diff --git a/.github/workflows/ci.yml b/.github/workflows/ci.yml new file mode 100644 index 0000000..63ac1f8 --- /dev/null +++ b/.github/workflows/ci.yml @@ -0,0 +1,55 @@ +name: CI Test for Cropped Example + +on: + push: + branches: + - '**' + pull_request: + branches: + - '**' + +jobs: + oregon_metro_cropped_example: + strategy: + matrix: + python-version: + - "3.10" + fail-fast: false + + name: cropped-example-py${{ matrix.python-version }} + runs-on: ubuntu-latest + + steps: + - uses: actions/checkout@v4 + + # Clone ActivitySim from SimOR_pnr branch + - name: Clone ActivitySim + run: | + cd .. + git clone --branch SimOR_pnr https://github.com/RSGInc/activitysim.git + pwd + ls -la + + # Install UV + - name: Install UV + uses: astral-sh/setup-uv@v4 + with: + version: "latest" + + # Set up Python with UV + - name: Set up Python + run: uv python install ${{ matrix.python-version }} + + # Install dependencies using UV and the lock file from ActivitySim + - name: Install dependencies with UV + run: | + cd ../activitysim + uv sync --frozen + uv pip install -e . --no-deps + uv pip list + + # Run the test + - name: Run cropped example test + run: | + cd ../activitysim + uv run python $GITHUB_WORKSPACE/resident/test/test_cropped_dataset.py diff --git a/.gitignore b/.gitignore new file mode 100644 index 0000000..8dbe99b --- /dev/null +++ b/.gitignore @@ -0,0 +1,16 @@ +*__pycache__* +.vscode/* +resident/model_data/metro/data_full/* +resident/outputs/* + +# Unit test / coverage reports +htmlcov/ +.tox/ +.coverage +.cache +nosetests.xml +coverage.xml + +Metro_outputs/ + +skimming_and_assignment/visum/*.ver diff --git a/.vscode/launch.json b/.vscode/launch.json new file mode 100644 index 0000000..daa9893 --- /dev/null +++ b/.vscode/launch.json @@ -0,0 +1,43 @@ +{ + // Use IntelliSense to learn about possible attributes. + // Hover to view descriptions of existing attributes. + // For more information, visit: https://go.microsoft.com/fwlink/?linkid=830387 + "version": "0.2.0", + "configurations": [ + { + "name": "Run Full Example", + "type": "debugpy", + "request": "launch", + "cwd": "${workspaceFolder}/resident", + "program": "simulation.py", + "console": "integratedTerminal", + "args": [ + "-c", "configs", // check the settings.yaml file for run settings! + "-d", "model_data/metro/data_full", + "-o", "outputs/full", + ] + }, + { + "name": "Run Cropped Example", + "type": "debugpy", + "request": "launch", + "cwd": "${workspaceFolder}/resident", + "program": "simulation.py", + "console": "integratedTerminal", + "args": [ + "-c", "configs", // check the settings.yaml file for run settings! + "-d", "model_data/metro/data_cropped", + "-o", "outputs/cropped", + ] + }, + { + "name": "Run Preprocessor", + "type": "debugpy", + "request": "launch", + "cwd": "${workspaceFolder}/resident", + "program": "preprocessor.py", + "console": "integratedTerminal", + "args": "preprocessor_settings.yaml" + } + ] +} \ No newline at end of file diff --git a/README.md b/README.md index 278bafe..e16a437 100644 --- a/README.md +++ b/README.md @@ -1,3 +1,6 @@ # SimOR Simulate Oregon (SimOR) - Oregon's Jointly Estimated ActivitySim Model ![image](SimOR.png) + +## Currently in development +To run the prototype mini example, download and install the correct fork of Activitysim from [here](https://github.com/RSGInc/activitysim/tree/SimOR_pnr) and follow the launch commands in the .vscode/launch.json file. This version of ActivitySim contains the necessary park-and-ride changes to run the SimOR model. \ No newline at end of file diff --git a/misc/bike_analysis/.gitignore b/misc/bike_analysis/.gitignore new file mode 100644 index 0000000..41b592c --- /dev/null +++ b/misc/bike_analysis/.gitignore @@ -0,0 +1 @@ +src/__pycache__/ \ No newline at end of file diff --git a/misc/bike_analysis/scripts/01_clean.py b/misc/bike_analysis/scripts/01_clean.py new file mode 100644 index 0000000..5dfe41c --- /dev/null +++ b/misc/bike_analysis/scripts/01_clean.py @@ -0,0 +1,241 @@ +import pandas as pd +import numpy as np +import os +pd.set_option('display.max_columns', None) +os.chdir(os.path.dirname(os.path.dirname(__file__))) + +# %% +# Directories +data_dir = 'data' +raw_dir = f'{data_dir}/raw' +interim_dir = f'{data_dir}/interim' +processed_dir = f'{data_dir}/processed' + +# %% +# Load data +hh = pd.read_csv(f'{raw_dir}/ex_hh.csv') +# day = pd.read_csv(f'{raw_dir}/ex_day.csv') +person = pd.read_csv(f'{raw_dir}/ex_person.csv') +trips = pd.read_csv(f'{raw_dir}/ex_trip_unlinked.csv') +ltrips = pd.read_csv(f'{raw_dir}/ex_trip_linked.csv') + +# %% Rename variables +ltrips.rename( + columns= + {'linked_trip_weight': 'ltrip_weight'}, + inplace=True +) + +# Re-map values to labels +person_mapping = { + 'gender': {1: 'Female', 2: 'Male', 3:'Non-binary', 995:'Missing', 997:'Other', 999:'PNTA'}, + 'age': {1:'Under5', 2:'5-10', 3:'11-15', 4:'16-17', 5:'18-24', 6:'25-34', 7:'35-44', 8:'45-54', 9:'55-64', 10:'65-74', 11:'75-84', 12:'85plus'}, + 'bike_freq': {1: '6-7days', 2: '5days', 3: '4days', 4: '3days', 5: '2days', 6: '1day', 7: '1-3month', 8: '<1month', 996: 'Never', 995: 'Missing'}, + 'bike_attitude': {1:'PhysicallyUnable', 2:'DoesNotKnowHow', 3:'NotInterested', 4:'WantBikeLess', 5:'HappyWithCurrent', 6:'WantBikeMore', 995:'Missing'} +} + +# Re-map to broader categories +bike_freq_map = { + '6-7days': 'VeryFrequent', + '5days': 'VeryFrequent', + '4days': 'VeryFrequent', + '3days': 'Occasional', + '2days': 'Occasional', + '1day': 'Occasional', + '1-3month': 'Infrequent', + '<1month': 'Infrequent', + 'Never': 'Never', + 'Missing': 'Missing' +} + +bike_att_bin = { + 'PhysicallyUnable': 'NotInterestedCant', + 'DoesNotKnowHow': 'NotInterestedCant', + 'NotInterested': 'NotInterestedCant', + 'WantBikeLess': 'WantBikeLess', + 'HappyWithCurrent': 'HappyWithCurrent', + 'WantBikeMore': 'WantBikeMore', + 'Missing': 'Missing' +} + +income_bin = { + 1: 'Low', + 2: 'Low', + 3: 'Mid', + 4: 'Mid', + 5: 'High', + 6: 'High', + 995: 'Missing', + 999: 'Missing' +} + +income_num = { + 1: 25000, + 2: 37500, + 3: 62500, + 4: 87500, + 5: 150000, + 6: 200000, + 995: np.nan, + 999: np.nan +} + +age_bin = { + 'Under5': 'Under10', + '5-10': 'Under10', + '11-15': '10-17', + '16-17': '10-17', + '18-24': '18-34', + '25-34': '18-34', + '35-44': '35-64', + '45-54': '35-64', + '55-64': '35-64', + '65-74': '65+', + '75-84': '65+', + '85plus': '65+' +} + +age_num = { + 'Under5': 2.5, + '5_10': 8, + '11-15': 13, + '16-17': 16, + '18-24': 21, + '25-34': 30, + '35-44': 40, + '45-54': 50, + '55-64': 60, + '65-74': 70, + '75-84': 80, + '85plus': 85 +} + +person['gender_num'] = np.where( + person['gender'].isin([1,3,997]), 1, # Female & Others + np.where(person['gender'] == 2, 2, 3) # Male and Missing +) +person['gender_lab'] = person['gender'].map(person_mapping['gender']) +person['gender_bin'] = person['gender_num'].map({1:'Female & Others', 2: 'Male', 3: 'Missing'}) + +person['age_lab'] = person['age'].map(person_mapping['age']) +person['age_bin'] = person['age_lab'].map(age_bin) +person['age_num'] = person['age_lab'].map(age_num) + +person['bike_freq_lab'] = person['bike_freq'].map(person_mapping['bike_freq']) +person['bike_freq_bin'] = person['bike_freq_lab'].map(bike_freq_map) + +person['bike_att'] = person['bike_attitude'].map(person_mapping['bike_attitude']) +person['bike_att_bin'] = person['bike_att'].map(bike_att_bin) + +hh['income_bin'] = hh['income_broad'].map(income_bin) +hh['income_num'] = hh['income_broad'].map(income_num) + +# %% +# Define comfort variable +comfort_cols = [col for col in person.columns if 'bike_comfort' in col] +likert_scale = {1:4, 2:3, 3:2, 4:1} # Reverse scoring for comfort +person[comfort_cols] = person[comfort_cols].replace(likert_scale) +person['avg_comfort'] = person[comfort_cols].replace(995, pd.NA).mean(axis=1, skipna=True).fillna(0) +comfort_labels = ['Uncomfortable', 'Comfortable'] +person['comfort_bin'] = np.where( + person['avg_comfort'] >= 3, 'Comfortable', + np.where(person['avg_comfort'] != 0 , 'Uncomfortable', pd.NA) +) + +# %% +# Identify complete rmove households (for comfort classification - 04_classify.py) +print(f"Total households: {hh['hh_weight'].sum():,.2f}") + +hh_rmove = hh[hh['diary_platform'] == 'rmove'] +print(f"Total households with rmove: {hh_rmove['hh_weight'].sum():,.2f}") + +hh_rmove_complete = hh_rmove[hh_rmove['num_days_complete'] == 7] +print(f"Total number of households with 7 complete days: {hh_rmove_complete['hh_weight'].sum():,.2f}") +print(f"Total sample size of households with 7 complete days: {hh_rmove_complete.shape[0]:,}") + +bike_complete_hh_ids = hh_rmove_complete['hh_id'].unique().tolist() + +# Add flag to identify complete records +person['bike_complete_flag'] = 0 +person.loc[person['hh_id'].isin(bike_complete_hh_ids), 'bike_complete_flag'] = 1 + +# %% Merge bike trips with person table +# Count bike trips +bike_trip_mask = (ltrips['linked_trip_mode'] == 11) +bike_trips= ltrips[bike_trip_mask].copy() +bike_counts = bike_trips.groupby('person_id').size().reset_index(name='bike_trips') + +# Keep relevant cols +per_cols = ['hh_id', 'person_id', 'age_lab', 'age_bin', 'age_num', + 'gender_bin', 'num_days_complete', 'bike_complete_flag', + 'avg_comfort', 'comfort_bin', + 'student', 'person_weight'] +bike_cols_per = [col for col in person.columns if 'bike' in col] + +# Merge bike trip info with person data +person_btrips = pd.merge( + person[per_cols + bike_cols_per], + bike_counts, + on='person_id', + how='left', + validate='1:1' +).fillna({'bike_trips': 0}) + +# Merge with hh data +hh_cols = ['hh_id', 'num_bicycle_adult', 'num_bicycle_child', 'income_broad', 'income_bin', 'income_num'] +person_btrips = pd.merge( + person_btrips, + hh[hh_cols], + on = 'hh_id', + how = 'left' +) + +# Add dummy if person reported bike trip +person_btrips['recorded_btrip'] = np.where(person_btrips['bike_trips'] > 0, 1, 0) + +# %% Merge bike trips with person info +btrips_person = pd.merge( + bike_trips.drop(columns = ['hh_id']), + person[per_cols + bike_cols_per], + how = 'left', + on = 'person_id' +) + +# Add hh data +btrips_person = pd.merge( + btrips_person, + hh[hh_cols], + how = 'left', + on = 'hh_id' +) + +# Add bike type to bike trip info +ltrip_bike_ids = bike_trips['linked_trip_id'].to_list() +subset = trips[trips['linked_trip_id'].isin(ltrip_bike_ids)] # bike type is unlinked trip table + +bike_modes = [2, 3, 4, 5, 69, 70, 82, 103, 300] # bike modes and hierarchy + +def get_highest_bike_mode(modes): + found_bikes = [m for m in modes if m in bike_modes] + if found_bikes: + return max(found_bikes) + else: + return None + +modes = subset.groupby('linked_trip_id')['mode_1'].agg(list).reset_index().rename(columns = {'mode_1':'mode_list'}) +modes['bike_mode'] = modes['mode_list'].apply(get_highest_bike_mode) + +# Add bike type +btrips_person = pd.merge( + btrips_person, + modes[['linked_trip_id', 'bike_mode']], + how = 'left', + on = 'linked_trip_id', +) + +ebikes = [70, 82] # ebike from bikeshare, ebike in household +btrips_person['bike_type'] = np.where(btrips_person['bike_mode'].isin(ebikes), 'ebike', 'standard') + +# %% Export +person_btrips.to_csv(f'{interim_dir}/person_btrips.csv', index=False) +btrips_person.to_csv(f'{interim_dir}/btrips_person.csv', index=False) diff --git a/misc/bike_analysis/scripts/02_visualize.py b/misc/bike_analysis/scripts/02_visualize.py new file mode 100644 index 0000000..b7891f5 --- /dev/null +++ b/misc/bike_analysis/scripts/02_visualize.py @@ -0,0 +1,233 @@ +import pandas as pd +import matplotlib.pyplot as plt +import seaborn as sns +import numpy as np +import os +import importlib + +pd.set_option('display.max_columns', None) +os.chdir(os.path.dirname(os.path.dirname(__file__))) + +import src.visualize as viz +importlib.reload(viz) + +# %% +# Define directories +data_dir = 'data' +raw_dir = f'{data_dir}/raw' +interim_dir = f'{data_dir}/interim' +processed_dir = f'{data_dir}/processed' +# %% +# Load data +person_btrips = pd.read_csv(f'{interim_dir}/person_btrips.csv') +btrips_person = pd.read_csv(f'{interim_dir}/btrips_person.csv') +ltrips = pd.read_csv(f'{raw_dir}/ex_trip_linked.csv') +value_labels = pd.read_csv(f'{raw_dir}/ex_value_labels.csv') + +# Convert to categorical +age_lab_cat = ['Under5', '5-10', '11-15', '16-17', '18-24', '25-34', '35-44', '45-54', '55-64', '65-74', '75-84'] +freq_bin_cat = ['VeryFrequent', 'Occasional', 'Infrequent', 'Never', 'Missing'] +att_cat = ['PhysicallyUnable', 'DoesNotKnowHow', 'NotInterested', 'WantBikeLess', 'HappyWithCurrent', 'WantBikeMore', 'Missing'] +att_bin_cat = ['NotInterestedCant', 'WantBikeLess', 'HappyWithCurrent', 'WantBikeMore', 'Missing'] + +person_btrips['age_lab'] = pd.Categorical(person_btrips['age_lab'], categories=age_lab_cat, ordered=True) +person_btrips['bike_freq_bin'] = pd.Categorical(person_btrips['bike_freq_bin'], categories=freq_bin_cat, ordered=True) +person_btrips['bike_att'] = pd.Categorical(person_btrips['bike_att'], categories=att_cat, ordered=True) +person_btrips['bike_att_bin'] = pd.Categorical(person_btrips['bike_att_bin'], categories=att_bin_cat, ordered=True) +# person['gender'] = pd.Categorical(person['gender'], categories=person_mapping['gender'], ordered=True) + +# %% =============== +# Basic Statistics +print(f"Number of bike trips: {btrips_person['ltrip_weight'].sum():,.2f}") +print(f"Percent bike trips: {btrips_person['ltrip_weight'].sum() / ltrips['linked_trip_weight'].sum() * 100:.2f}%") + +# Demographics of Revealed Bikers +bikers = person_btrips[person_btrips['bike_trips'] > 0] +bikers_gender = viz.weighted_total(bikers, 'gender_bin', 'person_weight') +bikers_age = viz.weighted_total(bikers, 'age_lab', 'person_weight') +bikers_attitude = viz.weighted_total(bikers, 'bike_att', 'person_weight') +bikers_income = viz.weighted_total(bikers, 'income_bin', 'person_weight') + +viz.plot_hist(bikers_gender, 'gender_bin', 'pct', title='Revealed Bikers by Gender') +viz.plot_hist(bikers_age, 'age_lab', 'pct', title='Revealed Bikers by Age') +viz.plot_hist(bikers_income, 'income_bin', 'pct', title='Revealed Bikers by Income') +viz.plot_hist(bikers_attitude, 'bike_att', 'pct', title='Revealed Bikers by Stated Attitude') + +# Num of bike trips per day +bikers_bike_count = viz.weighted_total(bikers, 'bike_trips', 'person_weight') +bikers_bike_count['bin'] = pd.cut(bikers_bike_count['bike_trips'], bins=[-0.1,0,1,2,3,4,5,50]) +bin_labels = ['0', '1', '2', '3', '4', '5', '6+'] +ax = (bikers_bike_count + .groupby('bin')['pct'] + .sum() + .plot(kind='bar', + color='skyblue', + xlabel='Number of Bike Trips', + ylabel='Pct', + title='Bike Trips per Day by Revealed Bikers')) +ax.set_xticklabels(bin_labels, rotation=0) +ax.bar_label(ax.containers[0], fmt='%.1f') +# %% +# Bike Trips by Purpose +bike_purpose = viz.weighted_total(btrips_person, 'd_purpose_category', 'ltrip_weight') +top_purpose = bike_purpose.sort_values(by='pct', ascending=False).head(10) +labels = value_labels[value_labels['variable'] == 'd_purpose_category'][['value', 'label']] +bin_labels = labels[labels['value'].isin(top_purpose['d_purpose_category'])]['label'].tolist() + +ax = top_purpose.plot( + kind='barh', + x='d_purpose_category', + y='pct', + color='skyblue', + title="Bike Trips by Purpose", + legend=False +) +ax.set_yticks(range(len(bin_labels))) +ax.set_yticklabels(bin_labels, rotation=0) +ax.bar_label(ax.containers[0], fmt='%.1f', label_type='edge') + +# %% ==================== +# Cross tabulations +df = person_btrips.copy() +df = df[df['comfort_bin'].notna()] + +freq_vs_trips_col = viz.weighted_crosstab(df, 'bike_freq_bin', 'recorded_btrip', 'person_weight', 'col', 0) +freq_vs_trips_row = viz.weighted_crosstab(df, 'bike_freq_bin', 'recorded_btrip', 'person_weight', 'row', 0) + +att_vs_trips_row = viz.weighted_crosstab(df, 'bike_att_bin', 'recorded_btrip', 'person_weight', 'row', 0) +att_vs_trips_col = viz.weighted_crosstab(df, 'bike_att_bin', 'recorded_btrip', 'person_weight', 'col', 0) + +comfort_vs_trips_row = viz.weighted_crosstab(df, 'comfort_bin', 'recorded_btrip', 'person_weight', 'row', 0) +comfort_vs_trips_col = viz.weighted_crosstab(df, 'comfort_bin', 'recorded_btrip', 'person_weight', 'col', 0) + +comfort_vs_att_row = viz.weighted_crosstab(df, 'bike_att_bin', 'comfort_bin', 'person_weight', 'row', 0) +comfort_vs_att_col = viz.weighted_crosstab(df, 'bike_att_bin', 'comfort_bin', 'person_weight', 'col', 0) + +# %% ==================================== +# Bike trip distance by reported frequency +df = btrips_person[(btrips_person['bike_freq_bin'] != 'Missing') & + (btrips_person['d_purpose_category'] != 14)] + +bins = [0, 1, 5, 10, np.inf] +labels2 = ['<1', '1-5', '5-10', '10+'] +df['dist_bin'] = pd.cut(df['distance_miles'], bins=bins, labels=labels2, right=False) + +summary = [] +for freq in df['bike_freq_bin'].unique(): + tbl = df[df['bike_freq_bin'] == freq].groupby('dist_bin')['ltrip_weight'].sum().reset_index() + tbl['pct'] = tbl['ltrip_weight'] / tbl['ltrip_weight'].sum() * 100 + tbl['bike_freq_bin'] = freq + summary.append(tbl) + +summary = pd.concat(summary) + +pivot = summary.pivot(index='dist_bin', columns='bike_freq_bin', values='pct').fillna(0) +pivot = pivot[df['bike_freq_bin'].unique()] + +ax = pivot.plot(kind='bar', figsize=(10,6), edgecolor='black') +ax.tick_params(axis='x', rotation=0) +ax.set_xlabel('Trip distance (miles)') +ax.set_ylabel('Percent') +ax.set_title('Bike Trip Distances by Reported Frequency') +ax.legend(title='Reported Frequency') +plt.tight_layout() +plt.show() + +# %% ========================= +# Trip Distance by Bike Type +df = btrips_person.copy() +bins = [0, 1, 2, 3, 4, 5, 10, np.inf] +labels = ['<1', '1-2', '2-3', '3-4', '4-5', '5-10', '10+'] +df['dist_bin'] = pd.cut(df['distance_miles'], bins=bins, labels=labels, right=False) + +std_dist = df[df['bike_type'] == 'standard'].groupby('dist_bin')['ltrip_weight'].sum().reset_index() +std_dist['pct'] = std_dist['ltrip_weight'] / std_dist['ltrip_weight'].sum() * 100 + +ebike_dist = df[df['bike_type'] == 'ebike'].groupby('dist_bin')['ltrip_weight'].sum().reset_index() +ebike_dist['pct'] = ebike_dist['ltrip_weight'] / ebike_dist['ltrip_weight'].sum() * 100 + +x = np.arange(len(labels)) +width = 0.35 +fig, ax = plt.subplots(figsize=(10,6)) +ax.bar(x - width/2, std_dist['pct'], width, label='Standard') +ax.bar(x + width/2, ebike_dist['pct'], width, label='E-bike') +ax.set_xticks(x) +ax.set_xticklabels(labels) +ax.set_xlabel('Bike trip distance (miles)') +ax.set_ylabel('Percent of Trips') +ax.set_title('Trip Distance by Bike Type') +ax.legend() +plt.show() + +# %% +# Gender vs Bike Type +df = btrips_person[btrips_person['gender_bin']!= "Missing"] +btype_fem = df[df['gender_bin'] == 'Female & Others'].groupby('bike_type')['ltrip_weight'].sum().reset_index() +btype_fem['pct'] = btype_fem['ltrip_weight'] / btype_fem['ltrip_weight'].sum() * 100 + +btype_male = df[df['gender_bin'] == 'Male'].groupby('bike_type')['ltrip_weight'].sum().reset_index() +btype_male['pct'] = btype_male['ltrip_weight'] / btype_male['ltrip_weight'].sum() * 100 + +btype_fem['gender'] = 'Female & Others' +btype_male['gender'] = 'Male' + +btype_combined = pd.concat([btype_fem, btype_male], ignore_index=True) +btype_pivot = btype_combined.pivot(index='bike_type', columns='gender', values='pct') +btype_pivot.plot(kind='bar', figsize=(8, 5)) +plt.ylabel('Percent of Trips (%)') +plt.title('Bike Type Share by Gender') +plt.legend(title='Gender') +plt.xticks(rotation=0) +plt.tight_layout() +plt.show() + +# %% ============================== +# Reported frequency vs comfort +df = person_btrips[(person_btrips['avg_comfort'] > 0)] +avg_comfort_freq_plt, avg_comfort_freq_tbl = viz.plot_weighted_comfort_by_group(df, 'bike_freq_bin', 'person_weight') + +# Age vs average comfort ratinga +avg_comfort_age_plt, avg_comfort_age_tbl = viz.plot_weighted_comfort_by_group(df, 'age_bin', 'person_weight') +# %% ===================== +# Avg comfort by gender +df = person_btrips.copy() +df = df[(df['gender_bin'] != 'Missing') & (df['avg_comfort'] > 0)] +bins = [0, 1, 2, 3, 4] +df['rate_bin'] = pd.cut(df['avg_comfort'], bins=bins, right=True) + +comfort_fem_rate = ( + df[df['gender_bin'] == 'Female & Others'] + .groupby('rate_bin')['person_weight'] + .sum() + .div(df[df['gender_bin'] == 'Female & Others']['person_weight'].sum()) + .mul(100) + .reset_index() +) + +comfort_male_rate = ( + df[df['gender_bin'] == 'Male'] + .groupby('rate_bin')['person_weight'] + .sum() + .div(df[df['gender_bin'] == 'Male']['person_weight'].sum()) + .mul(100) + .reset_index() +) + +comfort_fem_rate['gender'] = 'Female & Others' +comfort_male_rate['gender'] = 'Male' + +merge = pd.concat([comfort_male_rate, comfort_fem_rate], axis=0) + +sns.barplot( + merge, + x = 'rate_bin', + y = 'person_weight', + hue = 'gender' +) +plt.xticks( + ticks=range(len(merge['rate_bin'].unique())), + labels=['0–1', '1–2', '2–3', '3–4'] +) +plt.xlabel("Average comfort") +plt.title("Average Comfort by Gender") +plt.show() diff --git a/misc/bike_analysis/scripts/03_analyze.py b/misc/bike_analysis/scripts/03_analyze.py new file mode 100644 index 0000000..ada25a8 --- /dev/null +++ b/misc/bike_analysis/scripts/03_analyze.py @@ -0,0 +1,268 @@ +import pandas as pd +import numpy as np +import seaborn as sns +import importlib +import os +pd.set_option('display.max_columns', None) +pd.options.display.float_format = '{:.2f}'.format +os.chdir(os.path.dirname(os.path.dirname(__file__))) + +import src.visualize +importlib.reload(src.visualize) + +from src.stats import weighted_ttest, weighted_pt +importlib.reload(src.stats) + +from scipy.stats import chi2_contingency + +# %% +# Directoriess +data_dir = 'data' +raw_dir = f'{data_dir}/raw' +interim_dir = f'{data_dir}/interim' +processed_dir = f'{data_dir}/processed' + +# %% +# Load data +btrips_person = pd.read_csv(f"{interim_dir}/btrips_person.csv") +person_btrips = pd.read_csv(f"{interim_dir}/person_btrips.csv") +# %% +# Difference between male/female & level of comfort +df = person_btrips[person_btrips['avg_comfort'] > 0] +results1 = weighted_ttest( + df, + group_col = 'gender_bin', + value_col = 'avg_comfort', + group1_val = 'Male', + group2_val = 'Female & Others', + weight_col = 'person_weight' +) +results1 + +sns.histplot(df[df['gender_bin'] != "Missing"], + x = 'avg_comfort', + bins = [0, 1, 2, 3, 4], + weights='person_weight', + hue='gender_bin', + stat = 'percent', + multiple='dodge') + +# Alternative method +# group1 = df.loc[df['gender_bin'] == 'Male', 'avg_comfort'] +# group2 = df.loc[df['gender_bin'] == 'Female & Others', 'avg_comfort'] + +# weights1 = df.loc[df['gender_bin'] == 'Male', 'person_weight'] +# weights2 = df.loc[df['gender_bin'] == 'Female & Others', 'person_weight'] + +# t_stat, p_value, degrees_of_freedom = ttest_ind(group1, group2, weights = (weights1, weights2)) +# %% ==================================== +# Gender vs number of bike trips reported +results2 = weighted_ttest( + person_btrips, + group_col = 'gender_bin', + group1_val='Male', + group2_val='Female & Others', + value_col='bike_trips', + weight_col='person_weight' +) +results2 +# %% +# Differences between comfort level & bike trips +df = person_btrips[person_btrips['comfort_bin'].notna()] +results3 = weighted_ttest( + df, + group_col = 'comfort_bin', + value_col = 'bike_trips', + group1_val = 'Comfortable', + group2_val = 'Uncomfortable', + weight_col = 'person_weight' +) +results3 + +# %% +# Reported bike frequency & bike trips +df = person_btrips[person_btrips['bike_freq_bin'] != 'Missing'] +results4 = weighted_ttest( + df, + group_col = 'bike_freq_bin', + value_col = 'bike_trips', + group1_val = 'VeryFrequent', + group2_val = 'Occasional', + weight_col = 'person_weight' +) +results4 +# %% +# Reported attitude & bike trips +results4 = weighted_ttest( + person_btrips[person_btrips['bike_att_bin'] != "Missing"], + group_col = 'bike_att_bin', + value_col = 'bike_trips', + group1_val = 'HappyWithCurrent', + group2_val = 'WantBikeMore', + weight_col = 'person_weight' +) +results4 +# %% +# Frequency vs distance +df = btrips_person[ + (btrips_person['d_purpose_category'] != 14) & + (btrips_person['distance_miles'].notna())] + +results5 = weighted_ttest( + df, + group_col = 'bike_freq_bin', + value_col = 'distance_miles', + group1_val = 'VeryFrequent', + group2_val = 'Infrequent', + weight_col = 'ltrip_weight' +) +results5 + +results6 = weighted_ttest( + df, + group_col = 'bike_freq_bin', + value_col = 'distance_miles', + group1_val = 'VeryFrequent', + group2_val = 'Occasional', + weight_col = 'ltrip_weight' +) +results6 +# %% +# Bike comfort vs age +df = person_btrips[person_btrips['avg_comfort'] > 0] +results7 = weighted_ttest( + df, + group_col = 'age_bin', + value_col = 'avg_comfort', + group1_val = '18-34', + group2_val = '35-64', + weight_col = 'person_weight' +) +results7 + +# %% ====================== +# Chi-square test +# Reported biking vs gender +table = person_btrips[person_btrips['gender_bin'].isin(['Male', 'Female & Others'])].pivot_table( + index = 'gender_bin', + columns = 'reported_btrip', + values = 'person_weight', + aggfunc = 'sum', + fill_value=0 +) +chi2, p_value, dof, expected = chi2_contingency(table) +# %% +# Reported biking vs comfort +table2 = person_btrips[person_btrips['comfort_bin'].notna()].pivot_table( + index = 'bike_freq_bin', + columns = 'reported_btrip', + aggfunc = 'sum', + values = 'person_weight', + fill_value = 0 +) +chi2, p_value, dof, expected = chi2_contingency(table2) +print(f"chi-square: {chi2}, p-value: {p_value}, expected {expected}") + +# %% +# Comfort vs gender +table3 = person_btrips[(person_btrips['comfort_bin'].notna()) & (person_btrips['gender_bin'] != 'Missing')].pivot_table( + index = 'comfort_bin', + columns = 'gender_bin', + aggfunc = 'sum', + values = 'person_weight', + fill_value = 0 +) +chi2, p_value, dof, expected = chi2_contingency(table3) +print(f"chi-square: {chi2}, p-value: {p_value}, expected {expected}") + +print(f"chi-square: {chi2}, p-value: {p_value}, expected {expected}")# %% +# Reported btrip and attitude +table4 = person_btrips[(person_btrips['bike_att_bin'] != "Missing")].pivot_table( + index = 'bike_att_bin', + columns = 'reported_btrip', + aggfunc = 'sum', + values = 'person_weight', + fill_value = 0 +) +chi2, p_value, dof, expected = chi2_contingency(table4) +print(f"chi-square: {chi2}, p-value: {p_value}, expected {expected}") + +# %% +# Reported frequency and comfort +df = person_btrips[(person_btrips['bike_att_bin'] != "Missing") & (person_btrips['bike_att_bin'] != "Missing")] +table5 = df.pivot_table( + index = 'bike_att_bin', + columns = 'bike_freq_bin', + aggfunc = 'sum', + values = 'person_weight', + fill_value = 0 +) +chi2, p_value, dof, expected = chi2_contingency(table5) +print(f"chi-square: {chi2}, p-value: {p_value}, expected {expected}") + +weighted_pt( + df, + index = 'bike_att_bin', + cols='bike_freq_bin', + values = 'person_weight' +) + +weighted_pt( + df, + index = 'bike_att_bin', + cols='bike_freq_bin', + values = 'person_weight', + prop = 'col' +) + +# %% +bike_comf_cols = [col for col in person_btrips.columns if "bike_comfort_" in col] +df = person_btrips[person_btrips[bike_comf_cols] != 995] + +# %% ================================== +# Logistic regression +import statsmodels.api as sm +from sklearn.preprocessing import StandardScaler + +# Prepare and filter dataframe +df = person_btrips.copy() +df = df[(df['comfort_bin'].notna()) & + (df['gender_bin'] != "Missing") & + (df['age_num'].notna()) & + (df['income_num'].notna()) & + (df['bike_freq_bin'] != 'Missing') & + (df['bike_att_bin'] != "Missing") & + (df['avg_comfort'] > 0 ) + ] + +# Dummies for categorical predictors +X_dummies = pd.get_dummies( + df[['gender_bin', 'comfort_bin', 'bike_freq_bin', 'bike_att_bin', 'age_bin']], + drop_first=True +).astype(float) + +# Scaled continuous predictors +cont = df[['income_num', 'avg_comfort']] +scaler = StandardScaler() +cont_scaled = pd.DataFrame( + scaler.fit_transform(cont), + columns=cont.columns, + index=df.index +) + +# Combine predictors +X = pd.concat([X_dummies, cont_scaled], axis=1) + +# Remove NaN's +y = df['reported_btrip'].astype(float) +mask_nonnull = X.notna().all(axis=1) & y.notna() +X = X.loc[mask_nonnull] +y = y.loc[mask_nonnull] +weights = df.loc[mask_nonnull, 'person_weight'] + +# Add intercept +X = sm.add_constant(X, has_constant='add') + +model = sm.GLM(y, X, family=sm.families.Binomial(), freq_weights=weights) +result = model.fit() +print(result.summary()) \ No newline at end of file diff --git a/misc/bike_analysis/scripts/04_classify.py b/misc/bike_analysis/scripts/04_classify.py new file mode 100644 index 0000000..7035831 --- /dev/null +++ b/misc/bike_analysis/scripts/04_classify.py @@ -0,0 +1,133 @@ +import pandas as pd +import numpy as np +import matplotlib.pyplot as plt +import importlib +import os + +pd.set_option('display.max_columns', None) +os.chdir(os.path.dirname(os.path.dirname(__file__))) + +import src.visualize as viz +importlib.reload(viz) + +# %% +def pct_summary(df, col='biker_type', weight_col='person_weight', label=None): + """Return weighted percent share of each category.""" + weighted = ( + df.groupby(col, observed=True)[weight_col] + .sum() + .div(df[weight_col].sum()) + .mul(100) + ) + return weighted.rename(label) + +def classify_bikers(df, comfort_cols): + """Classify and reclassify bikers, tracking weighted % change after each step.""" + + biker_types = [ + 'NoWayNoHow', + 'InterestedButConcerned', + 'EnthusedAndConfident', + 'StrongAndFearless' + ] + + biker_type_map = { + 'Never': biker_types[0], + '<1month': biker_types[1], + '1-3month': biker_types[1], + '1day': biker_types[2], + '2days': biker_types[2], + '3days': biker_types[2], + '4days': biker_types[3], + '5days': biker_types[3], + '6-7days': biker_types[3], + } + + # Step 0: Initial classification + df['biker_type'] = df['bike_freq_lab'].map(biker_type_map) + pct_table = pct_summary(df, label='initial') + + # Step 1: Reclassify based on actual frequency + df.loc[(df['biker_type'] == 'Never') & (df['bike_trips'] > 0), 'biker_type'] = biker_types[1] + df.loc[(df['biker_type'] == biker_types[2]) & (df['bike_trips'] == 0), 'biker_type'] = biker_types[1] + df.loc[(df['biker_type'] == biker_types[3]) & (df['bike_trips'] == 0), 'biker_type'] = biker_types[1] + df.loc[(df['biker_type'] == biker_types[3]) & (df['bike_trips'] < 2), 'biker_type'] = biker_types[2] + pct_table = pd.concat([pct_table, pct_summary(df, label='after_actual_freq')], axis=1) + + # Step 2: Reclassify based on Bike Attitude + print(f"Attitudes before att: {df['bike_att'].unique()}") + mask = df['biker_type'].isin(biker_types[1:]) + df.loc[mask & df['bike_att'].isin(['NotInterested', 'PhysicallyUnable']), 'biker_type'] = 'NoWayNoHow' + pct_table = pd.concat([pct_table, pct_summary(df, label='after_att')], axis=1) + + # Step 3: Reclassify based on Bike Comfort + df.loc[ + (df['biker_type'] == biker_types[2]) & + (~df['bike_comfort_four_lanes'].isin([3, 4])), + 'biker_type' + ] = biker_types[1] + + df.loc[ + (df['biker_type'] == biker_types[3]) & + (~df[comfort_cols] == 4).all(axis=1), + 'biker_type' + ] = biker_types[2] + + pct_table = pd.concat([pct_table, pct_summary(df, label='after_comfort')], axis=1) + + # Step 4: Reclassify by Bike Ownership + mask = df['biker_type'].isin(biker_types[1:]) + df.loc[mask & (df['num_bicycle_adult'] == 0), 'biker_type'] = 'InterestedButConcerned' + pct_table = pd.concat([pct_table, pct_summary(df, label='after_ownership')], axis=1) + + # Final tidy-up + pct_table = pct_table.reindex(biker_types) + pct_table = pct_table.fillna(0).round(2) + + return df, pct_table +# %% +# Define directories +data_dir = 'data' +raw_dir = f'{data_dir}/raw' +interim_dir = f'{data_dir}/interim' +processed_dir = f'{data_dir}/processed' +# %% +# Load data +person_btrips = pd.read_csv(f'{interim_dir}/person_btrips.csv') +# %% ============ +# Clean dataframe +df = person_btrips.copy() + +# Keep complete records only +print(f"Total records before: {df.shape[0]:,}") +print(f"Total persons before: {df['person_weight'].sum():,.2f}") + +comfort_cols = [col for col in df.columns if 'bike_comfort' in col] +df = df[df['bike_complete_flag'] == 1] # defined in 01_cleaning.py +df = df[df['bike_freq_lab'] != 'Missing'] +df = df[~(df[comfort_cols] == 995).all(axis=1)] # Remove missing + +print(f"Total records after: {df.shape[0]:,}") +print(f"Total persons after: {df['person_weight'].sum():,.2f}") + +# %% +# Classify +df_classified, tbl = classify_bikers(df, comfort_cols) +# %% ============== +# Analysis +age_within = viz.weighted_crosstab(df, 'biker_type', 'age_bin', 'person_weight', 'col') +age_across = viz.weighted_crosstab(df, 'biker_type', 'age_bin', 'person_weight', 'row') + +viz.plot_stacked(age_within, 'Age Distribution Within Each Biker Type') +viz.plot_stacked(age_across, 'Biker Type Distribution Across Age Groups') +# %% +gender_within = viz.weighted_crosstab(df, 'biker_type', 'gender_bin', 'person_weight', 'col') +gender_across = viz.weighted_crosstab(df, 'biker_type', 'gender_bin', 'person_weight', 'row') +viz.plot_stacked(gender_within, 'Gender Distribution Within Each Biker Type') +viz.plot_stacked(gender_across, 'Biker Type Distribution Across Gender Groups') + +# %% +income_within = viz.weighted_crosstab(df, 'biker_type', 'income_bin', 'person_weight', 'col') +income_across = viz.weighted_crosstab(df, 'biker_type', 'income_bin', 'person_weight', 'row') +viz.plot_stacked(income_within, 'Income Distribution Within Each Biker Type') +viz.plot_stacked(income_across, 'Biker Type Distribution Across Income') diff --git a/misc/bike_analysis/src/__init__.py b/misc/bike_analysis/src/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/misc/bike_analysis/src/stats.py b/misc/bike_analysis/src/stats.py new file mode 100644 index 0000000..4cc8775 --- /dev/null +++ b/misc/bike_analysis/src/stats.py @@ -0,0 +1,85 @@ +from scipy import stats +import numpy as np +from statsmodels.stats.weightstats import ttest_ind + + +def weighted_mean(x, w): + return np.sum(w * x) / np.sum(w) + +def weighted_std(x, w, unbiased=True): + mean = weighted_mean(x, w) + var = np.sum(w * (x - mean)**2) + if unbiased: + eff_n = np.sum(w) - (np.sum(w**2) / np.sum(w)) + return np.sqrt(var / eff_n) + else: + return np.sqrt(var / np.sum(w)) + +def weighted_ttest(df, group_col, value_col, group1_val, group2_val, weight_col): + """ + Performs an independent t-test between two groups and returns key statistics. + """ + group1 = df.loc[df[group_col] == group1_val, value_col] + group2 = df.loc[df[group_col] == group2_val, value_col] + + weights1 = df.loc[df[group_col] == group1_val, weight_col] + weights2 = df.loc[df[group_col] == group2_val, weight_col] + + # Sample sizes + n1, n2 = weights1.sum(), weights2.sum() + + # Means and SDs + mean1, mean2 = weighted_mean(group1, weights1), weighted_mean(group2, weights2) + std1, std2 = weighted_std(group1, weights1), weighted_std(group2, weights2) + + # Mean difference + mean_diff = mean1 - mean2 + + # t-test + t_stat, p_value, degrees_of_freedom = ttest_ind(group1, group2, weights = (weights1, weights2)) + + results = { + 'group1_val': group1_val, + 'group2_val': group2_val, + 'n1': n1, + 'n2': n2, + 'mean1': mean1, + 'mean2': mean2, + 'std1': std1, + 'std2': std2, + 'mean_diff': mean_diff, + 't_stat': t_stat, + 'p_val': p_value, + # 'cohen_d': cohen_d + } + + return results + +def weighted_total(df, gb_col, wgt_col, ): + """ + Incomplete + """ + freq = df.groupby(gb_col)[wgt_col].sum().reset_index(name='total') + freq['pct'] = freq['total'] / freq['total'].sum() * 100 + return freq + +def weighted_pt(df, index, cols, values, prop='row'): + """ + Creates a weighted pivot table showing percentages. + prop (row, col) + """ + pt = df.pivot_table( + index = index, + columns = cols, + values = values, + aggfunc = 'sum' + ) + + if prop == 'row': + pct = pt.div(pt.sum(axis=1), axis=0) * 100 + elif prop == 'col': + pct = pt.div(pt.sum(axis=0), axis=1) * 100 + else: + raise ValueError("prop must be either 'row' or 'col'") + + return pct \ No newline at end of file diff --git a/misc/bike_analysis/src/visualize.py b/misc/bike_analysis/src/visualize.py new file mode 100644 index 0000000..70a52e7 --- /dev/null +++ b/misc/bike_analysis/src/visualize.py @@ -0,0 +1,137 @@ +import matplotlib.pyplot as plt +import numpy as np +import pandas as pd +import seaborn as sns + +def weighted_total(df, gb_col, wgt_col): + freq = df.groupby(gb_col)[wgt_col].sum().reset_index(name='total') + freq['pct'] = freq['total'] / freq['total'].sum() * 100 + return freq + +def plot_hist(df, x, y, title = None): + fig, ax = plt.subplots(figsize=(8, 6)) + bars = ax.bar(df[x], df[y], color='skyblue') + for bar, pct in zip(bars, df['pct']): + ax.text( + bar.get_x() + bar.get_width() / 2, + bar.get_height(), + f"{pct:.1f}%", + ha='center', va='bottom', fontsize=10 + ) + ax.set_xlabel(f"{x}") + ax.set_ylabel(f"{y}") + ax.set_title(f"{title}") + plt.xticks(rotation=45) + plt.tight_layout() + plt.show() + + +def check_dup_cols(df): + if df.columns.duplicated().any(): + print("Duplicate column names:", df.columns[df.columns.duplicated()].unique()) + else: + print("No duplicate columns!") + +def plot_weighted_comfort_by_group( + df, + group_by, + weight_col='person_weight', + comfort_prefix='bike_comfort', + figsize=(10, 6), + ylabel='Average Comfort (1=Very Uncomfortable, 4=Very Comfortable)', + xlabel='Scenario', + title=None, + rotate_xticks=45, +): + """ + Compute weighted mean comfort for all columns containing `comfort_prefix`, grouped by `group_by`, + and plot a bar chart of the results. + """ + # comfort columns + comfort_cols = [col for col in df.columns if comfort_prefix in col] + + def weighted_mean(x): + w = df.loc[x.index, weight_col] + if (w.isna() | (w == 0)).all(): + return np.nan + return np.average(x.fillna(np.nan), weights=w) + + summary_comfort = pd.DataFrame() + for col in comfort_cols: + tbl = df.groupby(group_by)[col].apply(weighted_mean) + summary_comfort = pd.concat([summary_comfort, tbl], axis=1) + + summary_plot = summary_comfort.T + summary_plot.index = comfort_cols + + ax = summary_plot.plot( + kind='bar', + figsize=figsize, + ylabel=ylabel, + xlabel=xlabel, + title=title, + ) + ax.set_xticklabels(ax.get_xticklabels(), rotation=rotate_xticks, ha='right') + plt.tight_layout() + plt.show() + + return ax, summary_plot + +def weighted_crosstab(df, index, columns, weight=None, prop='col', fill_value=0): + """ + Creates a weighted cross-tab and returns percentages + either across rows or columns. + """ + pivot = pd.pivot_table( + df, + index=index, + columns=columns, + values=weight, + aggfunc='sum' if weight else 'count', + fill_value=fill_value + ) + + # Compute row or column percentages + if prop == 'row': + pct = pivot.div(pivot.sum(axis=1), axis=0) * 100 + elif prop == 'col': + pct = pivot.div(pivot.sum(axis=0), axis=1) * 100 + + return pct.round(0) + +def plot_stacked(pct_table, title, ylabel='Percent'): + pct_table = pct_table.reindex(biker_order) + + plt.figure(figsize=(8, 5)) + pct_table.T[biker_order].plot( + kind='bar', + stacked=True, + color=[biker_colors[b] for b in biker_order] + ) + + plt.title(title, fontsize=12) + plt.ylabel(ylabel) + plt.xlabel('') + plt.legend( + title='Biker Type', + bbox_to_anchor=(1.05, 1), + loc='upper left', + labels=biker_order + ) + plt.xticks(rotation = 0) + plt.tight_layout() + plt.show() + +biker_order = [ + 'NoWayNoHow', + 'InterestedButConcerned', + 'EnthusedAndConfident', + 'StrongAndFearless' +] + +biker_colors = { + 'NoWayNoHow': '#D9523B', + 'InterestedButConcerned': '#fc8d59', + 'EnthusedAndConfident': '#91bfdb', + 'StrongAndFearless': '#4575b4' +} \ No newline at end of file diff --git a/resident/configs/accessibility.csv b/resident/configs/accessibility.csv new file mode 100644 index 0000000..98f27b9 --- /dev/null +++ b/resident/configs/accessibility.csv @@ -0,0 +1,53 @@ +Description,Target,Expression +#,, +#,, auto peak +#,, +#,, assume peak occurs in AM for outbound and PM for inbound +peak round trip distance,_auPkTime,"skim_od[('SOV_M_TIME', 'AM')] + skim_do[('SOV_M_TIME', 'PM')]" +decay function,_decay, exp(_auPkTime * dispersion_parameter_automobile) +auto peak retail,auPkRetail,df.EMP_RET * _decay +auto peak total,auPkTotal,df.EMP_TOTAL * _decay +#,, +#,, auto off-peak +#,, +#,, assume midday occurs entirely in the midday period +off-peak round trip distance,_auOpTime,"skim_od[('SOV_M_TIME', 'MD')] + skim_do[('SOV_M_TIME', 'MD')]" +decay function,_decay, exp(_auOpTime * dispersion_parameter_automobile) +auto off-peak retail,auOpRetail,df.EMP_RET * _decay +auto off-peak total,auOpTotal,df.EMP_TOTAL * _decay +#,, +#,, transit peak +#,, assume peak outbound transit occurs in AM +o-d peak transit time,_inVehicletime_od,"skim_od[('WTW_TIV', 'AM')]" +o-d out of vehicle transit time,_outOfVehicleTime_od,"skim_od[('WTW_FWT', 'AM')] + skim_od[('WTW_XWT', 'AM')] + reindex(df.walk_dist_local_bus, df.orig) + skim_od[('WTW_AUX', 'AM')] + reindex(df.walk_dist_local_bus, df.dest)" +total o-d peak transit time,_trPkTime_od,(_inVehicletime_od + out_of_vehicle_time_weight * _outOfVehicleTime_od) +#,, assume peak inbound transit occurs in PM +o-d peak transit time,_inVehicletime_do,"skim_do[('WTW_TIV', 'PM')]" +o-d out of vehicle transit time,_outOfVehicleTime_do,"skim_do[('WTW_FWT', 'PM')] + skim_do[('WTW_XWT', 'PM')] + reindex(df.walk_dist_local_bus, df.orig) + skim_do[('WTW_AUX', 'PM')] + reindex(df.walk_dist_local_bus, df.dest)" +total o-d peak transit time,_trPkTime_do,(_inVehicletime_do + out_of_vehicle_time_weight * _outOfVehicleTime_do) +#,, +peak transit time,_trPkTime,(_trPkTime_od + _trPkTime_do).clip(0) +round trip path is available,_rt_available,(_trPkTime_od > 0) & (_trPkTime_do > 0) +decay function,_decay,_rt_available * exp(_trPkTime * dispersion_parameter_transit) +transit peak retail,trPkRetail,df.EMP_RET * _decay +transit peak total,trPkTotal,df.EMP_TOTAL * _decay +transit peak total,trPkHH,df.TOTHHS * _decay +####,, +####,, transit off-peak +####,, +####,, assume off-peak inbound and outbound transit occurs in the MD time period +o-d off-peak transit time,_trOpTime_od,"skim_od[('WTW_TIV', 'MD')]" +d-o off-peak transit time,_trOpTime_do,"skim_do[('WTW_TIV', 'MD')]" +off-peak transit time,_trOpTime,(_trOpTime_od + _trPkTime_do).clip(0) +round trip path is available,_rt_available,(_trOpTime_od > 0) & (_trOpTime_do > 0) +decay function,_decay,_rt_available * exp(_trOpTime * dispersion_parameter_transit) +transit off-peak retail,trOpRetail,df.EMP_RET * _decay +transit off-peak total,trOpTotal,df.EMP_TOTAL * _decay +#,, +#,, non motorized +#,, +non-motorized round trip distance,_nmDist,"skim_od[('SOV_M_DIST', 'MD')] + skim_do[('SOV_M_DIST', 'MD')]" +round trip path is available,_rt_available,_nmDist <= max_walk_distance +decay function,_decay,_rt_available * exp(_nmDist * dispersion_parameter_walk) +retail accessibility,nmRetail,df.EMP_RET * _decay +total accessibility,nmTotal,df.EMP_TOTAL * _decay diff --git a/resident/configs/accessibility.yaml b/resident/configs/accessibility.yaml new file mode 100644 index 0000000..13b96c2 --- /dev/null +++ b/resident/configs/accessibility.yaml @@ -0,0 +1,2 @@ +# columns from land_use table to add to df +land_use_columns: ['EMP_RET', 'EMP_TOTAL', 'TOTHHS', 'walk_dist_local_bus'] diff --git a/resident/configs/annotate_disaggregate_accessibility.csv b/resident/configs/annotate_disaggregate_accessibility.csv new file mode 100644 index 0000000..821dda4 --- /dev/null +++ b/resident/configs/annotate_disaggregate_accessibility.csv @@ -0,0 +1,11 @@ +# annotating the proto_disaggregate_accessibilty table +Description,Target,Expression +workplace location for zero auto only,workplace_location_accessibility_0,"np.where(df.auto_ownership == 0, df.workplace_location_accessibility, np.nan)" +workplace location for auto deficient,workplace_location_accessibility_1,"np.where(df.auto_ownership == 1, df.workplace_location_accessibility, np.nan)" +workplace location for auto sufficient,workplace_location_accessibility_2,"np.where(df.auto_ownership == 2, df.workplace_location_accessibility, np.nan)" +other discretionary for zero auto only,othdiscr_accessibility_0,"np.where(df.auto_ownership == 0, df.othdiscr_accessibility, np.nan)" +other discretionary for auto deficient,othdiscr_accessibility_1,"np.where(df.auto_ownership ==1, df.othdiscr_accessibility, np.nan)" +other discretionary for auto sufficient,othdiscr_accessibility_2,"np.where(df.auto_ownership == 2, df.othdiscr_accessibility, np.nan)" +shopping for zero auto only,shopping_accessibility_0,"np.where(df.auto_ownership == 0, df.shopping_accessibility, np.nan)" +shopping for auto deficient,shopping_accessibility_1,"np.where(df.auto_ownership ==1, df.shopping_accessibility, np.nan)" +shopping for auto sufficient,shopping_accessibility_2,"np.where(df.auto_ownership == 2, df.shopping_accessibility, np.nan)" \ No newline at end of file diff --git a/resident/configs/annotate_households.csv b/resident/configs/annotate_households.csv new file mode 100644 index 0000000..1f8040c --- /dev/null +++ b/resident/configs/annotate_households.csv @@ -0,0 +1,33 @@ +Description,Target,Expression +#,, annotate households table after import +,_PERSON_COUNT,"lambda query, persons, households: persons.query(query).groupby('household_id').size().reindex(households.index).fillna(0).astype(np.int8)" +#,,FIXME households.income can be negative - so we clip? +income_in_thousands,_income_in_thousands,(households.income / 1000).clip(lower=0) +income_segment,income_segment,"pd.cut(_income_in_thousands, bins=[-np.inf, 30, 60, 100, np.inf], labels=[INCOME_SEGMENT_LOW, INCOME_SEGMENT_MED, INCOME_SEGMENT_HIGH, INCOME_SEGMENT_VERYHIGH]).astype(int)" +# FIXME move below expressions to their respective preprocessor files,, +#,,we assume that everyone 16 and older is a potential driver +number of drivers,num_drivers,"_PERSON_COUNT('16 <= age', persons, households)" +num_adults,num_adults,"_PERSON_COUNT('age >= 18', persons, households)" +num_children,num_children,"_PERSON_COUNT('age < 18', persons, households)" +num_young_children,num_young_children,"_PERSON_COUNT('age <= 5', persons, households)" +num_children_6_to_12,num_children_6_to_12,"_PERSON_COUNT('6 <= age <= 12', persons, households)" +num_children_5_to_15,num_children_5_to_15,"_PERSON_COUNT('5 <= age <= 15', persons, households)" +num_children_16_to_17,num_children_16_to_17,"_PERSON_COUNT('16 <= age <= 17', persons, households)" +num_gradeschool,num_gradeschool,"_PERSON_COUNT('6 <= age <= 17', persons, households)" +num_highschool,num_highschool,"_PERSON_COUNT('14 <= age <= 17', persons, households)" +num_college_age,num_college_age,"_PERSON_COUNT('18 <= age <= 24', persons, households)" +num_young_adults,num_young_adults,"_PERSON_COUNT('25 <= age <= 34', persons, households)" +number of non_workers,num_non_workers,households.hhsize - households.num_workers +number of pre-driving age children in the household,num_predrive_child,"_PERSON_COUNT('ptype == 7', persons, households)" +number of non-working adult in the household,num_nonworker_adults,"_PERSON_COUNT('ptype == 4', persons, households)" +number of full time workers,num_fullTime_workers,"_PERSON_COUNT('pemploy == 1', persons, households)" +number of part time workers,num_partTime_workers,"_PERSON_COUNT('pemploy == 2', persons, households)" +number of retired adults in the household,num_retired_adults,"_PERSON_COUNT('ptype == 5', persons, households)" +#,,FIXME is this the best replacement for area_type?? +home_is_urban,home_is_urban,"reindex(land_use.pseudomsa, households.home_zone_id) <= urban_threshold" +home_is_rural,home_is_rural,"reindex(land_use.pseudomsa, households.home_zone_id) > rural_threshold" +#,, +,num_hh_in_zone,"reindex(households.groupby('home_zone_id').size(), households.home_zone_id)" +#,multiple_auto_hh_in_zone,"reindex_i(df.groupby('home_zone_id').multiple_auto_hh.sum(), df.index)" +#,multiple_auto_hh_in_zone_share,multiple_auto_hh_in_zone/num_hh_in_zone +hh owns an ebike,ebike_owner,"rng.random_for_df(households)[:,0] < ebikeownership" \ No newline at end of file diff --git a/resident/configs/annotate_households_auto_ownership.csv b/resident/configs/annotate_households_auto_ownership.csv new file mode 100644 index 0000000..d0c6e63 --- /dev/null +++ b/resident/configs/annotate_households_auto_ownership.csv @@ -0,0 +1,15 @@ +Description,Target,Expression +#,, annotate households table after auto_ownership model has run +#,, need to select the appropriate accessibility value from the persons table for the household +,_wla0,persons_merged.groupby('household_id').workplace_location_accessibility_0.mean().reindex(df.index) +,_wla1,persons_merged.groupby('household_id').workplace_location_accessibility_1.mean().reindex(df.index) +,_wla2,persons_merged.groupby('household_id').workplace_location_accessibility_2.mean().reindex(df.index) +,workplace_location_accessibility,"np.where(df.auto_ownership == 0, _wla0, np.where(df.auto_ownership == 1, _wla1, _wla2))" +,_sha0,persons_merged.groupby('household_id').shopping_accessibility_0.mean().reindex(df.index) +,_sha1,persons_merged.groupby('household_id').shopping_accessibility_1.mean().reindex(df.index) +,_sha2,persons_merged.groupby('household_id').shopping_accessibility_2.mean().reindex(df.index) +,shopping_accessibility,"np.where(df.auto_ownership == 0, _sha0, np.where(df.auto_ownership == 1, _sha1, _sha2))" +,_otha0,persons_merged.groupby('household_id').othdiscr_accessibility_0.mean().reindex(df.index) +,_otha1,persons_merged.groupby('household_id').othdiscr_accessibility_1.mean().reindex(df.index) +,_otha2,persons_merged.groupby('household_id').othdiscr_accessibility_2.mean().reindex(df.index) +,othdiscr_accessibility,"np.where(df.auto_ownership == 0, _otha0, np.where(df.auto_ownership == 1, _otha1, _otha2))" diff --git a/resident/configs/annotate_households_cdap.csv b/resident/configs/annotate_households_cdap.csv new file mode 100644 index 0000000..52be731 --- /dev/null +++ b/resident/configs/annotate_households_cdap.csv @@ -0,0 +1,8 @@ +Description,Target,Expression +#,, annotate households table after cdap model has run +num_travel_active,num_travel_active,persons.travel_active.astype(int).groupby(persons.household_id).sum().reindex(households.index).fillna(0).astype(np.int8) +num_travel_active_adults,num_travel_active_adults,(persons.adult & persons.travel_active).astype(int).groupby(persons.household_id).sum().reindex(households.index).fillna(0).astype(np.int8) +num_travel_active_preschoolers,num_travel_active_preschoolers,((persons.ptype == PTYPE_PRESCHOOL) & persons.travel_active).astype(int).groupby(persons.household_id).sum().reindex(households.index).fillna(0).astype(np.int8) +num_travel_active_children,num_travel_active_children,num_travel_active - num_travel_active_adults +num_travel_active_non_preschoolers,num_travel_active_non_preschoolers,num_travel_active - num_travel_active_preschoolers +joint tours determined in cdap model,participates_in_jtf_model,(df.has_joint_tour == 1) diff --git a/resident/configs/annotate_households_vehicle_type.csv b/resident/configs/annotate_households_vehicle_type.csv new file mode 100644 index 0000000..a3e0a26 --- /dev/null +++ b/resident/configs/annotate_households_vehicle_type.csv @@ -0,0 +1,2 @@ +Description,Target,Expression +number of household AVs,numAVowned,"vehicles[vehicles['vehicle_type'].str.contains('-AV', na=False)].groupby('household_id').size().reindex(households.index).fillna(0)" \ No newline at end of file diff --git a/resident/configs/annotate_landuse.csv b/resident/configs/annotate_landuse.csv new file mode 100644 index 0000000..8eddebb --- /dev/null +++ b/resident/configs/annotate_landuse.csv @@ -0,0 +1,24 @@ +Description,Target,Expression +#,, annotate landuse table after import +household_density,household_density,land_use.TOTHHS / land_use.ACRES.clip(lower=1) +population_density,population_density,land_use['TOTPOP'] / land_use.ACRES.clip(lower=1) +# FIXME pseudomsa is used in a few places but there is no equivalent in Metro landuse,, +,pseudomsa,0 +# FIXME missing in landuse but used in SANDAG -- setting to 0 for now,, +,ACTIVE_ACRES,0 +,totint,0 +,micro_dist_local_bus,0 +,microtransit,0 +,nev,0 +,ESCOOACCTIME,land_use.ESCOOACCTIME.fillna(0) +# FIXME need to decide on what the preschool target looks like,, +preschool target for mandatory constraint - sum of all size terms,preschool_target,land_use['TOTPOP'] + land_use.EMP_TOTAL +# FIME whats the criteria for parking zone?,, +Is a zone with parking,is_parking_zone,"np.where(land_use.PRKCST_MNTH > 0, True, False)" +employment_density,employment_density,(land_use['EMP_TOTAL'] / land_use.ACRES.clip(lower=1)).clip(upper=500) +density_index,density_index,(household_density *employment_density) / (household_density + employment_density).clip(lower=1) +# FIXME temporary density calculation -- again I don't think this calculation matches what's in sandag,, +dwelling unit density,duden,(land_use['TOTHHS'] / land_use.ACRES.clip(lower=1)).clip(upper=500) +population density,popden,(land_use['TOTPOP'] / land_use.ACRES.clip(lower=1)).clip(upper=500) +employment unit density,empden,(land_use['EMP_TOTAL'] / land_use.ACRES.clip(lower=1)).clip(upper=500) +retail employment density,retempden,(land_use['EMP_RET'] / land_use.ACRES.clip(lower=1)).clip(upper=500) \ No newline at end of file diff --git a/resident/configs/annotate_persons.csv b/resident/configs/annotate_persons.csv new file mode 100644 index 0000000..0a0902e --- /dev/null +++ b/resident/configs/annotate_persons.csv @@ -0,0 +1,68 @@ +Description,Target,Expression +#,, annotate persons table after import +age_16_to_19,age_16_to_19,"persons.age.between(16, 19)" +age_16_p,age_16_p,persons.age >= 16 +adult,adult,persons.age >= 18 +# FIXME --are these SEX categories correct?,, +male,male,persons.SEX == 1 +female,female,persons.SEX == 2 +employment status type,pemploy,"pd.Series(np.zeros(len(persons)), index=persons.index)" +,pemploy,"np.where(persons.age < 16, PEMPLOY_CHILD, PEMPLOY_PART)" +,pemploy,"np.where((persons.age >= 16) & ((persons.ESR == 3) | (persons.ESR == 6)), PEMPLOY_NOT, pemploy)" +,pemploy,"np.where((persons.age>=16) & (persons.ESR != 3) & (persons.ESR != 6) & (persons.WKHP >= 35) & (persons.WKW >= 1) & (persons.WKW <= 4), PEMPLOY_FULL, pemploy)" +student category,pstudent,"pd.Series(np.zeros(len(persons)), index=persons.index)" +,pstudent,"np.where((pemploy == 1) & (persons.age >= 16), PSTUDENT_NOT, pstudent)" +,pstudent,"np.where((pemploy == 1) & (persons.age < 16), PSTUDENT_GRADE_OR_HIGH, pstudent)" +,pstudent,"np.where((persons.SCHG < 1) & (persons.age >= 16), PSTUDENT_NOT, pstudent)" +,pstudent,"np.where((persons.SCHG < 1) & (persons.age < 16), PSTUDENT_GRADE_OR_HIGH, pstudent)" +,pstudent,"np.where((persons.SCHG >= 15) & (persons.age >= 16) & (pemploy != 1), PSTUDENT_UNIVERSITY, pstudent)" +,pstudent,"np.where((persons.SCHG >= 15) & (persons.age < 16) & (pemploy != 1), PSTUDENT_GRADE_OR_HIGH, pstudent)" +,pstudent,"np.where((persons.age <= 19) & (pemploy != 1) & (persons.SCHG >=1) & (persons.SCHG<=14), PSTUDENT_GRADE_OR_HIGH, pstudent)" +,pstudent,"np.where((persons.age > 19) & (pemploy != 1) & (persons.SCHG >=1) & (persons.SCHG<=14), PSTUDENT_UNIVERSITY, pstudent)" +,pstudent,"np.where(pstudent == 0, 3, pstudent)" +person type,ptype,"pd.Series(np.zeros(len(persons)), index=persons.index)" +,ptype,"np.where((pemploy == 1), PTYPE_FULL, PTYPE_NONWORK)" +,ptype,"np.where((pstudent == 3) & (pemploy == 2), PTYPE_PART, ptype)" +,ptype,"np.where((pstudent == 3) & (persons.age >= 65) & ((pemploy == 3) | (pemploy == 4)), PTYPE_RETIRED, ptype)" +,ptype,"np.where((pstudent == 3) & (persons.age < 6) & ((pemploy == 3) | (pemploy == 4)), PTYPE_PRESCHOOL, ptype)" +,ptype,"np.where((pstudent == 3) & (persons.age >= 6) & (persons.age <= 64) & ((pemploy == 3) | (pemploy == 4)), PTYPE_NONWORK, ptype)" +,ptype,"np.where((pstudent == 2) & ((pemploy == 2) | (pemploy == 3) | (pemploy == 4)), PTYPE_UNIVERSITY, ptype)" +,ptype,"np.where((pstudent == 1) & (persons.age < 6) & ((pemploy == 2) | (pemploy == 3) | (pemploy == 4)), PTYPE_PRESCHOOL, ptype)" +,ptype,"np.where((pstudent == 1) & (persons.age >= 6) & ((pemploy == 2) | (pemploy == 3) | (pemploy == 4)), PTYPE_SCHOOL, ptype)" +# FIXME are these rules for preschoolers still wanted?,, +is_student,is_student,"np.isin(pstudent, [PSTUDENT_GRADE_OR_HIGH, PSTUDENT_UNIVERSITY])" +preschool age can go to preschool,is_student,"np.where(persons.age > GRADE_SCHOOL_MIN_AGE, is_student, True)" +preschool age can go to preschool,pstudent,"np.where(persons.age > GRADE_SCHOOL_MIN_AGE, pstudent, PSTUDENT_GRADE_OR_HIGH)" +is_preschool,_is_preschool,(pstudent == PSTUDENT_GRADE_OR_HIGH) & (persons.age <= GRADE_SCHOOL_MIN_AGE) +is_gradeschool,_is_gradeschool,(pstudent == PSTUDENT_GRADE_OR_HIGH) & (persons.age <= GRADE_SCHOOL_MAX_AGE) & (persons.age > GRADE_SCHOOL_MIN_AGE) +is_highschool,_is_highschool,(pstudent == PSTUDENT_GRADE_OR_HIGH) & (persons.age > GRADE_SCHOOL_MAX_AGE) +# is_university is actually hard coded in mandatory scheduling(!) so need it here,, +is_university,is_university,pstudent == PSTUDENT_UNIVERSITY +school_segment preschool,school_segment,"np.where(_is_preschool, SCHOOL_SEGMENT_PREK, SCHOOL_SEGMENT_NONE)" +school_segment gradeschool,school_segment,"np.where(_is_gradeschool, SCHOOL_SEGMENT_GRADE, school_segment)" +school_segment highschool,school_segment,"np.where(_is_highschool, SCHOOL_SEGMENT_HIGH, school_segment)" +school_segment university,school_segment,"np.where(is_university, SCHOOL_SEGMENT_UNIV, school_segment).astype(np.int8)" +#,, +is_worker,is_worker,"np.isin(pemploy, [PEMPLOY_FULL, PEMPLOY_PART])" +#extrnal model variable initialization treating everyone as internal initially,, +,is_internal_worker,is_worker +,is_external_worker,0 +#,, +home_zone_id,home_zone_id,"reindex(households.home_zone_id, persons.household_id)" +#,, +,_mean_work,distributed_time_factor_work_mean +,_stddev_work,distributed_time_factor_work_stddev +,_mean_nonwork,distributed_time_factor_nonwork_mean +,_stddev_nonwork,distributed_time_factor_nonwork_stddev +,_min_time_factor,distributed_time_factor_min +,_max_time_factor,distributed_time_factor_max +#,,"Below expression was previously done using np.clip, but was producing a NotImplemented error for mixed DataFrame and Series Inputs. Reimplemented using min & max" +travel time sensitivity factor for work travel,time_factor_work,"np.minimum(_max_time_factor, np.maximum(rng.lognormal_for_df(persons, mu=_mean_work, sigma=_stddev_work, scale=True), _min_time_factor))" +travel time sensitivity factor for non-work travel,time_factor_nonwork,"np.minimum(_max_time_factor, np.maximum(rng.lognormal_for_df(persons, mu=_mean_nonwork, sigma=_stddev_nonwork, scale=True), _min_time_factor)) " +#,, +# FIXME need naics and SOC codes ,, +,_naics_code,"np.where(persons.naics2_original_code=='3M', 3000, np.where(persons.naics2_original_code=='4M', 4000, np.where(persons.naics2_original_code=='MIL',9000, persons.naics2_original_code))) if 'naics2_original_code' in persons.columns else 0" +,naics_code,_naics_code.astype('int') if all(_naics_code!=0) else 0 +#,, +# FIXME does filling with -1 make sense here? depends on what the missing values mean,, +occupation categories mapped to workplace segments,occupation,persons.OCCCAT.fillna(-1).map(occupation_xwalk) diff --git a/resident/configs/annotate_persons_after_hh.csv b/resident/configs/annotate_persons_after_hh.csv new file mode 100644 index 0000000..5123de4 --- /dev/null +++ b/resident/configs/annotate_persons_after_hh.csv @@ -0,0 +1,15 @@ +Description,Target,Expression +#,, annotate persons table after annotate_households +,_hh_income,"reindex(households.income, persons.household_id)" +,_num_adults,"reindex(households.num_adults, persons.household_id)" +,_num_predrive_child,"reindex(households.num_predrive_child, persons.household_id)" +,_num_nonworker_adults,"reindex(households.num_nonworker_adults, persons.household_id)" +,_num_full_time_workers,"reindex(households.num_fullTime_workers, persons.household_id)" +Income less than 25K,is_income_less25K,(_hh_income)<25000 +Income 25K to 60K,is_income_25K_to_60K,((_hh_income)>=25000) & ((_hh_income)<60000) +Income 60K to 120K,is_income_60K_to_120K, ((_hh_income)>=60000) & ((_hh_income)<120000) +Income greater than 60K,is_income_greater60K,((_hh_income)>=60000) +Income greater than 120K,is_income_greater120K,((_hh_income)>=120000) +Presence of nonworker in HHs,is_non_worker_in_HH,_num_nonworker_adults>0 +all the adults in the HH are full time workers,is_all_adults_full_time_workers,(_num_adults) == (_num_full_time_workers) +Presence of predrive child in HHs,is_pre_drive_child_in_HH,_num_predrive_child>0 diff --git a/resident/configs/annotate_persons_cdap.csv b/resident/configs/annotate_persons_cdap.csv new file mode 100644 index 0000000..38eb42e --- /dev/null +++ b/resident/configs/annotate_persons_cdap.csv @@ -0,0 +1,3 @@ +Description,Target,Expression +#,, annotate persons table after cdap model has run +travel_active,travel_active,persons.cdap_activity != CDAP_ACTIVITY_HOME \ No newline at end of file diff --git a/resident/configs/annotate_persons_jtp.csv b/resident/configs/annotate_persons_jtp.csv new file mode 100644 index 0000000..a72c866 --- /dev/null +++ b/resident/configs/annotate_persons_jtp.csv @@ -0,0 +1,3 @@ +Description,Target,Expression +#,, annotate persons table after joint_tour_participation model has run +num_joint_tours,num_joint_tours,"joint_tour_participants.groupby('person_id').size().reindex(persons.index).fillna(0).astype(np.int8)" diff --git a/resident/configs/annotate_persons_mtf.csv b/resident/configs/annotate_persons_mtf.csv new file mode 100644 index 0000000..47e4160 --- /dev/null +++ b/resident/configs/annotate_persons_mtf.csv @@ -0,0 +1,10 @@ +Description,Target,Expression +#,, annotate persons table after mandatory_tour_frequency model has run +,_PERSON_TOUR_COUNT,"lambda exp, persons, tours: tours.query(exp).groupby('person_id').size().reindex(persons.index).fillna(0).astype(np.int8)" +,_Q,lambda s: "'{}'".format(s) +work_and_school_and_worker,work_and_school_and_worker,(persons.mandatory_tour_frequency == 'work_and_school') & persons.is_worker +work_and_school_and_student,work_and_school_and_student,(persons.mandatory_tour_frequency == 'work_and_school') & persons.is_student +number of mandatory tours for each person,num_mand,"_PERSON_TOUR_COUNT('tour_category==%s' % _Q('mandatory'), persons, tours).fillna(0)" +number of work tours for each person,num_work_tours,"_PERSON_TOUR_COUNT('tour_type==%s' % _Q('work'), persons, tours).fillna(0)" +presence of pre school kid with mandatory tours,has_pre_school_child_with_mandatory,"other_than(persons.household_id, (persons.ptype == 8) & (num_mand > 0))" +presense of driving age school children with mandatory tours,has_driving_age_child_with_mandatory,"other_than(persons.household_id, (persons.ptype == 6) & (num_mand > 0))" diff --git a/resident/configs/annotate_persons_nmtf.csv b/resident/configs/annotate_persons_nmtf.csv new file mode 100644 index 0000000..11f8b11 --- /dev/null +++ b/resident/configs/annotate_persons_nmtf.csv @@ -0,0 +1,15 @@ +Description,Target,Expression +#,, annotate persons table after non_mandatory_tour_frequency model has run +num_non_mand,num_non_mand,tours[tours.tour_category=='non_mandatory'].groupby('person_id').size().reindex(persons.index).fillna(0).astype(np.int8) +num_escort_tours,num_escort_tours,tours[tours.tour_type == 'escort'].groupby('person_id').size().reindex(persons.index).fillna(0).astype(np.int8) +num_eatout_tours,num_eatout_tours,tours[tours.tour_type == 'eatout'].groupby('person_id').size().reindex(persons.index).fillna(0).astype(np.int8) +num_shop_tours,num_shop_tours,tours[tours.tour_type == 'shopping'].groupby('person_id').size().reindex(persons.index).fillna(0).astype(np.int8) +num_maint_tours,num_maint_tours,tours[tours.tour_type == 'othmaint'].groupby('person_id').size().reindex(persons.index).fillna(0).astype(np.int8) +num_discr_tours,num_discr_tours,tours[tours.tour_type == 'othdiscr'].groupby('person_id').size().reindex(persons.index).fillna(0).astype(np.int8) +num_social_tours,num_social_tours,tours[tours.tour_type == 'social'].groupby('person_id').size().reindex(persons.index).fillna(0).astype(np.int8) +num_non_escort_tours,num_non_escort_tours,num_non_mand-num_escort_tours +total shopping and maintenance tours,num_shop_maint_tours,num_shop_tours + num_maint_tours +"total shopping, maintenance and escort tours",num_shop_maint_escort_tours,num_shop_tours + num_maint_tours + num_escort_tours +number of additional shopping and maintenance tours,num_add_shop_maint_tours,"np.where (num_shop_maint_tours>0, 1, 0) * (num_shop_maint_tours - 1)" +total social and discretionary tours,num_soc_discr_tours,num_social_tours + num_discr_tours +number of additional social and discretionary,num_add_soc_discr_tours,"np.where (num_soc_discr_tours>0, 1, 0) * (num_soc_discr_tours - 1)" diff --git a/resident/configs/annotate_persons_school.csv b/resident/configs/annotate_persons_school.csv new file mode 100644 index 0000000..d6c18fe --- /dev/null +++ b/resident/configs/annotate_persons_school.csv @@ -0,0 +1,7 @@ +Description,Target,Expression +#,, annotate persons table after school_location model has run +,distance_to_school,"np.where(persons.school_zone_id >= 0,skim_dict.lookup(persons.home_zone_id, persons.school_zone_id, ('SOV_M_DIST', 'MD')), np.nan)" +#,, this uses the free flow travel time in both directions. MTC TM1 was MD and MD +temp auto_time_to_school,_auto_time_to_school,"skim_dict.lookup(persons.home_zone_id, persons.school_zone_id, ('SOV_M_TIME', 'MD'))" +temp auto_time_return,_auto_time_return,"skim_dict.lookup(persons.school_zone_id, persons.home_zone_id, ('SOV_M_TIME', 'MD'))" +free flow roundtrip_auto_time_to_school,roundtrip_auto_time_to_school,"np.where(persons.school_zone_id >= 0,_auto_time_to_school + _auto_time_return,0)" diff --git a/resident/configs/annotate_persons_workplace.csv b/resident/configs/annotate_persons_workplace.csv new file mode 100644 index 0000000..133f853 --- /dev/null +++ b/resident/configs/annotate_persons_workplace.csv @@ -0,0 +1,13 @@ +Description,Target,Expression +#,, annotate persons table after workplace_location model has run +,workplace_zone_id,"np.where(persons.get('external_workplace_zone_id',-1) > 0, persons.get('external_workplace_zone_id',-1), persons.workplace_zone_id)" +,workplace_location_logsum,"np.where(persons.get('external_workplace_zone_id',-1) > 0, persons.get('external_workplace_location_logsum',-1), persons.workplace_location_logsum)" +,workplace_modechoice_logsum,"np.where(persons.get('external_workplace_zone_id',-1) > 0, persons.get('external_workplace_modechoice_logsum',-1), persons.workplace_modechoice_logsum)" +,distance_to_work,"np.where(workplace_zone_id>=0,skim_dict.lookup(persons.home_zone_id, workplace_zone_id, ('SOV_M_DIST', 'MD')),np.nan)" +work_zone_area_type,work_zone_area_type,"reindex(land_use.pseudomsa, persons.workplace_zone_id)" +#,, auto time to work - free flow travel time in both directions. MTC TM1 was MD and MD +#,,roundtrip_auto_time_to_work +,auto_time_home_to_work,"skim_dict.lookup(persons.home_zone_id, workplace_zone_id, ('SOV_M_TIME', 'AM'))" +,_auto_time_work_to_home,"skim_dict.lookup(workplace_zone_id, persons.home_zone_id, ('SOV_M_TIME', 'PM'))" +,roundtrip_auto_time_to_work,"np.where(workplace_zone_id>=0, auto_time_home_to_work + _auto_time_work_to_home,0)" +Parking cost at work,exp_daily_work,"reindex(land_use.PRKCST_DAY, df.workplace_zone_id).fillna(0)" diff --git a/resident/configs/annotate_proto_persons.csv b/resident/configs/annotate_proto_persons.csv new file mode 100644 index 0000000..12ec1f4 --- /dev/null +++ b/resident/configs/annotate_proto_persons.csv @@ -0,0 +1,4 @@ +Description,Target,Expression +flag denoting proto person,is_proto_person,True +setting work time factor to 1 for disaggregate accessibilities,time_factor_work,1 +setting non-work time factor to 1 for disaggregate accessibilities,time_factor_nonwork,1 diff --git a/resident/configs/annotate_tours_external_joint_identification.csv b/resident/configs/annotate_tours_external_joint_identification.csv new file mode 100644 index 0000000..84de424 --- /dev/null +++ b/resident/configs/annotate_tours_external_joint_identification.csv @@ -0,0 +1,9 @@ +Description,Target,Expression +#,, annotate tours table after first external tour identification model has run +finding external workers,_is_external_worker,"reindex(persons.get('is_external_worker', False), df.person_id)" +#,, want to set external tour indicators for mandatory tours too +setting external work trips,is_external_tour,"np.where(_is_external_worker & (df.tour_category == 'mandatory') & (df.tour_type == 'work'), True, is_external_tour)" +setting internal work trips,is_external_tour,"np.where((~_is_external_worker) & (df.tour_category == 'mandatory') & (df.tour_type == 'work'), False, is_external_tour)" +#atwork subtours not created yet...,, +#atwork tours are internal,is_external_tour,"np.where((df.tour_category == 'atwork'), False, is_external_tour)" +setting corresponding internal tour indicator,is_internal_tour,"np.where(is_external_tour == True, False, True)" diff --git a/resident/configs/annotate_tours_tour_mode_choice.csv b/resident/configs/annotate_tours_tour_mode_choice.csv new file mode 100644 index 0000000..1ebe015 --- /dev/null +++ b/resident/configs/annotate_tours_tour_mode_choice.csv @@ -0,0 +1,9 @@ +Description,Target,Expression +non-motorized and walk tran and tnc set to null,selected_vehicle,np.nan +tours using sov option,selected_vehicle,"np.where(df.tour_mode.isin(['DRIVEALONE','PNR_LOC','PNR_PRM','PNR_MIX']), df.vehicle_occup_1, selected_vehicle)" +tours using sr2 option,selected_vehicle,"np.where(df.tour_mode.isin(['SHARED2','KNR_LOC', 'KNR_PRM', 'KNR_MIX']), df.vehicle_occup_2, selected_vehicle)" +tours using sr3 option,selected_vehicle,"np.where(df.tour_mode.isin(['SHARED3']), df['vehicle_occup_3.5'], selected_vehicle)" +parent tour vehicle,_parent_selected_vehicle,"reindex(df.selected_vehicle, df.parent_tour_id) if 'selected_vehicle' in df.columns else np.nan" +assign parent tour vehicle to atwork subtours,selected_vehicle,"np.where(df.tour_category == 'atwork', _parent_selected_vehicle, selected_vehicle)" +is non-driving tour,_is_non_driving_tour,"df.tour_mode.isin(['WALK','BIKE','WALK_LOC','WALK_PRM','WALK_MIX','TAXI','TNC_SINGLE','TNC_SHARED','SCH_BUS'])" +atwork subtours whose parent tour did not use a vehicle use non_hh_veh,selected_vehicle,"np.where(~_is_non_driving_tour & (df.tour_category == 'atwork') & pd.isna(selected_vehicle),'non_hh_veh', selected_vehicle)" diff --git a/resident/configs/atwork_subtour_destination.csv b/resident/configs/atwork_subtour_destination.csv new file mode 100644 index 0000000..bef8820 --- /dev/null +++ b/resident/configs/atwork_subtour_destination.csv @@ -0,0 +1,26 @@ +Label,Description,Expression,atwork +,,"_DIST@np.minimum(skims[('SOV_M_DIST', 'MD')], 20)",1 +,,_DIST_SQUARED@_DIST**2,1 +,,_DIST_CUBED@_DIST**3,1 +,,_DIST_LOGGED@np.log(_DIST + 0.001),1 +util_Distance,Distance,@_DIST,coef_dist_atwork +util_Distance_squared,Distance_squared,@_DIST_SQUARED,coef_distsqrd_atwork +util_Distance_fulltime_worker,Distance_fulltime_worker,@_DIST * (df.ptype==1),coef_dist_ftworker_atwork +util_Size_variable,Size_variable,@df['size_term'].apply(np.log1p),1 +util_No_attractions,No_attractions,@df['size_term']==0,-999 +util_mode_choice_logsum,Mode choice logsum,mode_choice_logsum,coef_mode_choice_logsum +#,,, +util_Calibration_Distance,Calibration_Distance,@_DIST,coef_Calibration_Distance_atwork +util_Calibration_Distance_squared,Calibration_Distance_squared,@_DIST_SQUARED,coef_Calibration_Distance_squared_atwork +util_Calibration_Distance_cubed,Calibration_Distance_cubed,@_DIST_CUBED,coef_Calibration_Distance_cubed_atwork +util_Calibration_Distance_logged,Calibration_Distance_logged,@_DIST_LOGGED,coef_Calibration_Distance_logged_atwork +util_ABM2_calibration_distance,ABM2_calibration_distance,@_DIST,coef_ABM2_calibration_distance_atwork +util_ABM2_calibration_distance_squared,ABM2_calibration_distance_squared,@_DIST_SQUARED,coef_ABM2_calibration_distance_squared_atwork +util_ABM2_calibration_distance_cubed,ABM2_calibration_distance_cubed,@_DIST_CUBED,coef_ABM2_calibration_distance_cubed_atwork +util_ABM2_calibration_distance_logged,ABM2_calibration_distance_logged,@_DIST_LOGGED,coef_ABM2_calibration_distance_logged_atwork +util_ABM2_calibration_0_1_miles,ABM2_calibration_0_1_miles,@(_DIST < 1),coef_ABM2_calibration_0_1_miles_atwork +util_ABM2_calibration_1_2_miles,ABM2_calibration_1_2_miles,@(_DIST < 2) * (_DIST >= 1),coef_ABM2_calibration_1_2_miles_atwork +util_ABM2_calibration_2_3_miles,ABM2_calibration_2_3_miles,@(_DIST < 3) * (_DIST >= 2),coef_ABM2_calibration_2_3_miles_atwork +util_ABM2_calibration_3_4_miles,ABM2_calibration_3_4_miles,@(_DIST < 4) * (_DIST >= 3),coef_ABM2_calibration_3_4_miles_atwork +util_ABM2_calibration_4_5_miles,ABM2_calibration_4_5_miles,@(_DIST < 5) * (_DIST >= 4),coef_ABM2_calibration_4_5_miles_atwork +util_ABM2_calibration_0_8_miles,ABM2_calibration_0_8_miles,@(_DIST < 8) * _DIST,coef_ABM2_calibration_0_8_miles_atwork diff --git a/resident/configs/atwork_subtour_destination.yaml b/resident/configs/atwork_subtour_destination.yaml new file mode 100644 index 0000000..232e1dc --- /dev/null +++ b/resident/configs/atwork_subtour_destination.yaml @@ -0,0 +1,33 @@ + +SPEC: atwork_subtour_destination.csv +SAMPLE_SPEC: atwork_subtour_destination_sample.csv +COEFFICIENTS: atwork_subtour_destination_coefficients.csv + +SAMPLE_SIZE: 30 + +SIZE_TERM_SELECTOR: atwork + +SEGMENTS: + - atwork + +ORIG_ZONE_ID: workplace_zone_id + +SIMULATE_CHOOSER_COLUMNS: + - person_id + - income_segment + - workplace_zone_id + - ptype + +LOGSUM_SETTINGS: tour_mode_choice.yaml + +# model-specific logsum-related settings +CHOOSER_ORIG_COL_NAME: workplace_zone_id +ALT_DEST_COL_NAME: alt_dest +IN_PERIOD: 14 +OUT_PERIOD: 14 + +# optional (comment out if not desired) +DEST_CHOICE_LOGSUM_COLUMN_NAME: destination_logsum + +# comment out DEST_CHOICE_LOGSUM_COLUMN_NAME if saved alt logsum table +DEST_CHOICE_SAMPLE_TABLE_NAME: tour_destination_sample diff --git a/resident/configs/atwork_subtour_destination_coefficients.csv b/resident/configs/atwork_subtour_destination_coefficients.csv new file mode 100644 index 0000000..7b13c01 --- /dev/null +++ b/resident/configs/atwork_subtour_destination_coefficients.csv @@ -0,0 +1,20 @@ +coefficient_name,value,constrain +coef_dist_atwork,-0.705802227,F +coef_distsqrd_atwork,0.014952721,F +coef_dist_ftworker_atwork,0.119028062,F +coef_mode_choice_logsum,0.5,T +#,, +coef_Calibration_Distance_atwork,-0.5666,T +coef_Calibration_Distance_squared_atwork,0.13,T +coef_Calibration_Distance_cubed_atwork,-0.0047,T +coef_Calibration_Distance_logged_atwork,-0.3494,T +coef_ABM2_calibration_distance_atwork,0.3065,T +coef_ABM2_calibration_distance_squared_atwork,-0.0037,T +coef_ABM2_calibration_distance_cubed_atwork,0,T +coef_ABM2_calibration_distance_logged_atwork,-1.5153,T +coef_ABM2_calibration_0_1_miles_atwork,-0.2192,T +coef_ABM2_calibration_1_2_miles_atwork,0.0836,T +coef_ABM2_calibration_2_3_miles_atwork,0.5432,T +coef_ABM2_calibration_3_4_miles_atwork,0.6058,T +coef_ABM2_calibration_4_5_miles_atwork,0.7145,T +coef_ABM2_calibration_0_8_miles_atwork,0.1737,T diff --git a/resident/configs/atwork_subtour_destination_sample.csv b/resident/configs/atwork_subtour_destination_sample.csv new file mode 100644 index 0000000..8c700b7 --- /dev/null +++ b/resident/configs/atwork_subtour_destination_sample.csv @@ -0,0 +1,7 @@ +Label,Description,Expression,atwork +,,"_DIST@skims[('SOV_M_DIST', 'MD')]",1 +util_Distance,Distance,"@_DIST.clip(0,20)",coef_dist_atwork +util_Distance_squared,Distance_squared,"@_DIST.clip(0,20)**2",coef_distsqrd_atwork +util_Distance_fulltime_worker,Distance_fulltime_worker,"@_DIST.clip(0,20) * (df.ptype==1)",coef_dist_ftworker_atwork +util_Size_variable,Size_variable,@df['size_term'].apply(np.log1p),1 +util_No_attractions,No_attractions,@df['size_term']==0,-999 diff --git a/resident/configs/atwork_subtour_frequency.csv b/resident/configs/atwork_subtour_frequency.csv new file mode 100644 index 0000000..36b1bf2 --- /dev/null +++ b/resident/configs/atwork_subtour_frequency.csv @@ -0,0 +1,23 @@ +Label,Expression,no_subtours,eat,business1,maint,business2,eat_business +util_dummy_for_full_time_worker,pemploy==1,coefficient_dummy_for_full_time_worker_no_subtours,coefficient_dummy_for_full_time_worker_eat,coefficient_dummy_for_full_time_worker_business1,coefficient_dummy_for_full_time_worker_maint,coefficient_dummy_for_full_time_worker_business2,coefficient_dummy_for_full_time_worker_eat_business +util_dummy_for_non_full_time_worker,pemploy!=1,coefficient_dummy_for_non_full_time_worker_no_subtours,coefficient_dummy_for_non_full_time_worker_eat,coefficient_dummy_for_non_full_time_worker_business1,coefficient_dummy_for_non_full_time_worker_maint,coefficient_dummy_for_non_full_time_worker_business2,coefficient_dummy_for_non_full_time_worker_eat_business +util_dummy_for_non_workers,"ptype in [4, 5]",coefficient_dummy_for_non_workers_no_subtours,coefficient_dummy_for_non_workers_eat,coefficient_dummy_for_non_workers_business1,coefficient_dummy_for_non_workers_maint,coefficient_dummy_for_non_workers_business2,coefficient_dummy_for_non_workers_eat_business +util_medium_hh_income_dummy,income_segment == 2,coefficient_medium_hh_income_dummy_no_subtours,coefficient_medium_hh_income_dummy_eat,coefficient_medium_hh_income_dummy_business1,coefficient_medium_hh_income_dummy_maint,coefficient_medium_hh_income_dummy_business2,coefficient_medium_hh_income_dummy_eat_business +util_high_hh_income_dummy,(income_segment > 2) & (income_segment < 5),coefficient_high_hh_income_dummy_no_subtours,coefficient_high_hh_income_dummy_eat,coefficient_high_hh_income_dummy_business1,coefficient_high_hh_income_dummy_maint,coefficient_high_hh_income_dummy_business2,coefficient_high_hh_income_dummy_eat_business +util_zero_cars_owned_by_hh_dummy, auto_ownership == 0,coefficient_zero_cars_owned_by_hh_dummy_no_subtours,coefficient_zero_cars_owned_by_hh_dummy_eat,coefficient_zero_cars_owned_by_hh_dummy_business1,coefficient_zero_cars_owned_by_hh_dummy_maint,coefficient_zero_cars_owned_by_hh_dummy_business2,coefficient_zero_cars_owned_by_hh_dummy_eat_business +util_individual_discretionary_tours_made_by_full_time_worker,@(df.pemploy==1)*df.num_discr_tours,coefficient_individual_discretionary_tours_made_by_full_time_worker_no_subtours,coefficient_individual_discretionary_tours_made_by_full_time_worker_eat,coefficient_individual_discretionary_tours_made_by_full_time_worker_business1,coefficient_individual_discretionary_tours_made_by_full_time_worker_maint,coefficient_individual_discretionary_tours_made_by_full_time_worker_business2,coefficient_individual_discretionary_tours_made_by_full_time_worker_eat_business +util_individual_discretionary_tours_made_by_part_time_worker,@(df.pemploy==2)*df.num_discr_tours,coefficient_individual_discretionary_tours_made_by_part_time_worker_no_subtours,coefficient_individual_discretionary_tours_made_by_part_time_worker_eat,coefficient_individual_discretionary_tours_made_by_part_time_worker_business1,coefficient_individual_discretionary_tours_made_by_part_time_worker_maint,coefficient_individual_discretionary_tours_made_by_part_time_worker_business2,coefficient_individual_discretionary_tours_made_by_part_time_worker_eat_business +util_individual_eating_out_tours_made_by_person,num_eatout_tours,coefficient_individual_eating_out_tours_made_by_person_no_subtours,coefficient_individual_eating_out_tours_made_by_person_eat,coefficient_individual_eating_out_tours_made_by_person_business1,coefficient_individual_eating_out_tours_made_by_person_maint,coefficient_individual_eating_out_tours_made_by_person_business2,coefficient_individual_eating_out_tours_made_by_person_eat_business +util_main_shop_escort_tours_allocated_to_full_time_worker,@(df.pemploy==1)*df.num_maint_shop_escort,coefficient_main_shop_escort_tours_allocated_to_full_time_worker_no_subtours,coefficient_main_shop_escort_tours_allocated_to_full_time_worker_eat,coefficient_main_shop_escort_tours_allocated_to_full_time_worker_business1,coefficient_main_shop_escort_tours_allocated_to_full_time_worker_maint,coefficient_main_shop_escort_tours_allocated_to_full_time_worker_business2,coefficient_main_shop_escort_tours_allocated_to_full_time_worker_eat_business +util_main_shop_escort_tours_allocated_to_part_time_worker,@(df.pemploy==2)*df.num_maint_shop_escort,coefficient_main_shop_escort_tours_allocated_to_part_time_worker_no_subtours,coefficient_main_shop_escort_tours_allocated_to_part_time_worker_eat,coefficient_main_shop_escort_tours_allocated_to_part_time_worker_business1,coefficient_main_shop_escort_tours_allocated_to_part_time_worker_maint,coefficient_main_shop_escort_tours_allocated_to_part_time_worker_business2,coefficient_main_shop_escort_tours_allocated_to_part_time_worker_eat_business +util_participation_in_joint_shop_main_eat_tours,num_joint_maint_shop_eat,coefficient_participation_in_joint_shop_main_eat_tours_no_subtours,coefficient_participation_in_joint_shop_main_eat_tours_eat,coefficient_participation_in_joint_shop_main_eat_tours_business1,coefficient_participation_in_joint_shop_main_eat_tours_maint,coefficient_participation_in_joint_shop_main_eat_tours_business2,coefficient_participation_in_joint_shop_main_eat_tours_eat_business +util_participation_in_joint_discretionary_tours,num_joint_discr,coefficient_participation_in_joint_discretionary_tours_no_subtours,coefficient_participation_in_joint_discretionary_tours_eat,coefficient_participation_in_joint_discretionary_tours_business1,coefficient_participation_in_joint_discretionary_tours_maint,coefficient_participation_in_joint_discretionary_tours_business2,coefficient_participation_in_joint_discretionary_tours_eat_business +util_log_of_the_work_tour_duration,@np.log(df.duration+0.5),coefficient_log_of_the_work_tour_duration_no_subtours,coefficient_log_of_the_work_tour_duration_eat,coefficient_log_of_the_work_tour_duration_business1,coefficient_log_of_the_work_tour_duration_maint,coefficient_log_of_the_work_tour_duration_business2,coefficient_log_of_the_work_tour_duration_eat_business +util_dummy_for_drive_alone_mode_for_work_tour,work_tour_is_SOV,coefficient_dummy_for_drive_alone_mode_for_work_tour_no_subtours,coefficient_dummy_for_drive_alone_mode_for_work_tour_eat,coefficient_dummy_for_drive_alone_mode_for_work_tour_business1,coefficient_dummy_for_drive_alone_mode_for_work_tour_maint,coefficient_dummy_for_drive_alone_mode_for_work_tour_business2,coefficient_dummy_for_drive_alone_mode_for_work_tour_eat_business +util_two_work_tours_by_person,num_work_tours==2,coefficient_two_work_tours_by_person_no_subtours,coefficient_two_work_tours_by_person_eat,coefficient_two_work_tours_by_person_business1,coefficient_two_work_tours_by_person_maint,coefficient_two_work_tours_by_person_business2,coefficient_two_work_tours_by_person_eat_business +util_workplace_urban_area_dummy,work_zone_area_type<4,coefficient_workplace_urban_area_dummy_no_subtours,coefficient_workplace_urban_area_dummy_eat,coefficient_workplace_urban_area_dummy_business1,coefficient_workplace_urban_area_dummy_maint,coefficient_workplace_urban_area_dummy_business2,coefficient_workplace_urban_area_dummy_eat_business +util_workplace_suburban_area_dummy,(work_zone_area_type>3) & (work_zone_area_type<6),coefficient_workplace_suburban_area_dummy_no_subtours,coefficient_workplace_suburban_area_dummy_eat,coefficient_workplace_suburban_area_dummy_business1,coefficient_workplace_suburban_area_dummy_maint,coefficient_workplace_suburban_area_dummy_business2,coefficient_workplace_suburban_area_dummy_eat_business +util_auto_accessibility_to_retail_for_work_taz,auOpRetail,coefficient_auto_accessibility_to_retail_for_work_taz_no_subtours,coefficient_auto_accessibility_to_retail_for_work_taz_eat,coefficient_auto_accessibility_to_retail_for_work_taz_business1,coefficient_auto_accessibility_to_retail_for_work_taz_maint,coefficient_auto_accessibility_to_retail_for_work_taz_business2,coefficient_auto_accessibility_to_retail_for_work_taz_eat_business +util_walk_accessibility_to_retail_for_work_taz,nmRetail,coefficient_walk_accessibility_to_retail_for_work_taz_no_subtours,coefficient_walk_accessibility_to_retail_for_work_taz_eat,coefficient_walk_accessibility_to_retail_for_work_taz_business1,coefficient_walk_accessibility_to_retail_for_work_taz_maint,coefficient_walk_accessibility_to_retail_for_work_taz_business2,coefficient_walk_accessibility_to_retail_for_work_taz_eat_business +util_dummy_for_worker_or_student_with_non_mandatory_tour,(is_worker | is_student) * num_non_mand,coefficient_dummy_for_worker_or_student_with_non_mandatory_tour_no_subtours,coefficient_dummy_for_worker_or_student_with_non_mandatory_tour_eat,coefficient_dummy_for_worker_or_student_with_non_mandatory_tour_business1,coefficient_dummy_for_worker_or_student_with_non_mandatory_tour_maint,coefficient_dummy_for_worker_or_student_with_non_mandatory_tour_business2,coefficient_dummy_for_worker_or_student_with_non_mandatory_tour_eat_business +util_at_work_sub_tour_alternative_specific_constant,1,coefficient_at_work_sub_tour_asc_no_subtours,coefficient_at_work_sub_tour_asc_eat,coefficient_at_work_sub_tour_asc_business1,coefficient_at_work_sub_tour_asc_maint,coefficient_at_work_sub_tour_asc_business2,coefficient_at_work_sub_tour_asc_eat_business diff --git a/resident/configs/atwork_subtour_frequency.yaml b/resident/configs/atwork_subtour_frequency.yaml new file mode 100644 index 0000000..ed3ab3e --- /dev/null +++ b/resident/configs/atwork_subtour_frequency.yaml @@ -0,0 +1,11 @@ + +SPEC: atwork_subtour_frequency.csv +COEFFICIENTS: atwork_subtour_frequency_coefficients.csv + +preprocessor: + SPEC: atwork_subtour_frequency_annotate_tours_preprocessor + DF: df + TABLES: + - land_use + - tours + - joint_tour_participants diff --git a/resident/configs/atwork_subtour_frequency_alternatives.csv b/resident/configs/atwork_subtour_frequency_alternatives.csv new file mode 100644 index 0000000..ba99419 --- /dev/null +++ b/resident/configs/atwork_subtour_frequency_alternatives.csv @@ -0,0 +1,8 @@ +#,,,alt file for building tours even though simulation is simple_simulate not interaction_simulate +alt,eat,business,maint +no_subtours,0,0,0 +eat,1,0,0 +business1,0,1,0 +maint,0,0,1 +business2,0,2,0 +eat_business,1,1,0 diff --git a/resident/configs/atwork_subtour_frequency_annotate_tours_preprocessor.csv b/resident/configs/atwork_subtour_frequency_annotate_tours_preprocessor.csv new file mode 100644 index 0000000..3e4f151 --- /dev/null +++ b/resident/configs/atwork_subtour_frequency_annotate_tours_preprocessor.csv @@ -0,0 +1,9 @@ +Description,Target,Expression +,num_maint_shop_escort,df.num_maint_tours+df.num_shop_tours+df.num_escort_tours +#,num_joint_maint_shop_eat,"reindex_i(tours[(tours.tour_category=='joint') & tours.tour_type.isin(['othmaint', 'shopping', 'eatout'])].groupby('person_id').size(), df.person_id)" +#,num_eatout_tours,"reindex_i(tours[~tours.is_joint & (tours.tour_type==EATOUT_TOUR)].groupby('person_id').size(), df.person_id)" +joint tour participants annotated with tour type,_PARTICIPANTS,"pd.merge(joint_tour_participants[['tour_id', 'person_id']], tours[['tour_type']], left_on='tour_id', right_index=True, how='left')" +,num_joint_discr,"reindex_i(_PARTICIPANTS[_PARTICIPANTS.tour_type=='othdiscr'].groupby('person_id').size(), df.person_id)" +,num_joint_maint_shop_eat,"reindex_i(_PARTICIPANTS[_PARTICIPANTS.tour_type.isin(['othmaint', 'shopping', 'eatout'])].groupby('person_id').size(), df.person_id)" +#,, +,work_tour_is_SOV,"df.tour_mode.isin(['DRIVEALONEFREE','DRIVEALONEPAY'])" diff --git a/resident/configs/atwork_subtour_frequency_coefficients.csv b/resident/configs/atwork_subtour_frequency_coefficients.csv new file mode 100644 index 0000000..f719a69 --- /dev/null +++ b/resident/configs/atwork_subtour_frequency_coefficients.csv @@ -0,0 +1,133 @@ +coefficient_name,value,constrain +coefficient_dummy_for_full_time_worker_business1,-7.375,F +coefficient_dummy_for_full_time_worker_business2,-14.28,F +coefficient_dummy_for_full_time_worker_eat,-7.28,F +coefficient_dummy_for_full_time_worker_eat_business,-14.79,F +coefficient_dummy_for_full_time_worker_maint,-8.093,F +coefficient_dummy_for_full_time_worker_no_subtours,-0.6,F +coefficient_dummy_for_non_full_time_worker_business1,-8.319,F +coefficient_dummy_for_non_full_time_worker_business2,-14.28,F +coefficient_dummy_for_non_full_time_worker_eat,-8.604,F +coefficient_dummy_for_non_full_time_worker_eat_business,-14.79,F +coefficient_dummy_for_non_full_time_worker_maint,-8.214,F +coefficient_dummy_for_non_full_time_worker_no_subtours,-0.6,F +coefficient_dummy_for_non_workers_business1,-5,T +coefficient_dummy_for_non_workers_business2,-5,T +coefficient_dummy_for_non_workers_eat,0,T +coefficient_dummy_for_non_workers_eat_business,-5,T +coefficient_dummy_for_non_workers_maint,-5,T +coefficient_dummy_for_non_workers_no_subtours,0,T +coefficient_medium_hh_income_dummy_business1,0.5555,F +coefficient_medium_hh_income_dummy_business2,1.111,F +coefficient_medium_hh_income_dummy_eat,0.61,F +coefficient_medium_hh_income_dummy_eat_business,1.1655,F +coefficient_medium_hh_income_dummy_maint,0.1527,F +coefficient_medium_hh_income_dummy_no_subtours,0,T +coefficient_high_hh_income_dummy_business1,1.066,F +coefficient_high_hh_income_dummy_business2,2.132,F +coefficient_high_hh_income_dummy_eat,0.8693,F +coefficient_high_hh_income_dummy_eat_business,1.9353,F +coefficient_high_hh_income_dummy_maint,0.1651,F +coefficient_high_hh_income_dummy_no_subtours,0,T +coefficient_zero_cars_owned_by_hh_dummy_business1,-0.3391,F +coefficient_zero_cars_owned_by_hh_dummy_business2,0,T +coefficient_zero_cars_owned_by_hh_dummy_eat,0,T +coefficient_zero_cars_owned_by_hh_dummy_eat_business,-0.3391,F +coefficient_zero_cars_owned_by_hh_dummy_maint,0.1762,F +coefficient_zero_cars_owned_by_hh_dummy_no_subtours,0,T +coefficient_individual_discretionary_tours_made_by_full_time_worker_business1,0.7045,F +coefficient_individual_discretionary_tours_made_by_full_time_worker_business2,1.409,F +coefficient_individual_discretionary_tours_made_by_full_time_worker_eat,0.2334,F +coefficient_individual_discretionary_tours_made_by_full_time_worker_eat_business,0.9379,F +coefficient_individual_discretionary_tours_made_by_full_time_worker_maint,0.5061,F +coefficient_individual_discretionary_tours_made_by_full_time_worker_no_subtours,0,T +coefficient_individual_discretionary_tours_made_by_part_time_worker_business1,0.7045,F +coefficient_individual_discretionary_tours_made_by_part_time_worker_business2,1.409,F +coefficient_individual_discretionary_tours_made_by_part_time_worker_eat,0.6776,F +coefficient_individual_discretionary_tours_made_by_part_time_worker_eat_business,1.3821,F +coefficient_individual_discretionary_tours_made_by_part_time_worker_maint,0.5061,F +coefficient_individual_discretionary_tours_made_by_part_time_worker_no_subtours,0,T +coefficient_individual_eating_out_tours_made_by_person_business1,0.5434,F +coefficient_individual_eating_out_tours_made_by_person_business2,1.0868,F +coefficient_individual_eating_out_tours_made_by_person_eat,0.5491,F +coefficient_individual_eating_out_tours_made_by_person_eat_business,1.0925,F +coefficient_individual_eating_out_tours_made_by_person_maint,0.9166,F +coefficient_individual_eating_out_tours_made_by_person_no_subtours,0,T +coefficient_main_shop_escort_tours_allocated_to_full_time_worker_business1,-0.1903,F +coefficient_main_shop_escort_tours_allocated_to_full_time_worker_business2,-0.3806,F +coefficient_main_shop_escort_tours_allocated_to_full_time_worker_eat,0.052,F +coefficient_main_shop_escort_tours_allocated_to_full_time_worker_eat_business,-0.2423,F +coefficient_main_shop_escort_tours_allocated_to_full_time_worker_maint,0.1446,F +coefficient_main_shop_escort_tours_allocated_to_full_time_worker_no_subtours,0,T +coefficient_main_shop_escort_tours_allocated_to_part_time_worker_business1,-0.1903,F +coefficient_main_shop_escort_tours_allocated_to_part_time_worker_business2,-0.3806,F +coefficient_main_shop_escort_tours_allocated_to_part_time_worker_eat,-0.3099,F +coefficient_main_shop_escort_tours_allocated_to_part_time_worker_eat_business,-0.5002,F +coefficient_main_shop_escort_tours_allocated_to_part_time_worker_maint,-0.2723,F +coefficient_main_shop_escort_tours_allocated_to_part_time_worker_no_subtours,0,T +coefficient_participation_in_joint_shop_main_eat_tours_business1,0.083,F +coefficient_participation_in_joint_shop_main_eat_tours_business2,0.166,F +coefficient_participation_in_joint_shop_main_eat_tours_eat,0.2458,F +coefficient_participation_in_joint_shop_main_eat_tours_eat_business,0.3288,F +coefficient_participation_in_joint_shop_main_eat_tours_maint,0.0803,F +coefficient_participation_in_joint_shop_main_eat_tours_no_subtours,0,T +coefficient_participation_in_joint_discretionary_tours_business1,-0.2637,F +coefficient_participation_in_joint_discretionary_tours_business2,-0.5274,F +coefficient_participation_in_joint_discretionary_tours_eat,0.3588,F +coefficient_participation_in_joint_discretionary_tours_eat_business,0.0951,F +coefficient_participation_in_joint_discretionary_tours_maint,0.5822,F +coefficient_participation_in_joint_discretionary_tours_no_subtours,0,T +coefficient_log_of_the_work_tour_duration_business1,1.142,F +coefficient_log_of_the_work_tour_duration_business2,2.284,F +coefficient_log_of_the_work_tour_duration_eat,1.55,F +coefficient_log_of_the_work_tour_duration_eat_business,2.692,F +coefficient_log_of_the_work_tour_duration_maint,1.659,F +coefficient_log_of_the_work_tour_duration_no_subtours,0,T +coefficient_dummy_for_drive_alone_mode_for_work_tour_business1,0.9901,F +coefficient_dummy_for_drive_alone_mode_for_work_tour_business2,1.9802,F +coefficient_dummy_for_drive_alone_mode_for_work_tour_eat,0.4804,F +coefficient_dummy_for_drive_alone_mode_for_work_tour_eat_business,1.4705,F +coefficient_dummy_for_drive_alone_mode_for_work_tour_maint,1.153,F +coefficient_dummy_for_drive_alone_mode_for_work_tour_no_subtours,0,T +coefficient_two_work_tours_by_person_business1,0.3753,F +coefficient_two_work_tours_by_person_business2,0.7506,F +coefficient_two_work_tours_by_person_eat,-0.9862,F +coefficient_two_work_tours_by_person_eat_business,-0.6109,F +coefficient_two_work_tours_by_person_maint,-0.2312,F +coefficient_two_work_tours_by_person_no_subtours,0,T +coefficient_workplace_urban_area_dummy_business1,-0.2235,F +coefficient_workplace_urban_area_dummy_business2,-0.447,F +coefficient_workplace_urban_area_dummy_eat,-0.4182,F +coefficient_workplace_urban_area_dummy_eat_business,-0.6417,F +coefficient_workplace_urban_area_dummy_maint,-0.1479,F +coefficient_workplace_urban_area_dummy_no_subtours,0,T +coefficient_workplace_suburban_area_dummy_business1,-0.1102,F +coefficient_workplace_suburban_area_dummy_business2,-0.2204,F +coefficient_workplace_suburban_area_dummy_eat,-0.2916,F +coefficient_workplace_suburban_area_dummy_eat_business,-0.4018,F +coefficient_workplace_suburban_area_dummy_maint,0,T +coefficient_workplace_suburban_area_dummy_no_subtours,0,T +coefficient_auto_accessibility_to_retail_for_work_taz_business1,0.0534,F +coefficient_auto_accessibility_to_retail_for_work_taz_business2,0.1067,F +coefficient_auto_accessibility_to_retail_for_work_taz_eat,0.015,F +coefficient_auto_accessibility_to_retail_for_work_taz_eat_business,0.0683,F +coefficient_auto_accessibility_to_retail_for_work_taz_maint,0.0265,F +coefficient_auto_accessibility_to_retail_for_work_taz_no_subtours,0,T +coefficient_walk_accessibility_to_retail_for_work_taz_business1,0,T +coefficient_walk_accessibility_to_retail_for_work_taz_business2,0,T +coefficient_walk_accessibility_to_retail_for_work_taz_eat,0.06,F +coefficient_walk_accessibility_to_retail_for_work_taz_eat_business,0.06,F +coefficient_walk_accessibility_to_retail_for_work_taz_maint,0.04,F +coefficient_walk_accessibility_to_retail_for_work_taz_no_subtours,0,T +coefficient_dummy_for_worker_or_student_with_non_mandatory_tour_business1,0,T +coefficient_dummy_for_worker_or_student_with_non_mandatory_tour_business2,0,T +coefficient_dummy_for_worker_or_student_with_non_mandatory_tour_eat,0,T +coefficient_dummy_for_worker_or_student_with_non_mandatory_tour_eat_business,0,T +coefficient_dummy_for_worker_or_student_with_non_mandatory_tour_maint,-0.3573,F +coefficient_dummy_for_worker_or_student_with_non_mandatory_tour_no_subtours,0,T +coefficient_at_work_sub_tour_asc_business1,-0.5372,F +coefficient_at_work_sub_tour_asc_business2,-2.1337,F +coefficient_at_work_sub_tour_asc_eat,0.8576,F +coefficient_at_work_sub_tour_asc_eat_business,-0.9721,F +coefficient_at_work_sub_tour_asc_maint,-0.6198,F +coefficient_at_work_sub_tour_asc_no_subtours,0,T diff --git a/resident/configs/auto_ownership.csv b/resident/configs/auto_ownership.csv new file mode 100644 index 0000000..144f617 --- /dev/null +++ b/resident/configs/auto_ownership.csv @@ -0,0 +1,31 @@ +Label,Description,Expression,cars0,cars1,cars2,cars3,cars4 +util_drivers_1,alternative specific constant for 1 driver household,@df.num_drivers==1,coef_alternative_specific_constant_for_1_driver_household_0_CARS,,coef_alternative_specific_constant_for_1_driver_household_2_CARS,coef_alternative_specific_constant_for_1_driver_household_3_CARS,coef_alternative_specific_constant_for_1_driver_household_4_CARS +util_drivers_2,alternative specific constant for 2 driver household,@df.num_drivers==2,coef_alternative_specific_constant_for_2_driver_household_0_CARS,coef_alternative_specific_constant_for_2_driver_household_1_CAR,,coef_alternative_specific_constant_for_2_driver_household_3_CARS,coef_alternative_specific_constant_for_2_driver_household_4_CARS +util_drivers_3,alternative specific constant for 3 driver household,@df.num_drivers==3,coef_alternative_specific_constant_for_3_driver_household_0_CARS,coef_alternative_specific_constant_for_3_driver_household_1_CAR,coef_alternative_specific_constant_for_3_driver_household_2_CARS,,coef_alternative_specific_constant_for_3_driver_household_4_CARS +util_drivers_4p,alternative specific constant for 4+ driver household,@df.num_drivers>=4,coef_alternative_specific_constant_for_4_driver_household_0_CARS,coef_alternative_specific_constant_for_4_driver_household_1_CAR,coef_alternative_specific_constant_for_4_driver_household_2_CARS,coef_alternative_specific_constant_for_4_driver_household_3_CARS, +util_has_0_4,Presence of children age 0-4,(num_young_children>0),coef_cars0_has_0_4,,coef_cars2_has_0_4,coef_cars34_has_0_4,coef_cars34_has_0_4 +util_has_18_24,Presence of persons age 18-24,(num_college_age > 0),coef_cars0_has_18_24,,coef_cars2_has_18_24,coef_cars34_has_18_24,coef_cars34_has_18_24 +util_has_25_34,Presence of persons age 35-34,(num_young_adults > 0),coef_cars0_has_25_34,,coef_cars2_has_25_34,coef_cars34_has_25_34,coef_cars34_has_25_34 +util_has_80plus,Presence of persons age 80+,(num_old_retirees > 0),coef_cars0_has_80plus,,coef_cars2_has_80plus,coef_cars34_has_80plus,coef_cars34_has_80plus +util_has_1_worker,Has one worker,@df.num_workers==1,coef_cars0_workers_1,,coef_cars2_workers_1,coef_cars34_workers_1,coef_cars34_workers_1 +util_has_2_workers,Has two workers,@df.num_workers==2,coef_cars0_workers_2,,coef_cars2_workers_2,coef_cars34_workers_2,coef_cars34_workers_1 +util_has_3plus_workers,Has three or more workers,@df.num_workers>2,coef_cars0_workers_3plus,,coef_cars2_workers_3plus,coef_cars34_workers_3plus,coef_cars34_workers_3plus +util_hh_income_verylow,household income <15k,@(df.income<15000),coef_household_income_15k_0_CARS,,coef_household_income_15k_2_CARS,coef_household_income_15k_3_CARS,coef_household_income_15k_4_CARS +util_hh_income_low,household income 15k-30k,@((df.income>=15000) & (df.income<30000)),coef_household_income_1530k_0_CARS,,coef_household_income_1530k_2_CARS,coef_household_income_1530k_3_CARS,coef_household_income_1530k_4_CARS +util_hh_income_mid,household income 30-60k,@((df.income>=30000) & (df.income<60000)),coef_household_income_3060k_0_CARS,,coef_household_income_3060k_2_CARS,coef_household_income_3060k_3_CARS,coef_household_income_3060k_4_CARS +util_hh_income_veryhigh,household income 100k+,@(df.income>=100000),coef_household_income_100k_0_CARS,,coef_household_income_100k_2_CARS,coef_household_income_100k_3_CARS,coef_household_income_100k_4_CARS +# FIXME no bldgsz equivalent in the Metro data,,,,,, +# util_attached,attached dwelling,"@np.where(df.bldgsz == 2, 0, 1)",coef_attached_0_CARS,,coef_attached_2_CARS,coef_attached_3_CARS,coef_attached_4_CARS +# 0.785 sq miles in 1/2 mile radius * 640 acres per square mile = 502,,,,,,, +util_intersection_density,Intersection count in 1/2 mile radius of household,totint/502,coef_intersection_density_0_CARS,,coef_intersection_density_2_CARS,coef_intersection_density_3_CARS,coef_intersection_density_4_CARS +util_pop_density,Population Density in 1/2 mile radius of household,popden,coef_population_density_0_CARS,,coef_population_density_2_CARS,coef_population_density_34_CARS,coef_population_density_34_CARS +util_emp_density,Employment Density in 1/2 mile radius of household,empden,coef_emp_density_0_CARS,,coef_emp_density_2_CARS,coef_emp_density_3_CARS,coef_emp_density_4_CARS +util_nm_accessibility,Non-motorized zonal accessbility,nmRetail,coef_nonmotorized_zonal_accessbility_0_CARS,,coef_nonmotorized_zonal_accessbility_2_CARS,coef_nonmotorized_zonal_accessbility_3_CARS,coef_nonmotorized_zonal_accessbility_4_CARS +# Disaggregate accessibilities,,,,,,, +util_nonmand_logsum_0auto,Average non-mandatory disaggrage accessibility -- 0 autos,(shopping_accessibility_0 + othdiscr_accessibility_0) / 2,coef_nonmand_logsum_0,,,, +util_nonmand_logsum_1auto,Average non-mandatory disaggrage accessibility -- 1 autos,(shopping_accessibility_1 + othdiscr_accessibility_1) / 2,,coef_nonmand_logsum_1,,, +util_nonmand_logsum_2pauto,Average non-mandatory disaggrage accessibility -- 2+ autos,(shopping_accessibility_2 + othdiscr_accessibility_2) / 2,,,coef_nonmand_logsum_2p,coef_nonmand_logsum_2p,coef_nonmand_logsum_2p +util_asc,alternative specific constant adjustment for all households,1,asc_allhhs_0_autos,,asc_allhhs_2_autos,asc_allhs_3_autos,asc_allhhs_4plus_autos +util_asc_gq,alternative specific constant adjustment for GQ households,@(df.HHT==0),asc_GQ_0_autos,,coef_unavailable,coef_unavailable,coef_unavailable +# coefficients for av ownership,,,,,,, +#util_household_owns_av,Decreasing the number of household autos if hh owns an AV,av_ownership,coef_unavailable,,coef_cars2_av_ownership,coef_cars3_av_ownership,coef_unavailable diff --git a/resident/configs/auto_ownership.yaml b/resident/configs/auto_ownership.yaml new file mode 100644 index 0000000..ecfc122 --- /dev/null +++ b/resident/configs/auto_ownership.yaml @@ -0,0 +1,19 @@ + +SPEC: auto_ownership.csv +COEFFICIENTS: auto_ownership_coefficients.csv + +LOGIT_TYPE: MNL + +preprocessor: + SPEC: auto_ownership_preprocessor + DF: choosers + TABLES: + - persons + - households + - persons_merged + +annotate_households: + SPEC: annotate_households_auto_ownership + DF: households + TABLES: + - persons_merged \ No newline at end of file diff --git a/resident/configs/auto_ownership_coefficients.csv b/resident/configs/auto_ownership_coefficients.csv new file mode 100644 index 0000000..fe526af --- /dev/null +++ b/resident/configs/auto_ownership_coefficients.csv @@ -0,0 +1,99 @@ +coefficient_name,value,constrain +coef_alternative_specific_constant_for_1_driver_household_0_CARS,-3.400614855,F +coef_alternative_specific_constant_for_1_driver_household_2_CARS,-0.352797459,F +coef_alternative_specific_constant_for_1_driver_household_3_CARS,-1.65228754,F +coef_alternative_specific_constant_for_1_driver_household_4_CARS,-2.217521436,F +coef_alternative_specific_constant_for_2_driver_household_0_CARS,-5.644614423,F +coef_alternative_specific_constant_for_2_driver_household_1_CAR,-2.078124802,F +coef_alternative_specific_constant_for_2_driver_household_3_CARS,-1.110305396,F +coef_alternative_specific_constant_for_2_driver_household_4_CARS,-1.672613333,F +coef_alternative_specific_constant_for_3_driver_household_0_CARS,-5.663986002,F +coef_alternative_specific_constant_for_3_driver_household_1_CAR,-2.219196761,F +coef_alternative_specific_constant_for_3_driver_household_2_CARS,-0.428191009,F +coef_alternative_specific_constant_for_3_driver_household_4_CARS,-0.158614724,F +coef_alternative_specific_constant_for_4_driver_household_0_CARS,-6.09109227,F +coef_alternative_specific_constant_for_4_driver_household_1_CAR,-3.245812862,F +coef_alternative_specific_constant_for_4_driver_household_2_CARS,-1.256377783,F +coef_alternative_specific_constant_for_4_driver_household_3_CARS,-0.736107251,F +coef_cars0_has_16_17,0,F +coef_cars0_has_18_24,0.303347961,F +coef_cars0_has_25_34,0,T +coef_cars0_has_65_79,0,T +coef_cars0_has_80plus,0,T +coef_cars0_has_0_4,0,T +coef_cars0_has_5_17,0,T +coef_cars0_workers_1,-0.626860919,F +coef_cars0_workers_2,-0.687643006,F +coef_cars0_workers_3plus,1.247591794,F +coef_household_income_15k_0_CARS,2.026792987,F +coef_household_income_1530k_0_CARS,1.115116386,F +coef_household_income_3060k_0_CARS,0,T +coef_household_income_100k_0_CARS,-0.901938407,F +coef_attached_0_CARS,0.583266391,F +coef_intersection_density_0_CARS,0.788935628,F +coef_population_density_0_CARS,0.016016885,F +coef_emp_density_0_CARS,0.014187024,F +coef_nonmotorized_zonal_accessbility_0_CARS,0.304730094,F +coef_autotransit_zonal_accessibility_0_CARS,0,T +coef_cars2_has_16_17,0,F +coef_cars2_has_18_24,0,T +coef_cars2_has_25_34,0,T +coef_cars2_has_0_4,0.588834468,F +coef_cars2_has_5_17,0,T +coef_cars2_has_65_79,0,T +coef_cars2_has_80plus,-0.718935811,F +coef_cars2_workers_1,0,T +coef_cars2_workers_2,0.775530112,F +coef_cars2_workers_3plus,0,T +coef_household_income_15k_2_CARS,-1.264502448,F +coef_household_income_1530k_2_CARS,-0.823659861,F +coef_household_income_3060k_2_CARS,-0.284592043,F +coef_household_income_100k_2_CARS,0,T +coef_attached_2_CARS,-0.65382238,F +coef_intersection_density_2_CARS,0.00678398,F +coef_population_density_2_CARS,-0.012314708,F +coef_emp_density_2_CARS,0,T +coef_nonmotorized_zonal_accessbility_2_CARS,-0.051503169,F +coef_autotransit_zonal_accessibility_2_CARS,0,T +coef_cars34_has_16_17,0,T +coef_cars34_has_18_24,0,T +coef_cars34_has_25_34,-0.205410684,F +coef_cars34_has_0_4,0,T +coef_cars34_has_5_17,0,T +coef_cars34_has_65_79,0,T +coef_cars34_has_80plus,-0.973202303,F +coef_cars34_workers_1,0.415094751,F +coef_cars34_workers_2,0.964206165,F +coef_cars34_workers_3plus,1.190507925,F +coef_household_income_15k_3_CARS,-1.827402022,F +coef_household_income_1530k_3_CARS,-1.243285944,F +coef_household_income_3060k_3_CARS,-0.510748344,F +coef_household_income_100k_3_CARS,0,T +coef_attached_3_CARS,-1.426386743,F +coef_intersection_density_3_CARS,-0.875111252,F +coef_population_density_34_CARS,-0.015701116,F +coef_emp_density_3_CARS,-0.01090242,F +coef_nonmotorized_zonal_accessbility_3_CARS,-0.068584652,F +coef_autotransit_zonal_accessibility_3_CARS,-0.000872604,F +coef_cars4_num_workers_clip_3,0,F +coef_household_income_15k_4_CARS,-2.743515835,F +coef_household_income_1530k_4_CARS,-2.050237487,F +coef_household_income_3060k_4_CARS,-0.903012312,F +coef_household_income_100k_4_CARS,0,T +coef_attached_4_CARS,-1.85939905,F +coef_intersection_density_4_CARS,-0.593060324,F +coef_emp_density_4_CARS,-0.016815877,F +coef_nonmotorized_zonal_accessbility_4_CARS,-0.133879458,F +coef_autotransit_zonal_accessibility_4_CARS,-0.002525807,F +coef_nonmand_logsum_0,0.422778026,F +coef_nonmand_logsum_1,0.517335299,F +coef_nonmand_logsum_2p,0.495923942,F +coef_cars2_av_ownership,0,T +coef_cars3_av_ownership,0,T +coef_unavailable,-999,T +asc_GQ_0_autos,2.468181736,F +# Calibration constants (2021 ACS Data),, +asc_allhhs_0_autos,-0.205031923,F +asc_allhhs_2_autos,0.103098218,F +asc_allhs_3_autos,0.217732491,F +asc_allhhs_4plus_autos,0.178545411,F diff --git a/resident/configs/auto_ownership_preprocessor.csv b/resident/configs/auto_ownership_preprocessor.csv new file mode 100644 index 0000000..9d7b904 --- /dev/null +++ b/resident/configs/auto_ownership_preprocessor.csv @@ -0,0 +1,10 @@ +Description,Target,Expression +,_PERSON_COUNT,"lambda query, persons, households: persons.query(query).groupby('household_id').size().reindex(households.index).fillna(0).astype(np.int8)" +,num_old_retirees,"_PERSON_COUNT('80 <= age', persons, households)" +# averaging disaggregate accessibilities across all household members +,shopping_accessibility_0,persons_merged.groupby('household_id').shopping_accessibility_0.mean().reindex(df.index) +,shopping_accessibility_1,persons_merged.groupby('household_id').shopping_accessibility_1.mean().reindex(df.index) +,shopping_accessibility_2,persons_merged.groupby('household_id').shopping_accessibility_2.mean().reindex(df.index) +,othdiscr_accessibility_0,persons_merged.groupby('household_id').othdiscr_accessibility_0.mean().reindex(df.index) +,othdiscr_accessibility_1,persons_merged.groupby('household_id').othdiscr_accessibility_1.mean().reindex(df.index) +,othdiscr_accessibility_2,persons_merged.groupby('household_id').othdiscr_accessibility_2.mean().reindex(df.index) \ No newline at end of file diff --git a/resident/configs/av_ownership.csv b/resident/configs/av_ownership.csv new file mode 100644 index 0000000..677db13 --- /dev/null +++ b/resident/configs/av_ownership.csv @@ -0,0 +1,8 @@ +Label,Description,Expression,owns_av,does_not_own_av +util_low_income,Household income less than 50K,income < 50000,coef_low_income,0 +util_high_income,Household income greater than 100K,income > 100000,coef_high_income,0 +# util_hh_head_age_lt_35,Head of household under age 35,hhhead_age < 35,coef_hh_head_age_lt_35,0 +# util_hh_head_age_gt_65,Head of household over age 65,hhhead_age > 65,coef_hh_head_age_gt_65,0 +# util_per_hour_commute_time,Coefficient per hour of total household commute time,hh_commute_time,coef_per_hour_commute_time,0 +util_static_constant,Static constant that does not change via iterations,1,coef_static,0 +util_av_target_share,Constant to adjust to match set AV target,1,coef_av_target_share,0 diff --git a/resident/configs/av_ownership.yaml b/resident/configs/av_ownership.yaml new file mode 100644 index 0000000..b72482d --- /dev/null +++ b/resident/configs/av_ownership.yaml @@ -0,0 +1,18 @@ + +SPEC: av_ownership.csv +COEFFICIENTS: av_ownership_coeffs.csv + +LOGIT_TYPE: MNL + +preprocessor: + SPEC: av_ownership_preprocessor + DF: choosers + TABLES: + - persons + +# iterative what-if analysis example +# omit these settings to not iterate +AV_OWNERSHIP_ITERATIONS: 5 +AV_OWNERSHIP_TARGET_PERCENT: 0.0 +AV_OWNERSHIP_TARGET_PERCENT_TOLERANCE: 0.01 +AV_OWNERSHIP_COEFFICIENT_CONSTANT: coef_av_target_share \ No newline at end of file diff --git a/resident/configs/av_ownership_coeffs.csv b/resident/configs/av_ownership_coeffs.csv new file mode 100644 index 0000000..18a6a7a --- /dev/null +++ b/resident/configs/av_ownership_coeffs.csv @@ -0,0 +1,8 @@ +coefficient_name,value,constrain +coef_low_income,-1.0,F +coef_high_income,1.0,F +coef_hh_head_age_lt_35,0.5,F +coef_hh_head_age_gt_65,-1.0,F +coef_per_hour_commute_time,0.25,F +coef_static,-3.5,F +coef_av_target_share,0,F diff --git a/resident/configs/av_ownership_preprocessor.csv b/resident/configs/av_ownership_preprocessor.csv new file mode 100644 index 0000000..f6db3c2 --- /dev/null +++ b/resident/configs/av_ownership_preprocessor.csv @@ -0,0 +1,3 @@ +Description,Target,Expression +age of the head of household,hhhead_age,persons.groupby('household_id').age.max().reindex(df.index) +#total household commute time -- cant compute if before workplace location choice,hh_commute_time,persons.groupby('household_id').roundtrip_auto_time_to_work.sum().reindex(df.index) \ No newline at end of file diff --git a/resident/configs/bike_comfort.csv b/resident/configs/bike_comfort.csv new file mode 100644 index 0000000..a91b62c --- /dev/null +++ b/resident/configs/bike_comfort.csv @@ -0,0 +1,10 @@ +Label,Description,Expression,StrongAndFearless,EnthusedAndConfident,InterstedButConcerned,NoWayNoHow +# dummy spec in here for demonstration in preparation for estimation,,,,,, +util_age,Age of persons in years,@df.age,coef_age_strong,coef_age_enthused,coef_age_concerned,0 +util_female,Female dummy,@df.female,coef_female_strong,coef_female_enthused,coef_female_concerned,0 +util_worker,Is worker dummy,@df.is_worker,coef_worker_strong,coef_worker_enthused,coef_worker_concerned,0 +#util_holds_license,Person holds a license,@has_license,coef_has_license_strong,coef_has_license_enthused,coef_has_license_concerned,0 +#util_mobility_difficulty,Has mobility difficulty dummy,@df.has_mobility_difficulty,coef_mobility_difficulty_strong,coef_mobility_difficulty_enthused,coef_mobility_difficulty_concerned,0 +util_logsum_work_0auto,Work accessibility -- 0 autos,workplace_location_accessibility_0,coef_logsum_work_0auto_strong,coef_logsum_work_0auto_enthused,coef_logsum_work_0auto_concerned,0 +util_logsum_shop_0auto,Shopping accessibility -- 0 auto,shopping_accessibility_0,coef_logsum_shop_0auto_strong,coef_logsum_shop_0auto_enthused,coef_logsum_shop_0auto_concerned,0 +util_logsum_other_0auto,Other accessibility -- 0 autos,othdiscr_accessibility_0,coef_logsum_other_0auto_strong,coef_logsum_other_0auto_enthused,coef_logsum_other_0auto_concerned,0 \ No newline at end of file diff --git a/resident/configs/bike_comfort.yaml b/resident/configs/bike_comfort.yaml new file mode 100644 index 0000000..ceb8295 --- /dev/null +++ b/resident/configs/bike_comfort.yaml @@ -0,0 +1,5 @@ + +SPEC: bike_comfort.csv +COEFFICIENTS: bike_comfort_coefficients.csv + +LOGIT_TYPE: MNL \ No newline at end of file diff --git a/resident/configs/bike_comfort_coefficients.csv b/resident/configs/bike_comfort_coefficients.csv new file mode 100644 index 0000000..b321524 --- /dev/null +++ b/resident/configs/bike_comfort_coefficients.csv @@ -0,0 +1,25 @@ +coefficient_name,value,constrain +coef_age_strong,1,F +coef_age_enthused,1,F +coef_age_concerned,1,F +coef_female_strong,0,F +coef_female_enthused,0,F +coef_female_concerned,0,F +coef_worker_strong,0,F +coef_worker_enthused,0,F +coef_worker_concerned,0,F +coef_has_license_strong,0,F +coef_has_license_enthused,0,F +coef_has_license_concerned,0,F +coef_mobility_difficulty_strong,0,F +coef_mobility_difficulty_enthused,0,F +coef_mobility_difficulty_concerned,0,F +coef_logsum_work_0auto_strong,0,F +coef_logsum_work_0auto_enthused,0,F +coef_logsum_work_0auto_concerned,0,F +coef_logsum_shop_0auto_strong,0,F +coef_logsum_shop_0auto_enthused,0,F +coef_logsum_shop_0auto_concerned,0,F +coef_logsum_other_0auto_strong,0,F +coef_logsum_other_0auto_enthused,0,F +coef_logsum_other_0auto_concerned,0,F \ No newline at end of file diff --git a/resident/configs/cdap.yaml b/resident/configs/cdap.yaml new file mode 100644 index 0000000..ea6ae20 --- /dev/null +++ b/resident/configs/cdap.yaml @@ -0,0 +1,47 @@ + +INDIV_AND_HHSIZE1_SPEC: cdap_indiv_and_hhsize1.csv +COEFFICIENTS: cdap_coefficients.csv +INTERACTION_COEFFICIENTS: cdap_interaction_coefficients.csv + +FIXED_RELATIVE_PROPORTIONS_SPEC: cdap_fixed_relative_proportions.csv + +# set to True if want to introduce joint tour utility in cdap +# otherwise set to False or comment out, defaulted to False +ADD_JOINT_TOUR_UTILITY: True +JOINT_TOUR_COEFFICIENTS: cdap_joint_tour_coefficients.csv +JOINT_TOUR_USEFUL_COLUMNS: + - auto_ownership + - income + - num_workers + - shopping_accessibility + - workplace_modechoice_logsum + +CONSTANTS: + FULL: 1 + PART: 2 + UNIVERSITY: 3 + NONWORK: 4 + RETIRED: 5 + DRIVING: 6 + SCHOOL: 7 + PRESCHOOL: 8 + +PERSON_TYPE_MAP: + WORKER: + - 1 + - 2 + CHILD: + - 6 + - 7 + - 8 + +annotate_persons: + SPEC: annotate_persons_cdap + DF: persons + + +annotate_households: + SPEC: annotate_households_cdap + DF: households + TABLES: + - persons diff --git a/resident/configs/cdap_coefficients.csv b/resident/configs/cdap_coefficients.csv new file mode 100644 index 0000000..af7e246 --- /dev/null +++ b/resident/configs/cdap_coefficients.csv @@ -0,0 +1,198 @@ +coefficient_name,value,constrain +coef_UNAVAILABLE,-999.0,T +coef_age_80_plus_N,-0.40552014545569237,F +coef_base_work_from_home_2016_N,-0.412755915492793,F +coef_base_work_from_home_N,-0.36137098196813544,F +coef_base_zero_auto_M,-0.3750683512059161,F +coef_base_zero_auto_N,-0.5866902617568075,F +coef_driving_age_child_2016_asc_M,0.9762795235480467,F +coef_driving_age_child_2016_asc_N,-0.24488773870065403,F +coef_driving_age_child_asc_M,-0.028791867283176956,F +coef_driving_age_child_asc_N,-0.35221765455627785,F +coef_driving_age_child_auto_deficient_N,-0.9468282864008857,F +coef_full_time_worker_2016_asc_M,1.223481320989442,F +coef_full_time_worker_2016_asc_N,0.6920533294338312,F +coef_full_time_worker_age_less_than_35_M,-0.12285898207075491,F +coef_full_time_worker_age_less_than_35_N,-0.15920673068983657,F +coef_full_time_worker_asc_M,1.5644720845560531,F +coef_full_time_worker_asc_N,1.4614984268627733,F +coef_full_time_worker_income_less_than_30k_H,0.15736137368023284,F +coef_non_working_adult_2016_asc_N,0.232621494267051,F +coef_non_working_adult_asc_N,1.1808900957814006,F +coef_non_working_adult_auto_deficient_N,-0.8370587331043843,F +coef_non_working_adult_zero_auto_N,0.0,F +coef_part_time_worker_2016_asc_M,0.2983857953450747,F +coef_part_time_worker_2016_asc_N,0.35204682792828224,F +coef_part_time_worker_age_less_than_35_M,0.23178722435239257,F +coef_part_time_worker_asc_M,1.6387513010169674,F +coef_part_time_worker_asc_N,1.606230763394354,F +coef_part_time_worker_auto_deficient_M,0.37748443629482326,F +coef_part_time_worker_income_less_than_30k_H,0.23400742652772027,F +coef_part_time_worker_work_from_home_2016_N,1.4299461243823042,F +coef_part_time_worker_work_from_home_N,-1.2281808814800665,F +coef_pre_driving_age_child_2016_asc_M,0.11101687817213937,F +coef_pre_driving_age_child_2016_asc_N,0.10384140613549096,F +coef_pre_driving_age_child_asc_M,0.8049646260769578,F +coef_pre_driving_age_child_asc_N,-0.06982784895274624,F +coef_pre_driving_age_child_auto_deficient_M,0.0,F +coef_pre_driving_age_child_auto_deficient_N,0.0,F +coef_preschool_child_2016_asc_M,0.06913803289094414,F +coef_preschool_child_2016_asc_N,0.8594372063809559,F +coef_preschool_child_asc_M,-0.4237607056861521,F +coef_preschool_child_asc_N,-0.7825949113888206,F +coef_preschool_child_auto_deficient_M,-0.6176520350813967,F +coef_preschool_child_income_between_60k_and_100k_M,0.27169762977541834,F +coef_retired_2016_asc_N,0.3104586071157866,F +coef_retired_asc_N,1.1538566746686725,F +coef_retired_auto_deficient_N,-0.5763380569296244,F +coef_telecommute_1_day_week_2016_H,0.6205919905910637,F +coef_telecommute_1_day_week_2016_N,0.5297551190509434,F +coef_telecommute_1_day_week_H,-0.7795490402549963,F +coef_telecommute_1_day_week_N,-0.7150941297946883,F +coef_telecommute_2_3_days_week_2016_H,-0.4085117546195023,F +coef_telecommute_2_3_days_week_2016_N,-0.5948535892511913,F +coef_telecommute_2_3_days_week_H,1.487776947249398,F +coef_telecommute_2_3_days_week_N,0.995563845622819,F +coef_telecommute_4_days_week_2016_H,0.8369274593715018,F +coef_telecommute_4_days_week_2016_N,1.022480425972746,F +coef_telecommute_4_days_week_H,0.7932098123998844,F +coef_telecommute_4_days_week_N,0.2758341263673416,F +coef_university_student_2016_asc_M,1.3474703710372755,F +coef_university_student_2016_asc_N,0.6441076940029465,F +coef_university_student_asc_M,0.2934044679640938,F +coef_university_student_asc_N,0.7161370279501159,F +coef_university_student_auto_deficient_M,0.0,F +coef_university_student_auto_deficient_N,-0.4793921704199631,F +coef_university_student_income_between_60k_and_100k_H,0.0,F +coef_university_student_zero_auto_N,0.0,F +coef_non_mand_accessibility_med_low_N,0.2098418874105526,F +coef_non_mand_accessibility_med_high_N,0.23778715336968032,F +coef_non_mand_accessibility_high_N,0.4489226773950751,F +coef_non_mand_accessibility_med_low_2016_N,-0.14676390165726438,F +coef_non_mand_accessibility_med_high_2016_N,-0.1657971018672975,F +coef_non_mand_accessibility_high_2016_N,-0.3075771697132568,F +coef_H_11,0.9081676720163869,F +coef_H_12,0.5879605906687824,F +coef_H_13,0.5293653823036347,F +coef_H_14,0.6121582837956262,F +coef_H_15,0.785784651471339,F +coef_H_16,0.0,F +coef_H_17,0.1687232310036398,F +coef_H_18,0.370497961792488,F +coef_H_22,1.0023866203429688,F +coef_H_23,0.0,F +coef_H_24,0.0,F +coef_H_25,0.5841783984603512,F +coef_H_26,0.0,F +coef_H_27,0.0,F +coef_H_28,0.0,F +coef_H_33,1.3535972783148953,F +coef_H_34,0.4815185221939276,F +coef_H_35,0.0,F +coef_H_36,0.0,T +coef_H_37,0.0,T +coef_H_38,0.0,T +coef_H_44,0.5566224376246068,F +coef_H_45,0.5008668115587485,F +coef_H_46,0.0,T +coef_H_47,0.0,T +coef_H_48,0.0,T +coef_H_55,0.27176137157094504,F +coef_H_56_57,0.0,T +coef_H_58,0.0,T +coef_H_66,3.14482613013081,F +coef_H_67,0.0,F +coef_H_68,0.0,F +coef_H_77,1.9693172113603012,F +coef_H_78,1.622709457576422,F +coef_H_88,1.9456615126911383,F +coef_M_11,0.20582236458467118,F +coef_M_12,0.0,F +coef_M_13,0.13759875304842933,F +coef_M_16,0.0,F +coef_M_17,0.1008692647165656,F +coef_M_18,0.30906951422329104,F +coef_M_22,0.0,F +coef_M_23,0.0,F +coef_M_26,0.0,F +coef_M_27,0.24621546849867038,F +coef_M_28,0.0,F +coef_M_33,0.2552795327531223,F +coef_M_36,0.0,T +coef_M_37,0.0,T +coef_M_38,0.0,T +coef_M_66,0.0,F +coef_M_67,0.0,F +coef_M_68,0.0,F +coef_M_77,1.180347121732678,F +coef_M_78,0.5390396802361401,F +coef_M_88,1.3066654391714445,F +coef_N_11,0.0,F +coef_N_12,0.0,F +coef_N_13,0.0,F +coef_N_14,0.0,F +coef_N_15,-0.3059631767785066,F +coef_N_16,0.0,F +coef_N_17,0.14867426687105245,F +coef_N_18,0.22534260567363343,F +coef_N_22,0.0,F +coef_N_23,0.0,F +coef_N_24,0.0,F +coef_N_25,0.0,F +coef_N_26,0.9791798564652399,F +coef_N_27,0.0,F +coef_N_28,0.3010315611792873,F +coef_N_33,0.0,F +coef_N_34,0.0,F +coef_N_35,-0.8021689612128808,F +coef_N_36,0.0,T +coef_N_37,0.0,T +coef_N_38,0.0,T +coef_N_44,0.0,F +coef_N_45,0.0,F +coef_N_46,0.0,T +coef_N_47,0.0,T +coef_N_48,0.0,T +coef_N_55,-0.15036765019338055,F +coef_N_56_57_58,0.0,T +coef_N_66,2.241353046656697,F +coef_N_67,1.9957786284419086,F +coef_N_68,0.0,F +coef_N_77,1.6220342829913563,F +coef_N_78,1.1156499097895638,F +coef_N_88,1.2180412202476305,F +coef_H_114,0.0,F +coef_H_117_118,0.0,F +coef_H_127_128,0.0,F +coef_H_147_148,0.0,T +coef_H_177_178_187_188,0.0,F +coef_H_277_278_287_288,0.0,F +coef_H_447_448,0.0,T +coef_H_477_478_487_488,0.0,T +coef_H_777_778_788_888,-1.1692289537913036,F +coef_M_111,-0.32284228918318064,F +coef_M_112,0.0,F +coef_M_122,0.5302446008838457,F +coef_M_127_128,0.2847930736739325,F +coef_M_177_178_187_188,0.0,F +coef_M_227_228,0.0,F +coef_M_277_278_287_288,0.0,F +coef_M_777_778_788_888,-0.5669303177188842,F +coef_N_117_118,0.0,F +coef_N_144,0.0,F +coef_N_127_128,0.0,F +coef_N_147_148,0.0,T +coef_N_177_178_187_188,0.0,F +coef_N_222,0.0,T +coef_N_277_278_287_288,0.0,F +coef_N_477_478_487_488,0.0,T +coef_N_777_778_788_888,-0.7322489904595986,F +coef_M_xxx,0.0,T +coef_N_xxx,0.0,T +coef_H_xxx,0.0,T +coef_M_xxxx,0.0,T +coef_N_xxxx,0.0,T +coef_H_xxxx,0.0,T +coef_M_xxxxx,0.3270563409817982,F +coef_N_xxxxx,0.3500149649035247,F +coef_H_xxxxx,0.0,F diff --git a/resident/configs/cdap_fixed_relative_proportions.csv b/resident/configs/cdap_fixed_relative_proportions.csv new file mode 100644 index 0000000..49ab7c4 --- /dev/null +++ b/resident/configs/cdap_fixed_relative_proportions.csv @@ -0,0 +1,13 @@ +Description,Expression,M,N,H +Full-time worker,(ptype == 1) & ~work_from_home,0.79647,0.09368,0.10985 +Full-time worker,(ptype == 1) & work_from_home,0,0.460276126,0.539723874 +Part-time worker,(ptype == 2) & ~work_from_home,0.61678,0.25757,0.12565 +Part-time worker,(ptype == 2) & work_from_home,0,0.672120453,0.327879547 +University student,ptype == 3,0.69229,0.15641,0.1513 +Non-working adult,ptype == 4,0,0.67169,0.32831 +Retired,ptype == 5,0,0.54295,0.45705 +Driving-age child who is in school,ptype == 6,0.77609,0.06004,0.16387 +Pre-driving-age child who is in school,ptype == 7,0.68514,0.09144,0.22342 +#Child who is too young for school,ptype == 8,0.14056,0.06512,0.79432 +Child who is too young for school,(ptype == 8) & (school_zone_id >= 0),0.14056,0.06512,0.79432 +Child who is too young for school,(ptype == 8) & (school_zone_id < 0),0,0.1354,0.8646 \ No newline at end of file diff --git a/resident/configs/cdap_indiv_and_hhsize1.csv b/resident/configs/cdap_indiv_and_hhsize1.csv new file mode 100644 index 0000000..90f7ae0 --- /dev/null +++ b/resident/configs/cdap_indiv_and_hhsize1.csv @@ -0,0 +1,66 @@ +Description,Expression,M,N,H +Full-time worker ASC,ptype == 1,coef_full_time_worker_asc_M,coef_full_time_worker_asc_N, +Part-time worker ASC,ptype == 2,coef_part_time_worker_asc_M,coef_part_time_worker_asc_N, +University student ASC,ptype == 3,coef_university_student_asc_M,coef_university_student_asc_N, +Non-working adult ASC,ptype == 4,,coef_non_working_adult_asc_N, +Retired ASC,ptype == 5,,coef_retired_asc_N, +Driving-age child who is in school ASC,ptype == 6,coef_driving_age_child_asc_M,coef_driving_age_child_asc_N, +Pre-driving-age child who is in school ASC,ptype == 7,coef_pre_driving_age_child_asc_M,coef_pre_driving_age_child_asc_N, +Preschool child ASC,ptype == 8,coef_preschool_child_asc_M,coef_preschool_child_asc_N, +Full-time worker interaction with age less than 35,(ptype == 1) & (age < 35),coef_full_time_worker_age_less_than_35_M,coef_full_time_worker_age_less_than_35_N, +Part-time worker interaction with age less than 35,(ptype == 2) & (age < 35),coef_part_time_worker_age_less_than_35_M,coef_full_time_worker_age_less_than_35_N, +Age over 80,(age >= 80),,coef_age_80_plus_N, +Zero auto ownership base,(auto_ownership == 0),coef_base_zero_auto_M,coef_base_zero_auto_N, +Auto Deficient part-time worker,(ptype == 2) & (auto_ownership > 0) & (auto_ownership < num_adults),coef_part_time_worker_auto_deficient_M,, +Auto Deficient university_student,(ptype == 3) & (auto_ownership > 0) & (auto_ownership < num_adults),,coef_university_student_auto_deficient_N, +Auto Deficient non_working_adult,(ptype == 4) & (auto_ownership > 0) & (auto_ownership < num_adults),,coef_non_working_adult_auto_deficient_N, +Auto Deficient retired,(ptype == 5) & (auto_ownership > 0) & (auto_ownership < num_adults),,coef_retired_auto_deficient_N, +Auto Deficient driving_age_child,(ptype == 6) & (auto_ownership > 0) & (auto_ownership < num_adults),,coef_driving_age_child_auto_deficient_N, +Auto Deficient preschool_child,(ptype == 8) & (auto_ownership > 0) & (auto_ownership < num_adults),coef_preschool_child_auto_deficient_M,, +Full-time worker interaction with income less than $30k,(ptype == 1) & (income < 30000),,,coef_full_time_worker_income_less_than_30k_H +Part-time worker interaction with income less than $30k,(ptype == 2) & (income < 30000),,,coef_part_time_worker_income_less_than_30k_H +Preschool child interaction with income between $60k and $100k,(ptype == 8) & (income >= 60000) & (income <= 100000),coef_preschool_child_income_between_60k_and_100k_M,, +Base works from home,(work_from_home),,coef_base_work_from_home_N, +Base works from home 2016,(work_from_home),,coef_base_work_from_home_2016_N, +Part time worker who works from home,(ptype == 2) & (work_from_home),,coef_part_time_worker_work_from_home_N, +Part time worker who works from home,@(df.ptype == 2) & (df.work_from_home) & (PRE_COVID),,coef_part_time_worker_work_from_home_2016_N, +Telecommutes 1 day per week,telecommute_frequency=='1_day_week',,coef_telecommute_1_day_week_N,coef_telecommute_1_day_week_H +Telecommutes 2-3 days per week,telecommute_frequency=='2_3_days_week',,coef_telecommute_2_3_days_week_N,coef_telecommute_2_3_days_week_H +Telecommutes 4 days per week,telecommute_frequency=='4_days_week',,coef_telecommute_4_days_week_N,coef_telecommute_4_days_week_H +Full-time worker 2016 ASC,@(df.ptype == 1) & (PRE_COVID),coef_full_time_worker_2016_asc_M,coef_full_time_worker_2016_asc_N, +Part-time worker 2016 ASC,@(df.ptype == 2) & (PRE_COVID),coef_part_time_worker_2016_asc_M,coef_part_time_worker_2016_asc_N, +University student 2016 ASC,@(df.ptype == 3) & (PRE_COVID),coef_university_student_2016_asc_M,coef_university_student_2016_asc_N, +Non-working adult 2016 ASC,@(df.ptype == 4) & (PRE_COVID),,coef_non_working_adult_2016_asc_N, +Retired 2016 ASC,@(df.ptype == 5) & (PRE_COVID),,coef_retired_2016_asc_N, +Driving-age child who is in school 2016 ASC,@(df.ptype == 6) & (PRE_COVID),coef_driving_age_child_2016_asc_M,coef_driving_age_child_2016_asc_N, +Pre-driving-age child who is in school 2016 ASC,@(df.ptype == 7) & (PRE_COVID),coef_pre_driving_age_child_2016_asc_M,coef_pre_driving_age_child_2016_asc_N, +Preschool child 2016 ASC,@(df.ptype == 8) & (PRE_COVID),coef_preschool_child_2016_asc_M,coef_preschool_child_2016_asc_N, +Telecommutes 1 day per week 2016,@(df.telecommute_frequency=='1_day_week') & (PRE_COVID),,coef_telecommute_1_day_week_2016_N,coef_telecommute_1_day_week_2016_H +Telecommutes 2-3 days per week 2016,@(df.telecommute_frequency=='2_3_days_week') & (PRE_COVID),,coef_telecommute_2_3_days_week_2016_N,coef_telecommute_2_3_days_week_2016_H +Telecommutes 4 days per week 2016,@(df.telecommute_frequency=='4_days_week') & (PRE_COVID),,coef_telecommute_4_days_week_2016_N,coef_telecommute_4_days_week_2016_H +Mandatory pattern unavailable if not worker or student,(is_student == False) & (is_worker == False),coef_UNAVAILABLE,, +Mandatory pattern unavailable if not works from home,(work_from_home == True) & (is_student == False),coef_UNAVAILABLE,, +# cutting non-mandatory accessibility into roughly quartiles with low as base,,,, +Non-mandatory accessibility med low,@(((df.shopping_accessibility + df.othdiscr_accessibility) / 2) >= 12) & (((df.shopping_accessibility + df.othdiscr_accessibility) / 2) < 13.5),,coef_non_mand_accessibility_med_low_N, +Non-mandatory accessibility med high,@(((df.shopping_accessibility + df.othdiscr_accessibility) / 2) >= 13.5) & (((df.shopping_accessibility + df.othdiscr_accessibility) / 2) < 15),,coef_non_mand_accessibility_med_high_N, +Non-mandatory accessibility high,@(((df.shopping_accessibility + df.othdiscr_accessibility) / 2) > 15),,coef_non_mand_accessibility_high_N, +Non-mandatory accessibility med low,@(((df.shopping_accessibility + df.othdiscr_accessibility) / 2) >= 12) & (((df.shopping_accessibility + df.othdiscr_accessibility) / 2) < 13.5) & (PRE_COVID),,coef_non_mand_accessibility_med_low_2016_N, +Non-mandatory accessibility med high,@(((df.shopping_accessibility + df.othdiscr_accessibility) / 2) >= 13.5) & (((df.shopping_accessibility + df.othdiscr_accessibility) / 2) < 15) & (PRE_COVID),,coef_non_mand_accessibility_med_high_2016_N, +Non-mandatory accessibility high,@(((df.shopping_accessibility + df.othdiscr_accessibility) / 2) > 15) & (PRE_COVID),,coef_non_mand_accessibility_high_2016_N, +#,,,, +Full-time worker weighted target ASC,ptype == 1,-0.076,0.033,0.288 +Part-time worker weighted target ASC,ptype == 2,0.118,-0.124,0.029 +University student weighted target ASC,ptype == 3,-0.081,0.038,0.164 +Non-working adult weighted target ASC,ptype == 4,,-0.110,0.284 +Retired weighted target ASC,ptype == 5,,-0.131,0.313 +Driving-age child who is in school weighted target ASC,ptype == 6,-0.134,0.314,0.026 +Pre-driving-age child who is in school weighted target ASC,ptype == 7,0.028,-0.084,0.010 +Preschool child weighted target ASC,ptype == 8,0.104,-0.223,0.116 +ABM3 calibration ASC ptype=1,ptype==1,0.600,0.00,-0.527 +ABM3 calibration ASC ptype=2,ptype==2,-0.107,0.0276,0.190 +ABM3 calibration ASC ptype=3,ptype==3,0.427,-0.120,-0.200 +ABM3 calibration ASC ptype=4,ptype==4,0.0,0.00,0.0889 +ABM3 calibration ASC ptype=5,ptype==5,0.0,0.134,-0.227 +ABM3 calibration ASC ptype=6,ptype==6,3.750,-0.241,0.298 +ABM3 calibration ASC ptype=7,ptype==7,2.200,-0.171,0.096 +ABM3 calibration ASC ptype=8,ptype==8,0.536,0.454,-0.731 \ No newline at end of file diff --git a/resident/configs/cdap_interaction_coefficients.csv b/resident/configs/cdap_interaction_coefficients.csv new file mode 100644 index 0000000..f94fa17 --- /dev/null +++ b/resident/configs/cdap_interaction_coefficients.csv @@ -0,0 +1,177 @@ +activity,interaction_ptypes,coefficient +# 2-way interactions,, +H,11,coef_H_11 +H,12,coef_H_12 +H,13,coef_H_13 +H,14,coef_H_14 +H,15,coef_H_15 +H,16,coef_H_16 +H,17,coef_H_17 +H,18,coef_H_18 +H,22,coef_H_22 +H,23,coef_H_23 +H,24,coef_H_24 +H,25,coef_H_25 +H,26,coef_H_26 +H,27,coef_H_27 +H,28,coef_H_28 +H,33,coef_H_33 +H,34,coef_H_34 +H,35,coef_H_35 +H,36,coef_H_36 +H,37,coef_H_37 +H,38,coef_H_38 +H,44,coef_H_44 +H,45,coef_H_45 +H,46,coef_H_46 +H,47,coef_H_47 +H,48,coef_H_48 +H,55,coef_H_55 +H,56,coef_H_56_57 +H,57,coef_H_56_57 +H,58,coef_H_58 +H,66,coef_H_66 +H,67,coef_H_67 +H,68,coef_H_68 +H,77,coef_H_77 +H,78,coef_H_78 +H,88,coef_H_88 +M,11,coef_M_11 +M,12,coef_M_12 +M,13,coef_M_13 +M,16,coef_M_16 +M,17,coef_M_17 +M,18,coef_M_18 +M,22,coef_M_22 +M,23,coef_M_23 +M,26,coef_M_26 +M,27,coef_M_27 +M,28,coef_M_28 +M,33,coef_M_33 +M,36,coef_M_36 +M,37,coef_M_37 +M,38,coef_M_38 +M,66,coef_M_66 +M,67,coef_M_67 +M,68,coef_M_68 +M,77,coef_M_77 +M,78,coef_M_78 +M,88,coef_M_88 +N,11,coef_N_11 +N,12,coef_N_12 +N,13,coef_N_13 +N,14,coef_N_14 +N,15,coef_N_15 +N,16,coef_N_16 +N,17,coef_N_17 +N,18,coef_N_18 +N,22,coef_N_22 +N,23,coef_N_23 +N,24,coef_N_24 +N,25,coef_N_25 +N,26,coef_N_26 +N,27,coef_N_27 +N,28,coef_N_28 +N,33,coef_N_33 +N,34,coef_N_34 +N,35,coef_N_35 +N,36,coef_N_36 +N,37,coef_N_37 +N,38,coef_N_38 +N,44,coef_N_44 +N,45,coef_N_45 +N,46,coef_N_46 +N,47,coef_N_47 +N,48,coef_N_48 +N,55,coef_N_55 +N,56,coef_N_56_57_58 +N,57,coef_N_56_57_58 +N,58,coef_N_56_57_58 +N,66,coef_N_66 +N,67,coef_N_67 +N,68,coef_N_68 +N,77,coef_N_77 +N,78,coef_N_78 +N,88,coef_N_88 +# 3-way interactions,, +H,114,coef_H_114 +H,117,coef_H_117_118 +H,118,coef_H_117_118 +H,127,coef_H_127_128 +H,128,coef_H_127_128 +H,147,coef_H_147_148 +H,148,coef_H_147_148 +H,177,coef_H_177_178_187_188 +H,178,coef_H_177_178_187_188 +H,187,coef_H_177_178_187_188 +H,188,coef_H_177_178_187_188 +H,277,coef_H_277_278_287_288 +H,278,coef_H_277_278_287_288 +H,287,coef_H_277_278_287_288 +H,288,coef_H_277_278_287_288 +H,447,coef_H_447_448 +H,448,coef_H_447_448 +H,477,coef_H_477_478_487_488 +H,478,coef_H_477_478_487_488 +H,487,coef_H_477_478_487_488 +H,488,coef_H_477_478_487_488 +H,777,coef_H_777_778_788_888 +H,778,coef_H_777_778_788_888 +H,788,coef_H_777_778_788_888 +H,888,coef_H_777_778_788_888 +M,111,coef_M_111 +M,112,coef_M_112 +M,122,coef_M_122 +M,127,coef_M_127_128 +M,128,coef_M_127_128 +M,177,coef_M_177_178_187_188 +M,178,coef_M_177_178_187_188 +M,187,coef_M_177_178_187_188 +M,188,coef_M_177_178_187_188 +M,227,coef_M_227_228 +M,228,coef_M_227_228 +M,277,coef_M_277_278_287_288 +M,278,coef_M_277_278_287_288 +M,287,coef_M_277_278_287_288 +M,288,coef_M_277_278_287_288 +M,777,coef_M_777_778_788_888 +M,778,coef_M_777_778_788_888 +M,788,coef_M_777_778_788_888 +M,888,coef_M_777_778_788_888 +N,117,coef_N_117_118 +N,118,coef_N_117_118 +N,144,coef_N_144 +N,127,coef_N_127_128 +N,128,coef_N_127_128 +N,147,coef_N_147_148 +N,148,coef_N_147_148 +N,177,coef_N_177_178_187_188 +N,178,coef_N_177_178_187_188 +N,187,coef_N_177_178_187_188 +N,188,coef_N_177_178_187_188 +N,222,coef_N_222 +N,277,coef_N_277_278_287_288 +N,278,coef_N_277_278_287_288 +N,287,coef_N_277_278_287_288 +N,288,coef_N_277_278_287_288 +N,477,coef_N_477_478_487_488 +N,478,coef_N_477_478_487_488 +N,487,coef_N_477_478_487_488 +N,488,coef_N_477_478_487_488 +N,777,coef_N_777_778_788_888 +N,778,coef_N_777_778_788_888 +N,788,coef_N_777_778_788_888 +N,888,coef_N_777_778_788_888 +# cdap_final_rules,, +M,5,coef_UNAVAILABLE +M,4,coef_UNAVAILABLE +# cdap_all_people,, +M,***,coef_M_xxx +N,***,coef_N_xxx +H,***,coef_H_xxx +M,****,coef_M_xxxx +N,****,coef_N_xxxx +H,****,coef_H_xxxx +M,*****,coef_M_xxxxx +N,*****,coef_N_xxxxx +H,*****,coef_H_xxxxx diff --git a/resident/configs/cdap_joint_tour_coefficients.csv b/resident/configs/cdap_joint_tour_coefficients.csv new file mode 100644 index 0000000..1eb8d6a --- /dev/null +++ b/resident/configs/cdap_joint_tour_coefficients.csv @@ -0,0 +1,19 @@ +Label,description,Expression,dependency,coefficient +,constant - joint tour,1,,-3.1506 +,joint tour for hhsize 2,hhsize == 2,,-0.550050686 +,joint tour for hhsize 3,hhsize == 3,,-1.066203116 +,joint tour for hhsize 4,hhsize == 4,,-1.041167491 +,joint tour for hhsize 5+,hhsize >= 5,,-1.281586989 +,person x is adult and DAP is M,ptype_px < 7,M_px,0.008 +,person x is adult and DAP is N,ptype_px < 7,N_px,1.2557 +,person x is kid and DAP is M,ptype_px > 6,M_px,0.1088 +,person x is kid and DAP is N,ptype_px > 6,N_px,1.6898 +,Accessibility to retail employment/Non-Mandatory Attractions,shopping_accessibility_p1,,0.055031985 +,Income less than $30k,income_p1 < 30000,,-0.192506367 +,Income between $60k and $100k,(income_p1 >= 60000) & (income_p1 < =100000),,0.104325349 +,Income more than $100k,income_p1 > 100000,,0.104325349 +,No Car Households,auto_ownership_p1 == 0,,0 +,Cars Less than Workers,auto_ownership_p1 < num_workers_p1,,0.088402389 +,Cars More than Workers,auto_ownership_p1 > num_workers_p1,,-0.005896499 +,WorkAccessForMandatoryDap,"@df.workplace_modechoice_logsum_px.fillna(0)",M_px,0.17217579 +,If All Adults stay at Home/ None of the Adults have Dap 1 or 2,(ptype_pxprod < 7) | (ptype_pxprod > 6),H_px,-0.988838929 diff --git a/resident/configs/constants.yaml b/resident/configs/constants.yaml new file mode 100644 index 0000000..3519117 --- /dev/null +++ b/resident/configs/constants.yaml @@ -0,0 +1,257 @@ +## ActivitySim +## See full license in LICENSE.txt. + +scenarioYear: 2022 +# set below to 1 for everything to be internal, 0 otherwise +NO_EXTERNAL: 0 + +# set below to be 1 for years before 2021, 0 otherwise +PRE_COVID: 0 + +# convenience for expression files +HHT_NONFAMILY: [4, 5, 6, 7] +HHT_FAMILY: [1, 2, 3] + +PSTUDENT_GRADE_OR_HIGH: 1 +PSTUDENT_UNIVERSITY: 2 +PSTUDENT_NOT: 3 + +GRADE_SCHOOL_MAX_AGE: 14 +GRADE_SCHOOL_MIN_AGE: 5 + +SCHOOL_SEGMENT_NONE: 0 +SCHOOL_SEGMENT_PREK: 1 +SCHOOL_SEGMENT_GRADE: 2 +SCHOOL_SEGMENT_HIGH: 3 +SCHOOL_SEGMENT_UNIV: 4 + + +INCOME_SEGMENT_LOW: 1 +INCOME_SEGMENT_MED: 2 +INCOME_SEGMENT_HIGH: 3 +INCOME_SEGMENT_VERYHIGH: 4 + +PEMPLOY_FULL: 1 +PEMPLOY_PART: 2 +PEMPLOY_NOT: 3 +PEMPLOY_CHILD: 4 + +PTYPE_FULL: 1 +PTYPE_PART: 2 +PTYPE_UNIVERSITY: 3 +PTYPE_NONWORK: 4 +PTYPE_RETIRED: 5 +PTYPE_SCHOOL: 6 +PTYPE_PRESCHOOL: 7 + +PTYPE_VALUE: + 1: PTYPE_FULL + 2: PTYPE_PART + 3: PTYPE_UNIVERSITY + 4: PTYPE_NONWORK + 5: PTYPE_RETIRED + 6: PTYPE_SCHOOL + 7: PTYPE_PRESCHOOL + + +CDAP_ACTIVITY_MANDATORY: M +CDAP_ACTIVITY_NONMANDATORY: N +CDAP_ACTIVITY_HOME: H + +# Defines cuts on psedomsa in landuse file. Replacement for area_type +cbd_threshold: 2 +urban_threshold: 5 +rural_threshold: 5 + +distributed_time_factor_work_mean: 1.0 +distributed_time_factor_work_stddev: 0.7 +distributed_time_factor_nonwork_mean: 1.0 +distributed_time_factor_nonwork_stddev: 0.6 +distributed_time_factor_min: 0.1 +distributed_time_factor_max: 10 + +c_drive: 1.5 + +AV_OWNERSHIP_TARGET_PERCENT: 0.0 +autoIVTFactorAV: 0.75 +autoParkingCostFactorAV: 0.5 +autoCostPerMileFactorAV: 0.7 +autoTerminalTimeFactorAV: 0.65 +minAgeDriveAloneAV: 13 + + +#valueOfTime: 8.00 +costPerMile: 32.5 +shortWalk: 0.333 +walkSpeed: 3.00 +bikeSpeed: 7.80 +ebikeSpeed: 10.00 +escooterSpeed: 6.70 +driveSpeed: 25.00 +indivTour: 1.00000 + +### Crosswalk between OCCCAT and size terms +# FIXME -- this xwalk is completely made up!! Needs to be updated with real data. +occupation_xwalk: + 1: mngt_busi_scic_arts + 2: services + 3: health + 4: services + 5: sales_office + 6: constr_maint + 7: prod_trans_move + 999: military + -1: # no occupation + +# RIDEHAIL Settings +Taxi_baseFare: 3.00 +Taxi_costPerMile: 3.3 +Taxi_costPerMinute: 0.46 +Taxi_waitTime_mean: + 1: 5.5 + 2: 9.5 + 3: 13.3 + 4: 17.3 + 5: 26.5 +#### [NICK] Updated all SD numbers from sandag_abm.properties. Were all 0 before +Taxi_waitTime_sd: + 1: 6.4 + 2: 6.4 + 3: 6.4 + 4: 6.4 + 5: 6.4 +TNC_single_baseFare: 3.31 +TNC_single_costPerMile: 0.96 +TNC_single_costPerMinute: 0.33 +TNC_single_costMinimum: 9.19 +TNC_single_waitTime_mean: + 1: 3.0 + 2: 6.3 + 3: 8.4 + 4: 8.5 + 5: 10.3 +TNC_single_waitTime_sd: + 1: 4.1 + 2: 4.1 + 3: 4.1 + 4: 4.1 + 5: 4.1 +TNC_shared_baseFare: 1.66 +TNC_shared_costPerMile: 0.48 +TNC_shared_costPerMinute: 0.16 +TNC_shared_costMinimum: 4.6 +TNC_shared_IVTFactor: 1.5 +TNC_shared_waitTime_mean: + 1: 5.0 + 2: 8.0 + 3: 11.0 + 4: 15.0 + 5: 15.0 +TNC_shared_waitTime_sd: + 1: 4.1 + 2: 4.1 + 3: 4.1 + 4: 4.1 + 5: 4.1 +min_waitTime: 0 +max_waitTime: 50 + +# rapid bus wait time discount +WAIT_TIME_DISC: 1.0 + +ivt_lrt_multiplier: 0.85 +ivt_brt_multiplier: 0.95 +ivt_cmr_multiplier: 0.85 +ivt_ltd_multiplier: 1.0 +# line-haul mode constants; note commuter rail is based on CMRIVTT. Also currently hyperloop is not applied because we do not skim hyperloop IVTT +eb_equiv_asc: 10 +brt_equiv_asc: -5 +lrt_equiv_asc: -30 +pnr_lrt_equiv_asc: -20 +# +density_index_multiplier: -0.2 +# joint_sr2_ASC_no_auto: 0 +# joint_sr2_ASC_auto_deficient: 0 +# joint_sr2_ASC_auto_sufficient: 0 +# joint_drive_transit_ASC_no_auto: 0 +cost_share_s2: 0.57 +cost_share_s3: 0.37 +vot_threshold_low: 8.81 +vot_threshold_med: 18.00 +max_walk_time: 60 +max_bike_time: 60 +max_walk_distance: 3 +max_bike_distance: 12 +# Location of attractions for associated special size terms +cabrillo_maz: 23831 +seaworld_maz: 8127 +legoland_maz: 24245 +safari_maz: 14345 +midway_maz: 10646 +zoo_maz: 10561 +torrypines_maz: 4093 + +# Micromobility constants +microVarCost: 39 #cents +microFixedCost: 100 #cents +microRentTime: 1 +microAccessThreshold: 100 +ebikeownership: 0.008 +ebikeMaxDist: 10.50 +escooterMaxDist: 2.00 + +# Microtransit and NEV constants +microtransitSpeed: 30 +microtransitCost: 125 #cents +microtransitWaitTime: 12 +microtransitMaxDist: 4.5 +microtransitDiversionConstant: 6 +microtransitDiversionFactor: 1.25 +microtransitStartPeriod: 9 +microtransitEndPeriod: 32 +nevSpeed: 17 +nevCost: 125 #cents +nevWaitTime: 12 +nevMaxDist: 3 +nevDwellMinutesPerMile: 0.5 +nevDiversionConstant: 6 +nevDiversionFactor: 1.25 +nevStartPeriod: 9 +nevEndPeriod: 38 +maxWalkIfMTAccessAvailable: 1.0 # Maximum distance to walk to premium transit if microtransit access is available + +# cost of "average" monthly transit pass cost. Used in transit pass ownership model. +# cost of pass divided by 2 for age < 18 and 65+. +monthly_transit_pass_cost: 100 + +# year after which a vehicle (therefore its household) must have transponder +hhTR_Vehyear: 2029 + +# SHARED2 and SHARED3 Occupancies +OCC_SHARED2: 2.0 +OCC_SHARED3: 3.33 +# add TNC and Taxi Occupancies +OCC_TAXI: 1.1 +OCC_TNC_SINGLE: 1.2 +OCC_TNC_SHARED: 2.0 + +# EV Rebate constants +LowIncomeEVRebateCutoff: 3 +MedIncomeEVRebateCutoff: 4 +LowIncomeBEVRebate: 0 +LowIncomePEVRebate: 0 +MedIncomeBEVRebate: 0 +MedIncomePEVRebate: 0 + +# MGRAs for Ports of Entry +SanYsidro_maz_id: 9279 +OtayMesa_maz_id: 9387 +Tecate_maz_id: 22324 +OtayMesaEast_maz_id: 7123 + +# dispersion parameters used for accessibility calculations +dispersion_parameter_automobile: -0.05 +dispersion_parameter_transit: -0.05 +dispersion_parameter_walk: -1.00 +# perceived minute of in-vehicle time for every minute of out-of-vehicle time +out_of_vehicle_time_weight: 2.0 \ No newline at end of file diff --git a/resident/configs/destination_choice_size_terms.csv b/resident/configs/destination_choice_size_terms.csv new file mode 100644 index 0000000..cdfacae --- /dev/null +++ b/resident/configs/destination_choice_size_terms.csv @@ -0,0 +1,38 @@ +model_selector,segment,TOTHHS,TOTPOP,EMP_NRM,EMP_CON,EMP_MFG,EMP_WT,EMP_TWU,EMP_RET,EMP_IFRPBS,EMP_HCS,EMP_OSV,EMP_GOV,EMP_EDU,EMP_AER,EMP_AFS,EMP_TOTAL,ENROLLGRADEKto8,ENROLLGRADE9to12,COLLEGEENROLL,ACTIVE_ACRES,EXTERNAL +workplace,constr_maint,0,0,0.114467809,1,0.019444044,0.053921216,0.042038744,0.057720006,0.149701138,0.007849633,0.160935475,0.137710644,0.014673052,0.021406453,0.007723607,0,0,0,0,0,0 +workplace,health,0,0,0,0.000229486,0.000272515,0.028269818,0,0.056869523,0.064585993,1,0.078627673,0.181566529,0.019735804,0.006167439,0.001161773,0,0,0,0,0,0 +workplace,military,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0 +workplace,mngt_busi_scic_arts,0,0,0.023150473,0.105478261,0.033921273,0.520588696,0.019155288,0.063194096,1,0.12997306,0.101893393,0.357056089,0.046020203,0.044384337,0.014582422,0,0,0,0,0,0 +workplace,prod_trans_move,0,0,0.044941354,0.059838173,0.160289782,0.055761149,0.692090573,0.577996613,0.289719626,0.051119614,0.300100358,0.169102427,0.010851157,0.045537226,0.039515775,0,0,0,0,0,0 +workplace,sales_office,0,0,0.011768937,0.051276887,0.174045033,1,0.206618229,0.914135674,0.494698223,0.170534494,0.142953744,0.337702324,0.048183584,0.052183296,0.031177613,0,0,0,0,0,0 +workplace,services,0,0,0.03214445,0.013782902,0.002031041,0.064277161,0.007689779,0.04669046,0.477412008,0.145096152,0.403566649,0.565075489,0.264904083,0.154746531,0.079950222,0,0,0,0,0,0 +school,preschool,0,0.1888,0,0,0,0,0,0,0,1,1,1,1,0,0,0,0,0,0,0,0 +school,gradeschool,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0 +school,highschool,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0 +school,university,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0 +non_mandatory,escort,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0 +non_mandatory,shopping,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0 +non_mandatory,othmaint,0,0,0,0,0,0,0,1.60296,0.42255,0.42255,0,0.24001,0,0,0,0,0,0,0,0,0 +non_mandatory,eatout,0.5512,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 +non_mandatory,social,0.3006,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 +non_mandatory,othdiscr,0.04333,0,0,0,0,0.004465,0,0.042025,0.004465,0,1,0.005953,0,0.20337,0.03453,0,0.03167,0.05136,0.02258,3.71685,0 +atwork,atwork,0,0,0,0,0,0,0,0.104,0.0145,0,0,0,0,0,0,0,0,0,0,0,0 +trip,work,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0 +trip,escort,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0 +trip,shopping,0.000001,0,0,0,0,0,0,0.375,0,0,0,0,0,0,0,0.0001,0,0,0,0,0 +trip,othmaint,0.000001,0,0,0,0,0,0,1.2379255,0.025,1.464014,0,0.661904,0,0,0,0.0001,0,0,0,0,0 +trip,eatout,0.010162,0,0,0,0,0,0,0.0689145,0,0,0,0,0,0,0,0,0,0,0,0,0 +trip,social,0.495249,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 +trip,othdiscr,0.027342,0,0,0,0,0,0,0.0194215,0,0,1,0,0,0.473744,0.092343,0,0,0,0,3.71685,0.05 +trip,univ,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0 +# not needed as school is not chosen as an intermediate trip destination,,,,,,,,,,,,,,,,,,,,,, +#trip,gradeschool,0,,,,,,,,,,,,,,,,,,,, +#trip,highschool,0,,,,,,,,,,,,,,,,,,,, +external_workplace,external_workplace,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1 +external_nonwork,external_nonwork,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1 +external_non_mandatory,escort,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1 +external_non_mandatory,shopping,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1 +external_non_mandatory,othmaint,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1 +external_non_mandatory,eatout,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1 +external_non_mandatory,social,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1 +external_non_mandatory,othdiscr,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1 diff --git a/resident/configs/disaggregate_accessibility.yaml b/resident/configs/disaggregate_accessibility.yaml new file mode 100644 index 0000000..e87dbcd --- /dev/null +++ b/resident/configs/disaggregate_accessibility.yaml @@ -0,0 +1,192 @@ +# Sampling size. 0 = no limit +# can be whole integer value or a sample rate (percent of zones) +# zero or missing defaults to full sample! +# DESTINATION_SAMPLE_SIZE: 100 +# ORIGIN_SAMPLE_SIZE: 5000 +DESTINATION_SAMPLE_SIZE: 10 +ORIGIN_SAMPLE_SIZE: 100 + +# select origin zones weighted by population (or another landuse variable) +ORIGIN_WEIGHTING_COLUMN: TOTPOP + +# skim to use to find nearest zone when distributing accessibilities to un-sampled origins +NEAREST_ZONE_SKIM: SOV_M_DIST__MD + +# Specify the tables to be created and their variables. +# Single values are constants +# Lists are varying and will generate the cartesian product (i.e., all possible non-repeating combinations) + +# Example result for households below: +#hhid veh hinccat1 hinc hworkers persons hht bldgsz +# 1 0 1 14000 1 2 1 2 +# 2 1 1 14000 1 2 1 2 +# 3 2 1 14000 1 2 1 2 +# 4 0 2 67000 1 2 1 2 +# 5 1 2 67000 1 2 1 2 +# 6 2 2 67000 1 2 1 2 +# .... + +#Note: parameters in ALL CAPS below are required by the program for synthesis +CREATE_TABLES: + PROTO_HOUSEHOLDS: + index_col: proto_household_id + zone_col: home_zone_id + rename_columns: + zone_id: home_zone_id + VARIABLES: + hinccat1: [1, 2, 3, 4] # Income categories + hworkers: 1 # Household workers + auto_ownership: [0, 1, 2] # Household vehicles + persons: 2 # Two persons household + HHT: 1 # Married-couple family household + bldgsz: 2 # Building size - Single family detached + # Additional columns that are mapped to another (e.g., hhinccat1: 1 = hinc: 14000) + mapped_fields: + hinccat1: # List new fields mapped to this field + income: # Median income within each quartile + 1: 15000 # 14000 (for 3 level) + 2: 45000 # 67000 (for 3 level) + 3: 80000 # 120000 (for 3 level) + 4: 150000 + persons: + hhsize: + 1: 1 + 2: 2 + 3: 3 + hworkers: + num_workers: + 1: 1 + 2: 2 + 3: 3 + + PROTO_PERSONS: + index_col: proto_person_id + VARIABLES: + PERSON_NUM: [1, 2] # Person number + MIL: 4 # Not military + SCHG: -1 # Not attending school + mapped_fields: + PERSON_NUM: + age: + 1: 35 + 2: 55 + SEX: # Female male + 1: 2 + 2: 1 + ptype: # Person type, full-time and non-working adult + 1: 1 + 2: 4 + pemploy: # Full-time and unemployed + 1: 1 + 2: 3 + WKW: # 50-52 weeks, none + 1: 1 + 2: 0 + WKHP: # Hours per week + 1: 35 + 2: 0 + ESR: # Employment code, employed and unemployed + 1: 1 + 2: 3 + DAP: # Mandatory, Non-mandatory + 1: "M" + 2: "N" + # FIXME this should be updated when occupation_xwalk is updated + OCCCAT: # occupation codes + 1: 1 # Management Occupations + 2: -1 # Non-worker + + PROTO_TOURS: + index_col: proto_tour_id + VARIABLES: + tour_num: 1 # Tour number, 1 tour per person + purpose: [1, 2, 3] + mapped_fields: + purpose: + person_num: # In this case it was easier to map the person number directly to the purposes + 1: 1 + 2: 2 + 3: 2 + tour_type: + 1: "work" + 2: "shopping" + 3: "othdiscr" + tour_category: # tour purpose category, mandatory/non-mandatory + 1: "mandatory" + 2: "non_mandatory" + 3: "non_mandatory" + filter_rows: # list any matching conditions as pandas expression + - ~((df.tour_type == "work") & (df.person_num == 1)) + JOIN_ON: + person_num: PERSON_NUM # Specifies which person variable to join the tours on + +# Merge on variables +# will merge onto the persons table +MERGE_ON: + by: # These should be categorical variables at the household level + - home_zone_id + - income_segment +# Note that auto ownership is not included in the merge above. +# Below columns are created in the post-processor and separate out accessibility by auto ownership. +# Since auto ownership is not yet decided in the synthetic population, we want them all. +# Disaggreate accessibility table is grouped by the "by" cols above and the KEEP_COLS are averaged +# across the group. Initializing the below as NA if not in the auto ownership level, they are skipped +# in the groupby mean and the values are correct. +# (It's a way to avoid having to update code to reshape the table and introduce new functionality there.) +KEEP_COLS: + - workplace_location_accessibility_0 + - workplace_location_accessibility_1 + - workplace_location_accessibility_2 + - othdiscr_accessibility_0 + - othdiscr_accessibility_1 + - othdiscr_accessibility_2 + - shopping_accessibility_0 + - shopping_accessibility_1 + - shopping_accessibility_2 + +# Include any annotations for persons, households, land_use, or tours. +# The purpose of a separate annotation setup is that annotation expressions for the main model +# may require data that aren't in the proto population. +# This step enables users to annotate the proto-population by referencing custom annotation scripts. +# Of course, users can also just reference existing configs if they work, but they must be referenced here too. + +annotate_proto_tables: + - tablename: proto_persons + annotate: + SPEC: annotate_persons + DF: proto_persons + TABLES: + - proto_households + +# specific annotation for proto_persons + - tablename: proto_persons + annotate: + SPEC: annotate_proto_persons + DF: proto_persons + TABLES: + - proto_households + +# Annotate the proto_households table using the main model annotations files + - tablename: proto_households + annotate: + SPEC: annotate_households + DF: proto_households + TABLES: + - proto_persons + - land_use + +# Annotate the proto_persons table using the main model annotations files + - tablename: proto_persons + annotate: + SPEC: annotate_persons_after_hh + DF: proto_persons + TABLES: + - proto_households + +postprocess_proto_tables: + - tablename: proto_disaggregate_accessibility + annotate: + SPEC: annotate_disaggregate_accessibility + DF: proto_disaggregate_accessibility + TABLES: + - land_use \ No newline at end of file diff --git a/resident/configs/external_joint_tour_destination.yaml b/resident/configs/external_joint_tour_destination.yaml new file mode 100644 index 0000000..ca25967 --- /dev/null +++ b/resident/configs/external_joint_tour_destination.yaml @@ -0,0 +1 @@ +include_settings: external_non_mandatory_destination.yaml diff --git a/resident/configs/external_joint_tour_identification.yaml b/resident/configs/external_joint_tour_identification.yaml new file mode 100644 index 0000000..1b9e997 --- /dev/null +++ b/resident/configs/external_joint_tour_identification.yaml @@ -0,0 +1 @@ +include_settings: external_non_mandatory_identification.yaml diff --git a/resident/configs/external_non_mandatory_destination.csv b/resident/configs/external_non_mandatory_destination.csv new file mode 100644 index 0000000..e521cb1 --- /dev/null +++ b/resident/configs/external_non_mandatory_destination.csv @@ -0,0 +1,5 @@ +Label,Description,Expression,escort,shopping,eatout,othmaint,social,othdiscr +local_dist,Distance capped at 20 miles,"_DIST@np.minimum(skims[('SOV_M_DIST', 'MD')], 20)",1,1,1,1,1,1 +util_dist,dist,@_DIST,coef_dist_escort,coef_dist_shopping,coef_dist_eatout,coef_dist_maint,coef_dist_social,coef_dist_discr +util_size_term,Size variable,@df['size_term'].apply(np.log1p),coef_size,coef_size,coef_size,coef_size,coef_size,coef_size +util_mode_choice_logsum,Mode choice logsum,mode_choice_logsum,coef_mode_logsum,coef_mode_logsum,coef_mode_logsum,coef_mode_logsum,coef_mode_logsum,coef_mode_logsum diff --git a/resident/configs/external_non_mandatory_destination.yaml b/resident/configs/external_non_mandatory_destination.yaml new file mode 100644 index 0000000..82af7ed --- /dev/null +++ b/resident/configs/external_non_mandatory_destination.yaml @@ -0,0 +1,43 @@ +SAMPLE_SPEC: external_non_mandatory_destination_sample.csv +SPEC: external_non_mandatory_destination.csv +COEFFICIENTS: external_non_mandatory_destination_coefficients.csv + +SAMPLE_SIZE: 30 + +SIZE_TERM_SELECTOR: external_non_mandatory + +CHOOSER_SEGMENT_COLUMN_NAME: tour_type + +# optional (comment out if not desired) +DEST_CHOICE_LOGSUM_COLUMN_NAME: destination_logsum + +# comment out DEST_CHOICE_LOGSUM_COLUMN_NAME if saved alt logsum table +DEST_CHOICE_SAMPLE_TABLE_NAME: external_tour_destination_sample + +# these segments need to match in the destination size term table +SEGMENTS: + - shopping + - othmaint + - othdiscr + - eatout + - social + - escort + +SIMULATE_CHOOSER_COLUMNS: + - tour_type + - home_zone_id + - person_id + - female + - income + - income_segment + - age + - tour_type_count + - tour_type_num + +LOGSUM_SETTINGS: tour_mode_choice.yaml + +# model-specific logsum-related settings +CHOOSER_ORIG_COL_NAME: home_zone_id +ALT_DEST_COL_NAME: alt_dest +IN_PERIOD: 14 +OUT_PERIOD: 14 diff --git a/resident/configs/external_non_mandatory_destination_coefficients.csv b/resident/configs/external_non_mandatory_destination_coefficients.csv new file mode 100644 index 0000000..62c9a42 --- /dev/null +++ b/resident/configs/external_non_mandatory_destination_coefficients.csv @@ -0,0 +1,9 @@ +coefficient_name,value,constrain +coef_mode_logsum,0.27,F +coef_dist_escort,-0.356,F +coef_dist_shopping,-0.42,F +coef_dist_maint,-0.18,F +coef_dist_eatout,-0.312,F +coef_dist_social,-0.273,F +coef_dist_discr,-0.345,F +coef_size,1,T diff --git a/resident/configs/external_non_mandatory_destination_sample.csv b/resident/configs/external_non_mandatory_destination_sample.csv new file mode 100644 index 0000000..f3a22a7 --- /dev/null +++ b/resident/configs/external_non_mandatory_destination_sample.csv @@ -0,0 +1,5 @@ +Label,Description,Expression,escort,shopping,eatout,othmaint,social,othdiscr +local_dist,Distance capped at 20 miles,"_DIST@np.minimum(skims[('SOV_M_DIST', 'MD')], 20)",1,1,1,1,1,1 +util_dist,dist,@_DIST,coef_dist_escort,coef_dist_shopping,coef_dist_eatout,coef_dist_maint,coef_dist_social,coef_dist_discr +util_size_term,Size variable,@df['size_term'].apply(np.log1p),coef_size,coef_size,coef_size,coef_size,coef_size,coef_size +utl_zero_size_term,,@df['size_term']==0,-999,-999,-999,-999,-999,-999 \ No newline at end of file diff --git a/resident/configs/external_non_mandatory_identification.csv b/resident/configs/external_non_mandatory_identification.csv new file mode 100644 index 0000000..2c84300 --- /dev/null +++ b/resident/configs/external_non_mandatory_identification.csv @@ -0,0 +1,20 @@ +Label,Description,Expression,external_tour,internal_tour +util_dist_to_nearest_ext_station,Distance to nearest external station,dist_to_external_zone,coef_dist_to_nearest_ext_station, +# FIXME need actual external counts at external station - fix in destination choice size terms too +# util_log_size,Log size of nearest station,"@np.log1p(reindex(land_use.external_nonwork, df.closest_external_zone))",coef_log_size_of_nearest_ext_station, +util_log_size,Log size of nearest station,"@np.log1p(reindex(land_use.EXTERNAL, df.closest_external_zone))",coef_log_size_of_nearest_ext_station, +util_escort,escort tour ASC,"@np.where(df.tour_type == 'escort', 1, 0)",coef_escort, +util_shopping,shopping tour ASC,"@np.where(df.tour_type == 'shopping', 1, 0)",coef_shopping, +util_othmaint,othmaint tour ASC,"@np.where(df.tour_type == 'othmaint', 1, 0)",coef_othmaint, +util_eatout,eatout tour ASC,"@np.where(df.tour_type == 'eatout', 1, 0)",coef_eatout, +util_social,social tour ASC,"@np.where(df.tour_type == 'social', 1, 0)",coef_social, +util_othdiscr,othdiscr tour ASC,"@np.where(df.tour_type == 'othdiscr', 1, 0)",coef_othdiscr, +util_inc_lt15,Income less than $15k,"@np.where(df['income']<15000,1,0)",coef_income_lt_15, +util_inc_15_50,Income 15 to 50k,"@np.where((df['income']>=15000) * (df['income']<=49999),1,0)",coef_income_15_50, +util_inc_100_250,Income 100k to 250k,"@np.where((df['income']>=100000) * (df['income']<=249999),1,0)",coef_income_100_250, +util_inc_250plus,Income 250k plus,"@np.where(df['income']>=250000,1,0)",coef_income_250_plus, +util_autos_0,0 auto household,"@np.where(df.auto_ownership==0,1,0)",coef_autos_0, +util_is_pure_escort,Pure escort tours cannot have an external destination,"@np.where((df.school_esc_outbound == 'pure_escort')|(df.school_esc_inbound == 'pure_escort'), 1, 0)",-999, +util_global_switch,Global switch to have everything internal,@NO_EXTERNAL,-999, +util_dist_lt_2p5,Distance less than 2.5 miles,"@np.where(df.dist_to_external_zone<2.5,1,0)",coef_dist_lt_2p5, +util_calibration,,1,0.43, diff --git a/resident/configs/external_non_mandatory_identification.yaml b/resident/configs/external_non_mandatory_identification.yaml new file mode 100644 index 0000000..c2a882b --- /dev/null +++ b/resident/configs/external_non_mandatory_identification.yaml @@ -0,0 +1,11 @@ +# using the same spec for both non-mandatory and joint tours +SPEC: external_non_mandatory_identification.csv +COEFFICIENTS: external_non_mandatory_identification_coefficients.csv + +#LOGIT_TYPE: NL +LOGIT_TYPE: MNL + +# Adds this column to the persons table and is set to True if workplace is external +EXTERNAL_COL_NAME: is_external_tour +# set to True if not external but CHOOSER_FILTER_COLUMN_NAME is True +INTERNAL_COL_NAME: is_internal_tour diff --git a/resident/configs/external_non_mandatory_identification_coefficients.csv b/resident/configs/external_non_mandatory_identification_coefficients.csv new file mode 100644 index 0000000..be5f021 --- /dev/null +++ b/resident/configs/external_non_mandatory_identification_coefficients.csv @@ -0,0 +1,15 @@ +coefficient_name,value,constrain +coef_dist_to_nearest_ext_station,-0.06913447619232407,F +coef_log_size_of_nearest_ext_station,0.40435031197856747,F +coef_escort,-8.056823449462565,F +coef_shopping,-7.413630670471193,F +coef_othmaint,-6.453644115005783,F +coef_eatout,-7.27668098767437,F +coef_social,-6.566754062811491,F +coef_othdiscr,-7.415691288097906,F +coef_income_lt_15,-0.706806612224066,F +coef_income_15_50,-0.2533987244055551,F +coef_income_100_250,0.11811329788815791,F +coef_income_250_plus,0.6221129017397624,F +coef_autos_0,-0.5505407390480237,F +coef_dist_lt_2p5,2.6495984313112557,F diff --git a/resident/configs/external_worker_identification.csv b/resident/configs/external_worker_identification.csv new file mode 100644 index 0000000..d70b683 --- /dev/null +++ b/resident/configs/external_worker_identification.csv @@ -0,0 +1,16 @@ +Label,Description,Expression,work_external,work_internal +util_dist_to_nearest_ext_station,Distance to nearest external station,dist_to_external_zone,coef_dist_to_nearest_ext_station, +# FIXME just have a single external indicator not counts at the station here and in destination choice size terms,,,, +#util_size_of_nearest_ext_station,Size of nearest external station,"@np.log1p(reindex(land_use.external_work,df.closest_external_zone))",coef_size_of_nearest_ext_station, +util_size_of_nearest_ext_station,Size of nearest external station,"@np.log1p(reindex(land_use.EXTERNAL,df.closest_external_zone))",coef_size_of_nearest_ext_station, +util_dist_lt_2p5,Distance less than 2.5 miles,"@np.where(df.dist_to_external_zone<2.5,1,0)",coef_dist_lt_2p5, +util_part_time,Part time worker,"@np.where(df.pemploy == 2,1,0)",coef_part_time, +util_inc_lt15,Household Income less than $15k,@(df.income<15000),coef_inc_lt15, +util_inc_15_25,Household income $15k-$25k,@(df.income>=15000) & (df.income<25000) ,coef_inc_15_25, +util_inc_25_50,Household Income $25k-$50k,@(df.income>=25000) & (df.income<50000) ,coef_inc_25_50, +util_inc_150_250,Household Income $150k-$50k,@(df.income>=150000) * (df.income<250000) ,coef_inc_150_250, +util_inc_250plus,Household Income $250k+,@(df.income>=250000),coef_inc_250plus, +util_asc,Alternative-specific constant for external worker,1,asc_external_worker, +util_global_switch,Global switch to have everything internal,@NO_EXTERNAL,-999, +util_2016,Constant for pre-COVID conditions,@PRE_COVID,asc_external_2016, +util_calib,Constant for calibration,1,0.35, diff --git a/resident/configs/external_worker_identification.yaml b/resident/configs/external_worker_identification.yaml new file mode 100644 index 0000000..e9660b6 --- /dev/null +++ b/resident/configs/external_worker_identification.yaml @@ -0,0 +1,14 @@ + +SPEC: external_worker_identification.csv +COEFFICIENTS: external_worker_identification_coeffs.csv + +LOGIT_TYPE: MNL + +# boolean column to filter choosers (True means keep) +# will only expose these people to the model +CHOOSER_FILTER_COLUMN_NAME: is_out_of_home_worker + +# Adds this column to the persons table and is set to True if workplace is external +EXTERNAL_COL_NAME: is_external_worker +# set to True if not external but CHOOSER_FILTER_COLUMN_NAME is True +INTERNAL_COL_NAME: is_internal_worker diff --git a/resident/configs/external_worker_identification_coeffs.csv b/resident/configs/external_worker_identification_coeffs.csv new file mode 100644 index 0000000..d316d86 --- /dev/null +++ b/resident/configs/external_worker_identification_coeffs.csv @@ -0,0 +1,12 @@ +coefficient_name,value,constrain +coef_dist_to_nearest_ext_station,-0.03637843085617849,F +coef_size_of_nearest_ext_station,0.7890441071064493,F +coef_part_time,-0.22281386510906526,F +asc_external_worker,-10.723198329563822,F +asc_external_2016,0.7108412488374917,F +coef_inc_lt15,-1.3314204997581536,F +coef_inc_25_50,-0.5733965244215453,F +coef_inc_250plus,1.2296738201382527,F +coef_dist_lt_2p5,2.302815581601645,F +coef_inc_15_25,-0.9334826063624445,F +coef_inc_150_250,0.8316476615543997,F diff --git a/resident/configs/external_workplace_location.csv b/resident/configs/external_workplace_location.csv new file mode 100644 index 0000000..f8b270d --- /dev/null +++ b/resident/configs/external_workplace_location.csv @@ -0,0 +1,6 @@ +Label,Description,Expression,external_workplace +local_dist,,"_DIST@skims[('SOV_M_DIST', 'MD')]",1 +util_dist,,"@np.minimum(_DIST,10)",coef_dist_capped +util_size_variable,Size variable,@(df['size_term']).apply(np.log1p),coef_size +util_no_attractions,No attractions,@df['size_term']==0,-999 +mode_choice_logsum,Mode choice logsum,@df.mode_choice_logsum if 'mode_choice_logsum' in df.columns else 0,coef_mode_logsum diff --git a/resident/configs/external_workplace_location.yaml b/resident/configs/external_workplace_location.yaml new file mode 100644 index 0000000..f106547 --- /dev/null +++ b/resident/configs/external_workplace_location.yaml @@ -0,0 +1,53 @@ +SAMPLE_SIZE: 30 + +SIMULATE_CHOOSER_COLUMNS: + - home_zone_id + - household_id + +# model-specific logsum-related settings +CHOOSER_ORIG_COL_NAME: home_zone_id +ALT_DEST_COL_NAME: alt_dest +IN_PERIOD: 14 +OUT_PERIOD: 8 + +DEST_CHOICE_COLUMN_NAME: external_workplace_zone_id +# comment out DEST_CHOICE_LOGSUM_COLUMN_NAME if not desired in persons table +DEST_CHOICE_LOGSUM_COLUMN_NAME: external_workplace_location_logsum +# comment out DEST_CHOICE_LOGSUM_COLUMN_NAME if not desired in persons table +MODE_CHOICE_LOGSUM_COLUMN_NAME: external_workplace_modechoice_logsum + +# comment out DEST_CHOICE_LOGSUM_COLUMN_NAME if saved alt logsum table +DEST_CHOICE_SAMPLE_TABLE_NAME: external_workplace_location_sample + +SAMPLE_SPEC: external_workplace_location.csv +SPEC: external_workplace_location.csv +COEFFICIENTS: external_workplace_location_coefficients.csv + +LOGSUM_SETTINGS: tour_mode_choice.yaml +LOGSUM_PREPROCESSOR: nontour_preprocessor + +LOGSUM_TOUR_PURPOSE: work + +# required by initialize_households when creating workplace_destination_size table +CHOOSER_TABLE_NAME: persons + +# size_terms model_selector +MODEL_SELECTOR: external_workplace + +# chooser column with segment_id for this segment type +CHOOSER_SEGMENT_COLUMN_NAME: is_external_worker + +# boolean column to filter choosers (True means keep) +# CHOOSER_FILTER_COLUMN_NAME: is_worker +CHOOSER_FILTER_COLUMN_NAME: is_external_worker + +# FIXME - these are assigned to persons in annotate_persons. we need a better way to manage this +SEGMENT_IDS: + external_workplace: True # using only one segment + +# model adds these tables (informational - not added if commented out) +# SHADOW_PRICE_TABLE: workplace_shadow_prices +# MODELED_SIZE_TABLE: workplace_modeled_size + +# not loaded if commented out +# SAVED_SHADOW_PRICE_TABLE_NAME: workplace_shadow_prices.csv diff --git a/resident/configs/external_workplace_location_archive.csv b/resident/configs/external_workplace_location_archive.csv new file mode 100644 index 0000000..1b113c4 --- /dev/null +++ b/resident/configs/external_workplace_location_archive.csv @@ -0,0 +1,6 @@ +Label,Description,Expression,external_workplace +local_dist,,"_DIST@skims[('SOV_M_DIST', 'MD')]",1 +util_dist,,"@np.minimum(_DIST,10)",coef_dist_capped +util_size_variable,Size variable,@(df['size_term']).apply(np.log1p),coef_size +util_no_attractions,No attractions,@df['size_term']==0,-999 +mode_choice_logsum not available for sample,Mode choice logsum,@df.mode_choice_logsum if 'mode_choice_logsum' in df.columns else 0,coef_mode_logsum \ No newline at end of file diff --git a/resident/configs/external_workplace_location_coefficients.csv b/resident/configs/external_workplace_location_coefficients.csv new file mode 100644 index 0000000..7ba9aa4 --- /dev/null +++ b/resident/configs/external_workplace_location_coefficients.csv @@ -0,0 +1,4 @@ +coefficient_name,value,constrain +coef_mode_logsum,0.064105,F +coef_dist_capped,-0.519016,F +coef_size,1,T diff --git a/resident/configs/free_parking.csv b/resident/configs/free_parking.csv new file mode 100644 index 0000000..8fa0f37 --- /dev/null +++ b/resident/configs/free_parking.csv @@ -0,0 +1,6 @@ +Label,Description,Expression,free,pay +util_income_very_high,Very high income household dummy,@df.income>=100000,coef_income_very_high,0.0 +util_income_high,High income housheold dummy,@(df.income>=60000) & (df.income<100000),coef_income_high,0.0 +util_hh_size_4_up,Household size is greater than 3 dummy,@df.hhsize>3,coef_hh_size_4_up,0.0 +util_more_autos_than_workers,More automobiles than workers dummy,@df.auto_ownership>df.num_workers,coef_more_autos_than_workers,0.0 +util_fewer_autos_than_workers,Fewer automobiles than workers dummy,@df.auto_ownership num_workers) * othmaint,coef_hh_has_more_autos_than_workers_maintenance +util_income_gtr_100k_maintenance,Income > $100k /Maintenance,(income>100000) * othmaint,coef_income_gtr_100k_maintenance +util_income_less_than_29999__eating_out,"Income Less than $29,999 / Eating Out",(income<30000) * eatout,coef_income_less_than_29999__eating_out +util_income_between_30000_to_59999__eating_out,"Income Between $30,000 to $59,999 / Eating Out",((income>=30000) & (income<60000)) * othmaint,coef_income_between_30000_to_59999__eating_out +util_income_less_than_29999__discretionary,"Income Less than $29,999 / Discretionary",(income<30000) * othdiscr,coef_income_less_than_29999__discretionary +util_income_between_30000_to_59999__discretionary,"Income Between $30,000 to $59,999 / Discretionary",((income>=30000) & (income<60000)) * othdiscr,coef_income_between_30000_to_59999__discretionary +# util_shopping_hov_accessibility_for_2_tours,Shopping HOV accessibility for 2 Tours,((auto_ownershipnum_workers)*shop_hov_oversufficient_accessibility)*(num_joint_tours==2)*shopping,coef_shopping_hov_accessibility_for_2_tours +# util_maintenance_hov_accessibility,Maintenance HOV Accessibility,((auto_ownershipnum_workers)*maint_hov_oversufficient_accessibility)*othmaint,coef_maintenance_hov_accessibility +# util_discretionary_hov_accessibility,Discretionary HOV Accessibility,((auto_ownershipnum_workers)*discr_hov_oversufficient_accessibility)*othdiscr,coef_discretionary_hov_accessibility +util_shopping_hov_accessibility_for_2_tours,Shopping HOV accessibility for 2 Tours,(shopping_accessibility) * (num_joint_tours == 2) * shopping,coef_shopping_hov_accessibility_for_2_tours +util_maintenance_hov_accessibility,Maintenance HOV Accessibility,shopping_accessibility * othmaint,coef_maintenance_hov_accessibility +util_discretionary_hov_accessibility,Discretionary HOV Accessibility,othdiscr_accessibility * othdiscr,coef_discretionary_hov_accessibility +util_constant_for_children_party_shopping_tour,Constant for Children Party/ Shopping Tour,@(df.purpose1==5)*(df.party1==2)+(df.purpose2==5)*(df.party2==2),coef_constant_for_children_party_shopping_tour +util_constant_for_children_party_maintenance_tour,Constant for Children Party/ Maintenance Tour,@(df.purpose1==6)*(df.party1==2)+(df.purpose2==6)*(df.party2==2),coef_constant_for_children_party_maintenance_tour +util_constant_for_children_party_eating_out_tour,Constant for Children Party/ Eating Out Tour,@(df.purpose1==7)*(df.party1==2)+(df.purpose2==7)*(df.party2==2),coef_constant_for_children_party_eating_out_tour +util_constant_for_children_party_visiting_tour,Constant for Children Party/ Visiting Tour,@(df.purpose1==8)*(df.party1==2)+(df.purpose2==8)*(df.party2==2),coef_constant_for_children_party_visiting_tour +util_constant_for_children_party_discretionary_tour,Constant for Children Party/ Discretionary Tour,@(df.purpose1==9)*(df.party1==2)+(df.purpose2==9)*(df.party2==2),coef_constant_for_children_party_discretionary_tour +util_constant_for_mixed_party_shopping_tour,Constant for Mixed Party/ Shopping Tour,@(df.purpose1==5)*(df.party1==3)+(df.purpose2==5)*(df.party2==3),coef_constant_for_mixed_party_shopping_tour +util_constant_for_mixed_party_maintenance_tour,Constant for Mixed Party/ Maintenance Tour,@(df.purpose1==6)*(df.party1==3)+(df.purpose2==6)*(df.party2==3),coef_constant_for_mixed_party_maintenance_tour +util_constant_for_mixed_party_eating_out_tour,Constant for Mixed Party/ Eating Out Tour,@(df.purpose1==7)*(df.party1==3)+(df.purpose2==7)*(df.party2==3),coef_constant_for_mixed_party_eating_out_tour +util_constant_for_mixed_party_visiting_tour,Constant for Mixed Party/ Visiting Tour,@(df.purpose1==8)*(df.party1==3)+(df.purpose2==8)*(df.party2==3),coef_constant_for_mixed_party_visiting_tour +util_constant_for_mixed_party_discretionary_tour,Constant for Mixed Party/ Discretionary Tour,@(df.purpose1==9)*(df.party1==3)+(df.purpose2==9)*(df.party2==3),coef_constant_for_mixed_party_discretionary_tour +util_number_of_active_full_time_workers_adult_party,Number of Active Full time workers /Adult Party,num_travel_active_full_time_workers * (party1==1) + num_travel_active_full_time_workers * (party2==1),coef_number_of_active_full_time_workers_adult_party +util_number_of_active_part_time_workers_adult_party,Number of Active Part time workers /Adult Party,num_travel_active_part_time_workers * (party1==1) + num_travel_active_part_time_workers * (party2==1),coef_number_of_active_part_time_workers_adult_party +util_number_of_active_university_students_adult_party,Number of Active University Students /Adult Party,num_travel_active_university_students * (party1==1) + num_travel_active_university_students * (party2==1),coef_number_of_active_university_students_adult_party +util_number_of_active_nonworkers_adult_party,Number of Active Non-workers /Adult Party,num_travel_active_non_workers * (party1==1) + num_travel_active_non_workers * (party2==1),coef_number_of_active_nonworkers_adult_party +util_number_of_active_retirees_adult_party,Number of Active Retirees /Adult Party,num_travel_active_retirees * (party1==1) + num_travel_active_retirees * (party2==1),coef_number_of_active_retirees_adult_party +util_number_of_active_driving_age_school_children_children_party,Number of Active Driving Age School Children /Children Party,num_travel_active_driving_age_students * (party1==1) + num_travel_active_driving_age_students * (party2==1),coef_number_of_active_driving_age_school_children_children_party +util_number_of_active_pre_driving_age_school_children_children_party,Number of Active Pre- Driving Age School Children /Children Party,num_travel_active_pre_driving_age_school_kids * (party1==2) + num_travel_active_pre_driving_age_school_kids * (party2==2),coef_number_of_active_pre_driving_age_school_children_children_party +util_number_of_active_part_time_workers_mixed_party,Number of Active Part time workers /Mixed Party,num_travel_active_part_time_workers * (party1==3) + num_travel_active_part_time_workers * (party2==3),coef_number_of_active_part_time_workers_mixed_party +util_number_of_active_driving_age_school_children_mixed_party,Number of Active Driving Age School Children /Mixed Party,num_travel_active_driving_age_students * (party1==3) + num_travel_active_driving_age_students * (party2==3),coef_number_of_active_driving_age_school_children_mixed_party +util_number_of_active_pre_driving_age_school_children_mixed_party,Number of Active Pre- Driving Age School Children /Mixed Party,num_travel_active_pre_driving_age_school_kids * (party1==3) + num_travel_active_pre_driving_age_school_kids * (party2==3),coef_number_of_active_pre_driving_age_school_children_mixed_party +util_number_of_active_preschool_children_mixed_party,Number of Active Preschool Children /Mixed Party,num_travel_active_pre_school_kids * (party1==3) + num_travel_active_pre_school_kids * (party2==3),coef_number_of_active_preschool_children_mixed_party +util_hh_has_no_autos__mixed_party,HH has no autos / Mixed Party,@(df.auto_ownership==0)*(df.party1==3)+(df.auto_ownership==0)*(df.party2==3),coef_hh_has_no_autos__mixed_party +util_hh_has_less_autos_than_workers_mixed_party,HH has less autos than workers/ Mixed Party,@(df.auto_ownership100000)*(df.party1==2)+(df.income>100000)*(df.party2==2),coef_income_more_than_100k_child_party +util_income_more_than_100k_mixed_party,Income more than $100k /Mixed Party,@(df.income>100000)*(df.party1==3)+(df.income>100000)*(df.party2==3),coef_income_more_than_100k_mixed_party +util_log_of_max_window_overlaps_between_adults,Log of max window overlaps between adults,@df.log_time_window_overlap_adult*((df.party1==1)+(df.party2==1)),coef_log_of_max_window_overlaps_between_adults +util_log_of_max_window_overlaps_between_children,Log of max window overlaps between children,@df.log_time_window_overlap_child*((df.party1==2)+(df.party2==2)),coef_log_of_max_window_overlaps_between_children +util_log_of_max_window_overlaps_between_adult_child,Log of max window overlaps between adult & child,@df.log_time_window_overlap_adult_child*((df.party1==3)+(df.party2==3)),coef_log_of_max_window_overlaps_between_adult_child +util_not_more_than_one_active_adult,Not more than 1 travel active adult in HH,@(df.num_travel_active_adults < 2)*(((df.party1==1)+(df.party2==1))>0),coef_unavailable +util_not_more_than_one_active_child,Not more than 1 travel active child in HH,@(df.num_travel_active_children < 2)*(((df.party1==2)+(df.party2==2))>0),coef_unavailable +util_not_more_than_one_active_mixed,No travel-active pair adult-child in HH ,@((df.num_travel_active_adults*df.num_travel_active_children) ==0)*(((df.party1==3)+(df.party2==3))>0),coef_unavailable +util_adjustment_for_children_party_shopping_tour,Adjustment for Children Party/ Shopping Tour,@(df.purpose1==5)*(df.party1==2)+(df.purpose2==5)*(df.party2==2),coef_adjustment_for_children_party_shopping_tour +util_adjustment_for_children_party_maintenance_tour,Adjustment for Children Party/ Maintenance Tour,@(df.purpose1==6)*(df.party1==2)+(df.purpose2==6)*(df.party2==2),coef_adjustment_for_children_party_maintenance_tour +util_adjustment_for_children_party_eating_out_tour,Adjustment for Children Party/ Eating Out Tour,@(df.purpose1==7)*(df.party1==2)+(df.purpose2==7)*(df.party2==2),coef_adjustment_for_children_party_eating_out_tour +util_adjustment_for_children_party_visiting_tour,Adjustment for Children Party/ Visiting Tour,@(df.purpose1==8)*(df.party1==2)+(df.purpose2==8)*(df.party2==2),coef_adjustment_for_children_party_visiting_tour +util_adjustment_for_children_party_discretionary_tour,Adjustment for Children Party/ Discretionary Tour,@(df.purpose1==9)*(df.party1==2)+(df.purpose2==9)*(df.party2==2),coef_adjustment_for_children_party_discretionary_tour +util_adjustment_for_mixed_party_shopping_tour,Adjustment for Mixed Party/ Shopping Tour,@(df.purpose1==5)*(df.party1==3)+(df.purpose2==5)*(df.party2==3),coef_adjustment_for_mixed_party_shopping_tour +util_adjustment_for_mixed_party_maintenance_tour,Adjustment for Mixed Party/ Maintenance Tour,@(df.purpose1==6)*(df.party1==3)+(df.purpose2==6)*(df.party2==3),coef_adjustment_for_mixed_party_maintenance_tour +util_adjustment_for_mixed_party_eating_out_tour,Adjustment for Mixed Party/ Eating Out Tour,@(df.purpose1==7)*(df.party1==3)+(df.purpose2==7)*(df.party2==3),coef_adjustment_for_mixed_party_eating_out_tour +util_adjustment_for_mixed_party_visiting_tour,Adjustment for Mixed Party/ Visiting Tour,@(df.purpose1==8)*(df.party1==3)+(df.purpose2==8)*(df.party2==3),coef_adjustment_for_mixed_party_visiting_tour +util_adjustment_for_mixed_party_discretionary_tour,Adjustment for Mixed Party/ Discretionary Tour,@(df.purpose1==9)*(df.party1==3)+(df.purpose2==9)*(df.party2==3),coef_adjustment_for_mixed_party_discretionary_tour +util_adjustment_for_shopping_tour,Adjustment for shopping tour,shopping,coef_adjustment_for_shopping_tour +util_adjustment_for_maintenance_tour,Adjustment for Maintenance tour,othmaint,coef_adjustment_for_maintenance_tour +util_adjustment_for_eating_out_tour,Adjustment for eating out tour,eatout,coef_adjustment_for_eating_out_tour +util_adjustment_for_visiting_tour,Adjustment for visiting tour,social,coef_adjustment_for_visiting_tour +util_adjustment_for_discretionary_tour,Adjustment for discretionary tour,othdiscr,coef_adjustment_for_discretionary_tour +util_adjustment_for_share_of_2_joint_tours,Adjustment for share of 2 Joint Tours,num_joint_tours==2,coef_adjustment_for_share_of_2_joint_tours +util_tm2_adjustment_for_children_party,TM2 Adjustment for Children Party,@(df.party1==2)+(df.party2==2),coef_tm2_adjustment_for_children_party +util_tm2_adjustment_for_mixed_party,TM2 Adjustment for Mixed Party,@(df.party1==3)+(df.party2==3),coef_tm2_adjustment_for_mixed_party +util_tm2_adjustment_for_share_of_2_joint_tours,TM2 adjustment for share of 2 Joint Tours,num_joint_tours==2,coef_tm2_adjustment_for_share_of_2_joint_tours +util_tm2_adjustment_for_share_of_1_joint_tours,TM2 adjustment for share of 1 Joint Tours,num_joint_tours==1,coef_tm2_adjustment_for_share_of_1_joint_tours +util_tm2_adjustment_for_share_of_0_joint_tours,TM2 adjustment for share of 0 Joint Tours,num_joint_tours==0,coef_tm2_adjustment_for_share_of_0_joint_tours diff --git a/resident/configs/joint_tour_frequency_composition.yaml b/resident/configs/joint_tour_frequency_composition.yaml new file mode 100644 index 0000000..452ec92 --- /dev/null +++ b/resident/configs/joint_tour_frequency_composition.yaml @@ -0,0 +1,35 @@ +LOGIT_TYPE: MNL + +SPEC: joint_tour_frequency_composition.csv +COEFFICIENTS: joint_tour_frequency_composition_coeffs.csv + +preprocessor: + SPEC: joint_tour_frequency_composition_annotate_households_preprocessor.csv + DF: households + TABLES: + - persons_merged + +ALTS_PREPROCESSOR: + SPEC: joint_tour_frequency_composition_annotate_alt_preprocessor.csv + DF: alt_tdd + +# define the structure of alternative table +ALTS_TABLE_STRUCTURE: + PURPOSE: + COLUMNS: + - purpose1 + - purpose2 + VALUE_MAP: + 5: shopping + 6: othmaint + 7: eatout + 8: social + 9: othdiscr + COMPOSITION: + COLUMNS: + - party1 + - party2 + VALUE_MAP: + 1: adults + 2: children + 3: mixed \ No newline at end of file diff --git a/resident/configs/joint_tour_frequency_composition_alternatives.csv b/resident/configs/joint_tour_frequency_composition_alternatives.csv new file mode 100644 index 0000000..657f987 --- /dev/null +++ b/resident/configs/joint_tour_frequency_composition_alternatives.csv @@ -0,0 +1,151 @@ +alt,purpose1,purpose2,party1,party2 +1,5,0,1,0 +2,5,0,2,0 +3,5,0,3,0 +4,6,0,1,0 +5,6,0,2,0 +6,6,0,3,0 +7,7,0,1,0 +8,7,0,2,0 +9,7,0,3,0 +10,8,0,1,0 +11,8,0,2,0 +12,8,0,3,0 +13,9,0,1,0 +14,9,0,2,0 +15,9,0,3,0 +16,5,5,1,1 +17,5,5,1,2 +18,5,5,1,3 +19,5,5,2,1 +20,5,5,2,2 +21,5,5,2,3 +22,5,5,3,1 +23,5,5,3,2 +24,5,5,3,3 +25,5,6,1,1 +26,5,6,1,2 +27,5,6,1,3 +28,5,6,2,1 +29,5,6,2,2 +30,5,6,2,3 +31,5,6,3,1 +32,5,6,3,2 +33,5,6,3,3 +34,5,7,1,1 +35,5,7,1,2 +36,5,7,1,3 +37,5,7,2,1 +38,5,7,2,2 +39,5,7,2,3 +40,5,7,3,1 +41,5,7,3,2 +42,5,7,3,3 +43,5,8,1,1 +44,5,8,1,2 +45,5,8,1,3 +46,5,8,2,1 +47,5,8,2,2 +48,5,8,2,3 +49,5,8,3,1 +50,5,8,3,2 +51,5,8,3,3 +52,5,9,1,1 +53,5,9,1,2 +54,5,9,1,3 +55,5,9,2,1 +56,5,9,2,2 +57,5,9,2,3 +58,5,9,3,1 +59,5,9,3,2 +60,5,9,3,3 +61,6,6,1,1 +62,6,6,1,2 +63,6,6,1,3 +64,6,6,2,1 +65,6,6,2,2 +66,6,6,2,3 +67,6,6,3,1 +68,6,6,3,2 +69,6,6,3,3 +70,6,7,1,1 +71,6,7,1,2 +72,6,7,1,3 +73,6,7,2,1 +74,6,7,2,2 +75,6,7,2,3 +76,6,7,3,1 +77,6,7,3,2 +78,6,7,3,3 +79,6,8,1,1 +80,6,8,1,2 +81,6,8,1,3 +82,6,8,2,1 +83,6,8,2,2 +84,6,8,2,3 +85,6,8,3,1 +86,6,8,3,2 +87,6,8,3,3 +88,6,9,1,1 +89,6,9,1,2 +90,6,9,1,3 +91,6,9,2,1 +92,6,9,2,2 +93,6,9,2,3 +94,6,9,3,1 +95,6,9,3,2 +96,6,9,3,3 +97,7,7,1,1 +98,7,7,1,2 +99,7,7,1,3 +100,7,7,2,1 +101,7,7,2,2 +102,7,7,2,3 +103,7,7,3,1 +104,7,7,3,2 +105,7,7,3,3 +106,7,8,1,1 +107,7,8,1,2 +108,7,8,1,3 +109,7,8,2,1 +110,7,8,2,2 +111,7,8,2,3 +112,7,8,3,1 +113,7,8,3,2 +114,7,8,3,3 +115,7,9,1,1 +116,7,9,1,2 +117,7,9,1,3 +118,7,9,2,1 +119,7,9,2,2 +120,7,9,2,3 +121,7,9,3,1 +122,7,9,3,2 +123,7,9,3,3 +124,8,8,1,1 +125,8,8,1,2 +126,8,8,1,3 +127,8,8,2,1 +128,8,8,2,2 +129,8,8,2,3 +130,8,8,3,1 +131,8,8,3,2 +132,8,8,3,3 +133,8,9,1,1 +134,8,9,1,2 +135,8,9,1,3 +136,8,9,2,1 +137,8,9,2,2 +138,8,9,2,3 +139,8,9,3,1 +140,8,9,3,2 +141,8,9,3,3 +142,9,9,1,1 +143,9,9,1,2 +144,9,9,1,3 +145,9,9,2,1 +146,9,9,2,2 +147,9,9,2,3 +148,9,9,3,1 +149,9,9,3,2 +150,9,9,3,3 diff --git a/resident/configs/joint_tour_frequency_composition_annotate_alt_preprocessor.csv b/resident/configs/joint_tour_frequency_composition_annotate_alt_preprocessor.csv new file mode 100644 index 0000000..389d28a --- /dev/null +++ b/resident/configs/joint_tour_frequency_composition_annotate_alt_preprocessor.csv @@ -0,0 +1,7 @@ +Description,Target,Expression +,shopping,"np.where(alt_tdd.purpose1 == 5, 1, 0) + np.where(alt_tdd.purpose2 == 5, 1, 0)" +,othmaint,"np.where(alt_tdd.purpose1 == 6, 1, 0) + np.where(alt_tdd.purpose2 == 6, 1, 0)" +,eatout,"np.where(alt_tdd.purpose1 == 7, 1, 0) + np.where(alt_tdd.purpose2 == 7, 1, 0)" +,social,"np.where(alt_tdd.purpose1 == 8, 1, 0) + np.where(alt_tdd.purpose2 == 8, 1, 0)" +,othdiscr,"np.where(alt_tdd.purpose1 == 9, 1, 0) + np.where(alt_tdd.purpose2 == 9, 1, 0)" +,num_joint_tours,shopping+othmaint+eatout+social+othdiscr \ No newline at end of file diff --git a/resident/configs/joint_tour_frequency_composition_annotate_households_preprocessor.csv b/resident/configs/joint_tour_frequency_composition_annotate_households_preprocessor.csv new file mode 100644 index 0000000..2185a09 --- /dev/null +++ b/resident/configs/joint_tour_frequency_composition_annotate_households_preprocessor.csv @@ -0,0 +1,21 @@ +Description,Target,Expression +,_HH_OVERLAPS,"hh_time_window_overlap(households, persons_merged)" +,time_window_overlap_adult,_HH_OVERLAPS['aa'] +,time_window_overlap_child,_HH_OVERLAPS['cc'] +,time_window_overlap_adult_child,_HH_OVERLAPS['ac'] +logTimeWindowOverlapAdult,log_time_window_overlap_adult,np.log1p(time_window_overlap_adult) +logTimeWindowOverlapChild,log_time_window_overlap_child,np.log1p(time_window_overlap_child) +logTimeWindowOverlapAdultChild,log_time_window_overlap_adult_child,np.log1p(time_window_overlap_adult_child) +,_PERSON_COUNT,"lambda query, persons_merged, households: persons_merged.query(query).groupby('household_id').size().reindex(households.index).fillna(0).astype(np.int8)" +number of travel active fulltime workers,num_travel_active_full_time_workers,"_PERSON_COUNT('(ptype == 1) & (cdap_activity != \'H\')', persons_merged, households)" +number of travel active parttime workers,num_travel_active_part_time_workers,"_PERSON_COUNT('(ptype == 2) & (cdap_activity != \'H\')', persons_merged, households)" +number of travel active university student,num_travel_active_university_students,"_PERSON_COUNT('(ptype == 3) & (cdap_activity != \'H\')', persons_merged, households)" +number of travel active non-workers,num_travel_active_non_workers,"_PERSON_COUNT('(ptype == 4) & (cdap_activity != \'H\')', persons_merged, households)" +number of travel active retirees,num_travel_active_retirees,"_PERSON_COUNT('(ptype == 5) & (cdap_activity != \'H\')', persons_merged, households)" +number of travel active driving age students,num_travel_active_driving_age_students,"_PERSON_COUNT('(ptype == 6) & (cdap_activity != \'H\')', persons_merged, households)" +number of travel active pre-driving age school kids,num_travel_active_pre_driving_age_school_kids,"_PERSON_COUNT('(ptype == 7) & (cdap_activity != \'H\')', persons_merged, households)" +number of travel active pre-school kids,num_travel_active_pre_school_kids,"_PERSON_COUNT('(ptype == 8) & (cdap_activity != \'H\')', persons_merged, households)" +number of travel active adults,num_travel_active_adults,"_PERSON_COUNT('adult & (cdap_activity != \'H\')', persons_merged, households)" +number of travel active chilren,num_travel_active_children,"_PERSON_COUNT('~adult & (cdap_activity != \'H\')', persons_merged, households)" +shopping_accessibility,shopping_accessibility,"persons_merged.groupby('household_id')['shopping_accessibility'].mean().reindex(households.index)" +othdiscr_accessibility,othdiscr_accessibility,"persons_merged.groupby('household_id')['othdiscr_accessibility'].mean().reindex(households.index)" \ No newline at end of file diff --git a/resident/configs/joint_tour_frequency_composition_coeffs.csv b/resident/configs/joint_tour_frequency_composition_coeffs.csv new file mode 100644 index 0000000..e0d15c2 --- /dev/null +++ b/resident/configs/joint_tour_frequency_composition_coeffs.csv @@ -0,0 +1,102 @@ +coefficient_name,value,constrain +coef_unavailable,-999,T +coef_constant_for_shopping_tour,0,T +coef_constant_for_maintenance_tour,-1.477,F +coef_constant_for_eating_out_tour,0.5796,F +coef_constant_for_visiting_tour,-1.0037,F +coef_constant_for_discretionary_tour,-1.1195,F +coef_constant_for_2_shopping_tour,-12.009,F +coef_1_shopping_and_1_maintenance_tour,-12.13684801,F +coef_1_shopping_and_1_eating_out_tour,-12.79892713,F +coef_1_shopping_and_1_visiting_tour,-12.22077221,F +coef_1_shopping_and_1_discretionary_tour,-12.57867869,F +coef_constant_for_2_maintenance_tour,-13.43572169,F +coef_1_maintenance_and_1_eating_out_tour,-12.18915033,F +coef_1_maintenance_and_1_visiting_tour,-11.81267905,F +coef_1_maintenance_and_1_discretionary_tour,-12.28867197,F +coef_constant_for_2_eating_out_tour,-13.15388273,F +coef_1_eating_out_and_1_visiting_tour,-13.15388273,F +coef_1_eating_out_and_1_discretionary_tour,-12.56102569,F +coef_constant_for_2_visiting_tour,-13.15388273,F +coef_1_visiting_and_1_discretionary_tour,-12.37222202,F +coef_constant_for_2_discretionary_tour,-13.23532342,F +coef_constant_for_1_discretionary_tour,-1.200,F +coef_constant_for_1_eating_out_tour,0.460,F +coef_constant_for_1_visiting_tour,1.720,F +coef_number_of_active_full_time_workers_shopping,0.09864381,F +coef_number_of_active_nonworkers_shopping,0.393630718,F +coef_number_of_active_pre_driving_age_school_children_shopping,-0.313021042,F +coef_number_of_active_preschool_children_shopping,-1.213950718,F +coef_number_of_active_nonworkers_maintenance,0.322738327,F +coef_number_of_active_retirees_maintenance,0.298632515,F +coef_number_of_active_driving_age_school_children_maintenance,0.503901856,F +coef_number_of_active_preschool_children_maintenance,-1.160523204,F +coef_number_of_active_full_time_workers_eating_out,-0.305934663,F +coef_number_of_active_university_students_eating_out,-0.65706029,F +coef_number_of_active_retirees_eating_out,-0.391710731,F +coef_number_of_active_pre_driving_age_school_children_eating_out,-0.25140082,F +coef_number_of_active_preschool_children_eating_out,-1.700731169,F +coef_number_of_active_pre_driving_age_school_children_visiting,0.162335324,F +coef_number_of_active_preschool_children_visiting,-0.96955678,F +coef_number_of_active_part_time_workers_discretionary,0.217898126,F +coef_number_of_active_university_students_discretionary,-0.610578234,F +coef_number_of_active_driving_age_school_children_discretionary,0.359485499,F +coef_number_of_active_preschool_children_discretionary,-1.244458661,F +coef_hh_has_more_autos_than_workers_maintenance,-0.336188392,F +coef_income_gtr_100k_maintenance,-0.475730683,F +coef_income_less_than_29999__eating_out,-1.282190776,F +coef_income_between_30000_to_59999__eating_out,-0.275046208,F +coef_income_less_than_29999__discretionary,-0.352579013,F +coef_income_between_30000_to_59999__discretionary,-0.191735343,F +coef_shopping_hov_accessibility_for_2_tours,0.039513822,F +coef_maintenance_hov_accessibility,0.128132691,F +coef_discretionary_hov_accessibility,0.089590553,F +coef_constant_for_children_party_shopping_tour,-5.374907972,F +coef_constant_for_children_party_maintenance_tour,-5.144798184,F +coef_constant_for_children_party_eating_out_tour,-4.09806907,F +coef_constant_for_children_party_visiting_tour,-4.09806907,F +coef_constant_for_children_party_discretionary_tour,-4.09806907,F +coef_constant_for_mixed_party_shopping_tour,0.575879495,F +coef_constant_for_mixed_party_maintenance_tour,0.515873866,F +coef_constant_for_mixed_party_eating_out_tour,0.168592084,F +coef_constant_for_mixed_party_visiting_tour,0.078060666,F +coef_constant_for_mixed_party_discretionary_tour,0.856042068,F +coef_number_of_active_full_time_workers_adult_party,0.599335502,F +coef_number_of_active_part_time_workers_adult_party,1.113944272,F +coef_number_of_active_university_students_adult_party,0.231138167,F +coef_number_of_active_nonworkers_adult_party,0.341446999,F +coef_number_of_active_retirees_adult_party,0.657220801,F +coef_number_of_active_driving_age_school_children_children_party,0.580109231,F +coef_number_of_active_pre_driving_age_school_children_children_party,0.580109231,F +coef_number_of_active_part_time_workers_mixed_party,0.522327137,F +coef_number_of_active_driving_age_school_children_mixed_party,0.216908669,F +coef_number_of_active_pre_driving_age_school_children_mixed_party,0.31440104,F +coef_number_of_active_preschool_children_mixed_party,0.897670235,F +coef_hh_has_no_autos__mixed_party,-2.920728233,F +coef_hh_has_less_autos_than_workers_mixed_party,-0.546339245,F +coef_income_more_than_100k_child_party,-1.189112151,F +coef_income_more_than_100k_mixed_party,-0.303217156,F +coef_log_of_max_window_overlaps_between_adults,2.96902119,F +coef_log_of_max_window_overlaps_between_children,4.673601828,F +coef_log_of_max_window_overlaps_between_adult_child,3.523795377,F +coef_adjustment_for_children_party_shopping_tour,0.407745781,F +coef_adjustment_for_children_party_maintenance_tour,0.284925252,F +coef_adjustment_for_children_party_eating_out_tour,0,T +coef_adjustment_for_children_party_visiting_tour,0.966264444,F +coef_adjustment_for_children_party_discretionary_tour,0.144740532,F +coef_adjustment_for_mixed_party_shopping_tour,0.681178558,F +coef_adjustment_for_mixed_party_maintenance_tour,0.476324741,F +coef_adjustment_for_mixed_party_eating_out_tour,0.827297043,F +coef_adjustment_for_mixed_party_visiting_tour,0.691826384,F +coef_adjustment_for_mixed_party_discretionary_tour,0.697808415,F +coef_adjustment_for_shopping_tour,0,T +coef_adjustment_for_maintenance_tour,0.06575155,F +coef_adjustment_for_eating_out_tour,0.0643679,F +coef_adjustment_for_visiting_tour,0.00785518,F +coef_adjustment_for_discretionary_tour,0.076075677,F +coef_adjustment_for_share_of_2_joint_tours,-1.000,F +coef_tm2_adjustment_for_children_party,-4.0,F +coef_tm2_adjustment_for_mixed_party,-1.800,F +coef_tm2_adjustment_for_share_of_2_joint_tours,0.00,F +coef_tm2_adjustment_for_share_of_1_joint_tours,1.50,F +coef_tm2_adjustment_for_share_of_0_joint_tours,-1.00,F \ No newline at end of file diff --git a/resident/configs/joint_tour_participation.csv b/resident/configs/joint_tour_participation.csv new file mode 100644 index 0000000..29f5548 --- /dev/null +++ b/resident/configs/joint_tour_participation.csv @@ -0,0 +1,67 @@ +Label,Description,Expression,participate,not_participate +util_full_time_worker_adults_only_party,Dummy for full-time worker in adult party,(ptype==1) & (composition=='adults'),coef_full_time_worker_adults_only_party, +util_part_time_worker_adults_only_party,Dummy for part-time worker in adult party,(ptype==2) & (composition=='adults'),coef_part_time_worker_adults_only_party, +util_university_students_adults_only_party,Dummy for university student in adult party,(ptype==3) & (composition=='adults'),coef_university_students_adults_only_party, +util_non_worker_adults_only_party,Dummy for nonworker in adult party,(ptype==4) & (composition=='adults'),coef_non_worker_adults_only_party, +util_retiree_adults_only_party,Dummy for retiree in adult party,(ptype==5) & (composition=='adults'),coef_retiree_adults_only_party, +util_driving_age_student_children_only_party,Dummy for driving school child in children party,(ptype==6) & (composition=='children'),coef_driving_age_student_children_only_party, +util_pre_driving_age_student_children_only_party,Dummy for pre-driving school child in children party,(ptype==7) & (composition=='children'),coef_pre_driving_age_student_children_only_party, +util_child_too_young_for_school_children_only_party,Dummy for preschool child in children party,(ptype==8) & (composition=='children'),coef_child_too_young_for_school_children_only_party, +util_full_time_worker_mixed_party,Dummy for full-time worker in mixed party,(ptype==1) & (composition=='mixed'),coef_full_time_worker_mixed_party, +util_part_time_worker_mixed_party,Dummy for part-time worker in mixed party,(ptype==2) & (composition=='mixed'),coef_part_time_worker_mixed_party, +util_university_student_mixed_party,Dummy for university student in mixed party,(ptype==3) & (composition=='mixed'),coef_university_student_mixed_party, +util_non_worker_mixed_party,Dummy for nonworker in mixed party,(ptype==4) & (composition=='mixed'),coef_non_worker_mixed_party, +util_retiree_mixed_party,Dummy for retiree in mixed party,(ptype==5) & (composition=='mixed'),coef_retiree_mixed_party, +util_child_too_young_for_school_mixed_party,Dummy for driving school child in mixed party,(ptype==6) & (composition=='mixed'),coef_child_too_young_for_school_mixed_party, +util_pre_driving_age_student_mixed_party,Dummy for pre-driving school child in mixed party,(ptype==7) & (composition=='mixed'),coef_pre_driving_age_student_mixed_party, +util_driving_age_student_mixed_party,Dummy for preschool child in mixed party,(ptype==8) & (composition=='mixed'),coef_driving_age_student_mixed_party, +#,,,, +util_part_time_worker_specific_to_maintenance_joint_tours,Dummy for part-time worker on maintenance joint tour ,(ptype==2) & (tour_type=='othmaint'),coef_part_time_worker_specific_to_maintenance_joint_tours, +util_non_worker_specific_to_maintenance_joint_tours,Dummy for non-worker on maintenance joint tour ,(ptype==4) & (tour_type=='othmaint'),coef_non_worker_specific_to_maintenance_joint_tours, +util_pre_school_kid_specific_to_maintenance_joint_tours,Dummy for preschool child on maintenance joint tour ,(ptype==8) & (tour_type=='othmaint'),coef_pre_school_kid_specific_to_maintenance_joint_tours, +util_full_time_worker_specific_to_eating_out_joint_tours,Dummy for full-time worker on eating out joint tour ,(ptype==1) & (tour_type=='eatout'),coef_full_time_worker_specific_to_eating_out_joint_tours, +util_part_time_worker_specific_to_eating_out_joint_tours,Dummy for part-time worker on eating out joint tour ,(ptype==2) & (tour_type=='eatout'),coef_part_time_worker_specific_to_eating_out_joint_tours, +util_pre_driving_age_student_specific_to_eating_out_joint_tours,Dummy for pre-driving age school child on eating out joint tour ,(ptype==7) & (tour_type=='eatout'),coef_pre_driving_age_student_specific_to_eating_out_joint_tours, +util_part_time_worker_specific_to_discretionary_joint_tours,Dummy for part-time worker on discretionary joint tour ,(ptype==2) & (tour_type=='othdiscr'),coef_part_time_worker_specific_to_discretionary_joint_tours, +util_retiree_specific_to_discretionary_joint_tours,Dummy for retiree on discretionary joint tour ,(ptype==5) & (tour_type=='othdiscr'),coef_retiree_specific_to_discretionary_joint_tours, +util_driving_age_student_specific_to_discretionary_joint_tours,Dummy for driving school child on discretionary joint tour ,(ptype==6) & (tour_type=='othdiscr'),coef_driving_age_student_specific_to_discretionary_joint_tours, +util_pre_driving_age_student_specific_to_discretionary_joint_tours,Dummy for pre-driving school child on discretionary joint tour ,(ptype==7) & (tour_type=='othdiscr'),coef_pre_driving_age_student_specific_to_discretionary_joint_tours, +util_part_time_worker_specific_to_visiting_joint_tours,Dummy for part-time worker on visiting joint tour ,(ptype==2) & (tour_type=='social'),coef_part_time_worker_specific_to_visiting_joint_tours, +util_non_worker_specific_to_visiting_joint_tours,Dummy for non-worker on visiting joint tour ,(ptype==4) & (tour_type=='social'),coef_non_worker_specific_to_visiting_joint_tours, +util_retiree_specific_to_visiting_joint_tours,Dummy for retiree on visiting joint tour ,(ptype==5) & (tour_type=='social'),coef_retiree_specific_to_visiting_joint_tours, +util_driving_age_student_specific_to_visiting_joint_tours,Dummy for driving school child on visiting joint tour ,(ptype==6) & (tour_type=='social'),coef_driving_age_student_specific_to_visiting_joint_tours, +util_pre_school_kid_specific_to_visiting_joint_tours,Dummy for preschool child on visiting joint tour ,(ptype==8) & (tour_type=='social'),coef_pre_school_kid_specific_to_visiting_joint_tours, +#,,,, +util_adult_fewer_automobiles_than_workers_adult_only_party,Dummy for low car ownership for adult in adult party,(auto_ownershipnum_workers)&(composition=='mixed')&adult,coef_adult_more_automobiles_than_workers_mixed_party, +util_child_zero_automobiles_mixed_party,Dummy for no car for child in mixed party,(auto_ownership==0)&(composition=='mixed')&child,coef_child_zero_automobiles_mixed_party, +#,,,, +util_dummy_for_low_income_for_adult_in_adult_party,Dummy for low income for adult in adult party,(income<30000)&(composition=='adults')&adult,coef_dummy_for_low_income_for_adult_in_adult_party, +util_dummy_for_high_income_for_adult_in_mixed_party,Dummy for high income for adult in mixed party,(income>100000)&(composition=='mixed')&adult,coef_dummy_for_high_income_for_adult_in_mixed_party, +util_dummy_for_high_income_for_child_in_mixed_party,Dummy for high income for child in mixed party,(income>100000)&(composition=='mixed')&child,coef_dummy_for_high_income_for_child_in_mixed_party, +#,,,, +util_adult_number_of_joint_tours_adult_only,No of HH joint tours for adult in adult party,(adult&(composition=='adults'))*num_hh_joint_tours,coef_adult_number_of_joint_tours_adult_only, +util_adult_number_of_joint_tours_mixed,No of HH joint tours for adult in mixed party,(adult&(composition=='mixed'))*num_hh_joint_tours,coef_adult_number_of_joint_tours_mixed, +util_child_number_of_joint_tours_child_only,No of HH joint tours for child in children party,(child&(composition=='children'))*num_hh_joint_tours,coef_child_number_of_joint_tours_child_only, +util_child_number_of_joint_tours_mixed,No of HH joint tours for child in mixed party,(child&(composition=='mixed'))*num_hh_joint_tours,coef_child_number_of_joint_tours_mixed, +#,,,, +util_adult_number_of_other_adults_in_the_household_adults_only_party,No of other HH adults for adult in adult party,(adult & (composition=='adults')) * (num_adults.clip(upper=4) - 1),coef_adult_number_of_other_adults_in_the_household_adults_only_party, +util_adult_number_of_other_adults_in_the_household_mixed_party,No of other HH adults for adult in mixed party,(adult & (composition=='mixed')) * (num_adults.clip(upper=4) - 1),coef_adult_number_of_other_adults_in_the_household_mixed_party, +util_child_number_of_other_children_in_the_household_child_only_party,No of other HH children for child in children party,(~adult & (composition=='children')) * (num_children.clip(upper=4) - 1),coef_child_number_of_other_children_in_the_household_child_only_party, +util_child_number_of_other_children_in_the_household_mixed,No of other HH children for child in mixed party,(~adult & (composition=='mixed')) * (num_children.clip(upper=4) - 1),coef_child_number_of_other_children_in_the_household_mixed, +#,,,, +util_adult_log_of_max_window_overlap_with_an_adult_adult_only_party,"Adult, log of max window overlap with an adult, adult-only party",(adult & (composition=='adults')) * log_time_window_overlap_adult,coef_adult_log_of_max_window_overlap_with_an_adult_adult_only_party, +util_adult_log_of_max_window_overlap_with_a_child_mixed,"Adult, log of max window overlap with a child, mixed",(adult & (composition=='mixed')) * log_time_window_overlap_adult_child,coef_adult_log_of_max_window_overlap_with_a_child_mixed, +util_child_log_of_max_window_overlap_with_an_adult_mixed,"Child, log of max window overlap with an adult, mixed",(~adult &(composition=='mixed')) * log_time_window_overlap_adult_child,coef_child_log_of_max_window_overlap_with_an_adult_mixed, +util_child_log_of_max_window_overlap_with_a_child_child,"Child, log of max window overlap with a child, child",(~adult & (composition=='children')) * log_time_window_overlap_child,coef_child_log_of_max_window_overlap_with_a_child_child, +#,,,, +util_adults_are_prohibited_in_participating_in_child_only_tours,Adults are prohibited in participating in child-only tours,adult & (composition=='children'),coef_unavailable, +util_children_are_prohibited_in_participating_in_adult_only_tours,Children are prohibited in participating in adult-only tours,~adult & (composition=='adults'),coef_unavailable, +util_persons_with_home_activity_patterns_are_prohibilted_from_participating,Persons with Home activity patterns are prohibilted from participating,~travel_active,coef_unavailable, +#,,,, +util_if_only_two_available_adults_both_must_participate_in_adult_only_tour,"If only two available adults, both must participate in adult-only tour",adult & travel_active & (composition=='adults') & (num_travel_active_adults<3),,coef_unavailable +util_if_only_one_available_adult_traveler_must_participate_in_mixed_tour,"If only one available adult, traveler must participate in mixed tour",adult & travel_active & (composition=='mixed') & (num_travel_active_adults<2),,coef_unavailable +util_if_only_two_available_children_both_must_participate_in_child_only_tour,"If only two available children, both must participate in child-only tour",~adult & travel_active & (composition=='children') & (num_travel_active_children<3),,coef_unavailable +util_if_only_one_available_child_traveler_must_participate_in_mixed_tour,"If only one available child, traveler must participate in mixed tour",~adult & travel_active & (composition == 'mixed') & (num_travel_active_children<2),,coef_unavailable +#,,,, +util_globabl_adjustment_constant,Global adjustment constant for whether person participated or not,1,coef_global_adj_for_paticipation, diff --git a/resident/configs/joint_tour_participation.yaml b/resident/configs/joint_tour_participation.yaml new file mode 100644 index 0000000..1d17a22 --- /dev/null +++ b/resident/configs/joint_tour_participation.yaml @@ -0,0 +1,24 @@ + +SPEC: joint_tour_participation.csv +COEFFICIENTS: joint_tour_participation_coefficients.csv + +LOGIT_TYPE: MNL + +max_participation_choice_iterations: 1000 + +# will force anyone with a probability > 0 onto the tour after the max iterations +FORCE_PARTICIPATION: True + +preprocessor: + SPEC: joint_tour_participation_annotate_participants_preprocessor + DF: participants + TABLES: + - tours +# - persons +# - accessibility + +annotate_persons: + SPEC: annotate_persons_jtp + DF: persons + TABLES: + - joint_tour_participants diff --git a/resident/configs/joint_tour_participation_annotate_participants_preprocessor.csv b/resident/configs/joint_tour_participation_annotate_participants_preprocessor.csv new file mode 100644 index 0000000..38d26c7 --- /dev/null +++ b/resident/configs/joint_tour_participation_annotate_participants_preprocessor.csv @@ -0,0 +1,14 @@ +Description,Target,Expression +,_P_OVERLAPS,person_time_window_overlap(persons) +,time_window_overlap_adult,"reindex(_P_OVERLAPS.aa, participants.person_id)" +,time_window_overlap_child,"reindex(_P_OVERLAPS.cc, participants.person_id)" +,time_window_overlap_adult_child,"reindex(_P_OVERLAPS.ac, participants.person_id)" +logTimeWindowOverlapAdult,log_time_window_overlap_adult,np.log1p(time_window_overlap_adult) +logTimeWindowOverlapChild,log_time_window_overlap_child,np.log1p(time_window_overlap_child) +logTimeWindowOverlapAdultChild,log_time_window_overlap_adult_child,np.log1p(time_window_overlap_adult_child) +#,, +,_JOINT_TOURS,tours[tours.tour_category=='joint'] +,num_hh_joint_tours,"reindex_i(_JOINT_TOURS.groupby('household_id').size(), participants.household_id)" +#,, +,person_is_preschool,participants.ptype == 8 +,child,~participants.adult \ No newline at end of file diff --git a/resident/configs/joint_tour_participation_coefficients.csv b/resident/configs/joint_tour_participation_coefficients.csv new file mode 100644 index 0000000..8126d30 --- /dev/null +++ b/resident/configs/joint_tour_participation_coefficients.csv @@ -0,0 +1,59 @@ +coefficient_name,value,constrain +coef_unavailable,-999,T +coef_full_time_worker_adults_only_party,-0.844885174,F +coef_part_time_worker_adults_only_party,-1.837563723,F +coef_university_students_adults_only_party,-0.970251833,F +coef_non_worker_adults_only_party,-0.757939204,F +coef_retiree_adults_only_party,1.196798335,F +coef_driving_age_student_children_only_party,-12.08924396,F +coef_pre_driving_age_student_children_only_party,-16.16999328,F +coef_child_too_young_for_school_children_only_party,-16.16999328,F +coef_full_time_worker_mixed_party,0.453048547,F +coef_part_time_worker_mixed_party,1.263225489,F +coef_university_student_mixed_party,1.561741231,F +coef_non_worker_mixed_party,2.90040237,F +coef_retiree_mixed_party,1.043276837,F +coef_child_too_young_for_school_mixed_party,-1.916267403,F +coef_pre_driving_age_student_mixed_party,-1.91645719,F +coef_driving_age_student_mixed_party,-0.933863842,F +#,, +coef_part_time_worker_specific_to_maintenance_joint_tours,0.766072366,F +coef_non_worker_specific_to_maintenance_joint_tours,0.971450303,F +coef_pre_school_kid_specific_to_maintenance_joint_tours,-0.52817951,F +coef_full_time_worker_specific_to_eating_out_joint_tours,0.535579735,F +coef_part_time_worker_specific_to_eating_out_joint_tours,1.23257663,F +coef_pre_driving_age_student_specific_to_eating_out_joint_tours,1.535608884,F +coef_part_time_worker_specific_to_discretionary_joint_tours,0.539337302,F +coef_retiree_specific_to_discretionary_joint_tours,1.105118702,F +coef_driving_age_student_specific_to_discretionary_joint_tours,-1.151066894,F +coef_pre_driving_age_student_specific_to_discretionary_joint_tours,0.798684189,F +coef_part_time_worker_specific_to_visiting_joint_tours,1.075535443,F +coef_non_worker_specific_to_visiting_joint_tours,1.075535443,F +coef_retiree_specific_to_visiting_joint_tours,-1.930328716,F +coef_driving_age_student_specific_to_visiting_joint_tours,-1.334515344,F +coef_pre_school_kid_specific_to_visiting_joint_tours,1.552698221,F +#,, +coef_adult_fewer_automobiles_than_workers_adult_only_party,1.292641814,F +coef_adult_more_automobiles_than_workers_mixed_party,-0.390671553,F +coef_child_zero_automobiles_mixed_party,-1.546687521,F +#,, +coef_dummy_for_low_income_for_adult_in_adult_party,-0.680688378,F +coef_dummy_for_high_income_for_adult_in_mixed_party,-0.20257569,F +coef_dummy_for_high_income_for_child_in_mixed_party,-0.74160668,F +#,, +coef_adult_number_of_joint_tours_adult_only,-0.599443484,F +coef_adult_number_of_joint_tours_mixed,-0.218541079,F +coef_child_number_of_joint_tours_child_only,-0.313832442,F +coef_child_number_of_joint_tours_mixed,-0.241748337,F +#,, +coef_adult_number_of_other_adults_in_the_household_adults_only_party,-0.747528682,F +coef_adult_number_of_other_adults_in_the_household_mixed_party,-0.285988025,F +coef_child_number_of_other_children_in_the_household_child_only_party,-2.305908575,F +coef_child_number_of_other_children_in_the_household_mixed,-0.471545203,F +#,, +coef_adult_log_of_max_window_overlap_with_an_adult_adult_only_party,1.634378325,F +coef_adult_log_of_max_window_overlap_with_a_child_mixed,0.057012356,F +coef_child_log_of_max_window_overlap_with_an_adult_mixed,1.616796615,F +coef_child_log_of_max_window_overlap_with_a_child_child,10.70256432,F +#,, +coef_global_adj_for_paticipation,-1,T diff --git a/resident/configs/joint_tour_scheduling.yaml b/resident/configs/joint_tour_scheduling.yaml new file mode 100644 index 0000000..1a8cdcd --- /dev/null +++ b/resident/configs/joint_tour_scheduling.yaml @@ -0,0 +1,13 @@ + +SPEC: tour_scheduling_joint.csv +COEFFICIENTS: tour_scheduling_joint_coefficients.csv + +LOGIT_TYPE: MNL + +preprocessor: + SPEC: joint_tour_scheduling_annotate_tours_preprocessor + DF: joint_tours + TABLES: + - land_use + - households + - joint_tour_participants diff --git a/resident/configs/joint_tour_scheduling_annotate_tours_preprocessor.csv b/resident/configs/joint_tour_scheduling_annotate_tours_preprocessor.csv new file mode 100644 index 0000000..c220297 --- /dev/null +++ b/resident/configs/joint_tour_scheduling_annotate_tours_preprocessor.csv @@ -0,0 +1,2 @@ +Description,Target,Expression +,origin_to_destination_distance,"skim_dict.lookup(joint_tours.origin, joint_tours.destination, ('SOV_M_DIST', 'MD'))" diff --git a/resident/configs/license_holding_status.csv b/resident/configs/license_holding_status.csv new file mode 100644 index 0000000..b78a571 --- /dev/null +++ b/resident/configs/license_holding_status.csv @@ -0,0 +1,11 @@ +Label,Description,Expression,has_license,does_not_have_license +# dummy specification for license holding status model,,, +util_constant,alternative specific constant,1,coef_has_license_ASC,0 +util_unavailable_for_under_16,unavailable for persons under 16 years old,@df.age < 16,coef_unavailable,0 +util_age, Age of person in years,@df.age,coef_age,0 +util_female, Female dummy, @df.female,coef_female,0 +util_worker, Is worker dummy,@df.is_worker,coef_is_worker,0 +#util_mobility_difficulty, Has mobility difficulty dummy,@df.mobility_difficulty,coef_mobility_difficulty,0 +util_logsum_work_0auto,Work accessibility -- 0 autos,workplace_location_accessibility_0,coef_logsum_work_0auto,0 +util_logsum_shop_0auto,Shopping accessibility -- 0 autos,shopping_accessibility_0,coef_logsum_shop_0auto,0 +util_logsum_other_0auto,Other accessibility -- 0 autos,othdiscr_accessibility_0,coef_logsum_other_0auto,0 \ No newline at end of file diff --git a/resident/configs/license_holding_status.yaml b/resident/configs/license_holding_status.yaml new file mode 100644 index 0000000..5ab124d --- /dev/null +++ b/resident/configs/license_holding_status.yaml @@ -0,0 +1,12 @@ +SPEC: license_holding_status.csv +COEFFICIENTS: license_holding_status_coeff.csv + +# running for all people, using availability feature in the spec +# can use this to limit to a subset of persons if desired +# CHOOSE_FILTER_COLUMN_NAME: + +# 0-based index of the "has_license" alternative in the spec +LICENSE_STATUS_ALT: 0 + +LOGIT_TYPE: MNL + diff --git a/resident/configs/license_holding_status_coeff.csv b/resident/configs/license_holding_status_coeff.csv new file mode 100644 index 0000000..308f5f1 --- /dev/null +++ b/resident/configs/license_holding_status_coeff.csv @@ -0,0 +1,10 @@ +coefficient_name,value,constrain +coef_unavailable,-999,T +coef_has_license_ASC,1,F +coef_age,0,F +coef_female,0,F +coef_is_worker,0,F +# coef_mobility_difficulty,-0,F +coef_logsum_work_0auto,0,F +coef_logsum_shop_0auto,0,F +coef_logsum_other_0auto,0,F \ No newline at end of file diff --git a/resident/configs/logging.yaml b/resident/configs/logging.yaml new file mode 100644 index 0000000..71162f1 --- /dev/null +++ b/resident/configs/logging.yaml @@ -0,0 +1,59 @@ +# Config for logging +# ------------------ +# See http://docs.python.org/2.7/library/logging.config.html#configuration-dictionary-schema + +logging: + version: 1 + disable_existing_loggers: true + + + # Configuring the default (root) logger is highly recommended + root: + level: NOTSET + handlers: [console] + + loggers: + + activitysim: + level: DEBUG + handlers: [console, logfile] + propagate: false + + sharrow: + level: INFO + handlers: [console, logfile] + propagate: false + + handlers: + + logfile: + class: logging.FileHandler + filename: + get_log_file_path: 'activitysim.log' + mode: w + formatter: fileFormatter + level: NOTSET + + console: + class: logging.StreamHandler + stream: ext://sys.stdout + formatter: elapsedFormatter + level: INFO + + formatters: + + simpleFormatter: + class: logging.Formatter + # format: '%(levelname)s - %(name)s - %(message)s' + format: '%(levelname)s - %(message)s' + datefmt: '%d/%m/%Y %H:%M:%S' + + fileFormatter: + class: logging.Formatter + format: '%(asctime)s - %(levelname)s - %(name)s - %(message)s' + datefmt: '%d/%m/%Y %H:%M:%S' + + elapsedFormatter: + (): activitysim.core.tracing.ElapsedTimeFormatter + format: '[{elapsedTime}] {levelname:s}: {message:s}' + style: '{' \ No newline at end of file diff --git a/resident/configs/mandatory_tour_frequency.csv b/resident/configs/mandatory_tour_frequency.csv new file mode 100644 index 0000000..548e2dd --- /dev/null +++ b/resident/configs/mandatory_tour_frequency.csv @@ -0,0 +1,78 @@ +Label,Description,Expression,work1,work2,school1,school2,work_and_school +# availiability conditions just based on worker or student status,,,,,,, +util_work_unavailable_for_non_workers,work not available for non-workers,is_worker == False,coef_unavailable,coef_unavailable,,,coef_unavailable +util_work_unavailable_for_wfh,work not available if work from home,work_from_home == True,coef_unavailable,coef_unavailable,,,coef_unavailable +util_school_unavailable_for_non_students,school unavailable for non-students,is_student == False,,,coef_unavailable,coef_unavailable,coef_unavailable +#,,,,,,, +util_alternative_specific_constant_for_full_time_work,alternative specific constant for full_time work,@df.ptype==1,coef_zero,coef_ft_worker_work2_asc,coef_ft_worker_school1_asc,,coef_ft_worker_work_and_school_asc +util_alternative_specific_constant_for_part_time_work,alternative specific constant for part_time work,@df.ptype==2,coef_zero,coef_pt_worker_work2_asc,coef_pt_worker_school1_asc,,coef_pt_worker_work_and_school_asc +util_alternative_specific_constant_for_university_student,alternative specific constant for university student,@df.ptype==3,coef_univ_work1_asc,,coef_zero,coef_univ_school2_asc,coef_univ_work_and_school_asc +util_alternative_specific_constant_for_non_worker_U65,alternative specific constant for non_worker U65,@df.ptype==4,coef_zero,,coef_non_worker_school1_asc,coef_zero, +util_alternative_specific_constant_for_retiree_65,alternative specific constant for retiree 65+,@df.ptype==5,coef_zero,,coef_ret_worker_school1_asc,coef_zero, +util_alternative_specific_constant_for_school_child_16_17,alternative specific constant for school child 16_17,@df.ptype==6,coef_driving_age_child_work1_asc,,coef_zero,coef_driving_age_child_school2_asc,coef_driving_age_child_work_and_school_asc +util_alternative_specific_constant_for_school_child_6_15,alternative specific constant for school child 6_15,@df.ptype==7,coef_unavailable,coef_unavailable,coef_zero,coef_pre_driving_age_child_school2_asc,coef_unavailable +#,,,,,,, +util_person_is_female_full_time_worker,person is female_ full time worker,@(df.female==1)*(df.ptype==1),coef_zero,coef_female_ftworker_work2,coef_female_ftworker_school1,,coef_female_ftworker_work_and_school +util_person_is_female_part_time_worker,person is female_ part time worker,@(df.female==1)*(df.ptype==2),coef_zero,coef_female_ptworker_work2,coef_female_ptworker_school1,,coef_zero +util_person_is_female_university_student,person is female_ university student,@(df.female==1)*(df.ptype==3),coef_female_univ_work1,,coef_zero,coef_female_univ_school2,coef_female_univ_work_and_school +util_person_is_female_non_worker_Under_65,person is female_ non_worker Under 65,@(df.female==1)*(df.ptype==4),,,,, +util_person_is_female_retiree_65_and_plus,person is female_ retiree 65 and plus,@(df.female==1)*(df.ptype==5),,,,, +util_person_is_female_school_child_16_17,person is female_ school child 16_17,@(df.female==1)*(df.ptype==6),coef_female_driving_age_child_work1,,coef_zero,coef_female_driving_age_child_school2,coef_female_driving_age_child_work_and_school +util_person_is_female_school_child_6_15,person is female_ school child 6_15,@(df.female==1)*(df.ptype==7),,,coef_zero,coef_female_child_school2, +util_young_adult_full_time_worker,young adult_ full time worker,@(df.age>17)*(df.age<=35)*(df.ptype==1),coef_zero,coef_zero,coef_youngadult_ftworker_school1,,coef_zero +util_older_adult_university_student,older adult_ university student,@(df.age>35)*(df.ptype==3),coef_olderadult_univ_work1,,coef_zero,coef_zero, +#,,,,,,, +util_workplace_within_distance_band_1full_time_worker,workplace within distance band 1 (0 to 0.5 mile)_ full time worker,@(df.distance_to_work<=0.5)*(df.ptype==1),,coef_distband1_ftworker_work2,,, +util_workplace_within_distance_band_1part_time_worker,workplace within distance band 1 (0 to 0.5 mile)_ part time worker,@(df.distance_to_work<=0.5)*(df.ptype==2),,coef_distband1_ptworker_work2,,, +util_workplace_within_distance_band_2_full_time_worker,workplace within distance band 2 (0.5 to 3 mile)_ full time worker,@(df.distance_to_work>0.5)*(df.distance_to_work<=3)*(df.ptype==1),,coef_distband2_ftworker_work2,,, +util_workplace_within_distance_band_2_part_time_worker,workplace within distance band 2 (0.5 to 3 mile)_ part time worker,@(df.distance_to_work>0.5)*(df.distance_to_work<=3)*(df.ptype==2),,coef_distband2_ptworker_work2,,, +util_school_wtihin_distance_band_2_university_student,school wtihin distance band 2 (0.5 to 2 mile)_ university student,@(df.distance_to_school>0.5)*(df.distance_to_school<=2)*(df.ptype==3),,,,coef_distband2_univ_school2, +util_school_wtihin_distance_band_2_school_child_16_17,school wtihin distance band 2 (0.5 to 2 mile)_ school child 16_17,@(df.distance_to_school>0.5)*(df.distance_to_school<=2)*(df.ptype==6),,,,coef_distband2_driving_age_child_school2, +util_school_wtihin_distance_band_2_school_child_6_15,school wtihin distance band 2 (0.5 to 2 mile)_ school child 6_15,@(df.distance_to_school>0.5)*(df.distance_to_school<=2)*(df.ptype==7),,,,coef_distband2_child_school2, +util_work_or_school_distance_within_distance_band_1_full_time_worker,work or school distance within distance band 1_ full time worker,@((df.distance_to_work<=0.5)+(df.distance_to_school<=0.5))*(df.ptype==1),,,,,coef_distband1_ftworker_work_and_school +util_work_or_school_distance_within_distance_band_1_part_time_worker,work or school distance within distance band 1_ part time worker,@((df.distance_to_work<=0.5)+(df.distance_to_school<=0.5))*(df.ptype==2),,,,,coef_distband1_ptworker_work_and_school +util_work_or_school_distance_within_distance_band_1_university_student,work or school distance within distance band 1_ university student,@((df.distance_to_work<=0.5)+(df.distance_to_school<=0.5))*(df.ptype==3),,,,,coef_distband1_univ_work_and_school +util_work_or_school_distance_within_distance_band_1_school_child_16_17,work or school distance within distance band 1_ school child 16_17,@((df.distance_to_work<=0.5)+(df.distance_to_school<=0.5))*(df.ptype==6),,,,,coef_distband1_driving_age_child_work_and_school +util_work_or_school_distance_within_distance_band_2_full_time_worker,work or school distance within distance band 2_ full time worker,@((df.distance_to_work>0.5)*(df.distance_to_work<=3)+(df.distance_to_school>0.5)*(df.distance_to_school<=2))*(df.ptype==1),,,,,coef_distband2_ftworker_work_and_school +util_work_or_school_distance_within_distance_band_2_part_time_worker,work or school distance within distance band 2_ part time worker,@((df.distance_to_work>0.5)*(df.distance_to_work<=3)+(df.distance_to_school>0.5)*(df.distance_to_school<=2))*(df.ptype==2),,,,,coef_distband2_ptworker_work_and_school +util_work_or_school_distance_within_distance_band_2_university_student,work or school distance within distance band 2_ university student,@((df.distance_to_work>0.5)*(df.distance_to_work<=3)+(df.distance_to_school>0.5)*(df.distance_to_school<=2))*(df.ptype==3),,,,,coef_distband2_univ_work_and_school +util_work_or_school_distance_within_distance_band_2_school_child_16_17,work or school distance within distance band 2_ school child 16_17,@((df.distance_to_work>0.5)*(df.distance_to_work<=3)+(df.distance_to_school>0.5)*(df.distance_to_school<=2))*(df.ptype==6),,,,,coef_distband2_driving_age_child_work_and_school +#,,,,,,, +util_minimum_travel_time_to_workplace_full_time_worker,minimum travel time to workplace (exclues non_motorized)_ full time worker,@df.ptype==1 * df.roundtrip_auto_time_to_work,,coef_ftworker_timetowork_work2,,,coef_ftworker_timetowork_work_and_school +util_minimum_travel_time_to_workplace_part_time_worker,minimum travel time to workplace (excludes non_motorized)_ part time worker,@df.ptype==2 * df.roundtrip_auto_time_to_work,,coef_ptworker_timetowork_work2,,,coef_ptworker_timetowork_work_and_school +util_minimum_travel_time_to_workplace_university_student,minimum travel time to workplace (excludes non_motorized)_ university student,@df.ptype==3 * df.roundtrip_auto_time_to_work,,,,,coef_univ_timetowork_work_and_school +util_minimum_travel_time_to_workplace_non_worker_under_65,minimum travel time to workplace (excludes non_motorized)_ non_worker under 65,@df.ptype==4 * df.roundtrip_auto_time_to_work,,,,, +util_minimum_travel_time_to_workplace_retiree_65_and_plus,minimum travel time to workplace (excludes non_motorized)_ retiree 65 and plus,@df.ptype==5 * df.roundtrip_auto_time_to_work,,,,, +#,,,,,,, +util_zero_car_full_time_worker,zero car full time worker,@(df.auto_ownership==0)*(df.ptype==1),,coef_ftworker_zerocar_work2,,,coef_ftworker_zerocar_work_and_school +util_zero_car_part_time_worker,zero car part time worker,@(df.auto_ownership==0)*(df.ptype==2),,coef_ptworker_zerocar_work2,,,coef_ptworker_zerocar_work_and_school +util_zero_car_university_student,zero car university student,@(df.auto_ownership==0)*(df.ptype==3),,,,coef_univ_zerocar_school2,coef_univ_zerocar_work_and_school +util_zero_car_non_worker_under_65,zero car non_worker under 65,@(df.auto_ownership==0)*(df.ptype==4),,,,, +util_zero_car_retiree_65_and_plus,zero car retiree 65 and plus,@(df.auto_ownership==0)*(df.ptype==5),,,,, +util_zero_car_school_child_16_17,zero car school child 16_17,@(df.auto_ownership==0)*(df.ptype==6),,,,coef_driving_age_child_zerocar_school2,coef_driving_age_child_zerocar_work_and_school +util_zero_car_school_child_6_15,zero car school child 6_15,@(df.auto_ownership==0)*(df.ptype==7),,,,coef_child_zerocar_school2, +util_car_fewer_than_drivers_university_student,car fewer than drivers_ university student,@(df.auto_ownership=60000)*(df.ptype==1),,,,, +util_mid_and_high_household_income_part_time_worker,mid and high household income_ part time worker,@(df.income>=60000)*(df.ptype==2),,,,, +util_mid_and_high_household_income_university_student,mid and high household income_ university student,@(df.income>=60000)*(df.ptype==3),coef_univ_midhighincome_work1,,,, +#,,,,,,, +util_alternative_specific_constant_adjustment_for_full_time_work,alternative specific constant adjustment for full_time work,@df.ptype==1,,0.1423726,,, +util_alternative_specific_constant_adjustment_for_part_time_work,alternative specific constant adjustment for part_time work,@df.ptype==2,,0.2684171,,, +util_alternative_specific_constant_adjustment_for_university_student,alternative specific constant adjustment for university student,@df.ptype==3,0.362762501,,,0.52545937,1.0066602 +util_alternative_specific_constant_adjustment_for_school_child_16_17,alternative specific constant adjustment for school child 16_17,@df.ptype==6,,,,0.1478192,0.4715931 +util_alternative_specific_constant_adjustment_for_school_child_6_15,alternative specific constant adjustment for school child 6_15,@df.ptype==7,,,,0.0969167, +util_alternative_specific_constant_adjustment_for_school_child_0_5,alternative specific constant adjustment for school child 0_5,@df.ptype==8,,,0.72,-0.67, +#,,,,,,, diff --git a/resident/configs/mandatory_tour_frequency.yaml b/resident/configs/mandatory_tour_frequency.yaml new file mode 100644 index 0000000..a0d7fa6 --- /dev/null +++ b/resident/configs/mandatory_tour_frequency.yaml @@ -0,0 +1,15 @@ + +SPEC: mandatory_tour_frequency.csv +COEFFICIENTS: mandatory_tour_frequency_coeffs.csv + +preprocessor: + SPEC: mandatory_tour_frequency_preprocessor + DF: persons + TABLES: + - households + +annotate_persons: + SPEC: annotate_persons_mtf + DF: persons + TABLES: + - tours diff --git a/resident/configs/mandatory_tour_frequency_alternatives.csv b/resident/configs/mandatory_tour_frequency_alternatives.csv new file mode 100644 index 0000000..025decb --- /dev/null +++ b/resident/configs/mandatory_tour_frequency_alternatives.csv @@ -0,0 +1,7 @@ +#,,alt file for building tours even though simulation is simple_simulate not interaction_simulate +alt,work,school +work1,1,0 +work2,2,0 +school1,0,1 +school2,0,2 +work_and_school,1,1 diff --git a/resident/configs/mandatory_tour_frequency_coeffs.csv b/resident/configs/mandatory_tour_frequency_coeffs.csv new file mode 100644 index 0000000..d01a9e3 --- /dev/null +++ b/resident/configs/mandatory_tour_frequency_coeffs.csv @@ -0,0 +1,83 @@ +coefficient_name,value,constrain,,,, +coef_zero,0,T,,,, +coef_unavailable,-999,T,,,, +#,,,,,, +coef_ft_worker_work2_asc,-2.548,F,,,, +coef_ft_worker_school1_asc,-2.29548389,F,,,, +coef_ft_worker_work_and_school_asc,-2.124547196,F,,,, +coef_pt_worker_work2_asc,-3.255,F,,,, +coef_pt_worker_school1_asc,-1.523338502,F,,,, +coef_pt_worker_work_and_school_asc,-1.916270321,F,,,, +coef_univ_work1_asc,-0.935,F,,,, +coef_driving_age_child_work1_asc,-2.81,F,,,, +coef_univ_school2_asc,-2.329,F,,,, +coef_univ_work_and_school_asc,-0.898,F,,,, +coef_driving_age_child_school2_asc,-2.230,F,,,, +coef_pre_driving_age_child_school2_asc,-3.838,F,,,, +coef_driving_age_child_work_and_school_asc,-3.449,F,,,, +coef_non_worker_school1_asc,14.600,F,,,, +coef_ret_worker_school1_asc,14.600,F,,,, +#,,,,,, +coef_female_ftworker_work2,-0.171976,F,,,, +coef_female_ftworker_school1,-0.372102879,F,,,, +coef_female_ftworker_work_and_school,0.656500245,F,,,, +coef_female_ptworker_work2,0.72635447,F,,,, +coef_female_ptworker_school1,0.634251329,F,,,, +coef_female_univ_work1,-0.186104106,F,,,, +coef_female_univ_school2,-1.207464296,F,,,, +coef_female_univ_work_and_school,-0.627268064,F,,,, +coef_female_driving_age_child_work1,-0.84458299,F,,,, +coef_female_driving_age_child_school2,-1.565680968,F,,,, +coef_female_driving_age_child_work_and_school,2.22484406,F,,,, +coef_female_child_school2,-1.124163745,F,,,, +coef_youngadult_ftworker_school1,0.336552714,F,,,, +coef_olderadult_univ_work1,1.373765885,F,,,, +#,,,,,, +coef_distband1_ftworker_work2,0.64195998,F,,,, +coef_distband1_ptworker_work2,0.64195998,F,,,, +coef_distband2_ftworker_work2,1.217340344,F,,,, +coef_distband2_ptworker_work2,1.217340344,F,,,, +coef_distband2_univ_school2,0.491551442,F,,,, +coef_distband2_driving_age_child_school2,0.491551442,F,,,, +coef_distband2_child_school2,0.491551442,F,,,, +coef_distband1_ftworker_work_and_school,0.194304616,F,,,, +coef_distband1_ptworker_work_and_school,0.194304616,F,,,, +coef_distband1_univ_work_and_school,0.194304616,F,,,, +coef_distband1_driving_age_child_work_and_school,0.194304616,F,,,, +coef_distband2_ftworker_work_and_school,0.183705706,F,,,, +coef_distband2_ptworker_work_and_school,0.183705706,F,,,, +coef_distband2_univ_work_and_school,0.183705706,F,,,, +coef_distband2_driving_age_child_work_and_school,0.183705706,F,,,, +#,,,,,, +coef_ftworker_timetowork_work2,-0.022406282,F,,,, +coef_ptworker_timetowork_work2,-0.022406282,F,,,, +coef_ftworker_timetowork_work_and_school,-0.007707408,F,,,, +coef_ptworker_timetowork_work_and_school,-0.007707408,F,,,, +coef_univ_timetowork_work_and_school,-0.007707408,F,,,, +#,,,,,, +coef_ftworker_zerocar_work2,-0.661753779,F,,,, +coef_ptworker_zerocar_work2,-0.661753779,F,,,, +coef_univ_zerocar_school2,-0.661753779,F,,,, +coef_driving_age_child_zerocar_school2,-0.661753779,F,,,, +coef_child_zerocar_school2,-0.661753779,F,,,, +coef_univ_autodef_school2,-0.955356561,F,,,, +coef_driving_age_child_autodef_school2,-0.955356561,F,,,, +coef_child_autodef_school2,-0.955356561,F,,,, +coef_ftworker_zerocar_work_and_school,-0.661753779,F,,,, +coef_ptworker_zerocar_work_and_school,-0.661753779,F,,,, +coef_univ_zerocar_work_and_school,-0.661753779,F,,,, +coef_driving_age_child_zerocar_work_and_school,-0.661753779,F,,,, +#,,,,,, +coef_univ_nonfamily_work1,-1.094011735,F,,,, +coef_driving_age_child_nonfamily_work2,-1.094011735,F,,,, +coef_univ_midhighincome_work1,0.688596236,F,,,, +coef_ftworker_numprekchildren_work2,-0.039040872,F,,,, +coef_ptworker_numprekchildren_work2,-0.039040872,F,,,, +coef_ftworker_scng2school_work2,-0.436718886,F,,,, +coef_univ_numprekchildren_school2,-1.533952899,F,,,, +coef_ftworker_numprekchildren_work_and_school,-0.142979462,F,,,, +coef_ptworker_numprekchildren_work_and_school,-0.142979462,F,,,, +coef_univ_numprekchildren_work_and_school,-0.142979462,F,,,, +coef_univ_nonfamily_work_and_school,-1.094011735,F,,,, +coef_driving_age_child_nonfamily_work_and_school,-1.094011735,F,,,, +#,,,,,, diff --git a/resident/configs/mandatory_tour_frequency_preprocessor.csv b/resident/configs/mandatory_tour_frequency_preprocessor.csv new file mode 100644 index 0000000..d777580 --- /dev/null +++ b/resident/configs/mandatory_tour_frequency_preprocessor.csv @@ -0,0 +1,4 @@ +Description,Target,Expression +HHT from households,_HHT,"reindex(households.HHT, persons.household_id)" +non_family,non_family,_HHT.isin(HHT_NONFAMILY) +family,family,_HHT.isin(HHT_FAMILY) \ No newline at end of file diff --git a/resident/configs/mandatory_tour_scheduling.yaml b/resident/configs/mandatory_tour_scheduling.yaml new file mode 100644 index 0000000..4063f07 --- /dev/null +++ b/resident/configs/mandatory_tour_scheduling.yaml @@ -0,0 +1,51 @@ + +SIMULATE_CHOOSER_COLUMNS: + - age + - female + - ptype + - is_income_less25K + - is_income_25K_to_60K + - is_income_60K_to_120K + - is_income_greater120K + - is_pre_drive_child_in_HH + - is_non_worker_in_HH + - auto_ownership + - is_all_adults_full_time_workers + - distance_to_school + - roundtrip_auto_time_to_work + - roundtrip_auto_time_to_school + - free_parking_at_work + - workplace_zone_id + - school_zone_id + - home_zone_id + - TAZ + - transit_pass_ownership + - transit_pass_subsidy + +LOGSUM_SETTINGS: tour_mode_choice.yaml + +TOUR_SPEC_SEGMENTS: + work: work + school: school + univ: univ + +ALTS_PREPROCESSOR: + work: + SPEC: mandatory_tour_scheduling_annotate_alts_preprocessor.csv + DF: alt_tdd + +SPEC_SEGMENTS: + work: + 'SPEC': tour_scheduling_work.csv + 'COEFFICIENTS': tour_scheduling_work_coeffs.csv + school: + 'SPEC': tour_scheduling_school.csv + 'COEFFICIENTS': tour_scheduling_school_coeffs.csv + univ: + 'SPEC': tour_scheduling_university.csv + 'COEFFICIENTS': tour_scheduling_university_coeffs.csv + +DESTINATION_FOR_TOUR_PURPOSE: + work: workplace_zone_id + school: school_zone_id + univ: school_zone_id diff --git a/resident/configs/mandatory_tour_scheduling_annotate_alts_preprocessor.csv b/resident/configs/mandatory_tour_scheduling_annotate_alts_preprocessor.csv new file mode 100644 index 0000000..d961142 --- /dev/null +++ b/resident/configs/mandatory_tour_scheduling_annotate_alts_preprocessor.csv @@ -0,0 +1,15 @@ +Description,Target,Expression +# make expressions are consistent with reference bins,, +departure_reference_bin,departureRefBin,9 +arrival_reference_bin,arrivalRefBin,29 +duration_reference_bin,durationRefBin,20 +departure_shift,departureLinearShift1,"(9-df.start)*(df.start<=9) + (df.start-9)*(df.start>9)" +arrival_shift,arrivalLinearShift1,"(29-df.end)*(df.end<=29) + (df.end-29)*(df.end>29)" +duration_shift,durationShift,"(20-df.duration)*(df.duration<=20) + (df.duration-20)*(df.duration>20)" +# school specific bins +departure_reference_bin_school,departureRefBin_school,9 +arrival_reference_bin_school,arrivalRefBin_school,29 +duration_reference_bin_school,durationRefBin_school,16 +departure_shift_school,departureLinearShift1_school,"(9-df.start)*(df.start<=9) + (df.start-9)*(df.start>9)" +arrival_shift_school,arrivalLinearShift1_school,"(29-df.end)*(df.end<=29) + (df.end-29)*(df.end>29)" +duration_shift_school,durationShift_school,"(16-df.duration)*(df.duration<=16) + (df.duration-16)*(df.duration>16)" diff --git a/resident/configs/network_los.yaml b/resident/configs/network_los.yaml new file mode 100644 index 0000000..97494d4 --- /dev/null +++ b/resident/configs/network_los.yaml @@ -0,0 +1,49 @@ +inherit_settings: True + +zone_system: 2 + +skim_dict_factory: NumpyArraySkimFactory +#skim_dict_factory: MemMapSkimFactory + +# read cached skims (using numpy memmap) from output directory (memmap is faster than omx ) +read_skim_cache: False +# write memmapped cached skims to output directory after reading from omx, for use in subsequent runs +write_skim_cache: False + +# series15 +taz_skims: + - autoSkims*.omx + - transitSkims*.omx + - walkSkim.omx + +maz: land_use.csv + +maz_to_maz: + tables: + - maz_maz_walk.csv + + # maz_to_maz blending distance (missing or 0 means no blending) + # max_blend_distance: + # DIST: 5 + # # blend distance of 0 means no blending + # DISTBIKE: 0 + # DISTWALK: 1 + + +skim_time_periods: + time_window: 1440 + period_minutes: 30 + # Metro time periods: + # EA: midnight to 5 AM + # AM: 5 AM to 9 AM + # MD: 9 AM to 3 PM + # PM: 3 PM to 7 PM + # EV: 7 PM to midnight + periods: [0, 6, 12, 24, 32, 42, 48] # time periods to match documentation + labels: &skim_time_period_labels ['EA', 'AM', 'MD', 'PM', 'EV', 'EA'] + +demographic_segments: &demographic_segments + - &low_income_segment_id 0 + - &high_income_segment_id 1 + + diff --git a/resident/configs/non_mandatory_tour_destination.csv b/resident/configs/non_mandatory_tour_destination.csv new file mode 100644 index 0000000..116360a --- /dev/null +++ b/resident/configs/non_mandatory_tour_destination.csv @@ -0,0 +1,74 @@ +Label,Description,Expression,escort,shopping,eatout,othmaint,social,othdiscr +,,"_DIST@np.minimum(skims[('SOV_M_DIST', 'MD')], 20)",1,1,1,1,1,1 +,,_DIST_SQUARED@_DIST**2,1,1,1,1,1,1 +,,_DIST_CUBED@_DIST**3,1,1,1,1,1,1 +,,_DIST_LOGGED@np.log(_DIST + 0.001),1,1,1,1,1,1 +,,"_CALIB_DIST@np.where(df.tour_type == 'escort', 20, np.where(df.tour_type == 'othmaint', 8, 10))",1,1,1,1,1,1 +util_dist,dist,@_DIST,coef_dist_escort,coef_dist_shopping,coef_dist_eatout,coef_dist_maint,coef_dist_social,coef_dist_discr +util_dist_squared,dist_squared,@_DIST_SQUARED,coef_distsqrd_escort,coef_distsqrd_shopping,coef_distsqrd_eatout,coef_distsqrd_maint,coef_distsqrd_social,coef_distsqrd_discr +util_dist_cubed,dist_cubed,@_DIST_CUBED,coef_zero,coef_distcubed_shopping,coef_distcubed_eatout,coef_zero,coef_distcubed_social,coef_distcubed_discr +util_dist_logged,dist_logged,@_DIST_LOGGED,coef_zero,coef_distlogged_shopping,coef_distlogged_eatout,coef_zero,coef_distlogged_social,coef_distlogged_discr +util_dist_female,dist_female,"@_DIST* df.female * np.where(df.get('tour_category', default=False) == 'joint', 0, 1)",coef_zero,coef_distfemale_shopping,coef_zero,coef_zero,coef_zero,coef_distfemale_disc +util_dist_income_segment_escort,Distance - low income_segment_escort,@_DIST * (df.income < 30000),coef_distlowinc_escort,coef_zero,coef_zero,coef_zero,coef_zero,coef_zero +util_dist_lowincome_segment_maint,Distance - low income_segment_maint,@_DIST * (df.income_segment <= 2),coef_zero,coef_zero,coef_zero,coef_distlowinc_maint,coef_distlowinc_social,coef_zero +util_dist_lowmed_income_segment_maint,Distance - lowmed income_segment_maint,@_DIST * (df.income_segment <= 3),coef_zero,coef_zero,coef_zero,coef_zero,coef_distmidlowinc_social,coef_zero +#Distance - Time Pressure calculated as the log of the maxtime over tours left,#Distance - Time Pressure calculated as the log of the maxtime over tours left,@_DIST* np.log((person_max_window(persons)/(df.tour_type_count - df.tour_type_num + 1))+0.0001),coef_zero,coef_disttimepressure_shopping,coef_disttimepressure_eatout,coef_disttimepressure_maint,coef_zero,coef_disttimepressure_disc +Accessibility,#Accessibility,shopping_accessibility,coef_zero,coef_accessibility_shopping,coef_zero,coef_zero,coef_zero,coef_zero +util_dist_age_16to24,Distance - age 16 to 24,"@_DIST * df.age.between(16, 24) * np.where(df.get('tour_category', default=False) == 'joint', 0, 1)",coef_zero,coef_zero,coef_zero,coef_distage1624_maint,coef_zero,coef_zero +util_dist_age_56to64,Distance - age 56 to 64,"@_DIST * (df.age.between(56, 64))",coef_distage5664_escort,coef_zero,coef_zero,coef_zero,coef_zero,coef_zero +util_dist_age_65+,Distance - age 65+,@_DIST * (df.age > 64),coef_distage64plus_escort,coef_zero,coef_zero,coef_zero,coef_zero,coef_zero +#escort size is zero for escort tour,#escort size is zero for escort tour,@(@@householdsDestAlt * 1 + df.num_young_children * @@householdsDestAlt * 1 + df.num_gradeschool * @@gradeSchoolEnrollmentDestAlt * 100 + df.numHighSchoolStudents * @@highSchoolEnrollmentDestAlt * 100) == 0,-999,coef_zero,coef_zero,coef_zero,coef_zero,coef_zero +#escort size is non-zero for escort tour,#escort size is non-zero for escort tour,"@np.where(@@householdsDestAlt * 1 + df.num_young_children * @@householdsDestAlt * 1 + df.num_gradeschool * @@gradeSchoolEnrollmentDestAlt * 100 + df.numHighSchoolStudents * @@highSchoolEnrollmentDestAlt * 100 > 0, np.log(@@householdsDestAlt * 1 + df.num_y",1,coef_zero,coef_zero,coef_zero,coef_zero,coef_zero +util_size_term,Size variable,@df['size_term'].apply(np.log1p),1,1,1,1,1,1 +util_no_attractions,No attractions,@df['size_term']==0,-999,-999,-999,-999,-999,-999 +util_mode_choice_logsum,Mode choice logsum,mode_choice_logsum,coef_mode_logsum,coef_mode_logsum,coef_mode_logsum,coef_mode_logsum,coef_mode_logsum_socdisc,coef_mode_logsum_socdisc +util_sample_alts_correction,Sample of alternatives correction factor,"@np.minimum(np.log(df.pick_count/df.prob), 60)",1,1,1,1,1,1 +#Calibration constants,#Calibration constants,,,,,,, +util_calib_dist,Calibration - Distance,"@np.minimum(_DIST, _CALIB_DIST)",coef_Calibration_Distance_escort,coef_Calibration_Distance_shopping,coef_Calibration_Distance_eatout,coef_Calibration_Distance_maint,coef_Calibration_Distance_social,coef_Calibration_Distance_disc +util_calib_dist_squared,Calibration - Distance_squared,"@np.minimum(_DIST_SQUARED, _CALIB_DIST**2)",coef_Calibration_Distance_squared_escort,coef_Calibration_Distance_squared_shopping,coef_Calibration_Distance_squared_eatout,coef_Calibration_Distance_squared_maint,coef_Calibration_Distance_squared_social,coef_Calibration_Distance_squared_disc +util_calib_dist_cubed,Calibration - Distance_cubed,"@np.minimum(_DIST_CUBED, _CALIB_DIST**3)",coef_Calibration_Distance_cubed_escort,coef_Calibration_Distance_cubed_shopping,coef_Calibration_Distance_cubed_eatout,coef_Calibration_Distance_cubed_maint,coef_Calibration_Distance_cubed_social,coef_Calibration_Distance_cubed_disc +util_calib_dist_logged,Calibration - Distance_logged,"@np.minimum(_DIST_LOGGED, np.log(_CALIB_DIST))",coef_Calibration_Distance_logged_escort,coef_Calibration_Distance_logged_shopping,coef_Calibration_Distance_logged_eatout,coef_Calibration_Distance_logged_maint,coef_Calibration_Distance_logged_social,coef_Calibration_Distance_logged_disc +util_calib_0_1_miles,Calibration - 0-1 miles,@(_DIST<1),,coef_Calibration_0_1_miles_shopping,coef_Calibration_0_1_miles_eatout,coef_Calibration_0_1_miles_maint,coef_Calibration_0_1_miles_social, +util_calib_1_2_miles,Calibration - 1-2 miles,@(_DIST>=1) * (_DIST<2),,coef_Calibration_1_2_miles_shopping,,coef_Calibration_1_2_miles_maint,coef_Calibration_1_2_miles_social, +util_calib_2_5_miles,Calibration - 2-5 miles,@(_DIST>=2) * (_DIST<5),,coef_Calibration_2_5_miles_shopping,,coef_Calibration_2_5_miles_maint,, +util_abm2_calib_0_5_miles,ABM2 calibration - 0-5 miles,@(_DIST < 5) * _DIST,,,coef_ABM2_calibration_0_5_miles_eatout,,coef_ABM2_calibration_0_5_miles_social,coef_ABM2_calibration_0_5_miles_disc +util_abm2_calib_0to1_miles_all,"ABM2 calibration - 0-1 miles, all tours",@(_DIST < 1),,,coef_ABM2_calibration_0_1_miles_eatout,,coef_ABM2_calibration_0_1_miles_social,coef_ABM2_calibration_0_1_miles_disc +util_abm2_calib_1to2_miles_all,"ABM2 calibration - 1-2 miles, all tours",@(_DIST < 2) * (_DIST >= 1),,,coef_ABM2_calibration_1_2_miles_eatout,,coef_ABM2_calibration_1_2_miles_social,coef_ABM2_calibration_1_2_miles_disc +util_abm2_calib_2to3_miles_all,"ABM2 calibration - 2-3 miles, all tours",@(_DIST < 3) * (_DIST >= 2),,,,,coef_ABM2_calibration_2_3_miles_social,coef_ABM2_calibration_2_3_miles_disc +util_abm2_calib_3to4_miles_all,"ABM2 calibration - 3-4 miles, all tours",@(_DIST < 4) * (_DIST >= 3),,,,,coef_ABM2_calibration_3_4_miles_social,coef_ABM2_calibration_3_4_miles_disc +util_abm2_calib_0to1_miles_joint,"ABM2 calibration - 0-1 miles, joint tour","@(_DIST < 1) * np.where(df.get('tour_category', default=False) == 'joint', 0, 1)",coef_ABM2_calibration_0_1_miles_escort,,,,, +util_abm2_calib_1to2_miles_joint,"ABM2 calibration - 1-2 miles, joint tour","@(_DIST < 2) * (_DIST >= 1) * np.where(df.get('tour_category', default=False) == 'joint', 0, 1)",coef_ABM2_calibration_1_2_miles_escort,,,coef_ABM2_calibration_1_2_miles_maint,, +util_abm2_calib_2to3_miles_joint,"ABM2 calibration - 2-3 miles, joint tour","@(_DIST < 3) * (_DIST >= 2) * np.where(df.get('tour_category', default=False) == 'joint', 0, 1)",coef_ABM2_calibration_2_3_miles_escort,,,coef_ABM2_calibration_2_3_miles_maint,, +util_abm2_calib_distance_logged,"ABM2 calibration - distance logged, indiv tour","@np.minimum(_DIST_LOGGED, np.log(_CALIB_DIST)) * np.where(df.get('tour_category', default=False) == 'joint', 0, 1)",,,,coef_ABM2_calibration_distance_logged_maint,, +util_abm2_calib_4to5_miles,ABM2 calibration - 4-5 miles,@(_DIST < 5) * (_DIST >= 4),,coef_ABM2_calibration_4_5_miles_shopping,coef_ABM2_calibration_4_5_miles_eatout,coef_ABM2_calibration_4_5_miles_maint,coef_ABM2_calibration_4_5_miles_social,coef_ABM2_calibration_4_5_miles_disc +util_abm2_calib_5to6_miles,ABM2 calibration - 5-6 miles,@(_DIST < 6) * (_DIST >= 5),,coef_ABM2_calibration_5_6_miles_shopping,,coef_ABM2_calibration_5_6_miles_maint,, +util_abm2_calib_6to7_miles,ABM2 calibration - 6-7 miles,@(_DIST < 7) * (_DIST >= 6),,coef_ABM2_calibration_6_7_miles_shopping,,coef_ABM2_calibration_6_7_miles_maint,, +util_abm2_calib_0to20_miles,ABM2 calibration - 0-20 miles,@(_DIST < 20) * _DIST,coef_ABM2_calibration_0_20_miles_escort,coef_ABM2_calibration_0_20_miles_shopping,,coef_ABM2_calibration_0_20_miles_maint,, +util_abm2_calib_distance,ABM2 calibration - distance,@_DIST,coef_ABM2_calibration_distance_escort,coef_ABM2_calibration_distance_shopping,coef_ABM2_calibration_distance_eatout,coef_ABM2_calibration_distance_maint,coef_ABM2_calibration_distance_social,coef_ABM2_calibration_distance_disc +util_abm2_calib_distance_squared,ABM2 calibration - distance squared,@_DIST_SQUARED,coef_ABM2_calibration_distance_squared_escort,coef_ABM2_calibration_distance_squared_shopping,coef_ABM2_calibration_distance_squared_eatout,coef_ABM2_calibration_distance_squared_maint,coef_ABM2_calibration_distance_squared_social,coef_ABM2_calibration_distance_squared_disc +util_abm2_calib_distance_cubed,ABM2 calibration - distance cubed,@_DIST_CUBED,coef_ABM2_calibration_distance_cubed_escort,coef_ABM2_calibration_distance_cubed_shopping,coef_ABM2_calibration_distance_cubed_eatout,coef_ABM2_calibration_distance_cubed_maint,coef_ABM2_calibration_distance_cubed_social,coef_ABM2_calibration_distance_cubed_disc +util_abm2_calib_distance_logged2,ABM2 calibration - distance logged,"@np.minimum(_DIST_LOGGED, np.log(_CALIB_DIST)) ",coef_ABM2_calibration_distance_logged_escort,coef_ABM2_calibration_distance_logged_shopping,coef_ABM2_calibration_distance_logged_eatout,,coef_ABM2_calibration_distance_logged_social,coef_ABM2_calibration_distance_logged_disc +"util_abm2_calib_0to1_miles_joint_tour""","ABM2 calibration - 0-1 miles, joint tour","@np.where(df.get('tour_category', default=False) == 'joint', 1, 0) * (_DIST<1)",,coef_ABM2_calibration_0_1_miles_joint_shopping,coef_ABM2_calibration_0_1_miles_joint_eatout,coef_ABM2_calibration_0_1_miles_joint_maint,coef_ABM2_calibration_0_1_miles_joint_social,coef_ABM2_calibration_0_1_miles_joint_disc +"util_abm2_calib_1to2_miles_joint_tour""","ABM2 calibration - 1-2 miles, joint tour","@np.where(df.get('tour_category', default=False) == 'joint', 1, 0) * (_DIST>=1) * (_DIST<2)",,coef_ABM2_calibration_1_2_miles_joint_shopping,coef_ABM2_calibration_1_2_miles_joint_eatout,coef_ABM2_calibration_1_2_miles_joint_maint,coef_ABM2_calibration_1_2_miles_joint_social,coef_ABM2_calibration_1_2_miles_joint_disc +"util_abm2_calib_2to3_miles_joint_tour""","ABM2 calibration - 2-3 miles, joint tour","@np.where(df.get('tour_category', default=False) == 'joint', 1, 0) * (_DIST>=2) * (_DIST<3)",,coef_ABM2_calibration_2_3_miles_joint_shopping,,coef_ABM2_calibration_2_3_miles_joint_maint,, +"util_abm2_calib_1to2_miles_indiv_tour""","ABM2 calibration - 1-2 miles, indiv tour","@(_DIST < 2) * (_DIST >= 1) * np.where(df.get('tour_category', default=False) == 'joint', 0, 1)",,coef_ABM2_calibration_1_2_miles_shopping,,,, +"util_abm2_calib_2to3_miles_indiv_tour""","ABM2 calibration - 2-3 miles, indiv tour","@(_DIST < 3) * (_DIST >= 2) * np.where(df.get('tour_category', default=False) == 'joint', 0, 1)",,coef_ABM2_calibration_2_3_miles_shopping,,,, +util_abm2_calib_distance_logged3,ABM2+ calibration - distance logged,"@(_DIST < 20) * np.minimum(_DIST_LOGGED, np.log(_CALIB_DIST))",coef_ABM2p_calibration_distance_logged_escort,,,,, +util_abm2_calib_0to1_miles,ABM2+ calibration - 0-1 miles,@(_DIST < 1),coef_ABM2p_calibration_0_1_miles_escort,coef_ABM2p_calibration_0_1_miles_shopping,coef_ABM2p_calibration_0_1_miles_eatout,coef_ABM2p_calibration_0_1_miles_maint,coef_ABM2p_calibration_0_1_miles_social,coef_ABM2p_calibration_0_1_miles_disc +util_abm2_calib_1to2_miles,ABM2+ calibration - 1-2 miles,@(_DIST < 2) * (_DIST >= 1),coef_ABM2p_calibration_1_2_miles_escort,coef_ABM2p_calibration_1_2_miles_shopping,coef_ABM2p_calibration_1_2_miles_eatout,coef_ABM2p_calibration_1_2_miles_maint,coef_ABM2p_calibration_1_2_miles_social,coef_ABM2p_calibration_1_2_miles_disc +util_abm2_calib_2to3_miles,ABM2+ calibration - 2-3 miles,@(_DIST < 3) * (_DIST >= 2),coef_ABM2p_calibration_2_3_miles_escort,coef_ABM2p_calibration_2_3_miles_shopping,coef_ABM2p_calibration_2_3_miles_eatout,coef_ABM2p_calibration_2_3_miles_maint,coef_ABM2p_calibration_2_3_miles_social,coef_ABM2p_calibration_2_3_miles_disc +util_abm2_calib_3to4_miles,ABM2+ calibration - 3-4 miles,@(_DIST < 4) * (_DIST >= 3),coef_ABM2p_calibration_3_4_miles_escort,coef_ABM2p_calibration_3_4_miles_shopping,coef_ABM2p_calibration_3_4_miles_eatout,coef_ABM2p_calibration_3_4_miles_maint,coef_ABM2p_calibration_3_4_miles_social,coef_ABM2p_calibration_3_4_miles_disc +#,#,,,,,,, +#util_mission_valley_mall_constant,#Mission Valley Mall Constant,@df.destination==6187,,coef_Mission_Valley_Mall_Constant_shopping_6187,,,, +#util_mission_valley_mall_constant,#Mission Valley Mall Constant,@df.destination==6196,,coef_Mission_Valley_Mall_Constant_shopping_6196,,,, +#,#,,,,,,, +util_indiv_0_2_ASC,indiv tours in 0_2bin calibration constant,"@(_DIST.between(0,2)) * (df.get('tour_category', default=False) != 'joint')",coef_abm3_dist_0_2indivescort_asc,coef_abm3_dist_0_2indivmaint_asc,coef_abm3_dist_0_2indivdisc_asc,coef_abm3_dist_0_2indivmaint_asc,coef_abm3_dist_0_2indivdisc_asc,coef_abm3_dist_0_2indivdisc_asc +util_indiv_2_5_ASC,indiv tours in 2_5bin calibration constant,"@(_DIST.between(2,5)) * (df.get('tour_category', default=False) != 'joint')",coef_abm3_dist_2_5indivescort_asc,coef_abm3_dist_2_5indivmaint_asc,coef_abm3_dist_2_5indivdisc_asc,coef_abm3_dist_2_5indivmaint_asc,coef_abm3_dist_2_5indivdisc_asc,coef_abm3_dist_2_5indivdisc_asc +util_indiv_5_10_ASC,indiv tours in 5_10bin calibration constant,"@(_DIST.between(5,10)) * (df.get('tour_category', default=False) != 'joint')",coef_abm3_dist_5_10indivescort_asc,coef_abm3_dist_5_10indivmaint_asc,coef_abm3_dist_5_10indivdisc_asc,coef_abm3_dist_5_10indivmaint_asc,coef_abm3_dist_5_10indivdisc_asc,coef_abm3_dist_5_10indivdisc_asc +util_indiv_10_30_ASC,indiv tours in 10_30bin calibration constant,"@(_DIST.between(10,30)) * (df.get('tour_category', default=False) != 'joint')",coef_abm3_dist_10_30indivescort_asc,coef_abm3_dist_10_30indivmaint_asc,coef_abm3_dist_10_30indivdisc_asc,coef_abm3_dist_10_30indivmaint_asc,coef_abm3_dist_10_30indivdisc_asc,coef_abm3_dist_10_30indivdisc_asc +util_indiv_dist_ASC,indiv tours in distance calibration constant,"@(_DIST) * (df.get('tour_category', default=False) != 'joint')",,0.05,,0.05,, +util_joint_0_2_ASC,joint tours in 0_2bin calibration constant,"@(_DIST.between(0,2)) * (df.get('tour_category', default=False) == 'joint')",,coef_abm3_dist_0_2jointmaint_asc,coef_abm3_dist_0_2jointdisc_asc,coef_abm3_dist_0_2jointmaint_asc,coef_abm3_dist_0_2jointdisc_asc,coef_abm3_dist_0_2jointdisc_asc +util_joint_2_5_ASC,joint tours in 2_5bin calibration constant,"@(_DIST.between(2,5)) * (df.get('tour_category', default=False) == 'joint')",,coef_abm3_dist_2_5jointmaint_asc,coef_abm3_dist_2_5jointdisc_asc,coef_abm3_dist_2_5jointmaint_asc,coef_abm3_dist_2_5jointdisc_asc,coef_abm3_dist_2_5jointdisc_asc +util_joint_5_10_ASC,joint tours in 5_10bin calibration constant,"@(_DIST.between(5,10)) * (df.get('tour_category', default=False) == 'joint')",,coef_abm3_dist_5_10jointmaint_asc,coef_abm3_dist_5_10jointdisc_asc,coef_abm3_dist_5_10jointmaint_asc,coef_abm3_dist_5_10jointdisc_asc,coef_abm3_dist_5_10jointdisc_asc +util_joint_10_30_ASC,joint tours in 10_30bin calibration constant,"@(_DIST.between(10,30)) * (df.get('tour_category', default=False) == 'joint')",,coef_abm3_dist_10_30jointmaint_asc,coef_abm3_dist_10_30jointdisc_asc,coef_abm3_dist_10_30jointmaint_asc,coef_abm3_dist_10_30jointdisc_asc,coef_abm3_dist_10_30jointdisc_asc +util_indiv_dist_ASC,indiv tours in distance calibration constant,"@(_DIST) * (df.get('tour_category', default=False) == 'joint')",,0.10,,0.10,, \ No newline at end of file diff --git a/resident/configs/non_mandatory_tour_destination.yaml b/resident/configs/non_mandatory_tour_destination.yaml new file mode 100644 index 0000000..f8daa2c --- /dev/null +++ b/resident/configs/non_mandatory_tour_destination.yaml @@ -0,0 +1,58 @@ +SAMPLE_SPEC: non_mandatory_tour_destination_sample.csv +SPEC: non_mandatory_tour_destination.csv +COEFFICIENTS: non_mandatory_tour_destination_coefficients.csv + +SAMPLE_SIZE: 30 + +SIZE_TERM_SELECTOR: non_mandatory + +# we can't use use household income_segment as this will also be set for non-workers +CHOOSER_SEGMENT_COLUMN_NAME: tour_type + +# optional (comment out if not desired) +DEST_CHOICE_LOGSUM_COLUMN_NAME: destination_logsum + +# comment out DEST_CHOICE_LOGSUM_COLUMN_NAME if saved alt logsum table +DEST_CHOICE_SAMPLE_TABLE_NAME: tour_destination_sample + + +SEGMENTS: + - shopping + - othmaint + - othdiscr + - eatout + - social + - escort + +SIMULATE_CHOOSER_COLUMNS: + - tour_type + - home_zone_id + - person_id + - female + - income + - income_segment + - age + - tour_type_count + - tour_type_num + - tour_category + + +LOGSUM_SETTINGS: tour_mode_choice.yaml + +# model-specific logsum-related settings +CHOOSER_ORIG_COL_NAME: home_zone_id +ALT_DEST_COL_NAME: alt_dest +IN_PERIOD: + shopping: 14 + othmaint: 14 + othdiscr: 14 + eatout: 14 + social: 14 + escort: 14 +OUT_PERIOD: + shopping: 14 + othmaint: 14 + othdiscr: 14 + eatout: 14 + social: 14 + escort: 14 \ No newline at end of file diff --git a/resident/configs/non_mandatory_tour_destination_coefficients.csv b/resident/configs/non_mandatory_tour_destination_coefficients.csv new file mode 100644 index 0000000..09b4e02 --- /dev/null +++ b/resident/configs/non_mandatory_tour_destination_coefficients.csv @@ -0,0 +1,206 @@ +coefficient_name,value,constrain +coef_zero,0.0,T +coef_mode_logsum,0.5,T +coef_mode_logsum_socdisc,0.4,T +#,, +coef_dist_escort,-1.09538,F +coef_distsqrd_escort,0.022130838,F +coef_distlowinc_escort,0.157496938,F +coef_distage5664_escort,0.189508203,F +coef_distage64plus_escort,0.309309986,F +#,, +coef_dist_shopping,-0.27116,F +coef_distsqrd_shopping,-0.003098511,F +coef_distcubed_shopping,0.0,F +coef_distlogged_shopping,-0.229414306,F +coef_distfemale_shopping,0.0,F +coef_disttimepressure_shopping,0.029450792,F +coef_accessibility_shopping,0.377323356,F +#,, +coef_dist_eatout,0.10041,F +coef_distsqrd_eatout,-0.029120711,F +coef_distcubed_eatout,0.000647926,F +coef_distlogged_eatout,-0.664600521,F +coef_disttimepressure_eatout,0.027648186,F +#,, +coef_dist_maint,-0.03745,F +coef_distsqrd_maint,-0.007959,F +coef_distlowinc_maint,0.0,F +coef_disttimepressure_maint,0.025736,F +coef_distage1624_maint,-0.503857,F +#,, +coef_dist_social,-0.08731,F +coef_distsqrd_social,-0.003052,F +coef_distcubed_social,0.0,F +coef_distlogged_social,-0.430261,F +coef_distlowinc_social,0.038684,F +coef_distmidlowinc_social,0.0,F +#,, +coef_dist_discr,0.57603,F +coef_distsqrd_discr,-0.065310929,F +coef_distcubed_discr,0.001719762,F +coef_distlogged_discr,-1.524851692,F +coef_distfemale_disc,0.0,F +coef_disttimepressure_disc,0.054073655,F +#,, +coef_ABM2_calibration_0_1_miles_escort,-0.1714,T +coef_ABM2_calibration_1_2_miles_escort,-0.0966,T +coef_ABM2_calibration_2_3_miles_escort,-0.0739,T +coef_ABM2_calibration_0_20_miles_escort,0.13377,T +coef_ABM2_calibration_distance_escort,0.0,T +coef_ABM2_calibration_distance_squared_escort,-0.007376946,T +coef_ABM2_calibration_distance_cubed_escort,0.0,T +coef_ABM2_calibration_distance_logged_escort,-0.1373,T +coef_Calibration_Distance_squared_escort,0.0732726,T +coef_Calibration_Distance_cubed_escort,-0.0026709,T +coef_Calibration_Distance_escort,-0.2321275,T +coef_Calibration_Distance_logged_escort,-0.0801,T +coef_ABM2p_calibration_distance_logged_escort,0.1648,T +coef_ABM2p_calibration_0_1_miles_escort,0.222,T +coef_ABM2p_calibration_1_2_miles_escort,0.1599,T +coef_ABM2p_calibration_2_3_miles_escort,-0.0749,T +coef_ABM2p_calibration_3_4_miles_escort,-0.0095,T +#,, +coef_ABM2_calibration_0_1_miles_joint_shopping,-0.137065295,T +coef_ABM2_calibration_2_3_miles_joint_shopping,0.0,T +coef_ABM2_calibration_1_2_miles_joint_shopping,-0.389228729,T +coef_ABM2_calibration_0_1_miles_shopping,-0.338567459,T +coef_ABM2_calibration_1_2_miles_shopping,0.380266207,T +coef_ABM2_calibration_0_20_miles_shopping,0.0,T +coef_ABM2_calibration_2_3_miles_shopping,0.233495387,T +coef_ABM2_calibration_3_4_miles_shopping,-0.236188729,T +coef_ABM2_calibration_4_5_miles_shopping,-0.178169632,T +coef_ABM2_calibration_5_6_miles_shopping,0.0,T +coef_ABM2_calibration_6_7_miles_shopping,0.0,T +coef_Calibration_0_1_miles_shopping,-0.3477,T +coef_Calibration_1_2_miles_shopping,-0.2341,T +coef_Calibration_2_5_miles_shopping,-0.0208,T +coef_ABM2_calibration_distance_shopping,0.0,T +coef_ABM2_calibration_distance_squared_shopping,-0.0005121,T +coef_ABM2_calibration_distance_cubed_shopping,-7.584e-05,T +#coef_Mission_Valley_Mall_Constant_shopping_6187,-0.5,T +#coef_Mission_Valley_Mall_Constant_shopping_6196,-0.5,T +coef_Calibration_Distance_squared_shopping,-0.1357403,T +coef_Calibration_Distance_cubed_shopping,0.0090424,T +coef_Calibration_Distance_shopping,0.5816206,T +coef_Calibration_Distance_logged_shopping,-1.332207,T +coef_ABM2_calibration_distance_logged_shopping,-0.23912,T +coef_ABM2p_calibration_0_1_miles_shopping,-0.3386,T +coef_ABM2p_calibration_1_2_miles_shopping,-0.2771,T +coef_ABM2p_calibration_2_3_miles_shopping,-0.3181,T +coef_ABM2p_calibration_3_4_miles_shopping,-0.2362,T +#,, +coef_ABM2_calibration_1_2_miles_joint_eatout,-0.3004,T +coef_ABM2_calibration_0_1_miles_joint_eatout,-0.4739,T +coef_ABM2_calibration_0_1_miles_eatout,-0.7869,T +coef_ABM2_calibration_1_2_miles_eatout,0.5209,T +coef_ABM2_calibration_2_3_miles_eatout,0.4549,T +coef_ABM2_calibration_3_4_miles_eatout,0.1109,T +coef_ABM2_calibration_4_5_miles_eatout,-0.027,T +coef_ABM2_calibration_0_5_miles_eatout,0.107,T +coef_Calibration_0_1_miles_eatout,0.1074,T +coef_ABM2_calibration_distance_eatout,0.8713,T +coef_ABM2_calibration_distance_squared_eatout,-0.0353,T +coef_ABM2_calibration_distance_cubed_eatout,0.0004,T +coef_Calibration_Distance_squared_eatout,0.0333,T +coef_Calibration_Distance_cubed_eatout,-0.001,T +coef_Calibration_Distance_eatout,-0.19,T +coef_ABM2_calibration_distance_logged_eatout,-2.349,T +coef_Calibration_Distance_logged_eatout,-0.2569,T +coef_ABM2p_calibration_0_1_miles_eatout,-0.0563,T +coef_ABM2p_calibration_1_2_miles_eatout,-0.0582,T +coef_ABM2p_calibration_2_3_miles_eatout,-0.1056,T +coef_ABM2p_calibration_3_4_miles_eatout,-0.0262,T +#,, +coef_ABM2_calibration_0_1_miles_joint_maint,-0.1371,T +coef_ABM2_calibration_2_3_miles_joint_maint,0.0,T +coef_ABM2_calibration_1_2_miles_joint_maint,-0.3892,T +coef_ABM2_calibration_0_1_miles_maint,-0.3386,T +coef_ABM2_calibration_1_2_miles_maint,0.3803,T +coef_ABM2_calibration_0_20_miles_maint,0.0,T +coef_ABM2_calibration_2_3_miles_maint,0.2335,T +coef_ABM2_calibration_3_4_miles_maint,-0.2362,T +coef_ABM2_calibration_4_5_miles_maint,0.0,T +coef_ABM2_calibration_5_6_miles_maint,0.0,T +coef_ABM2_calibration_6_7_miles_maint,0.0,T +coef_Calibration_0_1_miles_maint,-0.0214,T +coef_Calibration_1_2_miles_maint,-0.1938,T +coef_Calibration_2_5_miles_maint,0.0,T +coef_ABM2_calibration_distance_maint,0.0,T +coef_ABM2_calibration_distance_squared_maint,-0.0005,T +coef_ABM2_calibration_distance_cubed_maint,-0.0001,T +coef_Calibration_Distance_squared_maint,-0.0396,T +coef_Calibration_Distance_cubed_maint,0.0023,T +coef_Calibration_Distance_maint,0.2641,T +coef_Calibration_Distance_logged_maint,-1.4297,T +coef_ABM2_calibration_distance_logged_maint,-0.2391,T +coef_ABM2p_calibration_0_1_miles_maint,-0.3386,T +coef_ABM2p_calibration_1_2_miles_maint,-0.2771,T +coef_ABM2p_calibration_2_3_miles_maint,-0.3181,T +coef_ABM2p_calibration_3_4_miles_maint,-0.2362,T +#,, +coef_ABM2_calibration_0_1_miles_joint_social,-0.4739,T +coef_ABM2_calibration_1_2_miles_joint_social,-0.3004,T +coef_ABM2_calibration_0_1_miles_social,-0.7869,T +coef_ABM2_calibration_1_2_miles_social,0.5209,T +coef_ABM2_calibration_2_3_miles_social,0.5605,T +coef_ABM2_calibration_3_4_miles_social,0.1371,T +coef_ABM2_calibration_4_5_miles_social,-0.027,T +coef_ABM2_calibration_0_5_miles_social,0.107,T +coef_Calibration_0_1_miles_social,1.0697,T +coef_Calibration_1_2_miles_social,0.5699,T +coef_ABM2_calibration_distance_social,0.8713,T +coef_ABM2_calibration_distance_squared_social,-0.0353,T +coef_ABM2_calibration_distance_cubed_social,0.0004,T +coef_Calibration_Distance_squared_social,0.0,T +coef_Calibration_Distance_cubed_social,0.0,T +coef_Calibration_Distance_social,0.0,T +coef_ABM2_calibration_distance_logged_social,-2.349,T +coef_Calibration_Distance_logged_social,0.0,T +coef_ABM2p_calibration_0_1_miles_social,-0.0563,T +coef_ABM2p_calibration_1_2_miles_social,-0.0582,T +coef_ABM2p_calibration_2_3_miles_social,-0.1056,T +coef_ABM2p_calibration_3_4_miles_social,-0.0262,T +#,, +coef_Distance_Calibration_adjustment_disc,0.0066,T +coef_ABM2_calibration_0_1_miles_joint_disc,-0.4739,T +coef_ABM2_calibration_1_2_miles_joint_disc,-0.3004,T +coef_ABM2_calibration_0_1_miles_disc,-0.7869,T +coef_ABM2_calibration_1_2_miles_disc,0.5209,T +coef_ABM2_calibration_2_3_miles_disc,0.5605,T +coef_ABM2_calibration_3_4_miles_disc,0.1371,T +coef_ABM2_calibration_4_5_miles_disc,-0.027,T +coef_ABM2_calibration_0_5_miles_disc,0.107,T +coef_ABM2_calibration_distance_disc,0.8713,T +coef_ABM2_calibration_distance_squared_disc,-0.0353,T +coef_ABM2_calibration_distance_cubed_disc,0.0004,T +coef_Calibration_Distance_squared_disc,-0.0523,T +coef_Calibration_Distance_cubed_disc,0.0027,T +coef_Calibration_Distance_disc,0.2181,T +coef_ABM2_calibration_distance_logged_disc,-2.349,T +coef_Calibration_Distance_logged_disc,-0.1883,T +coef_ABM2p_calibration_0_1_miles_disc,-0.0563,T +coef_ABM2p_calibration_1_2_miles_disc,-0.0582,T +coef_ABM2p_calibration_2_3_miles_disc,-0.1056,T +coef_ABM2p_calibration_3_4_miles_disc,-0.0262,T +#,, +coef_abm3_dist_0_2indivescort_asc,0.0743894542403208,F +coef_abm3_dist_0_2indivmaint_asc,-1.1013926972835268,F +coef_abm3_dist_0_2indivdisc_asc,-0.5188544554826798,F +coef_abm3_dist_2_5indivescort_asc,0.3267263770696683,F +coef_abm3_dist_2_5indivmaint_asc,-0.1229585362100356,F +coef_abm3_dist_2_5indivdisc_asc,0.3658142479785504,F +coef_abm3_dist_5_10indivescort_asc,0.3268135840383142,F +coef_abm3_dist_5_10indivmaint_asc,1.2615942662937147,F +coef_abm3_dist_5_10indivdisc_asc,0.970850923095182,F +coef_abm3_dist_10_30indivescort_asc,-1.210160116890714,F +coef_abm3_dist_10_30indivmaint_asc,2.244671931775837,F +coef_abm3_dist_10_30indivdisc_asc,1.7449730188184471,F +coef_abm3_dist_0_2jointmaint_asc,0.10814282191363894,F +coef_abm3_dist_0_2jointdisc_asc,-0.3257531020899993,F +coef_abm3_dist_2_5jointmaint_asc,-0.2256620842333652,F +coef_abm3_dist_2_5jointdisc_asc,0.7878311118085077,F +coef_abm3_dist_5_10jointmaint_asc,0.26451614721496275,F +coef_abm3_dist_5_10jointdisc_asc,0.7566707355720486,F +coef_abm3_dist_10_30jointmaint_asc,1.1623489449822964,F +coef_abm3_dist_10_30jointdisc_asc,0.17580063767755247,F diff --git a/resident/configs/non_mandatory_tour_destination_sample.csv b/resident/configs/non_mandatory_tour_destination_sample.csv new file mode 100644 index 0000000..e82f800 --- /dev/null +++ b/resident/configs/non_mandatory_tour_destination_sample.csv @@ -0,0 +1,19 @@ +Description,Expression,escort,shopping,eatout,othmaint,social,othdiscr +,"_DIST@skims[('SOV_M_DIST', 'MD')]",1,1,1,1,1,1 +Distance,"@_DIST.clip(0,20)",coef_dist_escort,coef_dist_shopping,coef_dist_eatout,coef_dist_maint,coef_dist_social,coef_dist_discr +Distance_squared,"@_DIST.clip(0,20)**2",coef_distsqrd_escort,coef_distsqrd_shopping,coef_distsqrd_eatout,coef_distsqrd_maint,coef_distsqrd_social,coef_distsqrd_discr +Distance_cubed,"@_DIST.clip(0,20)**3",coef_zero,coef_distcubed_shopping,coef_distcubed_eatout,coef_zero,coef_distcubed_social,coef_distcubed_discr +Distance_logged,"@np.log(_DIST.clip(0,20) + 0.001)",coef_zero,coef_distlogged_shopping,coef_distlogged_eatout,coef_zero,coef_distlogged_social,coef_distlogged_discr +Distance_female,"@_DIST.clip(0,20) * df.female * np.where(df.get('tour_category', default=False) == 'joint', 0, 1)",coef_zero,coef_distfemale_shopping,coef_zero,coef_zero,coef_zero,coef_distfemale_disc +Distance - low income_segment_escort,"@_DIST.clip(0,20) * (df.income < 30000)",coef_distlowinc_escort,coef_zero,coef_zero,coef_zero,coef_zero,coef_zero +Distance - low income_segment_maint,"@_DIST.clip(0,20) * (df.income_segment <= 2)",coef_zero,coef_zero,coef_zero,coef_distlowinc_maint,coef_distlowinc_social,coef_zero +Distance - lowmed income_segment_maint,"@_DIST.clip(0,20) * (df.income_segment <= 3)",coef_zero,coef_zero,coef_zero,coef_zero,coef_distmidlowinc_social,coef_zero +#Distance - Time Pressure calculated as the log of the maxtime over tours left,"@_DIST.clip(0,20) * np.log((df.maxWindow/df.toursLeft)+0.0001)",coef_zero,coef_disttimepressure_shopping,coef_disttimepressure_eatout,coef_disttimepressure_maint,coef_zero,coef_disttimepressure_disc +#Accessibility,@@nonMandatoryAccessibilityAlt,coef_zero,coef_accessibility_shopping,coef_zero,coef_zero,coef_zero,coef_zero +Distance - age 16 to 24,"@_DIST.clip(0,20) * df.age.between(16, 24) * np.where(df.get('tour_category', default=False) == 'joint', 0, 1)",coef_zero,coef_zero,coef_zero,coef_distage1624_maint,coef_zero,coef_zero +Distance - age 56 to 64,"@_DIST.clip(0,20) * (df.age.between(56, 64))",coef_distage5664_escort,coef_zero,coef_zero,coef_zero,coef_zero,coef_zero +Distance - age 65+,"@_DIST.clip(0,20) * (df.age > 64)",coef_distage64plus_escort,coef_zero,coef_zero,coef_zero,coef_zero,coef_zero +#escort size is zero for escort tour,@(@@householdsDestAlt * 1 + df.num_young_children * @@householdsDestAlt * 1 + df.num_gradeschool * @@gradeSchoolEnrollmentDestAlt * 100 + df.numHighSchoolStudents * @@highSchoolEnrollmentDestAlt * 100) == 0,-999,coef_zero,coef_zero,coef_zero,coef_zero,coef_zero +#escort size is non-zero for escort tour,"@np.where(@@householdsDestAlt * 1 + df.num_young_children * @@householdsDestAlt * 1 + df.num_gradeschool * @@gradeSchoolEnrollmentDestAlt * 100 + df.numHighSchoolStudents * @@highSchoolEnrollmentDestAlt * 100 > 0, np.log(@@householdsDestAlt * 1 + df.num_y",1,coef_zero,coef_zero,coef_zero,coef_zero,coef_zero +Size variable,@df['size_term'].apply(np.log1p),coef_zero,1,1,1,1,1 +No attractions,@df['size_term']==0,coef_zero,-999,-999,-999,-999,-999 diff --git a/resident/configs/non_mandatory_tour_frequency.csv b/resident/configs/non_mandatory_tour_frequency.csv new file mode 100644 index 0000000..55db7ac --- /dev/null +++ b/resident/configs/non_mandatory_tour_frequency.csv @@ -0,0 +1,106 @@ +Label,Description,Expression,PTYPE_FULL,PTYPE_PART,PTYPE_UNIVERSITY,PTYPE_NONWORK,PTYPE_RETIRED,PTYPE_SCHOOL,PTYPE_PRESCHOOL +util_naming_alts_explicitly,Explicitly naming what tot_tours is defined as,_total_indNM_tours@df.tot_tours,1,1,1,1,1,1,1 +util_escorting_tour,Escorting Tour,escort,coef_escorting_tour,coef_escorting_tour,coef_escorting_tour,coef_escorting_tour,coef_escorting_tour,coef_escorting_tour,coef_escorting_tour +util_discretionary_tour,Discretionary Tour,othdiscr,coef_discretionary_tour,coef_discretionary_tour,coef_discretionary_tour,coef_discretionary_tour,coef_discretionary_tour,coef_discretionary_tour,coef_discretionary_tour +util_shopping_tour,Shopping Tour,shopping,coef_shopping_tour,coef_shopping_tour,coef_shopping_tour,coef_shopping_tour,coef_shopping_tour,coef_shopping_tour,coef_shopping_tour +util_maintenance_tour,Maintenance Tour,othmaint,coef_maintenance_tour,coef_maintenance_tour,coef_maintenance_tour,coef_maintenance_tour,coef_maintenance_tour,coef_maintenance_tour,coef_maintenance_tour +util_visiting_or_social_tour,Visiting/Social Tour,social,coef_visiting_or_social_tour,coef_visiting_or_social_tour,coef_visiting_or_social_tour,coef_visiting_or_social_tour,coef_visiting_or_social_tour,coef_visiting_or_social_tour,coef_visiting_or_social_tour +util_eating_out_tour,Eating Out Tour,eatout,coef_eating_out_tour,coef_eating_out_tour,coef_eating_out_tour,coef_eating_out_tour,coef_eating_out_tour,coef_eating_out_tour,coef_eating_out_tour +util_total_indNM_tours_is_0_prior_tours,Total individual NM Tours = 0 (1 or more Prior Tours),(_total_indNM_tours == 0) & (has_previous_tour == 1),,coef_total_indNM_tours_is_0_prior_tours,coef_total_indNM_tours_is_0_prior_tours,,,coef_total_indNM_tours_is_0_prior_tours,coef_total_indNM_tours_is_0_prior_tours +util_total_indNM_tours_is_1,Total individual NM Tours = 1,(_total_indNM_tours == 1),coef_total_indNM_tours_is_1,coef_total_indNM_tours_is_1,coef_total_indNM_tours_is_1,coef_total_indNM_tours_is_1,coef_total_indNM_tours_is_1,coef_total_indNM_tours_is_1,coef_total_indNM_tours_is_1 +util_total_indNM_tours_is_2,Total individual NM Tours = 2,(_total_indNM_tours == 2),coef_total_indNM_tours_is_2,coef_total_indNM_tours_is_2,coef_total_indNM_tours_is_2,coef_total_indNM_tours_is_2,coef_total_indNM_tours_is_2,, +util_total_indNM_tours_is_2p,Total individual NM Tours >= 2,(_total_indNM_tours >= 2),,,,,,coef_total_indNM_tours_is_2p,coef_total_indNM_tours_is_2p +util_total_indNM_tours_is_3,Total individual NM Tours = 3,(_total_indNM_tours == 3),coef_total_indNM_tours_is_3,,,,coef_total_indNM_tours_is_3,, +util_total_indNM_tours_is_3p,Total individual NM Tours >= 3,(_total_indNM_tours >= 3),,coef_total_indNM_tours_is_3p,coef_total_indNM_tours_is_3p,coef_total_indNM_tours_is_3p,,, +util_total_indNM_tours_is_4,Total individual NM Tours = 4,(_total_indNM_tours >= 4),coef_total_indNM_tours_is_4p,,,,coef_total_indNM_tours_is_4p,, +util_has_mandatory_tours_and_iNM_tour_freq_1,One or more Mandatory tour & tour frequency =1,(num_mandatory_tours > 0) & (_total_indNM_tours == 1),coef_has_mandatory_tours_and_iNM_tour_freq_1,,,,,, +util_has_mandatory_tours_and_iNM_tour_freq_2p,One or more Mandatory tour & tour frequency =2p,(num_mandatory_tours > 0) & (_total_indNM_tours >= 2),coef_has_mandatory_tours_and_iNM_tour_freq_2p,,,,,, +util_has_joint_tours_and_iNM_tour_freq_1,One or more Joint tour & tour frequency =1,(num_person_joint_tours > 0) & (_total_indNM_tours == 1),coef_has_joint_tours_and_iNM_tour_freq_1,coef_has_joint_tours_and_iNM_tour_freq_1,,coef_has_joint_tours_and_iNM_tour_freq_1,coef_has_joint_tours_and_iNM_tour_freq_1,coef_has_joint_tours_and_iNM_tour_freq_1, +util_has_joint_tours_and_iNM_tour_freq_2p,One or more Joint tour & tour frequency =2p,(num_person_joint_tours > 0) & (_total_indNM_tours >= 2),coef_has_joint_tours_and_iNM_tour_freq_2p,,,coef_has_joint_tours_and_iNM_tour_freq_2p,coef_has_joint_tours_and_iNM_tour_freq_2p,, +util_number_of_school_escort_tours,Number of School Escort Tours,escort * num_school_escort_tours,coef_prev_school_escort_tours,coef_prev_school_escort_tours,,coef_prev_school_escort_tours,,, +# availability conditions,,,,,,,,, +util_availability_of_zero_tours,Requires NM tour if no previous tours,(has_previous_tour == 0) & (_total_indNM_tours == 0),coef_unavailable,coef_unavailable,coef_unavailable,coef_unavailable,coef_unavailable,coef_unavailable, +# income coefficients with medium as base,,,,,,,,, +util_low_income_escort,Low income household * number of escort tours,low_income * escort,,coef_low_income_escort,,coef_low_income_escort,coef_low_income_escort,, +util_low_income_shopping,Low income household * number of shopping tours,low_income * shopping,,,,,coef_low_income_shopping,, +util_low_income_othmaint,Low income household * number of othmaint tours,low_income * othmaint,coef_low_income_othmaint,,,coef_low_income_othmaint,coef_low_income_othmaint,, +util_low_income_eatout,Low income household * number of eatout tours,low_income * eatout,,,,,coef_low_income_eatout,, +util_low_income_othdiscr,Low income household * number of othdiscr tours,low_income * othdiscr,coef_low_income_othdiscr,coef_low_income_othdiscr,,,coef_low_income_othdiscr,, +util_medium_low_income_escort,Medium-low income household * number of escort tours,medium_low_income * escort,coef_medium_low_income_escort,coef_medium_low_income_escort,,coef_medium_low_income_escort,,, +util_medium_low_income_othmaint,Medium-low income household * number of othmaint tours,medium_low_income * othmaint,,,,coef_medium_low_income_othmaint,coef_medium_low_income_othmaint,, +util_medium_low_income_othdiscr,Medium-low income household * number of othdiscr tours,medium_low_income * othdiscr,coef_medium_low_income_othdiscr,,,,,, +util_medium_high_income_othdiscr,Medium-high income household * number of othdiscr tours,medium_high_income * othdiscr,coef_medium_high_income_othdiscr,,,coef_medium_high_income_othdiscr,,, +util_high_income_escort,High income household * number of escort tours,high_income * escort,coef_high_income_escort,,,,,, +util_high_income_shopping,High income household * number of shopping tours,high_income * shopping,,,,,coef_high_income_shopping,, +util_high_income_othdiscr,High income household * number of othdiscr tours,high_income * othdiscr,coef_high_income_othdiscr,,,,,, +# car ownership coefficients,,,,,,,,, +util_zero_auto_iNM_tour_freq_1,zero auto ownership & tour freq is 1,(auto_ownership == 0) & (_total_indNM_tours == 1),coef_zero_auto_iNM_tour_freq_1,coef_zero_auto_iNM_tour_freq_1,coef_zero_auto_iNM_tour_freq_1,,,, +util_zero_auto_iNM_tour_freq_2,zero auto ownership & tour freq is 2,(auto_ownership == 0) & (_total_indNM_tours == 2),coef_zero_auto_iNM_tour_freq_2,,,,,, +util_zero_auto_iNM_tour_freq_2p,zero auto ownership & tour freq is 2p,(auto_ownership == 0) & (_total_indNM_tours >= 2),,coef_zero_auto_iNM_tour_freq_2p,coef_zero_auto_iNM_tour_freq_2p,,,, +util_zero_auto_iNM_tour_freq_3p,zero auto ownership & tour freq is 3p,(auto_ownership == 0) & (_total_indNM_tours >= 3),coef_zero_auto_iNM_tour_freq_3p,,,coef_zero_auto_iNM_tour_freq_3p,,, +util_auto_deficient_iNM_tour_freq_1,auto deficient & tour freq is 1,(auto_ownership > 0) & (auto_ownership < num_adults) & (_total_indNM_tours == 1),coef_auto_deficient_iNM_tour_freq_1,coef_auto_deficient_iNM_tour_freq_1,coef_auto_deficient_iNM_tour_freq_1,coef_auto_deficient_iNM_tour_freq_1,coef_auto_deficient_iNM_tour_freq_1,, +util_auto_deficient_iNM_tour_freq_2,auto deficient & tour freq is 2,(auto_ownership > 0) & (auto_ownership < num_adults) & (_total_indNM_tours == 2),,,coef_auto_deficient_iNM_tour_freq_2,,coef_auto_deficient_iNM_tour_freq_2,, +util_auto_deficient_iNM_tour_freq_2p,auto deficient & tour freq is 2p,(auto_ownership > 0) & (auto_ownership < num_adults) & (_total_indNM_tours >= 2),coef_auto_deficient_iNM_tour_freq_2p,coef_auto_deficient_iNM_tour_freq_2p,,coef_auto_deficient_iNM_tour_freq_2p,,, +util_auto_deficient_iNM_tour_freq_3p,auto deficient & tour freq is 3p,(auto_ownership > 0) & (auto_ownership < num_adults) & (_total_indNM_tours >= 3),,,coef_auto_deficient_iNM_tour_freq_3p,,coef_auto_deficient_iNM_tour_freq_3p,, +# household member interactions,,,,,,,,, +util_num_children_escort,Number of household children * number of escort tours,num_children * escort,coef_num_children_escort,coef_num_children_escort,coef_num_children_escort,coef_num_children_escort,coef_num_children_escort,,coef_num_children_escort +util_num_children_shopping,Number of household children * number of shopping tours,num_children * shopping,coef_num_children_shopping,coef_num_children_shopping,,,,, +util_num_children_othmaint,Number of household children * number of othmaint tours,num_children * othmaint,coef_num_children_othmaint,,,,,, +util_num_children_social,Number of household children * number of social tours,num_children * social,coef_num_children_social,,,coef_num_children_social,,, +util_num_children_othdiscr,Number of household children * number of othdiscr tours,num_children * othdiscr,coef_num_children_othdiscr,coef_num_children_othdiscr,,,,, +util_num_full_time_workers_escort,Number of household full time workers * number of escort tours,num_fullTime_workers * escort,coef_num_full_time_workers_escort,,,coef_num_full_time_workers_escort,coef_num_full_time_workers_escort,coef_num_full_time_workers_escort, +util_num_full_time_workers_othmaint,Number of household full time workers * number of othmaint tours,num_fullTime_workers * othmaint,,,,coef_num_full_time_workers_othmaint,,, +util_num_full_time_workers_eatout,Number of household full time workers * number of eatout tours,num_fullTime_workers * eatout,,coef_num_full_time_workers_eatout,coef_num_full_time_workers_eatout,,,, +oef_num_full_time_workers_social,,,,,,,,, +util_num_full_time_workers_othdiscr,Number of household full time workers * number of othdiscr tours,num_fullTime_workers * othdiscr,coef_num_full_time_workers_othdiscr,,,,,, +util_hhsize_escort,household size * number of escort tours,hhsize * escort,,,,coef_hhsize_escort,,coef_hhsize_escort, +util_hhsize_shopping,household size * number of shopping tours,hhsize * shopping,coef_hhsize_shopping,,,,coef_hhsize_shopping,, +util_hhsize_othmaint,household size * number of othmaint tours,hhsize * othmaint,coef_hhsize_othmaint,,,coef_hhsize_othmaint,coef_hhsize_othmaint,, +util_hhsize_eatout,household size * number of eatout tours,hhsize * eatout,coef_hhsize_eatout,,coef_hhsize_eatout,coef_hhsize_eatout,coef_hhsize_eatout,, +util_hhsize_social,household size * number of social tours,hhsize * social,coef_hhsize_social,,,,,, +util_hhsize_othdiscr,household size * number of othdiscr tours,hhsize * othdiscr,coef_hhsize_othdiscr,,,,coef_hhsize_othdiscr,,coef_hhsize_othdiscr +util_retired_hh_escort,Retirees and Non-workers only household * Escorting,retiredHh * escort,,,,,coef_retired_hh_escort,, +# age effects by purpose,,,,,,,,, +util_retired_80p_escort,Retiree over age 80 escort,(age >= 80) * escort,,,,,coef_age_80p_escort,, +util_retired_80p_eatout,Retiree over age 80 eatout,(age >= 80) * eatout,,,,,coef_age_80p_eatout,, +util_adult_18to24_escort,Age 18 to 24 escort,((age > 18) & (age <= 24)) * escort,coef_age_18to24_escort,coef_age_18to24_escort,,,,, +util_adult_18to24_shopping,Age 18 to 24 shopping,((age > 18) & (age <= 24)) * shopping,,,coef_age_18to24_shopping,,,, +util_adult_18to24_eatout,Age 18 to 24 eatout,((age > 18) & (age <= 24)) * eatout,,,coef_age_18to24_eatout,,,, +util_adult_18to24_social,Age 18 to 24 social,((age > 18) & (age <= 24)) * social,,,,coef_age_18to24_social,,, +util_child_24to35_escort,Age 24 to 35 escort,((age > 24) & (age <= 35)) * escort,coef_age_24to35_escort,coef_age_24to35_escort,,coef_age_24to35_escort,,, +util_child_24to35_shopping,Age 24 to 35 shopping,((age > 24) & (age <= 35)) * shopping,,coef_age_24to35_shopping,coef_age_24to35_shopping,coef_age_24to35_shopping,,, +util_child_24to35_eatout,Age 24 to 35 eatout,((age > 24) & (age <= 35)) * eatout,coef_age_24to35_eatout,,,,,, +util_child_24to35_othdiscr,Age 24 to 35 othdiscr,((age > 24) & (age <= 35)) * othdiscr,,coef_age_24to35_othdiscr,,coef_age_24to35_othdiscr,,, +util_child_50to79_escort,Age 50 to 79 escort,((age >= 50) & (age <= 79)) * escort,coef_age_50to79_escort,coef_age_50to79_escort,,coef_age_50to79_escort,,, +util_child_50to79_eatout,Age 50 to 79 eatout,((age >= 50) & (age <= 79)) * eatout,coef_age_50to79_eatout,,,,,, +util_child_50to79_othdiscr,Age 50 to 79 othdiscr,((age >= 50) & (age <= 79)) * othdiscr,coef_age_50to79_othdiscr,,coef_age_50to79_othdiscr,,,, +# age effects by frequency,,,,,,,,, +util_retired_80p_and_iNM_tour_freq_2,Age 80+ & Tour Frequency =2,(age >= 80) & (_total_indNM_tours == 2),,,,,coef_age_80p_and_iNM_tour_freq_2,, +util_adult_18to24_and_iNM_tour_freq_2,Age 18 to 24 & Tour Frequency =2,((age > 18) & (age <= 24)) & (_total_indNM_tours == 2),coef_age_18to24_and_iNM_tour_freq_2,,,coef_age_18to24_and_iNM_tour_freq_2,,, +# residual time window,,,,,,,,, +util_log_total_hours_available_7am_to_10pm_window_escort,log_Total hours available 1am to 10pm tours escort,log_total_hours_available_7am_to_10pm * escort,coef_log_total_hours_available_7am_to_10pm_escort,coef_log_total_hours_available_7am_to_10pm_escort,coef_log_total_hours_available_7am_to_10pm_escort,coef_log_total_hours_available_7am_to_10pm_escort,coef_log_total_hours_available_7am_to_10pm_escort,coef_log_total_hours_available_7am_to_10pm_escort,coef_log_total_hours_available_7am_to_10pm_escort +util_log_total_hours_available_7am_to_10pm_window_shopping,log_Total hours available 1am to 10pm tours shopping,log_total_hours_available_7am_to_10pm * shopping,coef_log_total_hours_available_7am_to_10pm_shopping,coef_log_total_hours_available_7am_to_10pm_shopping,coef_log_total_hours_available_7am_to_10pm_shopping,,coef_log_total_hours_available_7am_to_10pm_shopping,coef_log_total_hours_available_7am_to_10pm_shopping,coef_log_total_hours_available_7am_to_10pm_shopping +util_log_total_hours_available_7am_to_10pm_window_othmaint,log_Total hours available 1am to 10pm tours othmaint,log_total_hours_available_7am_to_10pm * othmaint,coef_log_total_hours_available_7am_to_10pm_othmaint,coef_log_total_hours_available_7am_to_10pm_othmaint,coef_log_total_hours_available_7am_to_10pm_othmaint,,coef_log_total_hours_available_7am_to_10pm_othmaint,coef_log_total_hours_available_7am_to_10pm_othmaint,coef_log_total_hours_available_7am_to_10pm_othmaint +util_log_total_hours_available_7am_to_10pm_window_eatout,log_Total hours available 1am to 10pm tours eatout,log_total_hours_available_7am_to_10pm * eatout,coef_log_total_hours_available_7am_to_10pm_eatout,coef_log_total_hours_available_7am_to_10pm_eatout,coef_log_total_hours_available_7am_to_10pm_eatout,,,coef_log_total_hours_available_7am_to_10pm_eatout,coef_log_total_hours_available_7am_to_10pm_eatout +util_log_total_hours_available_7am_to_10pm_window_social,log_Total hours available 1am to 10pm tours social,log_total_hours_available_7am_to_10pm * social,coef_log_total_hours_available_7am_to_10pm_social,coef_log_total_hours_available_7am_to_10pm_social,,,,coef_log_total_hours_available_7am_to_10pm_social, +util_log_total_hours_available_7am_to_10pm_window_othdiscr,log_Total hours available 1am to 10pm tours othdiscr,log_total_hours_available_7am_to_10pm * othdiscr,coef_log_total_hours_available_7am_to_10pm_othdiscr,coef_log_total_hours_available_7am_to_10pm_othdiscr,,,coef_log_total_hours_available_7am_to_10pm_othdiscr,coef_log_total_hours_available_7am_to_10pm_othdiscr,coef_log_total_hours_available_7am_to_10pm_othdiscr +util_consecutive_hours_available_7am_to_10pm_window_escort,Consecutive hours available 1am to 10pmtours escort,consecutive_hours_available_7am_to_10pm * escort,,,,,coef_consecutive_hours_available_7am_to_10pm_escort,, +util_consecutive_hours_available_7am_to_10pm_window_shopping,Consecutive hours available 1am to 10pmtours shopping,consecutive_hours_available_7am_to_10pm * shopping,coef_consecutive_hours_available_7am_to_10pm_shopping,coef_consecutive_hours_available_7am_to_10pm_shopping,,coef_consecutive_hours_available_7am_to_10pm_shopping,,, +util_consecutive_hours_available_7am_to_10pm_window_othmaint,Consecutive hours available 1am to 10pmtours othmaint,consecutive_hours_available_7am_to_10pm * othmaint,coef_consecutive_hours_available_7am_to_10pm_othmaint,,,coef_consecutive_hours_available_7am_to_10pm_othmaint,,coef_consecutive_hours_available_7am_to_10pm_othmaint, +util_consecutive_hours_available_7am_to_10pm_window_eatout,Consecutive hours available 1am to 10pmtours eatout,consecutive_hours_available_7am_to_10pm * eatout,,,,,coef_consecutive_hours_available_7am_to_10pm_eatout,, +util_consecutive_hours_available_7am_to_10pm_window_othdiscr,Consecutive hours available 1am to 10pmtours othdiscr,consecutive_hours_available_7am_to_10pm * othdiscr,,,coef_consecutive_hours_available_7am_to_10pm_othdiscr,coef_consecutive_hours_available_7am_to_10pm_othdiscr,,, +# accessibilities,,,,,,,,, +util_othdiscr_accessibility_for_escorting,Retail Accessibility for Escorting,shopping_accessibility * escort,,coef_shopping_accessibility_escorting,,,,coef_shopping_accessibility_escorting, +util_shopping_accessibility_shopping,Retail Accessibility for Shopping,shopping_accessibility * shopping,coef_shopping_accessibility_shopping,coef_shopping_accessibility_shopping,coef_shopping_accessibility_shopping,coef_shopping_accessibility_shopping,,,coef_shopping_accessibility_shopping +util_shopping_accessibility_maintenance,Retail Accessibility for Maintenance,shopping_accessibility * othmaint,,,coef_shopping_accessibility_maintenance,,,coef_shopping_accessibility_maintenance,coef_shopping_accessibility_maintenance +util_othdiscr_accessibility_eating_out,Retail Accessibility for Eating Out,othdiscr_accessibility * eatout,coef_othdiscr_accessibility_eating_out,coef_othdiscr_accessibility_eating_out,coef_othdiscr_accessibility_eating_out,,coef_othdiscr_accessibility_eating_out,, +util_othdiscr_accessibility_discretionary,Retail Accessibility for Discretionary,othdiscr_accessibility * othdiscr,coef_othdiscr_accessibility_othdiscr,coef_othdiscr_accessibility_othdiscr,coef_othdiscr_accessibility_othdiscr,coef_othdiscr_accessibility_othdiscr,,,coef_othdiscr_accessibility_othdiscr +util_othdiscr_accessibility_visiting,Retail Accessibility for Visiting,othdiscr_accessibility * social,coef_othdiscr_accessibility_social,coef_othdiscr_accessibility_social,,coef_othdiscr_accessibility_social,,coef_othdiscr_accessibility_social,coef_othdiscr_accessibility_social +# work from home and telecommute_frequency,,,,,,,,, +util_work_from_home_and_iNM_tour_freq_2,Work From Home & Tour Frequency =2,(work_from_home) & (_total_indNM_tours == 2),coef_work_from_home_and_iNM_tour_freq_2,,,,,, +util_work_from_home_and_iNM_tour_freq_3p,Work From Home & Tour Frequency =3p,(work_from_home) & (_total_indNM_tours >= 3),coef_work_from_home_and_iNM_tour_freq_3p,,,,,, +util_telecommute_2_3_days_week_and_iNM_tour_freq_2,Telecommute 1 day per week & Tour Frequency =2,(telecommute_frequency == '2_3_days_week') & (_total_indNM_tours == 2),coef_telecommute_2_3_4_days_week_and_iNM_tour_freq_2,,,,,, +util_telecommute_2_3_days_week_and_iNM_tour_freq_3p,Telecommute 1 day per week & Tour Frequency =3p,(telecommute_frequency == '2_3_days_week') & (_total_indNM_tours >= 3),coef_telecommute_2_3_4_days_week_and_iNM_tour_freq_3p,,,,,, +util_telecommute_4_days_week_and_iNM_tour_freq_1,Telecommute 1 day per week & Tour Frequency =1,(telecommute_frequency == '4_days_week') & (_total_indNM_tours == 1),,coef_telecommute_4_days_week_and_iNM_tour_freq_1,,,,, +util_telecommute_4_days_week_and_iNM_tour_freq_2,Telecommute 1 day per week & Tour Frequency =2,(telecommute_frequency == '4_days_week') & (_total_indNM_tours == 2),coef_telecommute_2_3_4_days_week_and_iNM_tour_freq_2,,,,,, +util_telecommute_4_days_week_and_iNM_tour_freq_3p,Telecommute 1 day per week & Tour Frequency =3p,(telecommute_frequency == '4_days_week') & (_total_indNM_tours >= 3),coef_telecommute_2_3_4_days_week_and_iNM_tour_freq_3p,,,,,, diff --git a/resident/configs/non_mandatory_tour_frequency.yaml b/resident/configs/non_mandatory_tour_frequency.yaml new file mode 100644 index 0000000..d459bcc --- /dev/null +++ b/resident/configs/non_mandatory_tour_frequency.yaml @@ -0,0 +1,40 @@ + +SEGMENT_COL: ptype +SPEC: non_mandatory_tour_frequency.csv + +SPEC_SEGMENTS: + - NAME: PTYPE_FULL + PTYPE: 1 + COEFFICIENTS: non_mandatory_tour_frequency_coefficients_PTYPE_FULL.csv + - NAME: PTYPE_PART + PTYPE: 2 + COEFFICIENTS: non_mandatory_tour_frequency_coefficients_PTYPE_PART.csv + - NAME: PTYPE_UNIVERSITY + PTYPE: 3 + COEFFICIENTS: non_mandatory_tour_frequency_coefficients_PTYPE_UNIVERSITY.csv + - NAME: PTYPE_NONWORK + PTYPE: 4 + COEFFICIENTS: non_mandatory_tour_frequency_coefficients_PTYPE_NONWORK.csv + - NAME: PTYPE_RETIRED + PTYPE: 5 + COEFFICIENTS: non_mandatory_tour_frequency_coefficients_PTYPE_RETIRED.csv + - NAME: PTYPE_SCHOOL + PTYPE: 6 + COEFFICIENTS: non_mandatory_tour_frequency_coefficients_PTYPE_SCHOOL.csv + - NAME: PTYPE_PRESCHOOL + PTYPE: 7 + COEFFICIENTS: non_mandatory_tour_frequency_coefficients_PTYPE_PRESCHOOL.csv + +annotate_persons: + SPEC: annotate_persons_nmtf + DF: persons + TABLES: + - tours + +preprocessor: + SPEC: non_mandatory_tour_frequency_annotate_persons_preprocessor + DF: persons + TABLES: + - tours + - households + - joint_tour_participants diff --git a/resident/configs/non_mandatory_tour_frequency_alternatives.csv b/resident/configs/non_mandatory_tour_frequency_alternatives.csv new file mode 100644 index 0000000..e4d4f2e --- /dev/null +++ b/resident/configs/non_mandatory_tour_frequency_alternatives.csv @@ -0,0 +1,198 @@ +escort,shopping,othmaint,eatout,social,othdiscr +0,0,0,0,0,0 +0,0,0,0,0,1 +0,0,0,0,0,2 +0,0,0,0,1,0 +0,0,0,0,1,1 +0,0,0,0,1,2 +0,0,0,1,0,0 +0,0,0,1,0,1 +0,0,0,1,0,2 +0,0,0,1,1,0 +0,0,0,1,1,1 +0,0,0,1,1,2 +0,0,1,0,0,0 +0,0,1,0,0,1 +0,0,1,0,0,2 +0,0,1,0,1,0 +0,0,1,0,1,1 +0,0,1,0,1,2 +0,0,1,1,0,0 +0,0,1,1,0,1 +0,0,1,1,0,2 +0,0,1,1,1,0 +0,0,1,1,1,1 +0,0,1,1,1,2 +0,0,2,0,0,0 +0,0,2,0,0,1 +0,0,2,0,0,2 +0,0,2,0,1,0 +0,0,2,0,1,1 +0,0,2,0,1,2 +0,0,2,1,0,0 +0,0,2,1,0,1 +0,0,2,1,0,2 +0,0,2,1,1,0 +0,0,2,1,1,1 +0,1,0,0,0,0 +0,1,0,0,0,1 +0,1,0,0,0,2 +0,1,0,0,1,0 +0,1,0,0,1,1 +0,1,0,0,1,2 +0,1,0,1,0,0 +0,1,0,1,0,1 +0,1,0,1,0,2 +0,1,0,1,1,0 +0,1,0,1,1,1 +0,1,0,1,1,2 +0,1,1,0,0,0 +0,1,1,0,0,1 +0,1,1,0,0,2 +0,1,1,0,1,0 +0,1,1,0,1,1 +0,1,1,0,1,2 +0,1,1,1,0,0 +0,1,1,1,0,1 +0,1,1,1,0,2 +0,1,1,1,1,0 +0,1,1,1,1,1 +0,1,2,0,0,0 +0,1,2,0,0,1 +0,1,2,0,0,2 +0,1,2,0,1,0 +0,1,2,0,1,1 +0,1,2,1,0,0 +0,1,2,1,0,1 +0,1,2,1,1,0 +0,2,0,0,0,0 +0,2,0,0,0,1 +0,2,0,0,0,2 +0,2,0,0,1,0 +0,2,0,0,1,1 +0,2,0,0,1,2 +0,2,0,1,0,0 +0,2,0,1,0,1 +0,2,0,1,0,2 +0,2,0,1,1,0 +0,2,0,1,1,1 +0,2,1,0,0,0 +0,2,1,0,0,1 +0,2,1,0,0,2 +0,2,1,0,1,0 +0,2,1,0,1,1 +0,2,1,1,0,0 +0,2,1,1,0,1 +0,2,1,1,1,0 +0,2,2,0,0,0 +0,2,2,0,0,1 +0,2,2,0,1,0 +0,2,2,1,0,0 +1,0,0,0,0,0 +1,0,0,0,0,1 +1,0,0,0,0,2 +1,0,0,0,1,0 +1,0,0,0,1,1 +1,0,0,0,1,2 +1,0,0,1,0,0 +1,0,0,1,0,1 +1,0,0,1,0,2 +1,0,0,1,1,0 +1,0,0,1,1,1 +1,0,0,1,1,2 +1,0,1,0,0,0 +1,0,1,0,0,1 +1,0,1,0,0,2 +1,0,1,0,1,0 +1,0,1,0,1,1 +1,0,1,0,1,2 +1,0,1,1,0,0 +1,0,1,1,0,1 +1,0,1,1,0,2 +1,0,1,1,1,0 +1,0,1,1,1,1 +1,0,2,0,0,0 +1,0,2,0,0,1 +1,0,2,0,0,2 +1,0,2,0,1,0 +1,0,2,0,1,1 +1,0,2,1,0,0 +1,0,2,1,0,1 +1,0,2,1,1,0 +1,1,0,0,0,0 +1,1,0,0,0,1 +1,1,0,0,0,2 +1,1,0,0,1,0 +1,1,0,0,1,1 +1,1,0,0,1,2 +1,1,0,1,0,0 +1,1,0,1,0,1 +1,1,0,1,0,2 +1,1,0,1,1,0 +1,1,0,1,1,1 +1,1,1,0,0,0 +1,1,1,0,0,1 +1,1,1,0,0,2 +1,1,1,0,1,0 +1,1,1,0,1,1 +1,1,1,1,0,0 +1,1,1,1,0,1 +1,1,1,1,1,0 +1,1,2,0,0,0 +1,1,2,0,0,1 +1,1,2,0,1,0 +1,1,2,1,0,0 +1,2,0,0,0,0 +1,2,0,0,0,1 +1,2,0,0,0,2 +1,2,0,0,1,0 +1,2,0,0,1,1 +1,2,0,1,0,0 +1,2,0,1,0,1 +1,2,0,1,1,0 +1,2,1,0,0,0 +1,2,1,0,0,1 +1,2,1,0,1,0 +1,2,1,1,0,0 +1,2,2,0,0,0 +2,0,0,0,0,0 +2,0,0,0,0,1 +2,0,0,0,0,2 +2,0,0,0,1,0 +2,0,0,0,1,1 +2,0,0,0,1,2 +2,0,0,1,0,0 +2,0,0,1,0,1 +2,0,0,1,0,2 +2,0,0,1,1,0 +2,0,0,1,1,1 +2,0,1,0,0,0 +2,0,1,0,0,1 +2,0,1,0,0,2 +2,0,1,0,1,0 +2,0,1,0,1,1 +2,0,1,1,0,0 +2,0,1,1,0,1 +2,0,1,1,1,0 +2,0,2,0,0,0 +2,0,2,0,0,1 +2,0,2,0,1,0 +2,0,2,1,0,0 +2,1,0,0,0,0 +2,1,0,0,0,1 +2,1,0,0,0,2 +2,1,0,0,1,0 +2,1,0,0,1,1 +2,1,0,1,0,0 +2,1,0,1,0,1 +2,1,0,1,1,0 +2,1,1,0,0,0 +2,1,1,0,0,1 +2,1,1,0,1,0 +2,1,1,1,0,0 +2,1,2,0,0,0 +2,2,0,0,0,0 +2,2,0,0,0,1 +2,2,0,0,1,0 +2,2,0,1,0,0 +2,2,1,0,0,0 diff --git a/resident/configs/non_mandatory_tour_frequency_annotate_persons_preprocessor.csv b/resident/configs/non_mandatory_tour_frequency_annotate_persons_preprocessor.csv new file mode 100644 index 0000000..01895ec --- /dev/null +++ b/resident/configs/non_mandatory_tour_frequency_annotate_persons_preprocessor.csv @@ -0,0 +1,24 @@ +Description,Target,Expression +#,, +,low_income,persons.income<=30000 +,medium_low_income,(persons.income > 30000) & (persons.income <= 60000) +,medium_income,(persons.income > 60000) & (persons.income <= 100000) +,medium_high_income,(persons.income > 100000) & (persons.income <= 150000) +,high_income,(persons.income > 150000) +,total_hours_available_7am_to_10pm,"person_available_periods(persons, start_bin=8, end_bin=37, continuous=False) / 2" +,consecutive_hours_available_7am_to_10pm,"person_available_periods(persons, start_bin=8, end_bin=37, continuous=True) / 2" +,log_total_hours_available_7am_to_10pm,"np.log1p(total_hours_available_7am_to_10pm)" +#,, +# joint tour counts per persons,, +join joint tour participants with tour purpose,_JOINT_TOURS_PERSONS,"pd.merge(joint_tour_participants[['person_id', 'tour_id']], tours.reset_index()[['tour_id', 'tour_type']], on='tour_id', how='left')" +,num_person_joint_tours,"reindex_i(_JOINT_TOURS_PERSONS.groupby(['person_id']).size(), persons.index)" +,_TOUR_COUNT,"lambda query, tours, persons: tours.query(query).groupby('person_id').size().reindex(persons.index).fillna(0).astype(np.int8)" +# non_mandatory tour frequency extension,, +,_INDIV_TOURS,tours[tours.tour_category!='joint'] +,num_mandatory_tours,"_TOUR_COUNT('(tour_category == \'mandatory\')', tours, persons)" +,num_school_escort_tours,"_TOUR_COUNT('(tour_category == \'non_mandatory\') & (tour_type == \'escort\')', tours, persons)" +,has_mandatory_tour,(num_mandatory_tours > 0) * 1 +,has_joint_tour,(num_person_joint_tours > 0) * 1 +,has_school_escort_tour,(num_school_escort_tours > 0) * 1 +,has_previous_tour,((has_mandatory_tour + has_joint_tour + has_school_escort_tour) > 0) * 1 +,retiredHh,"np.where(df.num_retired_adults == df.hhsize, 1, 0)" diff --git a/resident/configs/non_mandatory_tour_frequency_coefficients_PTYPE_FULL.csv b/resident/configs/non_mandatory_tour_frequency_coefficients_PTYPE_FULL.csv new file mode 100644 index 0000000..18e2d7e --- /dev/null +++ b/resident/configs/non_mandatory_tour_frequency_coefficients_PTYPE_FULL.csv @@ -0,0 +1,64 @@ +coefficient_name,value,constrain +coef_unavailable,-999.0,T +coef_escorting_tour,-9.980738057374221,F +coef_discretionary_tour,-7.464263172179179,F +coef_shopping_tour,-8.726268980359634,F +coef_maintenance_tour,-11.50076886788746,F +coef_visiting_or_social_tour,-9.13878408618917,F +coef_eating_out_tour,-7.996439827456814,F +coef_total_indNM_tours_is_1,3.65902242986596,F +coef_total_indNM_tours_is_2,3.783116973334075,F +coef_total_indNM_tours_is_3,3.94980139461646,F +coef_total_indNM_tours_is_4p,4.044250929541874,F +coef_has_mandatory_tours_and_iNM_tour_freq_1,-2.712110237880168,F +coef_has_mandatory_tours_and_iNM_tour_freq_2p,-1.8591259334530463,F +coef_has_joint_tours_and_iNM_tour_freq_1,-2.681436185164753,F +coef_has_joint_tours_and_iNM_tour_freq_2p,-2.582456735203676,F +coef_prev_school_escort_tours,-0.5659071632851075,F +coef_low_income_othmaint,0.1684373052095534,F +coef_low_income_othdiscr,-0.4247110056225974,F +coef_medium_low_income_escort,-0.26216121389680647,F +coef_medium_low_income_othdiscr,-0.2178944567340515,F +coef_medium_high_income_othdiscr,0.28578380808780474,F +coef_high_income_escort,0.5293466734142718,F +coef_high_income_othdiscr,0.5523603590834895,F +coef_zero_auto_iNM_tour_freq_1,-0.7778478707312145,F +coef_zero_auto_iNM_tour_freq_2,-1.745830549610859,F +coef_zero_auto_iNM_tour_freq_3p,-1.493273297127197,F +coef_auto_deficient_iNM_tour_freq_1,-0.13330115546054844,F +coef_auto_deficient_iNM_tour_freq_2p,-0.16552557917619873,F +coef_num_children_escort,0.7840653709271845,F +coef_num_children_shopping,0.16433950279556886,F +coef_num_children_othmaint,0.23002702126710234,F +coef_num_children_social,0.4360126244341865,F +coef_num_children_othdiscr,0.21305244576581855,F +coef_num_full_time_workers_escort,0.26574742217239844,F +coef_num_full_time_workers_othdiscr,-0.1268307960384887,F +coef_hhsize_shopping,-0.12965078886635895,F +coef_hhsize_othmaint,-0.11388617419409013,F +coef_hhsize_eatout,-0.26843735070052444,F +coef_hhsize_social,-0.4955951679184127,F +coef_hhsize_othdiscr,-0.18715371728147756,F +coef_age_18to24_escort,-0.7850576862260017,F +coef_age_24to35_escort,-0.33234113400691667,F +coef_age_24to35_eatout,-0.14650997264922844,F +coef_age_50to79_escort,-0.16636961878236434,F +coef_age_50to79_eatout,-0.33718093997956605,F +coef_age_50to79_othdiscr,-0.11702301453418044,F +coef_age_18to24_and_iNM_tour_freq_2,0.5184071993755616,F +coef_log_total_hours_available_7am_to_10pm_escort,2.3084987446913487,F +coef_log_total_hours_available_7am_to_10pm_shopping,1.9947536383662492,F +coef_log_total_hours_available_7am_to_10pm_othmaint,3.361513223893925,F +coef_log_total_hours_available_7am_to_10pm_eatout,1.8577971133678188,F +coef_log_total_hours_available_7am_to_10pm_social,1.6066161223464683,F +coef_log_total_hours_available_7am_to_10pm_othdiscr,1.935955432868534,F +coef_consecutive_hours_available_7am_to_10pm_shopping,0.03850875066248657,F +coef_consecutive_hours_available_7am_to_10pm_othmaint,0.05407491523536202,F +coef_shopping_accessibility_shopping,0.024876328142334057,F +coef_othdiscr_accessibility_eating_out,0.046921175676984284,F +coef_othdiscr_accessibility_othdiscr,0.029252086458666476,F +coef_othdiscr_accessibility_social,0.07973729159196855,F +coef_work_from_home_and_iNM_tour_freq_2,0.2882503610075806,F +coef_work_from_home_and_iNM_tour_freq_3p,0.43258258391404014,F +coef_telecommute_2_3_4_days_week_and_iNM_tour_freq_2,0.060220889952691474,F +coef_telecommute_2_3_4_days_week_and_iNM_tour_freq_3p,0.1399978999743526,F diff --git a/resident/configs/non_mandatory_tour_frequency_coefficients_PTYPE_NONWORK.csv b/resident/configs/non_mandatory_tour_frequency_coefficients_PTYPE_NONWORK.csv new file mode 100644 index 0000000..c9a06db --- /dev/null +++ b/resident/configs/non_mandatory_tour_frequency_coefficients_PTYPE_NONWORK.csv @@ -0,0 +1,42 @@ +coefficient_name,value,constrain +coef_escorting_tour,-11.210780876486327,F +coef_discretionary_tour,-2.787013651357596,F +coef_shopping_tour,-3.3897422131085118,F +coef_maintenance_tour,-2.0561696310183835,F +coef_visiting_or_social_tour,-4.541591324213227,F +coef_eating_out_tour,-2.105295178854616,F +coef_total_indNM_tours_is_1,3.1482892220784664,F +coef_total_indNM_tours_is_2,3.200495227239918,F +coef_total_indNM_tours_is_3p,3.1567943211556018,F +coef_has_joint_tours_and_iNM_tour_freq_1,-3.603962491982708,F +coef_has_joint_tours_and_iNM_tour_freq_2p,-3.4626515562619895,F +coef_prev_school_escort_tours,-1.0061285103185091,F +coef_unavailable,-999.0,T +coef_low_income_escort,-0.35229582496605166,F +coef_low_income_othmaint,-0.4037879626637974,F +coef_medium_low_income_escort,-0.23160471596989218,F +coef_medium_low_income_othmaint,-0.2805507112503287,F +coef_medium_high_income_othdiscr,0.32498217994094,F +coef_zero_auto_iNM_tour_freq_3p,-1.062908160511384,F +coef_auto_deficient_iNM_tour_freq_1,-0.21649670970431778,F +coef_auto_deficient_iNM_tour_freq_2p,-0.158,F +coef_num_children_escort,0.5581986955579746,F +coef_num_children_social,0.21747519499167278,F +coef_num_full_time_workers_escort,0.26498806847910095,F +coef_num_full_time_workers_othmaint,-0.1802355063278875,F +coef_hhsize_escort,0.11111794157280563,F +coef_hhsize_othmaint,-0.10130464850722279,F +coef_hhsize_eatout,-0.13935663394655493,F +coef_age_18to24_social,1.1448624101673186,F +coef_age_24to35_escort,-0.5608881691528708,F +coef_age_24to35_shopping,-0.3108414980140646,F +coef_age_24to35_othdiscr,-0.34338639441727353,F +coef_age_50to79_escort,-0.3812786064549372,F +coef_age_18to24_and_iNM_tour_freq_2,-0.9472458465963635,F +coef_log_total_hours_available_7am_to_10pm_escort,3.2260518038123336,F +coef_consecutive_hours_available_7am_to_10pm_shopping,0.06806700796118008,F +coef_consecutive_hours_available_7am_to_10pm_othmaint,0.05307614489617537,F +coef_consecutive_hours_available_7am_to_10pm_othdiscr,0.062136717913049824,F +coef_shopping_accessibility_shopping,0.05776720283340465,F +coef_othdiscr_accessibility_othdiscr,0.026534922230928487,F +coef_othdiscr_accessibility_social,0.04531077718755425,F diff --git a/resident/configs/non_mandatory_tour_frequency_coefficients_PTYPE_PART.csv b/resident/configs/non_mandatory_tour_frequency_coefficients_PTYPE_PART.csv new file mode 100644 index 0000000..e44cb57 --- /dev/null +++ b/resident/configs/non_mandatory_tour_frequency_coefficients_PTYPE_PART.csv @@ -0,0 +1,43 @@ +coefficient_name,value,constrain +coef_escorting_tour,-6.8444776692013,F +coef_discretionary_tour,-7.061093571650552,F +coef_shopping_tour,-7.265878398154104,F +coef_maintenance_tour,-10.631962973623128,F +coef_visiting_or_social_tour,-7.513076659640363,F +coef_eating_out_tour,-8.032068601942477,F +coef_total_indNM_tours_is_0_prior_tours,4.350891211480593,F +coef_total_indNM_tours_is_1,5.4208851654881824,F +coef_total_indNM_tours_is_2,5.8949683354189375,F +coef_total_indNM_tours_is_3p,5.94305016560457,F +coef_has_joint_tours_and_iNM_tour_freq_1,-0.29050043860982105,F +coef_prev_school_escort_tours,-0.611900397388031,F +coef_unavailable,-999.0,T +coef_low_income_escort,-0.41827164724106775,F +coef_low_income_othdiscr,-0.19464500351479955,F +coef_medium_low_income_escort,-0.37906865383167276,F +coef_zero_auto_iNM_tour_freq_1,-1.1380175469769664,F +coef_zero_auto_iNM_tour_freq_2p,-1.3659866939532648,F +coef_auto_deficient_iNM_tour_freq_1,-0.42631646773848203,F +coef_auto_deficient_iNM_tour_freq_2p,-0.7609412509478872,F +coef_num_children_escort,0.6491494416510694,F +coef_num_children_shopping,0.1479897628434831,F +coef_num_children_othdiscr,0.09237804825842522,F +coef_num_full_time_workers_eatout,-0.24419160458661263,F +coef_age_18to24_escort,-1.2806214268139127,F +coef_age_24to35_escort,-0.7100598101390896,F +coef_age_24to35_shopping,-0.4586169269694417,F +coef_age_24to35_othdiscr,-0.3230013853315813,F +coef_age_50to79_escort,-0.8046532853606215,F +coef_log_total_hours_available_7am_to_10pm_escort,1.457991506538752,F +coef_log_total_hours_available_7am_to_10pm_shopping,1.0540599454856179,F +coef_log_total_hours_available_7am_to_10pm_othmaint,3.1263153979195755,F +coef_log_total_hours_available_7am_to_10pm_eatout,1.450002894756668,F +coef_log_total_hours_available_7am_to_10pm_social,1.0022229262201703,F +coef_log_total_hours_available_7am_to_10pm_othdiscr,1.592166386021326,F +coef_consecutive_hours_available_7am_to_10pm_shopping,0.08487848115827784,F +coef_shopping_accessibility_escorting,0.034914955261943735,F +coef_shopping_accessibility_shopping,0.05379740509782797,F +coef_othdiscr_accessibility_eating_out,0.06844529127625759,F +coef_othdiscr_accessibility_othdiscr,0.024135548666842303,F +coef_othdiscr_accessibility_social,0.034748385315176565,F +coef_telecommute_4_days_week_and_iNM_tour_freq_1,-1.0215421163576748,F diff --git a/resident/configs/non_mandatory_tour_frequency_coefficients_PTYPE_PRESCHOOL.csv b/resident/configs/non_mandatory_tour_frequency_coefficients_PTYPE_PRESCHOOL.csv new file mode 100644 index 0000000..6fe7800 --- /dev/null +++ b/resident/configs/non_mandatory_tour_frequency_coefficients_PTYPE_PRESCHOOL.csv @@ -0,0 +1,22 @@ +coefficient_name,value,constrain +coef_escorting_tour,-11.051836936413467,F +coef_discretionary_tour,-9.634146897194343,F +coef_shopping_tour,-9.036614322521848,F +coef_maintenance_tour,-19.37699515494972,F +coef_visiting_or_social_tour,-6.595936622418915,F +coef_eating_out_tour,-17.102175352222606,F +coef_total_indNM_tours_is_0_prior_tours,3.24758823306399,F +coef_total_indNM_tours_is_1,2.4595057378149705,F +coef_total_indNM_tours_is_2p,2.679341375562811,F +coef_num_children_escort,0.3449608531280371,F +coef_num_full_time_workers_social,-0.7989076159896628,F +coef_hhsize_othdiscr,0.10761055792559372,F +coef_log_total_hours_available_7am_to_10pm_escort,3.138584054552107,F +coef_log_total_hours_available_7am_to_10pm_shopping,1.6577579483896183,F +coef_log_total_hours_available_7am_to_10pm_othmaint,4.7337852273390135,F +coef_log_total_hours_available_7am_to_10pm_eatout,4.921758449660736,F +coef_log_total_hours_available_7am_to_10pm_othdiscr,2.055556483820614,F +coef_shopping_accessibility_shopping,0.1461117250482414,F +coef_shopping_accessibility_maintenance,0.26356027630549783,F +coef_othdiscr_accessibility_othdiscr,0.0632885406604063,F +coef_othdiscr_accessibility_social,0.20216296220808352,F diff --git a/resident/configs/non_mandatory_tour_frequency_coefficients_PTYPE_RETIRED.csv b/resident/configs/non_mandatory_tour_frequency_coefficients_PTYPE_RETIRED.csv new file mode 100644 index 0000000..7379051 --- /dev/null +++ b/resident/configs/non_mandatory_tour_frequency_coefficients_PTYPE_RETIRED.csv @@ -0,0 +1,41 @@ +coefficient_name,value,constrain +coef_escorting_tour,-13.324718928099896,F +coef_discretionary_tour,-5.033358573683751,F +coef_shopping_tour,-9.13631330258294,F +coef_maintenance_tour,-9.945067353298386,F +coef_visiting_or_social_tour,-4.189801745365118,F +coef_eating_out_tour,-4.445717330935003,F +coef_total_indNM_tours_is_1,6.00131407260292,F +coef_total_indNM_tours_is_2,5.883565852357907,F +coef_total_indNM_tours_is_3,5.828445878907544,F +coef_total_indNM_tours_is_4p,5.934428647072386,F +coef_has_joint_tours_and_iNM_tour_freq_1,-5.7952837722578465,F +coef_has_joint_tours_and_iNM_tour_freq_2p,-5.76,F +coef_unavailable,-999.0,T +coef_low_income_escort,-0.6839180517907705,F +coef_low_income_shopping,-0.36787550353215537,F +coef_low_income_othmaint,-0.7732373479965099,F +coef_low_income_eatout,-0.6616337553879514,F +coef_low_income_othdiscr,-0.5329988579150085,F +coef_medium_low_income_othmaint,-0.21589267232884296,F +coef_high_income_shopping,-0.25087649228047343,F +coef_auto_deficient_iNM_tour_freq_1,-0.7868235054750288,F +coef_auto_deficient_iNM_tour_freq_2,-1.0455559653756108,F +coef_auto_deficient_iNM_tour_freq_3p,-1.1157343188741007,F +coef_num_children_escort,0.754137688219863,F +coef_num_full_time_workers_escort,0.3339757816794557,F +coef_hhsize_shopping,-0.1425515978162942,F +coef_hhsize_othmaint,-0.20501266076301913,F +coef_hhsize_eatout,-0.29701211013117823,F +coef_hhsize_othdiscr,-0.17093648977342044,F +coef_retired_hh_escort,-0.3294828681385463,F +coef_age_80p_escort,-0.5906910988839469,F +coef_age_80p_eatout,-0.40828362020671494,F +coef_age_80p_and_iNM_tour_freq_2,-0.2537832085563407,F +coef_log_total_hours_available_7am_to_10pm_escort,4.560726446110003,F +coef_log_total_hours_available_7am_to_10pm_shopping,2.7270167242212993,F +coef_log_total_hours_available_7am_to_10pm_othmaint,3.1187871560709866,F +coef_log_total_hours_available_7am_to_10pm_othdiscr,1.4177528382551887,F +coef_consecutive_hours_available_7am_to_10pm_escort,-0.14521052349224925,F +coef_consecutive_hours_available_7am_to_10pm_eatout,0.07669930567537751,F +coef_othdiscr_accessibility_eating_out,0.0878608354323988,F diff --git a/resident/configs/non_mandatory_tour_frequency_coefficients_PTYPE_SCHOOL.csv b/resident/configs/non_mandatory_tour_frequency_coefficients_PTYPE_SCHOOL.csv new file mode 100644 index 0000000..c11b3c4 --- /dev/null +++ b/resident/configs/non_mandatory_tour_frequency_coefficients_PTYPE_SCHOOL.csv @@ -0,0 +1,24 @@ +coefficient_name,value,constrain +coef_unavailable,-999.0,T +coef_escorting_tour,-9.690966426250665,F +coef_discretionary_tour,-7.5690966961253565,F +coef_shopping_tour,-8.975997622510478,F +coef_maintenance_tour,-13.246253630607148,F +coef_visiting_or_social_tour,-14.6311575770962,F +coef_eating_out_tour,-6.7325400979612295,F +coef_total_indNM_tours_is_0_prior_tours,1.106305308197831,F +coef_total_indNM_tours_is_1,1.747170199655668,F +coef_total_indNM_tours_is_2p,1.3703636107226296,F +coef_has_joint_tours_and_iNM_tour_freq_1,-0.9122450724032953,F +coef_num_full_time_workers_escort,-0.1578338449120939,F +coef_hhsize_escort,0.1551946995851052,F +coef_log_total_hours_available_7am_to_10pm_escort,2.0528332539104683,F +coef_log_total_hours_available_7am_to_10pm_shopping,1.960668017568013,F +coef_log_total_hours_available_7am_to_10pm_othmaint,3.377458574954797,F +coef_log_total_hours_available_7am_to_10pm_eatout,0.9922950368954243,F +coef_log_total_hours_available_7am_to_10pm_social,2.494008974014185,F +coef_log_total_hours_available_7am_to_10pm_othdiscr,1.7579258741166293,F +coef_consecutive_hours_available_7am_to_10pm_othmaint,-0.14726873682504554,F +coef_shopping_accessibility_escorting,0.10532543446648758,F +coef_shopping_accessibility_maintenance,0.1770030247224446,F +coef_othdiscr_accessibility_social,0.20759071360015494,F diff --git a/resident/configs/non_mandatory_tour_frequency_coefficients_PTYPE_UNIVERSITY.csv b/resident/configs/non_mandatory_tour_frequency_coefficients_PTYPE_UNIVERSITY.csv new file mode 100644 index 0000000..1cc9f79 --- /dev/null +++ b/resident/configs/non_mandatory_tour_frequency_coefficients_PTYPE_UNIVERSITY.csv @@ -0,0 +1,33 @@ +coefficient_name,value,constrain +coef_escorting_tour,-8.498374870237669,F +coef_discretionary_tour,-4.812463904304644,F +coef_shopping_tour,-12.693463829895798,F +coef_maintenance_tour,-11.058840934154171,F +coef_visiting_or_social_tour,-4.459306542169845,F +coef_eating_out_tour,-6.157924697688962,F +coef_total_indNM_tours_is_0_prior_tours,4.999764921270562,F +coef_total_indNM_tours_is_1,5.705610555681865,F +coef_total_indNM_tours_is_2,6.832163735527388,F +coef_total_indNM_tours_is_3p,7.208937046301491,F +coef_unavailable,-999.0,T +coef_zero_auto_iNM_tour_freq_1,-1.4891145894268167,F +coef_zero_auto_iNM_tour_freq_2p,-2.1435322461710236,F +coef_auto_deficient_iNM_tour_freq_1,-0.4607257760453242,F +coef_auto_deficient_iNM_tour_freq_2,-0.69349273445043,F +coef_auto_deficient_iNM_tour_freq_3p,-1.2029607161678721,F +coef_num_children_escort,0.7701459374658138,F +coef_num_full_time_workers_eatout,0.42257286721446335,F +coef_hhsize_eatout,-0.38977424932597926,F +coef_age_18to24_shopping,-0.404262220153878,F +coef_age_18to24_eatout,0.6249447012564978,F +coef_age_24to35_shopping,-0.28923438967158355,F +coef_age_50to79_othdiscr,0.3076225296007352,F +coef_log_total_hours_available_7am_to_10pm_escort,1.8792910621880181,F +coef_log_total_hours_available_7am_to_10pm_shopping,3.0367849074959543,F +coef_log_total_hours_available_7am_to_10pm_othmaint,2.880344751247897,F +coef_log_total_hours_available_7am_to_10pm_eatout,0.8062448674910451,F +coef_consecutive_hours_available_7am_to_10pm_othdiscr,0.11890563179293542,F +coef_shopping_accessibility_shopping,0.1616020726995222,F +coef_shopping_accessibility_maintenance,0.07126681192848552,F +coef_othdiscr_accessibility_eating_out,0.08890904673851951,F +coef_othdiscr_accessibility_othdiscr,0.04477521803529839,F diff --git a/resident/configs/non_mandatory_tour_frequency_extension_probs.csv b/resident/configs/non_mandatory_tour_frequency_extension_probs.csv new file mode 100644 index 0000000..8308852 --- /dev/null +++ b/resident/configs/non_mandatory_tour_frequency_extension_probs.csv @@ -0,0 +1,193 @@ +ptype,has_mandatory_tour,has_joint_tour,nonmandatory_tour_type,0_tours,1_tours,2_tours +1,0,0,1,0.71087271,0.886861358,1 +2,0,0,1,0.640712953,0.863555773,1 +3,0,0,1,0.726456335,0.726456335,1 +4,0,0,1,0.665679012,0.903816044,1 +5,0,0,1,0.861430335,0.92284115,1 +6,0,0,1,1,1,1 +7,0,0,1,0,1,1 +8,0,0,1,0.754632385,0.918132897,1 +1,1,0,1,0.824975545,1,1 +2,1,0,1,0.881285721,0.964131273,1 +3,1,0,1,0.856158692,1,1 +4,1,0,1,1,1,1 +5,1,0,1,1,1,1 +6,1,0,1,1,1,1 +7,1,0,1,1,1,1 +8,1,0,1,1,1,1 +1,0,0,2,0.918802841,1,1 +2,0,0,2,1,1,1 +3,0,0,2,1,1,1 +4,0,0,2,1,1,1 +5,0,0,2,0.911351921,0.964395496,1 +6,0,0,2,1,1,1 +7,0,0,2,1,1,1 +8,0,0,2,0.91707294,1,1 +1,1,0,2,1,1,1 +2,1,0,2,1,1,1 +3,1,0,2,1,1,1 +4,1,0,2,1,1,1 +5,1,0,2,1,1,1 +6,1,0,2,1,1,1 +7,1,0,2,1,1,1 +8,1,0,2,1,1,1 +1,0,0,3,0.885080064,0.942540032,1 +2,0,0,3,0.832615585,1,1 +3,0,0,3,1,1,1 +4,0,0,3,1,1,1 +5,0,0,3,0.874364173,0.972270724,1 +6,0,0,3,1,1,1 +7,0,0,3,1,1,1 +8,0,0,3,1,1,1 +1,1,0,3,1,1,1 +2,1,0,3,1,1,1 +3,1,0,3,1,1,1 +4,1,0,3,1,1,1 +5,1,0,3,1,1,1 +6,1,0,3,1,1,1 +7,1,0,3,1,1,1 +8,1,0,3,1,1,1 +1,0,0,4,1,1,1 +2,0,0,4,0.99296599,1,1 +3,0,0,4,1,1,1 +4,0,0,4,0.976072814,1,1 +5,0,0,4,0.956476086,0.993572522,1 +6,0,0,4,1,1,1 +7,0,0,4,1,1,1 +8,0,0,4,0.991144007,1,1 +1,1,0,4,0.99740908,1,1 +2,1,0,4,0.939184617,1,1 +3,1,0,4,1,1,1 +4,1,0,4,0.749851433,1,1 +5,1,0,4,1,1,1 +6,1,0,4,1,1,1 +7,1,0,4,1,1,1 +8,1,0,4,0.949874016,1,1 +1,0,0,5,1,1,1 +2,0,0,5,1,1,1 +3,0,0,5,1,1,1 +4,0,0,5,0.954541082,1,1 +5,0,0,5,0.955216408,1,1 +6,0,0,5,1,1,1 +7,0,0,5,1,1,1 +8,0,0,5,0.931183243,1,1 +1,1,0,5,0.970742777,1,1 +2,1,0,5,0.882901912,1,1 +3,1,0,5,1,1,1 +4,1,0,5,0.334132623,1,1 +5,1,0,5,1,1,1 +6,1,0,5,0.934804611,0.934804611,1 +7,1,0,5,0.965932685,1,1 +8,1,0,5,1,1,1 +1,0,0,6,1,1,1 +2,0,0,6,1,1,1 +3,0,0,6,1,1,1 +4,0,0,6,0.86514867,1,1 +5,0,0,6,0.896321835,1,1 +6,0,0,6,1,1,1 +7,0,0,6,1,1,1 +8,0,0,6,1,1,1 +1,1,0,6,1,1,1 +2,1,0,6,1,1,1 +3,1,0,6,1,1,1 +4,1,0,6,1,1,1 +5,1,0,6,1,1,1 +6,1,0,6,1,1,1 +7,1,0,6,1,1,1 +8,1,0,6,1,1,1 +1,0,1,1,1,1,1 +2,0,1,1,1,1,1 +3,0,1,1,1,1,1 +4,0,1,1,1,1,1 +5,0,1,1,1,1,1 +6,0,1,1,1,1,1 +7,0,1,1,1,1,1 +8,0,1,1,1,1,1 +1,1,1,1,1,1,1 +2,1,1,1,1,1,1 +3,1,1,1,1,1,1 +4,1,1,1,1,1,1 +5,1,1,1,1,1,1 +6,1,1,1,1,1,1 +7,1,1,1,1,1,1 +8,1,1,1,1,1,1 +1,0,1,2,1,1,1 +2,0,1,2,1,1,1 +3,0,1,2,1,1,1 +4,0,1,2,1,1,1 +5,0,1,2,1,1,1 +6,0,1,2,1,1,1 +7,0,1,2,1,1,1 +8,0,1,2,1,1,1 +1,1,1,2,1,1,1 +2,1,1,2,1,1,1 +3,1,1,2,1,1,1 +4,1,1,2,1,1,1 +5,1,1,2,1,1,1 +6,1,1,2,1,1,1 +7,1,1,2,1,1,1 +8,1,1,2,1,1,1 +1,0,1,3,1,1,1 +2,0,1,3,1,1,1 +3,0,1,3,1,1,1 +4,0,1,3,1,1,1 +5,0,1,3,1,1,1 +6,0,1,3,1,1,1 +7,0,1,3,1,1,1 +8,0,1,3,1,1,1 +1,1,1,3,1,1,1 +2,1,1,3,1,1,1 +3,1,1,3,1,1,1 +4,1,1,3,1,1,1 +5,1,1,3,1,1,1 +6,1,1,3,1,1,1 +7,1,1,3,1,1,1 +8,1,1,3,1,1,1 +1,0,1,4,1,1,1 +2,0,1,4,1,1,1 +3,0,1,4,1,1,1 +4,0,1,4,1,1,1 +5,0,1,4,1,1,1 +6,0,1,4,1,1,1 +7,0,1,4,1,1,1 +8,0,1,4,1,1,1 +1,1,1,4,1,1,1 +2,1,1,4,1,1,1 +3,1,1,4,1,1,1 +4,1,1,4,1,1,1 +5,1,1,4,1,1,1 +6,1,1,4,1,1,1 +7,1,1,4,1,1,1 +8,1,1,4,1,1,1 +1,0,1,5,1,1,1 +2,0,1,5,1,1,1 +3,0,1,5,1,1,1 +4,0,1,5,1,1,1 +5,0,1,5,1,1,1 +6,0,1,5,1,1,1 +7,0,1,5,1,1,1 +8,0,1,5,1,1,1 +1,1,1,5,1,1,1 +2,1,1,5,1,1,1 +3,1,1,5,1,1,1 +4,1,1,5,1,1,1 +5,1,1,5,1,1,1 +6,1,1,5,1,1,1 +7,1,1,5,1,1,1 +8,1,1,5,1,1,1 +1,0,1,6,1,1,1 +2,0,1,6,1,1,1 +3,0,1,6,1,1,1 +4,0,1,6,1,1,1 +5,0,1,6,1,1,1 +6,0,1,6,1,1,1 +7,0,1,6,1,1,1 +8,0,1,6,1,1,1 +1,1,1,6,1,1,1 +2,1,1,6,1,1,1 +3,1,1,6,1,1,1 +4,1,1,6,1,1,1 +5,1,1,6,1,1,1 +6,1,1,6,1,1,1 +7,1,1,6,1,1,1 +8,1,1,6,1,1,1 diff --git a/resident/configs/non_mandatory_tour_scheduling.yaml b/resident/configs/non_mandatory_tour_scheduling.yaml new file mode 100644 index 0000000..1850c12 --- /dev/null +++ b/resident/configs/non_mandatory_tour_scheduling.yaml @@ -0,0 +1,92 @@ +LOGIT_TYPE: MNL + +preprocessor: + SPEC: non_mandatory_tour_scheduling_annotate_tours_preprocessor + DF: non_mandatory_tours + TABLES: + - land_use + - joint_tour_participants + - school_escort_tours + +SIMULATE_CHOOSER_COLUMNS: + - home_zone_id + - age + - female + - adult + - ptype + - has_pre_school_child_with_mandatory + - has_driving_age_child_with_mandatory + - is_income_less25K + - is_income_25K_to_60K + - is_income_60K_to_120K + - auto_ownership + - num_children + - num_adults + - num_retired_adults + - hhsize + - num_mand + - num_joint_tours + - num_escort_tours + - num_non_escort_tours + - num_add_shop_maint_tours + - num_add_soc_discr_tours + +LOGSUM_SETTINGS: tour_mode_choice.yaml + +# map : : +# segmentation of tours for processing may differ from segmentation of spec +# vectorize_tour_scheduling iterates over +# using to choose spec for scheduling alt chice +# using for logsums (if enabled) +# tour_mode_choice segmentation for logsums is: eatout,escort,othdiscr,othmaint,shopping,social,school,univ,work,atwork +TOUR_SPEC_SEGMENTS: + escort: escort + shopping: shopping + eatout: eatout + othdiscr: othdiscr + othmaint: othmaint + social: social + +# spec keyed by +SPEC_SEGMENTS: + escort: + 'SPEC': tour_scheduling_nonmandatory_escort.csv + 'COEFFICIENTS': tour_scheduling_nonmandatory_escort_coefficients.csv + shopping: + 'SPEC': tour_scheduling_nonmandatory_shopping.csv + 'COEFFICIENTS': tour_scheduling_nonmandatory_shopping_coefficients.csv + eatout: + 'SPEC': tour_scheduling_nonmandatory_eatout.csv + 'COEFFICIENTS': tour_scheduling_nonmandatory_eatout_coefficients.csv + othdiscr: + 'SPEC': tour_scheduling_nonmandatory_othdiscr.csv + 'COEFFICIENTS': tour_scheduling_nonmandatory_othdiscr_coefficients.csv + othmaint: + 'SPEC': tour_scheduling_nonmandatory_othmaint.csv + 'COEFFICIENTS': tour_scheduling_nonmandatory_othmaint_coefficients.csv + social: + 'SPEC': tour_scheduling_nonmandatory_social.csv + 'COEFFICIENTS': tour_scheduling_nonmandatory_social_coefficients.csv + +## alts preprocessor keyed by +#ALTS_PREPROCESSOR: +# escort: +# SPEC: tour_scheduling_nonmandatory_escort_annotate_alts_preprocessor.csv +# DF: alt_tdd +# shopping: +# SPEC: non_mandatory_tour_scheduling_shopping_annotate_alts_preprocessor.csv +# DF: alt_tdd +# eatout: +# SPEC: non_mandatory_tour_scheduling_annotate_alts_preprocessor.csv +# DF: alt_tdd +# othdiscr: +# SPEC: non_mandatory_tour_scheduling_annotate_alts_preprocessor.csv +# DF: alt_tdd +# othmaint: +# SPEC: non_mandatory_tour_scheduling_annotate_alts_preprocessor.csv +# DF: alt_tdd +# social: +# SPEC: non_mandatory_tour_scheduling_annotate_alts_preprocessor.csv +# DF: alt_tdd +# +DESTINATION_FOR_TOUR_PURPOSE: destination diff --git a/resident/configs/non_mandatory_tour_scheduling_annotate_tours_preprocessor.csv b/resident/configs/non_mandatory_tour_scheduling_annotate_tours_preprocessor.csv new file mode 100644 index 0000000..fb82384 --- /dev/null +++ b/resident/configs/non_mandatory_tour_scheduling_annotate_tours_preprocessor.csv @@ -0,0 +1,11 @@ +Description,Target,Expression +#,, +number of person joint tours,num_person_joint_tours,"reindex_i(joint_tour_participants.groupby('person_id').size(), non_mandatory_tours.person_id)" +#,, +,origin_to_destination_distance,"skim_dict.lookup(non_mandatory_tours.origin, non_mandatory_tours.destination, ('SOV_M_DIST', 'MD'))" +# included for school escorting model,, +flag to denote outbound school escort tours,is_outbound_school_escort_tour,"non_mandatory_tours.index.isin(school_escort_tours[school_escort_tours['school_escort_direction'] == 'outbound'].index)" +flag to denote inbound school escort tours,is_inbound_school_escort_tour,"non_mandatory_tours.index.isin(school_escort_tours[school_escort_tours['school_escort_direction'] == 'inbound'].index)" +school escort tour start time,school_escort_tour_start,"reindex(school_escort_tours.start.astype(int), non_mandatory_tours.index)" +school escort tour next start time,school_escort_tour_next_start,"reindex(school_escort_tours.next_pure_escort_start.astype(int), non_mandatory_tours.index)" +school escort tour end time,school_escort_tour_end,"reindex(school_escort_tours.end.astype(int), non_mandatory_tours.index)" \ No newline at end of file diff --git a/resident/configs/park_and_ride_lot_choice.csv b/resident/configs/park_and_ride_lot_choice.csv new file mode 100644 index 0000000..2d5b884 --- /dev/null +++ b/resident/configs/park_and_ride_lot_choice.csv @@ -0,0 +1,18 @@ +Label,Description,Expression,coefficient +# Driving utilities,,, +util_drive_time,drive time from origin to lot,@(olt_skims['SOV_M_TIME'] + lot_skims['SOV_M_TIME']),coef_ivt_work +util_drive_cost,auto operating cost,@df.sov_auto_op_cost * (olt_skims['SOV_M_DIST'] + lot_skims['SOV_M_DIST']) / df.cost_sensitivity,coef_income_work +# Transit utilties,,, +util_first_wait,first wait time,@(ldt_skims['WTW_FWT'] + dlt_skims['WTW_FWT']),coef_wait_work +util_xfer_wait,transfer wait time,@(ldt_skims['WTW_XWT'] + dlt_skims['WTW_XWT']),coef_xwait_work +util_walk_time,walk time,@(ldt_skims['WTW_AUX'] + dlt_skims['WTW_AUX']),coef_xwalk_work +util_ivtt,in vehcile transit time,@(ldt_skims['WTW_TIV'] + dlt_skims['WTW_TIV']),coef_ivt_work +util_xfer_penalty,transfer penalty,"@(-23+23*np.exp(0.414*np.clip(ldt_skims['WTW_XFR'], a_min=None,a_max=4))) + (-23+23*np.exp(0.414*np.clip(dlt_skims['WTW_XFR'], a_min=None,a_max=4)))",coef_xfer_work +util_stop_type_constant,stop type constant,@(ldt_skims['WTW_RST'] + dlt_skims['WTW_RST']),coef_ivt_work +util_vehicle_type_constant,vehicle type constant,@(ldt_skims['WTW_VTC'] + dlt_skims['WTW_VTC']),coef_ivt_work +# FIXME need fares,,, +#util_fare,transit fare,"@df.transitSubsidyPassDiscount * (odt_skims['WTW_FARE'] + dot_skims['WTW_FARE']) * 100*df.num_participants/df.cost_sensitivity",coef_income_work +util_small_lot,Small lot penalty -- 10 min penalty,"@np.where(df['PNR_SPACES'] < 100,10,0)",coef_ivt_work +#util_med_lot,med lot penalty -- no penalty,"@np.where((df['PNR_SPACES'] >= 100) & (df['PNR_SPACES'] < 500),1,0) * 0",coef_ivt_work +util_large_lot,large lot penalty -- 10 min back,"@np.where(df['PNR_SPACES'] >= 500,-10,0)",coef_ivt_work +util_capacity,lot capacity availability,@df['pnr_lot_full'],coef_unavailable diff --git a/resident/configs/park_and_ride_lot_choice.yaml b/resident/configs/park_and_ride_lot_choice.yaml new file mode 100644 index 0000000..ecf1eb0 --- /dev/null +++ b/resident/configs/park_and_ride_lot_choice.yaml @@ -0,0 +1,35 @@ +SPEC: park_and_ride_lot_choice.csv + +COEFFICIENTS: park_and_ride_lot_choice_coeffs.csv + +preprocessor: + SPEC: park_and_ride_lot_choice_preprocessor.csv + DF: df + TABLES: + - land_use + - vehicles + - tours + +LANDUSE_PNR_SPACES_COLUMN: PNR_SPACES + +# will check the tour destination to make sure transit access is available +# if no transit access, then no PNR lot will be available and could get +# zero probabilities error +TRANSIT_SKIMS_FOR_ELIGIBILITY: + - WTW_TIV__PM + - WTW_TIV__MD + +# Precompute destination eligibility for tours +# LANDUSE_COL_FOR_PNR_ELIGIBLE_DEST: is_pnr_eligible_destination + +ITERATE_WITH_TOUR_MODE_CHOICE: True +MAX_ITERATIONS: 5 +ACCEPTED_TOLERANCE: 0.95 +RESAMPLE_STRATEGY: latest +PARK_AND_RIDE_MODES: # should match tour_mode_choice configs + - PNR_TRANSIT + +compute_settings: + protect_columns: + - out_period + - in_period diff --git a/resident/configs/park_and_ride_lot_choice_coeffs.csv b/resident/configs/park_and_ride_lot_choice_coeffs.csv new file mode 100644 index 0000000..54457d7 --- /dev/null +++ b/resident/configs/park_and_ride_lot_choice_coeffs.csv @@ -0,0 +1,10 @@ +coefficient_name,value,constrain +coef_zero,0.0,T +# took work coefficients from tour mode choice model,, +coef_unavailable,-999,F +coef_ivt_work,-0.016,F +coef_income_work,-0.625,F +coef_xwalk_work,-0.04,F +coef_wait_work,-0.024,F +coef_xfer_work,-0.024,F +coef_xwait_work,-0.032,F diff --git a/resident/configs/park_and_ride_lot_choice_preprocessor.csv b/resident/configs/park_and_ride_lot_choice_preprocessor.csv new file mode 100644 index 0000000..f3063eb --- /dev/null +++ b/resident/configs/park_and_ride_lot_choice_preprocessor.csv @@ -0,0 +1,16 @@ +Description,Target,Expression, +local,_DF_IS_TOUR,'tour_type' in df.columns, +,is_atwork_subtour,(df.tour_category == 'atwork') if _DF_IS_TOUR else False, +,_parent_tour_veh,"reindex(tours['selected_vehicle'], df['parent_tour_id']) if 'selected_vehicle' in df.columns else np.nan", +,_parent_tour_mode,"reindex(tours['tour_mode'], df['parent_tour_id']) if 'tour_mode' in df.columns else np.nan", +,_sov_veh_option,"np.where(is_atwork_subtour, _parent_tour_veh, df.get('vehicle_occup_1', np.nan))", +,num_participants,df.number_of_participants if _DF_IS_TOUR else 1, +# Auto operating costs,,, +,sov_auto_op_cost,"reindex(vehicles.groupby('vehicle_type')['auto_operating_cost'].mean(), pd.Series(_sov_veh_option, df.index)) if 'vehicle_occup_1' in df.columns else np.nan", +,sov_auto_op_cost,"np.where(sov_auto_op_cost.isna() | (pd.Series(_sov_veh_option, df.index) == 'non_hh_veh'), costPerMile, sov_auto_op_cost)", +# cost coef,,, +,_income_exponent,"np.where(df.get('tour_type') == 'work', 0.6, 0.5) if _DF_IS_TOUR else 0.5", +,cost_sensitivity,"np.maximum(df.income,1000).pow(_income_exponent)", +transit pass subsidy,_transit_pass_subsidy,"df.get('transit_pass_subsidy', False)", +transit pass ownership,_transit_pass_ownership,"df.get('transit_pass_ownership', False)", +transit subsidy pass discount, transitSubsidyPassDiscount,"np.where(_transit_pass_subsidy | _transit_pass_ownership,0,1)", \ No newline at end of file diff --git a/resident/configs/parking_location_choice.csv b/resident/configs/parking_location_choice.csv new file mode 100644 index 0000000..9d65aa2 --- /dev/null +++ b/resident/configs/parking_location_choice.csv @@ -0,0 +1,12 @@ +Description,Expression,segment_1,segment_2,segment_3 +Person is worker,_person_is_worker@((df.ptype == 1) | (df.ptype == 2)),1,1,1 +Trip is work purpose,_work_trip@(df.purpose == 'work'),1,1,1 +Parking to destination distance skim,_walk_distance@pd_skims['DISTWALK'],1,1,1 +#,,,, +# actual utility terms starts from here,,,, +Log of parking size,"@np.where(df.PRKSPACES>0,np.log(df.PRKSPACES),-999)",1,1,1 +Cost work trip,"@np.where(df.purpose == 'work', df.cost_parking, 0)",coef_cost_work_trip,coef_cost_work_trip,coef_cost_work_trip +Cost other trip,"@np.where(df.purpose != 'work', df.cost_parking, 0)",coef_cost_other_trip,coef_cost_other_trip,coef_cost_other_trip +Walk distance,@_walk_distance,coef_walk_distance,coef_walk_distance,coef_walk_distance +Walk Unavailability,"@np.where(_walk_distance == 0, 1, 0)",coef_unavailable,coef_unavailable,coef_unavailable +Require parking zone to be less than 0.75 mile from dest,"@np.where(_walk_distance > 0.75, 1, 0) * (df.destination != df.parking_zone)",coef_unavailable,coef_unavailable,coef_unavailable diff --git a/resident/configs/parking_location_choice.yaml b/resident/configs/parking_location_choice.yaml new file mode 100644 index 0000000..625f4ea --- /dev/null +++ b/resident/configs/parking_location_choice.yaml @@ -0,0 +1,28 @@ +SPECIFICATION: parking_location_choice.csv + +COEFFICIENTS: parking_location_choice_coeffs.csv + +PREPROCESSOR: + SPEC: parking_location_choice_annotate_trips_preprocessor + DF: trips_merged + TABLES: + - land_use + - persons + - tours + +# boolean column to filter choosers (True means keep) +CHOOSER_FILTER_COLUMN_NAME: is_park_eligible +CHOOSER_SEGMENT_COLUMN_NAME: parking_segment + +ALTERNATIVE_FILTER_COLUMN_NAME: is_parking_zone +ALT_DEST_COL_NAME: parking_zone + +TRIP_DEPARTURE_PERIOD: depart + +TRIP_ORIGIN: origin +TRIP_DESTINATION: destination + +AUTO_MODES: + - DRIVEALONE + - SHARED2 + - SHARED3 diff --git a/resident/configs/parking_location_choice_annotate_trips_preprocessor.csv b/resident/configs/parking_location_choice_annotate_trips_preprocessor.csv new file mode 100644 index 0000000..e870188 --- /dev/null +++ b/resident/configs/parking_location_choice_annotate_trips_preprocessor.csv @@ -0,0 +1,15 @@ +Description,Target,Expression +Person Type,ptype,"reindex(persons.ptype, df.person_id)" +Trip mode is drive,drive_trip,"df.trip_mode.isin(['DRIVEALONE', 'SHARED2', 'SHARED3'])" +# putting all trips into the same parking segment,, +Parking segment,parking_segment,"np.where(df['tour_id'] % 3 == 0, 'segment_1',np.where(df['tour_id'] % 3 == 1, 'segment_2','segment_3'))" +Next Trip Departure,_next_trip_depart,df.groupby('tour_id')['depart'].shift() +Activity duration,activity_duration,"np.where(_next_trip_depart.isna(), 0, _next_trip_depart - df.depart)" +,_tour_participants,df.tour_id.map(tours.number_of_participants) +,_is_joint,(_tour_participants > 1) +,_cost_parking,0 +,_cost_parking,"_cost_parking + df.trip_mode.isin(['DRIVEALONE']) * (df.parkingCost * df.autoParkingCostFactor)" +,_cost_parking,"_cost_parking + df.trip_mode.isin(['SHARED2']) * (df.parkingCost * df.autoParkingCostFactor)/np.where(_is_joint,_tour_participants,OCC_SHARED2)" +,cost_parking,"_cost_parking + df.trip_mode.isin(['SHARED3']) * (df.parkingCost * df.autoParkingCostFactor)/np.where(_is_joint,_tour_participants,OCC_SHARED3)" +# FIXME better way to determine parking eligibility?,, +Parking eligible trip,is_park_eligible,"drive_trip & (df.purpose != 'home') & (cost_parking > 0)" \ No newline at end of file diff --git a/resident/configs/parking_location_choice_coeffs.csv b/resident/configs/parking_location_choice_coeffs.csv new file mode 100644 index 0000000..0edccfb --- /dev/null +++ b/resident/configs/parking_location_choice_coeffs.csv @@ -0,0 +1,5 @@ +coefficient_name,value,constrain +coef_unavailable,-9999,T +coef_walk_distance,-11.8,F +coef_cost_work_trip,-0.0072,F +coef_cost_other_trip,-0.0041,F diff --git a/resident/configs/school_escorting.yaml b/resident/configs/school_escorting.yaml new file mode 100644 index 0000000..b4524a9 --- /dev/null +++ b/resident/configs/school_escorting.yaml @@ -0,0 +1,49 @@ + +OUTBOUND_SPEC: school_escorting_outbound.csv +OUTBOUND_COEFFICIENTS: school_escorting_coefficients_outbound.csv + +INBOUND_SPEC: school_escorting_inbound.csv +INBOUND_COEFFICIENTS: school_escorting_coefficients_inbound.csv + +OUTBOUND_COND_SPEC: school_escorting_outbound_cond.csv +OUTBOUND_COND_COEFFICIENTS: school_escorting_coefficients_outbound_cond.csv + +ALTS: school_escorting_alts.csv + +LOGIT_TYPE: MNL + +NUM_ESCORTEES: 3 +NUM_CHAPERONES: 2 + +GENDER_COLUMN: SEX + +preprocessor_outbound: + SPEC: school_escorting_preprocessor_outbound + DF: df + TABLES: + - persons + - tours + +preprocessor_inbound: + SPEC: school_escorting_preprocessor_inbound + DF: df + TABLES: + - persons + - tours + +preprocessor_outbound_cond: + SPEC: school_escorting_preprocessor_outbound + DF: df + TABLES: + - persons + - tours + +SIMULATE_CHOOSER_COLUMNS: + - home_zone_id + - income + - auto_ownership + - num_workers + +CONSTANTS: + max_bin_difference_between_departure_times: 1 + mins_per_time_bin: 30 diff --git a/resident/configs/school_escorting_alts.csv b/resident/configs/school_escorting_alts.csv new file mode 100644 index 0000000..eca6617 --- /dev/null +++ b/resident/configs/school_escorting_alts.csv @@ -0,0 +1,158 @@ +Alt,bundle1,bundle2,bundle3,chauf1,chauf2,chauf3,nbund1,nbund2,nbundles,nrs1,npe1,nrs2,npe2,Description +1,0,0,0,0,0,0,0,0,0,0,0,0,0,no one is escorted +2,1,0,0,1,0,0,1,0,1,1,0,0,0,child 1 is escorted in bundle 1 by chauffeur 1 as ride sharing +3,1,0,0,2,0,0,1,0,1,0,1,0,0,child 1 is escorted in bundle 1 by chauffeur 1 as pure escort +4,1,0,0,3,0,0,0,1,1,0,0,1,0,child 1 is escorted in bundle 1 by chauffeur 2 as ride sharing +5,1,0,0,4,0,0,0,1,1,0,0,0,1,child 1 is escorted in bundle 1 by chauffeur 2 as pure escort +6,0,1,0,0,1,0,1,0,1,1,0,0,0,child 2 is escorted in bundle 1 by chauffeur 1 as ride sharing +7,0,1,0,0,2,0,1,0,1,0,1,0,0,child 2 is escorted in bundle 1 by chauffeur 1 as pure escort +8,0,1,0,0,3,0,0,1,1,0,0,1,0,child 2 is escorted in bundle 1 by chauffeur 2 as ride sharing +9,0,1,0,0,4,0,0,1,1,0,0,0,1,child 2 is escorted in bundle 1 by chauffeur 2 as pure escort +10,0,0,1,0,0,1,1,0,1,1,0,0,0,child 3 is escorted in bundle 1 by chauffeur 1 as ride sharing +11,0,0,1,0,0,2,1,0,1,0,1,0,0,child 3 is escorted in bundle 1 by chauffeur 1 as pure escort +12,0,0,1,0,0,3,0,1,1,0,0,1,0,child 3 is escorted in bundle 1 by chauffeur 2 as ride sharing +13,0,0,1,0,0,4,0,1,1,0,0,0,1,child 3 is escorted in bundle 1 by chauffeur 2 as pure escort +14,1,1,0,1,1,0,1,0,1,1,0,0,0,child 1 and 2 are escorted in bundle 1 by chauffeur 1 as ride sharing +15,1,1,0,2,2,0,1,0,1,0,1,0,0,child 1 and 2 are escorted in bundle 1 by chauffeur 1 as pure escort +16,1,1,0,3,3,0,0,1,1,0,0,1,0,child 1 and 2 are escorted in bundle 1 by chauffeur 2 as ride sharing +17,1,1,0,4,4,0,0,1,1,0,0,0,1,child 1 and 2 are escorted in bundle 1 by chauffeur 2 as pure escort +18,1,0,1,1,0,1,1,0,1,1,0,0,0,child 1 and 3 are escorted in bundle 1 by chauffeur 1 as ride sharing +19,1,0,1,2,0,2,1,0,1,0,1,0,0,child 1 and 3 are escorted in bundle 1 by chauffeur 1 as pure escort +20,1,0,1,3,0,3,0,1,1,0,0,1,0,child 1 and 3 are escorted in bundle 1 by chauffeur 2 as ride sharing +21,1,0,1,4,0,4,0,1,1,0,0,0,1,child 1 and 3 are escorted in bundle 1 by chauffeur 2 as pure escort +22,0,1,1,0,1,1,1,0,1,1,0,0,0,child 2 and 3 are escorted in bundle 1 by chauffeur 1 as ride sharing +23,0,1,1,0,2,2,1,0,1,0,1,0,0,child 2 and 3 are escorted in bundle 1 by chauffeur 1 as pure escort +24,0,1,1,0,3,3,0,1,1,0,0,1,0,child 2 and 3 are escorted in bundle 1 by chauffeur 2 as ride sharing +25,0,1,1,0,4,4,0,1,1,0,0,0,1,child 2 and 3 are escorted in bundle 1 by chauffeur 2 as pure escort +26,1,2,0,1,2,0,2,0,2,1,1,0,0,child 1 is escorted in bundle 1 by chauffeur 1 as ride sharing and child 2 is escorted in bundle 2 by chauffeur 1 as pure escort +27,1,2,0,1,3,0,1,1,2,1,0,1,0,child 1 is escorted in bundle 1 by chauffeur 1 as ride sharing and child 2 is escorted in bundle 2 by chauffeur 2 as ride sharing +28,1,2,0,1,4,0,1,1,2,1,0,0,1,child 1 is escorted in bundle 1 by chauffeur 1 as ride sharing and child 2 is escorted in bundle 2 by chauffeur 2 as pure escort +29,1,2,0,2,1,0,2,0,2,1,1,0,0,child 1 is escorted in bundle 1 by chauffeur 1 as pure escort and child 2 is escorted in bundle 2 by chauffeur 1 as ride sharing +30,1,2,0,2,2,0,2,0,2,0,2,0,0,child 1 is escorted in bundle 1 by chauffeur 1 as pure escort and child 2 is escorted in bundle 2 by chauffeur 1 as pure escort +31,1,2,0,2,3,0,1,1,2,0,1,1,0,child 1 is escorted in bundle 1 by chauffeur 1 as pure escort and child 2 is escorted in bundle 2 by chauffeur 2 as ride sharing +32,1,2,0,2,4,0,1,1,2,0,1,0,1,child 1 is escorted in bundle 1 by chauffeur 1 as pure escort and child 2 is escorted in bundle 2 by chauffeur 2 as pure escort +33,1,2,0,3,1,0,1,1,2,1,0,1,0,child 1 is escorted in bundle 1 by chauffeur 2 as ride sharing and child 2 is escorted in bundle 2 by chauffeur 1 as ride sharing +34,1,2,0,3,2,0,1,1,2,0,1,1,0,child 1 is escorted in bundle 1 by chauffeur 2 as ride sharing and child 2 is escorted in bundle 2 by chauffeur 1 as pure escort +35,1,2,0,3,4,0,0,2,2,0,0,1,1,child 1 is escorted in bundle 1 by chauffeur 2 as ride sharing and child 2 is escorted in bundle 2 by chauffeur 2 as pure escort +36,1,2,0,4,1,0,1,1,2,1,0,0,1,child 1 is escorted in bundle 1 by chauffeur 2 as pure escort and child 2 is escorted in bundle 2 by chauffeur 1 as ride sharing +37,1,2,0,4,2,0,1,1,2,0,1,0,1,child 1 is escorted in bundle 1 by chauffeur 2 as pure escort and child 2 is escorted in bundle 2 by chauffeur 1 as pure escort +38,1,2,0,4,3,0,0,2,2,0,0,1,1,child 1 is escorted in bundle 1 by chauffeur 2 as pure escort and child 2 is escorted in bundle 2 by chauffeur 2 as ride sharing +39,1,2,0,4,4,0,0,2,2,0,0,0,2,child 1 is escorted in bundle 1 by chauffeur 2 as pure escort and child 2 is escorted in bundle 2 by chauffeur 2 as pure escort +40,1,0,2,1,0,2,2,0,2,1,1,0,0,child 1 is escorted in bundle 1 by chauffeur 1 as ride sharing and child 3 is escorted in bundle 2 by chauffeur 1 as pure escorting +41,1,0,2,1,0,3,1,1,2,1,0,1,0,child 1 is escorted in bundle 1 by chauffeur 1 as ride sharing and child 3 is escorted in bundle 2 by chauffeur 2 as ride sharing +42,1,0,2,1,0,4,1,1,2,1,0,0,1,child 1 is escorted in bundle 1 by chauffeur 1 as ride sharing and child 3 is escorted in bundle 2 by chauffeur 2 as pure escorting +43,1,0,2,2,0,1,2,0,2,1,1,0,0,child 1 is escorted in bundle 1 by chauffeur 1 as pure escort and child 3 is escorted in bundle 2 by chauffeur 1 as ride sharing +44,1,0,2,2,0,2,2,0,2,0,2,0,0,child 1 is escorted in bundle 1 by chauffeur 1 as pure escort and child 3 is escorted in bundle 2 by chauffeur 1 as pure escorting +45,1,0,2,2,0,3,1,1,2,0,1,1,0,child 1 is escorted in bundle 1 by chauffeur 1 as pure escort and child 3 is escorted in bundle 2 by chauffeur 2 as ride sharing +46,1,0,2,2,0,4,1,1,2,0,1,0,1,child 1 is escorted in bundle 1 by chauffeur 1 as pure escort and child 3 is escorted in bundle 2 by chauffeur 2 as pure escorting +47,1,0,2,3,0,1,1,1,2,1,0,1,0,child 1 is escorted in bundle 1 by chauffeur 2 as ride sharing and child 3 is escorted in bundle 2 by chauffeur 1 as ride sharing +48,1,0,2,3,0,2,1,1,2,0,1,1,0,child 1 is escorted in bundle 1 by chauffeur 2 as ride sharing and child 3 is escorted in bundle 2 by chauffeur 1 as pure escorting +49,1,0,2,3,0,4,0,2,2,0,0,1,1,child 1 is escorted in bundle 1 by chauffeur 2 as ride sharing and child 3 is escorted in bundle 2 by chauffeur 2 as pure escorting +50,1,0,2,4,0,1,1,1,2,1,0,0,1,child 1 is escorted in bundle 1 by chauffeur 2 as pure escort and child 3 is escorted in bundle 2 by chauffeur 1 as ride sharing +51,1,0,2,4,0,2,1,1,2,0,1,0,1,child 1 is escorted in bundle 1 by chauffeur 2 as pure escort and child 3 is escorted in bundle 2 by chauffeur 1 as pure escorting +52,1,0,2,4,0,3,0,2,2,0,0,1,1,child 1 is escorted in bundle 1 by chauffeur 2 as pure escort and child 3 is escorted in bundle 2 by chauffeur 2 as ride sharing +53,1,0,2,4,0,4,0,2,2,0,0,0,2,child 1 is escorted in bundle 1 by chauffeur 2 as pure escort and child 3 is escorted in bundle 2 by chauffeur 2 as pure escorting +54,0,1,2,0,1,2,2,0,2,1,1,0,0,child 2 is escorted in bundle 1 by chauffeur 1 as ride sharing and child 3 is escorted in bundle 2 by chauffeur 1 as pure escorting +55,0,1,2,0,1,3,1,1,2,1,0,1,0,child 2 is escorted in bundle 1 by chauffeur 1 as ride sharing and child 3 is escorted in bundle 2 by chauffeur 2 as ride sharing +56,0,1,2,0,1,4,1,1,2,1,0,0,1,child 2 is escorted in bundle 1 by chauffeur 1 as ride sharing and child 3 is escorted in bundle 2 by chauffeur 2 as pure escorting +57,0,1,2,0,2,1,2,0,2,1,1,0,0,child 2 is escorted in bundle 1 by chauffeur 1 as pure escort and child 3 is escorted in bundle 2 by chauffeur 1 as ride sharing +58,0,1,2,0,2,2,2,0,2,0,2,0,0,child 2 is escorted in bundle 1 by chauffeur 1 as pure escort and child 3 is escorted in bundle 2 by chauffeur 1 as pure escorting +59,0,1,2,0,2,3,1,1,2,0,1,1,0,child 2 is escorted in bundle 1 by chauffeur 1 as pure escort and child 3 is escorted in bundle 2 by chauffeur 2 as ride sharing +60,0,1,2,0,2,4,1,1,2,0,1,0,1,child 2 is escorted in bundle 1 by chauffeur 1 as pure escort and child 3 is escorted in bundle 2 by chauffeur 2 as pure escorting +61,0,1,2,0,3,1,1,1,2,1,0,1,0,child 2 is escorted in bundle 1 by chauffeur 2 as ride sharing and child 3 is escorted in bundle 2 by chauffeur 1 as ride sharing +62,0,1,2,0,3,2,1,1,2,0,1,1,0,child 2 is escorted in bundle 1 by chauffeur 2 as ride sharing and child 3 is escorted in bundle 2 by chauffeur 1 as pure escorting +63,0,1,2,0,3,4,0,2,2,0,0,1,1,child 2 is escorted in bundle 1 by chauffeur 2 as ride sharing and child 3 is escorted in bundle 2 by chauffeur 2 as pure escorting +64,0,1,2,0,4,1,1,1,2,1,0,0,1,child 2 is escorted in bundle 1 by chauffeur 2 as pure escort and child 3 is escorted in bundle 2 by chauffeur 1 as ride sharing +65,0,1,2,0,4,2,1,1,2,0,1,0,1,child 2 is escorted in bundle 1 by chauffeur 2 as pure escort and child 3 is escorted in bundle 2 by chauffeur 1 as pure escorting +66,0,1,2,0,4,3,0,2,2,0,0,1,1,child 2 is escorted in bundle 1 by chauffeur 2 as pure escort and child 3 is escorted in bundle 2 by chauffeur 2 as ride sharing +67,0,1,2,0,4,4,0,2,2,0,0,0,2,child 2 is escorted in bundle 1 by chauffeur 2 as pure escort and child 3 is escorted in bundle 2 by chauffeur 2 as pure escorting +68,1,1,1,1,1,1,1,0,1,1,0,0,0,"child 1, 2 and 3 are escorted in bundle 1 by chauffeur 1 as ride sharing" +69,1,1,1,2,2,2,1,0,1,0,1,0,0,"child 1, 2 and 3 are escorted in bundle 1 by chauffeur 1 as pure escort" +70,1,1,1,3,3,3,0,1,1,0,0,1,0,"child 1, 2 and 3 are escorted in bundle 1 by chauffeur 2 as ride sharing" +71,1,1,1,4,4,4,0,1,1,0,0,0,1,"child 1, 2 and 3 are escorted in bundle 1 by chauffeur 2 as pure escort" +72,1,2,3,1,2,2,3,0,3,1,2,0,0,"child 1 is escorted in bundle 1 by chauffeur 1 as ride sharing, child 2 is escorted in bundle 2 by chauffeur 1 as pure escort and child 3 is escorted in bundle 3 by chauffeur 1 as pure escort" +73,1,2,3,1,2,3,2,1,3,1,1,1,0,"child 1 is escorted in bundle 1 by chauffeur 1 as ride sharing, child 2 is escorted in bundle 2 by chauffeur 1 as pure escort and child 3 is escorted in bundle 3 by chauffeur 2 as ride sharing" +74,1,2,3,1,2,4,2,1,3,1,1,0,1,"child 1 is escorted in bundle 1 by chauffeur 1 as ride sharing, child 2 is escorted in bundle 2 by chauffeur 1 as pure escort and child 3 is escorted in bundle 3 by chauffeur 2 as pure escort" +75,1,2,3,1,3,2,2,1,3,1,1,1,0,"child 1 is escorted in bundle 1 by chauffeur 1 as ride sharing, child 2 is escorted in bundle 2 by chauffeur 2 as ride sharing, and child 3 is escorted in bundle 3 by chauffeur 1 as pure escort" +76,1,2,3,1,3,4,1,2,3,1,0,1,1,"child 1 is escorted in bundle 1 by chauffeur 1 as ride sharing, child 2 is escorted in bundle 2 by chauffeur 2 as ride sharing, and child 3 is escorted in bundle 3 by chauffeur 2 as pure escort" +77,1,2,3,1,4,2,2,1,3,1,1,0,1,"child 1 is escorted in bundle 1 by chauffeur 1 as ride sharing, child 2 is escorted in bundle 2 by chauffeur 2 as pure escort and child 3 is escorted in bundle 3 by chauffeur 1 as pure escort" +78,1,2,3,1,4,3,1,2,3,1,0,1,1,"child 1 is escorted in bundle 1 by chauffeur 1 as ride sharing, child 2 is escorted in bundle 2 by chauffeur 2 as pure escort and child 3 is escorted in bundle 3 by chauffeur 2 as ride sharing" +79,1,2,3,1,4,4,1,2,3,1,0,0,2,"child 1 is escorted in bundle 1 by chauffeur 1 as ride sharing, child 2 is escorted in bundle 2 by chauffeur 2 as pure escort and child 3 is escorted in bundle 3 by chauffeur 2 as pure escort" +80,1,2,3,2,1,2,3,0,3,1,2,0,0,"child 1 is escorted in bundle 1 by chauffeur 1 as pure escort, child 2 is escorted in bundle 2 by chauffeur 1 as ride sharing, and child 3 is escorted in bundle 3 by chauffeur 1 as pure escort" +81,1,2,3,2,1,3,2,1,3,1,1,1,0,"child 1 is escorted in bundle 1 by chauffeur 1 as pure escort, child 2 is escorted in bundle 2 by chauffeur 1 as ride sharing, and child 3 is escorted in bundle 3 by chauffeur 2 as ride sharing" +82,1,2,3,2,1,4,2,1,3,1,1,0,1,"child 1 is escorted in bundle 1 by chauffeur 1 as pure escort, child 2 is escorted in bundle 2 by chauffeur 1 as ride sharing, and child 3 is escorted in bundle 3 by chauffeur 2 as pure escort" +83,1,2,3,2,2,1,3,0,3,1,2,0,0,"child 1 is escorted in bundle 1 by chauffeur 1 as pure escort, child 2 is escorted in bundle 2 by chauffeur 1 as pure escort and child 3 is escorted in bundle 3 by chauffeur 1 as ride sharing" +84,1,2,3,2,2,2,3,0,3,0,3,0,0,"child 1 is escorted in bundle 1 by chauffeur 1 as pure escort, child 2 is escorted in bundle 2 by chauffeur 1 as pure escort and child 3 is escorted in bundle 3 by chauffeur 1 as pure escort" +85,1,2,3,2,2,3,2,1,3,0,2,1,0,"child 1 is escorted in bundle 1 by chauffeur 1 as pure escort, child 2 is escorted in bundle 2 by chauffeur 1 as pure escort and child 3 is escorted in bundle 3 by chauffeur 2 as ride sharing" +86,1,2,3,2,2,4,2,1,3,0,2,0,1,"child 1 is escorted in bundle 1 by chauffeur 1 as pure escort, child 2 is escorted in bundle 2 by chauffeur 1 as pure escort and child 3 is escorted in bundle 3 by chauffeur 2 as pure escort" +87,1,2,3,2,3,1,2,1,3,1,1,1,0,"child 1 is escorted in bundle 1 by chauffeur 1 as pure escort, child 2 is escorted in bundle 2 by chauffeur 2 as ride sharing, and child 3 is escorted in bundle 3 by chauffeur 1 as ride sharing" +88,1,2,3,2,3,2,2,1,3,0,2,1,0,"child 1 is escorted in bundle 1 by chauffeur 1 as pure escort, child 2 is escorted in bundle 2 by chauffeur 2 as ride sharing, and child 3 is escorted in bundle 3 by chauffeur 1 as pure escort" +89,1,2,3,2,3,4,1,2,3,0,1,1,1,"child 1 is escorted in bundle 1 by chauffeur 1 as pure escort, child 2 is escorted in bundle 2 by chauffeur 2 as ride sharing, and child 3 is escorted in bundle 3 by chauffeur 2 as pure escort" +90,1,2,3,2,4,1,2,1,3,1,1,0,1,"child 1 is escorted in bundle 1 by chauffeur 1 as pure escort, child 2 is escorted in bundle 2 by chauffeur 2 as pure escort and child 3 is escorted in bundle 3 by chauffeur 1 as ride sharing" +91,1,2,3,2,4,2,2,1,3,0,2,0,1,"child 1 is escorted in bundle 1 by chauffeur 1 as pure escort, child 2 is escorted in bundle 2 by chauffeur 2 as pure escort and child 3 is escorted in bundle 3 by chauffeur 1 as pure escort" +92,1,2,3,2,4,3,1,2,3,0,1,1,1,"child 1 is escorted in bundle 1 by chauffeur 1 as pure escort, child 2 is escorted in bundle 2 by chauffeur 2 as pure escort and child 3 is escorted in bundle 3 by chauffeur 2 as ride sharing" +93,1,2,3,2,4,4,1,2,3,0,1,0,2,"child 1 is escorted in bundle 1 by chauffeur 1 as pure escort, child 2 is escorted in bundle 2 by chauffeur 2 as pure escort and child 3 is escorted in bundle 3 by chauffeur 2 as pure escort" +94,1,2,3,3,1,2,2,1,3,1,1,1,0,"child 1 is escorted in bundle 1 by chauffeur 2 as ride sharing, child 2 is escorted in bundle 2 by chauffeur 1 as ride sharing, and child 3 is escorted in bundle 3 by chauffeur 1 as pure escort" +95,1,2,3,3,1,4,1,2,3,1,0,1,1,"child 1 is escorted in bundle 1 by chauffeur 2 as ride sharing, child 2 is escorted in bundle 2 by chauffeur 1 as ride sharing, and child 3 is escorted in bundle 3 by chauffeur 2 as pure escort" +96,1,2,3,3,2,1,2,1,3,1,1,1,0,"child 1 is escorted in bundle 1 by chauffeur 2 as ride sharing, child 2 is escorted in bundle 2 by chauffeur 1 as pure escort and child 3 is escorted in bundle 3 by chauffeur 1 as ride sharing" +97,1,2,3,3,2,2,2,1,3,0,2,1,0,"child 1 is escorted in bundle 1 by chauffeur 2 as ride sharing, child 2 is escorted in bundle 2 by chauffeur 1 as pure escort and child 3 is escorted in bundle 3 by chauffeur 1 as pure escort" +98,1,2,3,3,2,4,1,2,3,0,1,1,1,"child 1 is escorted in bundle 1 by chauffeur 2 as ride sharing, child 2 is escorted in bundle 2 by chauffeur 1 as pure escort and child 3 is escorted in bundle 3 by chauffeur 2 as pure escort" +99,1,2,3,3,4,1,1,2,3,1,0,1,1,"child 1 is escorted in bundle 1 by chauffeur 2 as ride sharing, child 2 is escorted in bundle 2 by chauffeur 2 as pure escort and child 3 is escorted in bundle 3 by chauffeur 1 as ride sharing" +100,1,2,3,3,4,2,1,2,3,0,1,1,1,"child 1 is escorted in bundle 1 by chauffeur 2 as ride sharing, child 2 is escorted in bundle 2 by chauffeur 2 as pure escort and child 3 is escorted in bundle 3 by chauffeur 1 as pure escort" +101,1,2,3,3,4,4,0,3,3,0,0,1,2,"child 1 is escorted in bundle 1 by chauffeur 2 as ride sharing, child 2 is escorted in bundle 2 by chauffeur 2 as pure escort and child 3 is escorted in bundle 3 by chauffeur 2 as pure escort" +102,1,2,3,4,1,2,2,1,3,1,1,0,1,"child 1 is escorted in bundle 1 by chauffeur 2 as pure escort, child 2 is escorted in bundle 2 by chauffeur 1 as ride sharing, and child 3 is escorted in bundle 3 by chauffeur 1 as pure escort" +103,1,2,3,4,1,3,1,2,3,1,0,1,1,"child 1 is escorted in bundle 1 by chauffeur 2 as pure escort, child 2 is escorted in bundle 2 by chauffeur 1 as ride sharing, and child 3 is escorted in bundle 3 by chauffeur 2 as ride sharing" +104,1,2,3,4,1,4,1,2,3,1,0,0,2,"child 1 is escorted in bundle 1 by chauffeur 2 as pure escort, child 2 is escorted in bundle 2 by chauffeur 1 as ride sharing, and child 3 is escorted in bundle 3 by chauffeur 2 as pure escort" +105,1,2,3,4,2,1,2,1,3,1,1,0,1,"child 1 is escorted in bundle 1 by chauffeur 2 as pure escort, child 2 is escorted in bundle 2 by chauffeur 1 as pure escort and child 3 is escorted in bundle 3 by chauffeur 1 as ride sharing" +106,1,2,3,4,2,2,2,1,3,0,2,0,1,"child 1 is escorted in bundle 1 by chauffeur 2 as pure escort, child 2 is escorted in bundle 2 by chauffeur 1 as pure escort and child 3 is escorted in bundle 3 by chauffeur 1 as pure escort" +107,1,2,3,4,2,3,1,2,3,0,1,1,1,"child 1 is escorted in bundle 1 by chauffeur 2 as pure escort, child 2 is escorted in bundle 2 by chauffeur 1 as pure escort and child 3 is escorted in bundle 3 by chauffeur 2 as ride sharing" +108,1,2,3,4,2,4,1,2,3,0,1,0,2,"child 1 is escorted in bundle 1 by chauffeur 2 as pure escort, child 2 is escorted in bundle 2 by chauffeur 1 as pure escort and child 3 is escorted in bundle 3 by chauffeur 2 as pure escort" +109,1,2,3,4,3,1,1,2,3,1,0,1,1,"child 1 is escorted in bundle 1 by chauffeur 2 as pure escort, child 2 is escorted in bundle 2 by chauffeur 2 as ride sharing, and child 3 is escorted in bundle 3 by chauffeur 1 as ride sharing" +110,1,2,3,4,3,2,1,2,3,0,1,1,1,"child 1 is escorted in bundle 1 by chauffeur 2 as pure escort, child 2 is escorted in bundle 2 by chauffeur 2 as ride sharing, and child 3 is escorted in bundle 3 by chauffeur 1 as pure escort" +111,1,2,3,4,3,4,0,3,3,0,0,1,2,"child 1 is escorted in bundle 1 by chauffeur 2 as pure escort, child 2 is escorted in bundle 2 by chauffeur 2 as ride sharing, and child 3 is escorted in bundle 3 by chauffeur 2 as pure escort" +112,1,2,3,4,4,1,1,2,3,1,0,0,2,"child 1 is escorted in bundle 1 by chauffeur 2 as pure escort, child 2 is escorted in bundle 2 by chauffeur 2 as pure escort and child 3 is escorted in bundle 3 by chauffeur 1 as ride sharing" +113,1,2,3,4,4,2,1,2,3,0,1,0,2,"child 1 is escorted in bundle 1 by chauffeur 2 as pure escort, child 2 is escorted in bundle 2 by chauffeur 2 as pure escort and child 3 is escorted in bundle 3 by chauffeur 1 as pure escort" +114,1,2,3,4,4,3,0,3,3,0,0,1,2,"child 1 is escorted in bundle 1 by chauffeur 2 as pure escort, child 2 is escorted in bundle 2 by chauffeur 2 as pure escort and child 3 is escorted in bundle 3 by chauffeur 2 as ride sharing" +115,1,2,3,4,4,4,0,3,3,0,0,0,3,"child 1 is escorted in bundle 1 by chauffeur 2 as pure escort, child 2 is escorted in bundle 2 by chauffeur 2 as pure escort and child 3 is escorted in bundle 3 by chauffeur 2 as pure escort" +116,1,1,2,1,1,2,2,0,2,1,1,0,0,child 1 and 2 are escorted in bundle 1 by chauffeur 1 as ride sharing and child 3 is escorted in bundle 2 by chauffeur 1 as pure escort +117,1,1,2,1,1,3,1,1,2,1,0,1,0,child 1 and 2 are escorted in bundle 1 by chauffeur 1 as ride sharing and child 3 is escorted in bundle 2 by chauffeur 2 as ride sharing +118,1,1,2,1,1,4,1,1,2,1,0,0,1,child 1 and 2 are escorted in bundle 1 by chauffeur 1 as ride sharing and child 3 is escorted in bundle 2 by chauffeur 2 as pure escort +119,1,1,2,2,2,1,2,0,2,1,1,0,0,child 1 and 2 are escorted in bundle 1 by chauffeur 1 as pure escort and child 3 is escorted in bundle 2 by chauffeur 1 as ride sharing +120,1,1,2,2,2,2,2,0,2,0,2,0,0,child 1 and 2 are escorted in bundle 1 by chauffeur 1 as pure escort and child 3 is escorted in bundle 2 by chauffeur 1 as pure escort +121,1,1,2,2,2,3,1,1,2,0,1,1,0,child 1 and 2 are escorted in bundle 1 by chauffeur 1 as pure escort and child 3 is escorted in bundle 2 by chauffeur 2 as ride sharing +122,1,1,2,2,2,4,1,1,2,0,1,0,1,child 1 and 2 are escorted in bundle 1 by chauffeur 1 as pure escort and child 3 is escorted in bundle 2 by chauffeur 2 as pure escort +123,1,1,2,3,3,1,1,1,2,1,0,1,0,child 1 and 2 are escorted in bundle 1 by chauffeur 2 as ride sharing and child 3 is escorted in bundle 2 by chauffeur 1 as ride sharing +124,1,1,2,3,3,2,1,1,2,0,1,1,0,child 1 and 2 are escorted in bundle 1 by chauffeur 2 as ride sharing and child 3 is escorted in bundle 2 by chauffeur 1 as pure escort +125,1,1,2,3,3,4,0,2,2,0,0,1,1,child 1 and 2 are escorted in bundle 1 by chauffeur 2 as ride sharing and child 3 is escorted in bundle 2 by chauffeur 2 as pure escort +126,1,1,2,4,4,1,1,1,2,1,0,0,1,child 1 and 2 are escorted in bundle 1 by chauffeur 2 as pure escort and child 3 is escorted in bundle 2 by chauffeur 1 as ride sharing +127,1,1,2,4,4,2,1,1,2,0,1,0,1,child 1 and 2 are escorted in bundle 1 by chauffeur 2 as pure escort and child 3 is escorted in bundle 2 by chauffeur 1 as pure escort +128,1,1,2,4,4,3,0,2,2,0,0,1,1,child 1 and 2 are escorted in bundle 1 by chauffeur 2 as pure escort and child 3 is escorted in bundle 2 by chauffeur 2 as ride sharing +129,1,1,2,4,4,4,0,2,2,0,0,0,2,child 1 and 2 are escorted in bundle 1 by chauffeur 2 as pure escort and child 3 is escorted in bundle 2 by chauffeur 2 as pure escort +130,1,2,1,1,2,1,2,0,2,1,1,0,0,child 1 and 3 are escorted in bundle 1 by chauffeur 1 as ride sharing and child 2 is escorted in bundle 2 by chauffeur 1 as pure escort +131,1,2,1,1,3,1,1,1,2,1,0,1,0,child 1 and 3 are escorted in bundle 1 by chauffeur 1 as ride sharing and child 2 is escorted in bundle 2 by chauffeur 2 as ride sharing +132,1,2,1,1,4,1,1,1,2,1,0,0,1,child 1 and 3 are escorted in bundle 1 by chauffeur 1 as ride sharing and child 2 is escorted in bundle 2 by chauffeur 2 as pure escort +133,1,2,1,2,1,2,2,0,2,1,1,0,0,child 1 and 3 are escorted in bundle 1 by chauffeur 1 as pure escort and child 2 is escorted in bundle 2 by chauffeur 1 as ride sharing +134,1,2,1,2,2,2,2,0,2,0,2,0,0,child 1 and 3 are escorted in bundle 1 by chauffeur 1 as pure escort and child 2 is escorted in bundle 2 by chauffeur 1 as pure escort +135,1,2,1,2,3,2,1,1,2,0,1,1,0,child 1 and 3 are escorted in bundle 1 by chauffeur 1 as pure escort and child 2 is escorted in bundle 2 by chauffeur 2 as ride sharing +136,1,2,1,2,4,2,1,1,2,0,1,0,1,child 1 and 3 are escorted in bundle 1 by chauffeur 1 as pure escort and child 2 is escorted in bundle 2 by chauffeur 2 as pure escort +137,1,2,1,3,1,3,1,1,2,1,0,1,0,child 1 and 3 are escorted in bundle 1 by chauffeur 2 as ride sharing and child 2 is escorted in bundle 2 by chauffeur 1 as ride sharing +138,1,2,1,3,2,3,1,1,2,0,1,1,0,child 1 and 3 are escorted in bundle 1 by chauffeur 2 as ride sharing and child 2 is escorted in bundle 2 by chauffeur 1 as pure escort +139,1,2,1,3,4,3,0,2,2,0,0,1,1,child 1 and 3 are escorted in bundle 1 by chauffeur 2 as ride sharing and child 2 is escorted in bundle 2 by chauffeur 2 as pure escort +140,1,2,1,4,1,4,1,1,2,1,0,0,1,child 1 and 3 are escorted in bundle 1 by chauffeur 2 as pure escort and child 2 is escorted in bundle 2 by chauffeur 1 as ride sharing +141,1,2,1,4,2,4,1,1,2,0,1,0,1,child 1 and 3 are escorted in bundle 1 by chauffeur 2 as pure escort and child 2 is escorted in bundle 2 by chauffeur 1 as pure escort +142,1,2,1,4,3,4,0,2,2,0,0,1,1,child 1 and 3 are escorted in bundle 1 by chauffeur 2 as pure escort and child 2 is escorted in bundle 2 by chauffeur 2 as ride sharing +143,1,2,1,4,4,4,0,2,2,0,0,0,2,child 1 and 3 are escorted in bundle 1 by chauffeur 2 as pure escort and child 2 is escorted in bundle 2 by chauffeur 2 as pure escort +144,1,2,2,1,2,2,2,0,2,1,1,0,0,child 2 and 3 are escorted in bundle 2 by chauffeur 1 as ride sharing and child 1 is escorted in bundle 1 by chauffeur 1 as pure escort +145,1,2,2,1,3,3,1,1,2,1,0,1,0,child 2 and 3 are escorted in bundle 2 by chauffeur 1 as ride sharing and child 1 is escorted in bundle 1 by chauffeur 2 as ride sharing +146,1,2,2,1,4,4,1,1,2,1,0,0,1,child 2 and 3 are escorted in bundle 2 by chauffeur 1 as ride sharing and child 1 is escorted in bundle 1 by chauffeur 2 as pure escort +147,1,2,2,2,1,1,2,0,2,1,1,0,0,child 2 and 3 are escorted in bundle 2 by chauffeur 1 as pure escort and child 1 is escorted in bundle 1 by chauffeur 1 as ride sharing +148,1,2,2,2,2,2,2,0,2,0,2,0,0,child 2 and 3 are escorted in bundle 2 by chauffeur 1 as pure escort and child 1 is escorted in bundle 1 by chauffeur 1 as pure escort +149,1,2,2,2,3,3,1,1,2,0,1,1,0,child 2 and 3 are escorted in bundle 2 by chauffeur 1 as pure escort and child 1 is escorted in bundle 1 by chauffeur 2 as ride sharing +150,1,2,2,2,4,4,1,1,2,0,1,0,1,child 2 and 3 are escorted in bundle 2 by chauffeur 1 as pure escort and child 1 is escorted in bundle 1 by chauffeur 2 as pure escort +151,1,2,2,3,1,1,1,1,2,1,0,1,0,child 2 and 3 are escorted in bundle 2 by chauffeur 2 as ride sharing and child 1 is escorted in bundle 1 by chauffeur 1 as ride sharing +152,1,2,2,3,2,2,1,1,2,0,1,1,0,child 2 and 3 are escorted in bundle 2 by chauffeur 2 as ride sharing and child 1 is escorted in bundle 1 by chauffeur 1 as pure escort +153,1,2,2,3,4,4,0,2,2,0,0,1,1,child 2 and 3 are escorted in bundle 2 by chauffeur 2 as ride sharing and child 1 is escorted in bundle 1 by chauffeur 2 as pure escort +154,1,2,2,4,1,1,1,1,2,1,0,0,1,child 2 and 3 are escorted in bundle 2 by chauffeur 2 as pure escort and child 1 is escorted in bundle 1 by chauffeur 1 as ride sharing +155,1,2,2,4,2,2,1,1,2,0,1,0,1,child 2 and 3 are escorted in bundle 2 by chauffeur 2 as pure escort and child 1 is escorted in bundle 1 by chauffeur 1 as pure escort +156,1,2,2,4,3,3,0,2,2,0,0,1,1,child 2 and 3 are escorted in bundle 2 by chauffeur 2 as pure escort and child 1 is escorted in bundle 1 by chauffeur 2 as ride sharing +157,1,2,2,4,4,4,0,2,2,0,0,0,2,child 2 and 3 are escorted in bundle 2 by chauffeur 2 as pure escort and child 1 is escorted in bundle 1 by chauffeur 2 as pure escort diff --git a/resident/configs/school_escorting_coefficients_inbound.csv b/resident/configs/school_escorting_coefficients_inbound.csv new file mode 100644 index 0000000..d6bc194 --- /dev/null +++ b/resident/configs/school_escorting_coefficients_inbound.csv @@ -0,0 +1,41 @@ +coefficient_name,value,constrain +coef_unavail,-999,T +coef_child_age_16p_noes,0.17488,F +coef_child_age_6_to_15_noes,-0.41751,F +coef_child_age_u5_noes,-1.36718,F +coef_ln_dist_from_school_noes,-0.01787,F +coef_ln_dist_from_school_u6_noes,-0.23304,F +coef_ln_dist_from_school_6to9_noes,-0.07286,F +coef_child_age_16p_rs,1.97184 +coef_child_age_10to15_rs,1.73544 +coef_child_age_6to9_rs,1.73544 +coef_child_age_u6_rs,1.34996 +coef_hh_inc_u25k_noes,0.0,F +coef_hh_inc_25to50k_noes,0.0,F +coef_zero_auto_hh_noes,0.13165,F +coef_cars_lt_workers_rs,-3.35586,F +coef_cars_lt_workers_pe,-1.59062,F +coef_chauf_female_rs,-0.44827,F +coef_chauf_male_rs,-0.90832,F +coef_chauf_female_pe,-0.68399,F +coef_chauf_male_pe,-1.01783,F +coef_chauf_pt_worker_rs,0.51244,F +coef_chauf_pt_worker_pe,0.23496,F +coef_chauf_non_worker_pe,0.69245,F +coef_chauf_univ_stud_re,0.47395,F +coef_chauf_age_u35_pe,0.00000,F +coef_chauf_time_to_work_or_univ_rs,-0.01974,F +coef_chauf_walk_dist_to_work_or_univ_rs,-0.73155,F +coef_chauf_walk_dist_to_work_or_univ_pe,0.00000,F +coef_abs_dev_distance,-0.08011,F +coef_rel_dev_distance,0.0,F +coef_same_taz_escort,1.44053,F +coef_same_taz_no_escort,1.96760,F +coef_no_escort_outbound,2.04553,F +coef_outbound_rs,0.0,F +coef_same_chauf,1.14533,F +coef_calib_child_age_u6_rs,8.75011,F +coef_calib_child_age_16p_rs,-0.20,F +coef_calib_child_age_6to15_noes,-3.46362,F +coef_calib_child_age_u6_noes,-0.36565,F +coef_calib_child_age_6to15_rs,-1.45344,F diff --git a/resident/configs/school_escorting_coefficients_outbound.csv b/resident/configs/school_escorting_coefficients_outbound.csv new file mode 100644 index 0000000..14f1e7d --- /dev/null +++ b/resident/configs/school_escorting_coefficients_outbound.csv @@ -0,0 +1,40 @@ +coefficient_name,value,constrain +coef_unavail,-999,T +coef_child_age_16p_noes,1.07391,F +coef_child_age_10_to_15_noes,0.46127,F +coef_child_age_u9_noes,0.13590,F +coef_ln_dist_to_school_noes,-0.33583,F +coef_ln_dist_to_school_u6_noes,-1.00920,F +coef_ln_dist_to_school_6to9_noes,-0.11156,F +coef_child_age_16p_rs,0.34244,F +coef_child_age_10to15_rs,.27494,F +coef_child_age_6to9_rs,0.20757,F +coef_child_age_u6_rs,0.33051,F +coef_child_dist_pe,-0.04593,F +coef_hh_inc_u25k_noes,0.18901,F +coef_hh_inc_25to50k_noes,0.12172,F +coef_zero_auto_hh_noes,9.0,F +coef_cars_lt_workers_rs,-0.88274,F +coef_cars_lt_workers_pe,-0.55291,F +coef_chauf_female_rs,-0.27828,F +coef_chauf_male_rs,-0.67320,F +coef_chauf_female_pe,-0.84859,F +coef_chauf_male_pe,-0.80965,F +coef_chauf_pt_worker_rs,-0.11422,F +coef_chauf_pt_worker_pe,0.51792,F +coef_chauf_non_worker_pe,0.51577,F +coef_chauf_univ_stud_re,0.0,F +coef_chauf_age_u35_pe,-0.33715,F +coef_chauf_time_to_work_or_univ_rs,0.0,F +coef_chauf_walk_dist_to_work_or_univ_rs,0.77326,F +coef_chauf_walk_dist_to_work_or_univ_pe,0.0,F +coef_abs_dev_distance,-0.07136,F +coef_rel_dev_distance,-0.09951,F +coef_same_taz_escort,2.77591,F +coef_same_taz_no_escort,2.62969,F +coef_same_taz_no_escort_23,2.21201,F +coef_calib_child_age_u6_rs,8.55400,F +coef_calib_child_age_16p_rs,-0.2000,F +coef_calib_child_age_6to15_noes,-4.53067,F +coef_calib_child_age_6to15_rs,-2.41923,F +coef_calib_child_age_u6_noes,-0.67902,F diff --git a/resident/configs/school_escorting_coefficients_outbound_cond.csv b/resident/configs/school_escorting_coefficients_outbound_cond.csv new file mode 100644 index 0000000..079d1e8 --- /dev/null +++ b/resident/configs/school_escorting_coefficients_outbound_cond.csv @@ -0,0 +1,41 @@ +coefficient_name,value,constrain +coef_unavail,-999,T +coef_child_age_16p_noes,-0.33924,F +coef_child_age_10_to_15_noes,-0.78691,F +coef_child_age_u9_noes,-0.97142,F +coef_ln_dist_to_school_noes,-0.02112,F +coef_ln_dist_to_school_u6_noes,-0.81356,F +coef_ln_dist_to_school_6to9_noes,-0.12910,F +coef_child_age_16p_rs,0.93518,F +coef_child_age_10to15_rs,0.58009,F +coef_child_age_6to9_rs,0.36698,F +coef_child_age_u6_rs,0.29043,F +coef_child_dist_pe,-0.03624,F +coef_hh_inc_u25k_noes,0.33839,F +coef_hh_inc_25to50k_noes,0.05888,F +coef_zero_auto_hh_noes,9.00000,F +coef_cars_lt_workers_rs,-0.38588,F +coef_cars_lt_workers_pe,-0.01213,F +coef_chauf_female_rs,-0.49717,F +coef_chauf_male_rs,-0.94654,F +coef_chauf_female_pe,-0.98546,F +coef_chauf_male_pe,-1.05266,F +coef_chauf_pt_worker_rs,-0.74807,F +coef_chauf_pt_worker_pe,0.31729,F +coef_chauf_non_worker_pe,0.19211,F +coef_chauf_univ_stud_re,0.0,F +coef_chauf_age_u35_pe,-0.41194,F +coef_chauf_time_to_work_or_univ_rs,0.0,F +coef_chauf_walk_dist_to_work_or_univ_rs,0.38819,F +coef_chauf_walk_dist_to_work_or_univ_pe,0.0,F +coef_abs_dev_distance,-0.04497,F +coef_rel_dev_distance,-0.09067,F +coef_same_taz_escort,2.56855,F +coef_same_taz_no_escort,2.21201,F +coef_no_escort_inbound,1.72902,F +coef_same_chauf,0.99276,F +coef_calib_child_age_u6_rs,8.55400,F +coef_calib_child_age_16p_rs,-0.2000,F +coef_calib_child_age_6to15_noes,-4.53067,F +coef_calib_child_age_6to15_rs,-2.41923,F +coef_calib_child_age_u6_noes,-0.67902,F diff --git a/resident/configs/school_escorting_inbound.csv b/resident/configs/school_escorting_inbound.csv new file mode 100644 index 0000000..2912752 --- /dev/null +++ b/resident/configs/school_escorting_inbound.csv @@ -0,0 +1,175 @@ +Label,Description,Expression,Coefficient +# ,Availability Conditions,, +util_one_child_to_school,Availability based on number of eligible children,((bundle2 + bundle3) > 0) & (num_children_going_to_school==1),coef_unavail +util_two_children_to_school,Availability based on number of eligible children,(bundle3 > 0) & (num_children_going_to_school == 2),coef_unavail +util_one_potential_chauffeur,Availability based on number of eligible chauffeurs,((bundle1 + bundle2 + bundle3) > 0) & (num_potential_chauffeurs == 0),coef_unavail +util_two_potential_chauffeurs,Availability based on number of eligible chauffeurs,((chauf1 > 2) | (chauf2 > 2) | (chauf3 > 2)) & (num_potential_chauffeurs == 1),coef_unavail +util_avail_rs_cdap_child1_chauf1,Availability for RideSharing by daily pattern - Child 1 and Chauffeur 1,(bundle1 > 0) & (chauf1 == 1) & (cdap_chauf1 != 'M'),coef_unavail +util_avail_rs_cdap_child1_chauf2,Availability for RideSharing by daily pattern - Child 1 and Chauffeur 2,(bundle1 > 0) & (chauf1 == 3) & (cdap_chauf2 != 'M'),coef_unavail +util_avail_rs_cdap_child2_chauf1,Availability for RideSharing by daily pattern - Child 2 and Chauffeur 1,(bundle2 > 0) & (chauf2 == 1) & (cdap_chauf1 != 'M'),coef_unavail +util_avail_rs_cdap_child2_chauf2,Availability for RideSharing by daily pattern - Child 2 and Chauffeur 2,(bundle2 > 0) & (chauf2 == 3) & (cdap_chauf2 != 'M'),coef_unavail +util_avail_rs_cdap_child3_chauf1,Availability for RideSharing by daily pattern - Child 3 and Chauffeur 1,(bundle3 > 0) & (chauf3 == 1) & (cdap_chauf1 != 'M'),coef_unavail +util_avail_rs_cdap_child3_chauf2,Availability for RideSharing by daily pattern - Child 3 and Chauffeur 2,(bundle3 > 0) & (chauf3 == 3) & (cdap_chauf2 != 'M'),coef_unavail +util_avail_rs_sync_child1_chauf1,Availability for RideSharing by synchronization - Child 1 and Chauffeur 1,@(df.bundle1 > 0) & (df.chauf1 == 1) & (np.abs(df.pref_depart_time_chauf1 + (df.time_mand1_to_school1 / mins_per_time_bin) - df.pref_depart_time_school1) > max_bin_difference_between_departure_times),coef_unavail +util_avail_rs_sync_child1_chauf2,Availability for RideSharing by synchronization - Child 1 and Chauffeur 2,@(df.bundle1 > 0) & (df.chauf1 == 3) & (np.abs(df.pref_depart_time_chauf2 + (df.time_mand2_to_school1 / mins_per_time_bin) - df.pref_depart_time_school1) > max_bin_difference_between_departure_times),coef_unavail +util_avail_rs_sync_child2_chauf1,Availability for RideSharing by synchronization - Child 2 and Chauffeur 1,@(df.bundle2 > 0) & (df.chauf2 == 1) & (np.abs(df.pref_depart_time_chauf1 + (df.time_mand1_to_school2 / mins_per_time_bin) - df.pref_depart_time_school2) > max_bin_difference_between_departure_times),coef_unavail +util_avail_rs_sync_child2_chauf2,Availability for RideSharing by synchronization - Child 2 and Chauffeur 2,@(df.bundle2 > 0) & (df.chauf2 == 3) & (np.abs(df.pref_depart_time_chauf2 + (df.time_mand2_to_school2 / mins_per_time_bin) - df.pref_depart_time_school2) > max_bin_difference_between_departure_times),coef_unavail +util_avail_rs_sync_child3_chauf1,Availability for RideSharing by synchronization - Child 3 and Chauffeur 1,@(df.bundle3 > 0) & (df.chauf3 == 1) & (np.abs(df.pref_depart_time_chauf1 + (df.time_mand1_to_school3 / mins_per_time_bin) - df.pref_depart_time_school3) > max_bin_difference_between_departure_times),coef_unavail +util_avail_rs_sync_child3_chauf2,Availability for RideSharing by synchronization - Child 3 and Chauffeur 2,@(df.bundle3 > 0) & (df.chauf3 == 3) & (np.abs(df.pref_depart_time_chauf2 + (df.time_mand2_to_school3 / mins_per_time_bin) - df.pref_depart_time_school3) > max_bin_difference_between_departure_times),coef_unavail +util_avail_pe_cdap_child1_chauf1,Availability for Pure-Escorting by daily pattern - Child 1 and Chauffeur 1,(bundle1 > 0) & (chauf1 == 2) & (ptype_chauf1 < 6) & (cdap_chauf1 == 'H'),coef_unavail +util_avail_pe_cdap_child1_chauf2,Availability for Pure-Escorting by daily pattern - Child 1 and Chauffeur 2,(bundle1 > 0) & (chauf1 == 4) & (ptype_chauf2 < 6) & (cdap_chauf2 == 'H'),coef_unavail +util_avail_pe_cdap_child2_chauf1,Availability for Pure-Escorting by daily pattern - Child 2 and Chauffeur 1,(bundle2 > 0) & (chauf2 == 2) & (ptype_chauf1 < 6) & (cdap_chauf1 == 'H'),coef_unavail +util_avail_pe_cdap_child2_chauf2,Availability for Pure-Escorting by daily pattern - Child 2 and Chauffeur 2,(bundle2 > 0) & (chauf2 == 4) & (ptype_chauf2 < 6) & (cdap_chauf2 == 'H'),coef_unavail +util_avail_pe_cdap_child3_chauf1,Availability for Pure-Escorting by daily pattern - Child 3 and Chauffeur 1,(bundle3 > 0) & (chauf3 == 2) & (ptype_chauf1 < 6) & (cdap_chauf1 == 'H'),coef_unavail +util_avail_pe_cdap_child3_chauf2,Availability for Pure-Escorting by daily pattern - Child 3 and Chauffeur 2,(bundle3 > 0) & (chauf3 == 4) & (ptype_chauf2 < 6) & (cdap_chauf2 == 'H'),coef_unavail +util_avail_pe_sync_child1_chauf1,Availability for Pure-Escorting by synchronization - Child 1 and Chauffeur 1,@(df.bundle1 > 0) & (df.chauf1 == 2) & (df.cdap_chauf1 == 'H') & (((df.pref_depart_time_chauf1 + (df.time_mand_to_home1 / mins_per_time_bin)) - (df.pref_depart_time_school1 - (df.time_home_to_school1 / mins_per_time_bin))) > max_bin_difference_between_departure_times),coef_unavail +util_avail_pe_sync_child1_chauf2,Availability for Pure-Escorting by synchronization - Child 1 and Chauffeur 2,@(df.bundle1 > 0) & (df.chauf1 == 4) & (df.cdap_chauf2 == 'H') & (((df.pref_depart_time_chauf2 + (df.time_mand_to_home2 / mins_per_time_bin)) - (df.pref_depart_time_school1 - (df.time_home_to_school1 / mins_per_time_bin))) > max_bin_difference_between_departure_times),coef_unavail +util_avail_pe_sync_child2_chauf1,Availability for Pure-Escorting by synchronization - Child 2 and Chauffeur 1,@(df.bundle2 > 0) & (df.chauf2 == 2) & (df.cdap_chauf1 == 'H') & (((df.pref_depart_time_chauf1 + (df.time_mand_to_home1 / mins_per_time_bin)) - (df.pref_depart_time_school2 - (df.time_home_to_school2 / mins_per_time_bin))) > max_bin_difference_between_departure_times),coef_unavail +util_avail_pe_sync_child2_chauf2,Availability for Pure-Escorting by synchronization - Child 2 and Chauffeur 2,@(df.bundle2 > 0) & (df.chauf2 == 4) & (df.cdap_chauf2 == 'H') & (((df.pref_depart_time_chauf2 + (df.time_mand_to_home2 / mins_per_time_bin)) - (df.pref_depart_time_school2 - (df.time_home_to_school2 / mins_per_time_bin))) > max_bin_difference_between_departure_times),coef_unavail +util_avail_pe_sync_child3_chauf1,Availability for Pure-Escorting by synchronization - Child 3 and Chauffeur 1,@(df.bundle3 > 0) & (df.chauf3 == 2) & (df.cdap_chauf1 == 'H') & (((df.pref_depart_time_chauf1 + (df.time_mand_to_home1 / mins_per_time_bin)) - (df.pref_depart_time_school3 - (df.time_home_to_school3 / mins_per_time_bin))) > max_bin_difference_between_departure_times),coef_unavail +util_avail_pe_sync_child3_chauf2,Availability for Pure-Escorting by synchronization - Child 3 and Chauffeur 2,@(df.bundle3 > 0) & (df.chauf3 == 4) & (df.cdap_chauf2 == 'H') & (((df.pref_depart_time_chauf2 + (df.time_mand_to_home2 / mins_per_time_bin)) - (df.pref_depart_time_school3 - (df.time_home_to_school3 / mins_per_time_bin))) > max_bin_difference_between_departure_times),coef_unavail +util_avail_bundle_child_1and2,Availability for bundling child 1 and child 2,@(df.bundle1 == df.bundle2) & (df.bundle1 > 0) & (np.abs(df.pref_depart_time_school1 - df.pref_depart_time_school2) > max_bin_difference_between_departure_times),coef_unavail +util_avail_bundle_child_1and3,Availability for bundling child 1 and child 3,@(df.bundle1 == df.bundle3) & (df.bundle1 > 0) & (np.abs(df.pref_depart_time_school1 - df.pref_depart_time_school3) > max_bin_difference_between_departure_times),coef_unavail +util_avail_bundle_child_2and3,Availability for bundling child 2 and child 3,@(df.bundle2 == df.bundle3) & (df.bundle2 > 0) & (np.abs(df.pref_depart_time_school2 - df.pref_depart_time_school3) > max_bin_difference_between_departure_times),coef_unavail +util_avail_mult_bundles_outbound1,Availability Chauffeur 1 - Expected arrival from previous pure escort must be before departure for subsequent escort,((nrs1 + npe1) > 1) & ~avail_multiple_bundles,coef_unavail +util_avail_mult_bundles_outbound2,Availability Chauffeur 2 - Expected arrival from previous pure escort must be before departure for subsequent escort,((nrs2 + npe2) > 1) & ~avail_multiple_bundles,coef_unavail +util_time_span11,Chauffeur 1 and Child 1 - time span for this alternative must not overlap time span reserved from outbound conditional,(bundle1 > 0) & ((chauf1 == 1) | (chauf1 == 2)) & (return_bin_outbound_school_escorting1 > pref_depart_time_school1),coef_unavail +util_time_span12,Chauffeur 1 and Child 2 - time span for this alternative must not overlap time span reserved from outbound conditional,(bundle1 > 0) & ((chauf2 == 1) | (chauf2 == 2)) & (return_bin_outbound_school_escorting1 > pref_depart_time_school2),coef_unavail +util_time_span13,Chauffeur 1 and Child 3 - time span for this alternative must not overlap time span reserved from outbound conditional,(bundle1 > 0) & ((chauf3 == 1) | (chauf3 == 2)) & (return_bin_outbound_school_escorting1 > pref_depart_time_school3),coef_unavail +util_time_span21,Chauffeur 2 and Child 1 - time span for this alternative must not overlap time span reserved from outbound conditional,(bundle1 > 0) & ((chauf1 == 3) | (chauf1 == 4)) & (return_bin_outbound_school_escorting2 > pref_depart_time_school1),coef_unavail +util_time_span22,Chauffeur 2 and Child 2 - time span for this alternative must not overlap time span reserved from outbound conditional,(bundle1 > 0) & ((chauf2 == 3) | (chauf2 == 4)) & (return_bin_outbound_school_escorting2 > pref_depart_time_school2),coef_unavail +util_time_span23,Chauffeur 2 and Child 3 - time span for this alternative must not overlap time span reserved from outbound conditional,(bundle1 > 0) & ((chauf3 == 3) | (chauf3 == 4)) & (return_bin_outbound_school_escorting2 > pref_depart_time_school3),coef_unavail +util_avail_pe_during_mand_child1_chauf1,Availability pure escort tour must take place before mandatory tour - Child 1 and Chauffeur 1,@(df.bundle1 > 0) & (df.chauf1 == 2) & (df.cdap_chauf1 == 'M') & ((df.pref_depart_time_chauf1 + max_bin_difference_between_departure_times) >= df.pref_depart_time_school1),coef_unavail +util_avail_pe_during_mand_child1_chauf2,Availability pure escort tour must take place before mandatory tour - Child 1 and Chauffeur 2,@(df.bundle1 > 0) & (df.chauf1 == 4) & (df.cdap_chauf2 == 'M') & ((df.pref_depart_time_chauf2 + max_bin_difference_between_departure_times) >= df.pref_depart_time_school1),coef_unavail +util_avail_pe_during_mand_child2_chauf1,Availability pure escort tour must take place before mandatory tour - Child 2 and Chauffeur 1,@(df.bundle2 > 0) & (df.chauf2 == 2) & (df.cdap_chauf1 == 'M') & ((df.pref_depart_time_chauf1 + max_bin_difference_between_departure_times) >= df.pref_depart_time_school2),coef_unavail +util_avail_pe_during_mand_child2_chauf2,Availability pure escort tour must take place before mandatory tour - Child 2 and Chauffeur 2,@(df.bundle2 > 0) & (df.chauf2 == 4) & (df.cdap_chauf2 == 'M') & ((df.pref_depart_time_chauf2 + max_bin_difference_between_departure_times) >= df.pref_depart_time_school2),coef_unavail +util_avail_pe_during_mand_child3_chauf1,Availability pure escort tour must take place before mandatory tour - Child 3 and Chauffeur 1,@(df.bundle3 > 0) & (df.chauf3 == 2) & (df.cdap_chauf1 == 'M') & ((df.pref_depart_time_chauf1 + max_bin_difference_between_departure_times) >= df.pref_depart_time_school3),coef_unavail +util_avail_pe_during_mand_child3_chauf2,Availability pure escort tour must take place before mandatory tour - Child 3 and Chauffeur 2,@(df.bundle3 > 0) & (df.chauf3 == 4) & (df.cdap_chauf2 == 'M') & ((df.pref_depart_time_chauf2 + max_bin_difference_between_departure_times) >= df.pref_depart_time_school3),coef_unavail +# ,No escorting,, +util_child1_age_16p_noes,Child 1 age 16 years or older - No escort,(bundle1 == 0) & (child_id1 > 0) & (age_child1 > 15),coef_child_age_16p_noes +util_child2_age_16p_noes,Child 2 age 16 years or older - No escort,(bundle2 == 0) & (child_id2 > 0) & (age_child2 > 15),coef_child_age_16p_noes +util_child3_age_16p_noes,Child 3 age 16 years or older - No escort,(bundle3 == 0) & (child_id3 > 0) & (age_child3 > 15),coef_child_age_16p_noes +util_child1_age_6_to_15_noes,Child 1 age 6 to 15 years - No escort,(bundle1 == 0) & (child_id1 > 0) & (age_child1 > 5) & (age_child1 < 16),coef_child_age_6_to_15_noes +util_child2_age_6_to_15_noes,Child 2 age 6 to 15 years - No escort,(bundle2 == 0) & (child_id2 > 0) & (age_child2 > 5) & (age_child2 < 16),coef_child_age_6_to_15_noes +util_child3_age_6_to_15_noes,Child 3 age 6 to 15 years - No escort,(bundle3 == 0) & (child_id3 > 0) & (age_child3 > 5) & (age_child3 < 16),coef_child_age_6_to_15_noes +util_child1_age_u5_noes,Child 1 age 5 years or younger - No escort,(bundle1 == 0) & (child_id1 > 0) & (age_child1 < 6),coef_child_age_u5_noes +util_child2_age_u5_noes,Child 2 age 5 years or younger - No escort,(bundle2 == 0) & (child_id2 > 0) & (age_child2 < 6),coef_child_age_u5_noes +util_child3_age_u5_noes,Child 3 age 5 years or younger - No escort,(bundle3 == 0) & (child_id3 > 0) & (age_child3 < 6),coef_child_age_u5_noes +util_ln_dist_from_school_child1_noes,Logged distance to school for Child 1 - No escort,@(df.bundle1 == 0) * (df.child_id1 > 0) * np.log(1 + df.dist_school_to_home1),coef_ln_dist_from_school_noes +util_ln_dist_from_school_child2_noes,Logged distance to school for Child 2 - No escort,@(df.bundle2 == 0) * (df.child_id2 > 0) * np.log(1 + df.dist_school_to_home2),coef_ln_dist_from_school_noes +util_ln_dist_from_school_child3_noes,Logged distance to school for Child 3 - No escort,@(df.bundle3 == 0) * (df.child_id3 > 0) * np.log(1 + df.dist_school_to_home3),coef_ln_dist_from_school_noes +util_ln_dist_from_school_child1_u6_noes,Logged distance to school for Child 1 under 6 years old - No escort,@(df.bundle1 == 0) * (df.child_id1 > 0) * (df.age_child1 < 6) * np.log(1 + df.dist_school_to_home1),coef_ln_dist_from_school_u6_noes +util_ln_dist_from_school_child2_u6_noes,Logged distance to school for Child 2 under 6 years old - No escort,@(df.bundle2 == 0) * (df.child_id2 > 0) * (df.age_child2 < 6) * np.log(1 + df.dist_school_to_home2),coef_ln_dist_from_school_u6_noes +util_ln_dist_from_school_child3_u6_noes,Logged distance to school for Child 3 under 6 years old - No escort,@(df.bundle3 == 0) * (df.child_id3 > 0) * (df.age_child3 < 6) * np.log(1 + df.dist_school_to_home3),coef_ln_dist_from_school_u6_noes +util_ln_dist_from_school_child1_6to9_noes,Logged distance to school for Child 1 6 to 9 years old - No escort,@(df.bundle1 == 0) * (df.child_id1 > 0) * (df.age_child1 > 5) * (df.age_child1 < 10) * np.log(1 + df.dist_school_to_home1),coef_ln_dist_from_school_6to9_noes +util_ln_dist_from_school_child2_6to9_noes,Logged distance to school for Child 2 6 to 9 years old - No escort,@(df.bundle2 == 0) * (df.child_id2 > 0) * (df.age_child2 > 5) * (df.age_child2 < 10) * np.log(1 + df.dist_school_to_home2),coef_ln_dist_from_school_6to9_noes +util_ln_dist_from_school_child3_6to9_noes,Logged distance to school for Child 3 6 to 9 years old - No escort,@(df.bundle3 == 0) * (df.child_id3 > 0) * (df.age_child3 > 5) * (df.age_child3 < 10) * np.log(1 + df.dist_school_to_home3),coef_ln_dist_from_school_6to9_noes +# ,Ride Sharing,, +util_child1_age_16p_rs,Child 1 age 16 years or older - Ride Share,((chauf1 == 1) | (chauf1 == 3)) & (age_child1 > 15),coef_child_age_16p_rs +util_child2_age_16p_rs,Child 2 age 16 years or older - Ride Share,((chauf2 == 1) | (chauf2 == 3)) & (age_child2 > 15),coef_child_age_16p_rs +util_child3_age_16p_rs,Child 3 age 16 years or older - Ride Share,((chauf3 == 1) | (chauf3 == 3)) & (age_child3 > 15),coef_child_age_16p_rs +util_child1_age_10to15_rs,Child 1 age 10 to 15 years - Ride Share,((chauf1 == 1) | (chauf1 == 3)) & (age_child1 > 9) & (age_child1 < 16),coef_child_age_10to15_rs +util_child2_age_10to15_rs,Child 2 age 10 to 15 years - Ride Share,((chauf2 == 1) | (chauf2 == 3)) & (age_child2 > 9) & (age_child2 < 16),coef_child_age_10to15_rs +util_child3_age_10to15_rs,Child 3 age 10 to 15 years - Ride Share,((chauf3 == 1) | (chauf3 == 3)) & (age_child3 > 9) & (age_child3 < 16),coef_child_age_10to15_rs +util_child1_age_6to9_rs,Child 1 age 6 to 9 years - Ride Share,((chauf1 == 1) | (chauf1 == 3)) & (age_child1 > 5) & (age_child1 < 10),coef_child_age_6to9_rs +util_child2_age_6to9_rs,Child 2 age 6 to 9 years - Ride Share,((chauf2 == 1) | (chauf2 == 3)) & (age_child2 > 5) & (age_child2 < 10),coef_child_age_6to9_rs +util_child3_age_6to9_rs,Child 3 age 6 to 9 years - Ride Share,((chauf3 == 1) | (chauf3 == 3)) & (age_child3 > 5) & (age_child3 < 10),coef_child_age_6to9_rs +util_child1_age_u6_rs,Child 1 age under 6 years old - Ride Share,((chauf1 == 1) | (chauf1 == 3)) & (age_child1 < 6),coef_child_age_u6_rs +util_child2_age_u6_rs,Child 2 age under 6 years old - Ride Share,((chauf2 == 1) | (chauf2 == 3)) & (age_child2 < 6),coef_child_age_u6_rs +util_child3_age_u6_rs,Child 3 age under 6 years old - Ride Share,((chauf3 == 1) | (chauf3 == 3)) & (age_child3 < 6),coef_child_age_u6_rs +# ,Pure Escort Distance,, +util_child1_dist_pe_over30miles,Child 1 Pure escorting not allowed if over 30 miles,((chauf1 == 2) | (chauf1 == 4)) & (dist_school_to_home1 > 30),coef_unavail +util_child2_dist_pe_over30miles,Child 2 Pure escorting not allowed if over 30 miles,((chauf2 == 2) | (chauf2 == 4)) & (dist_school_to_home2 > 30),coef_unavail +util_child3_dist_pe_over30miles,Child 3 Pure escorting not allowed if over 30 miles,((chauf3 == 2) | (chauf3 == 4)) & (dist_school_to_home3 > 30),coef_unavail +# ,Household Interactions,, +util_hh_inc_u25k_noes,Household income less than 25k - No Escorting,(income <= 25000) & ((bundle1 == 0) | ((bundle2 == 0) & (num_children_going_to_school > 1)) | ((bundle3 == 0) & (num_children_going_to_school > 2))),coef_hh_inc_u25k_noes +util_hh_inc_25to50k_noes,Household income between 25 and 50k - No Escorting,((income > 25000) & (income <= 50000)) & ((bundle1 == 0) | ((bundle2 == 0) & (num_children_going_to_school > 1)) | ((bundle3 == 0) & (num_children_going_to_school > 2))),coef_hh_inc_25to50k_noes +util_zero_auto_hh_noes,Zero cars in the household - No Escorting,(auto_ownership == 0) & ((bundle1 == 0) | ((bundle2 == 0) & (num_children_going_to_school > 1)) | ((bundle3 == 0) & (num_children_going_to_school > 2))),coef_zero_auto_hh_noes +util_cars_lt_workers_rs,Cars fewer than household workers - Ride Share,(auto_ownership < num_workers) & ((chauf1 % 2 == 1) | (chauf2 % 2 == 1) | (chauf3 % 2 == 1)),coef_cars_lt_workers_rs +util_cars_lt_workers_pe,Cars fewer than household workers - Pure escort,(auto_ownership < num_workers) & ((chauf1 % 2 == 0) | (chauf2 % 2 == 0) | (chauf3 % 2 == 0)),coef_cars_lt_workers_pe +# ,Chauffer Interactions,, +util_chauf1_female_rs,Chauffeur 1 Female - Ride share,(gender_chauf1 == 2) & ((chauf1 == 1) | (chauf2 == 1) | (chauf3 == 1)),coef_chauf_female_rs +util_chauf1_male_rs,Chauffeur 1 Male - Ride share,(gender_chauf1 == 1) & ((chauf1 == 1) | (chauf2 == 1) | (chauf3 == 1)),coef_chauf_male_rs +util_chauf1_female_pe,Chauffeur 1 Female - Pure Escort,(gender_chauf1 == 2) & ((chauf1 == 2) | (chauf2 == 2) | (chauf3 == 2)),coef_chauf_female_pe +util_chauf1_male_pe,Chauffeur 1 Male - Pure Escort,(gender_chauf1 == 1) & ((chauf1 == 2) | (chauf2 == 2) | (chauf3 == 2)),coef_chauf_male_pe +util_chauf2_female_rs,Chauffeur 2 Female - Ride share,(gender_chauf2 == 2) & ((chauf1 == 3) | (chauf2 == 3) | (chauf3 == 3)),coef_chauf_female_rs +util_chauf2_male_rs,Chauffeur 2 Male - Ride share,(gender_chauf2 == 1) & ((chauf1 == 3) | (chauf2 == 3) | (chauf3 == 3)),coef_chauf_male_rs +util_chauf2_female_pe,Chauffeur 2 Female - Pure Escort,(gender_chauf2 == 2) & ((chauf1 == 4) | (chauf2 == 4) | (chauf3 == 4)),coef_chauf_female_pe +util_chauf2_male_pe,Chauffeur 2 Male - Pure Escort,(gender_chauf2 == 1) & ((chauf1 == 4) | (chauf2 == 4) | (chauf3 == 4)),coef_chauf_male_pe +util_chauf1_pt_worker_rs,Chauffer 1 part time worker - Ride share,(ptype_chauf1 == 2) & ((chauf1 == 1) | (chauf2 == 1) | (chauf3 == 1)),coef_chauf_pt_worker_rs +util_chauf1_pt_worker_pe,Chauffer 1 part time worker - Pure escort,(ptype_chauf1 == 2) & ((chauf1 == 2) | (chauf2 == 2) | (chauf3 == 2)),coef_chauf_pt_worker_pe +util_chauf2_pt_worker_rs,Chauffer 2 part time worker - Ride share,(ptype_chauf2 == 2) & ((chauf1 == 3) | (chauf2 == 3) | (chauf3 == 3)),coef_chauf_pt_worker_rs +util_chauf2_pt_worker_pe,Chauffer 2 part time worker - Pure escort,(ptype_chauf2 == 2) & ((chauf1 == 4) | (chauf2 == 4) | (chauf3 == 4)),coef_chauf_pt_worker_pe +util_chauf1_non_worker_pe,Chauffer 1 non worker - Pure escort,(ptype_chauf1 == 4) & ((chauf1 == 2) | (chauf2 == 2) | (chauf3 == 2)),coef_chauf_non_worker_pe +util_chauf2_non_worker_pe,Chauffer 2 non worker - Pure escort,(ptype_chauf2 == 4) & ((chauf1 == 4) | (chauf2 == 4) | (chauf3 == 4)),coef_chauf_non_worker_pe +util_chauf1_univ_stud_re,Chauffer 1 university student - Ride Share,(ptype_chauf1 == 3) & ((chauf1 == 1) | (chauf2 == 1) | (chauf3 == 1)),coef_chauf_univ_stud_re +util_chauf2_univ_stud_re,Chauffer 2 university student - Ride Share,(ptype_chauf2 == 3) & ((chauf1 == 3) | (chauf2 == 3) | (chauf3 == 3)),coef_chauf_univ_stud_re +util_chauf1_age_u35_pe,Chauffer 1 Age 35 years or younger - Pure escort,(ptype_chauf1 == 4) & ((chauf1 == 2) | (chauf2 == 2) | (chauf3 == 2)),coef_chauf_age_u35_pe +util_chauf2_age_u35_pe,Chauffer 2 Age 35 years or younger - Pure escort,(ptype_chauf2 == 4) & ((chauf1 == 4) | (chauf2 == 4) | (chauf3 == 4)),coef_chauf_age_u35_pe +util_chauf1_time_to_work_or_univ_rs,Chauffer 1 Auto time to work or university - Ride Share,time_mand_to_home1 * ((chauf1 == 1) | (chauf2 == 1) | (chauf3 == 1)),coef_chauf_time_to_work_or_univ_rs +util_chauf2_time_to_work_or_univ_rs,Chauffer 2 Auto time to work or university - Ride Share,time_mand_to_home2 * ((chauf1 == 3) | (chauf2 == 3) | (chauf3 == 3)),coef_chauf_time_to_work_or_univ_rs +util_chauf1_walk_dist_to_work_or_univ_rs,Chauffer 1 Walk dist to work or university - Ride Share,(dist_mand1_to_home < 3) & ((chauf1 == 1) | (chauf2 == 1) | (chauf3 == 1)),coef_chauf_walk_dist_to_work_or_univ_rs +util_chauf2_walk_dist_to_work_or_univ_rs,Chauffer 2 Walk dist to work or university - Ride Share,(dist_mand2_to_home < 3) & ((chauf1 == 3) | (chauf2 == 3) | (chauf3 == 3)),coef_chauf_walk_dist_to_work_or_univ_rs +util_chauf1_walk_dist_to_work_or_univ_pe,Chauffer 1 Walk dist to work or university - Pure Escort,(dist_home_to_mand1 < 3) & ((chauf1 == 2) | (chauf2 == 2) | (chauf3 == 2)),coef_chauf_walk_dist_to_work_or_univ_pe +util_chauf2_walk_dist_to_work_or_univ_pe,Chauffer 2 Walk dist to work or university - Pure Escort,(dist_home_to_mand2 < 3) & ((chauf1 == 4) | (chauf2 == 4) | (chauf3 == 4)),coef_chauf_walk_dist_to_work_or_univ_pe +# ,Chauffer deviation,, +util_chauf1_abs_dev_rs_child1only,Chauffer 1 Absolute deviation ride share - child 1 only,((bundle1 != bundle2) & (bundle1 != bundle3) & (chauf1 == 1)) * abs_dev_dist_in_child1_chauf1,coef_abs_dev_distance +util_chauf2_abs_dev_rs_child1only,Chauffer 2 Absolute deviation ride share - child 1 only,((bundle1 != bundle2) & (bundle1 != bundle3) & (chauf1 == 3)) * abs_dev_dist_in_child1_chauf2,coef_abs_dev_distance +util_chauf1_abs_dev_rs_child2only,Chauffer 1 Absolute deviation ride share - child 2 only,((bundle1 != bundle2) & (bundle2 != bundle3) & (chauf2 == 1)) * abs_dev_dist_in_child2_chauf1,coef_abs_dev_distance +util_chauf2_abs_dev_rs_child2only,Chauffer 2 Absolute deviation ride share - child 2 only,((bundle1 != bundle2) & (bundle2 != bundle3) & (chauf2 == 3)) * abs_dev_dist_in_child2_chauf2,coef_abs_dev_distance +util_chauf1_abs_dev_rs_child3only,Chauffer 1 Absolute deviation ride share - child 3 only,((bundle1 != bundle3) & (bundle2 != bundle3) & (chauf3 == 1)) * abs_dev_dist_in_child3_chauf1,coef_abs_dev_distance +util_chauf2_abs_dev_rs_child3only,Chauffer 2 Absolute deviation ride share - child 3 only,((bundle1 != bundle3) & (bundle2 != bundle3) & (chauf3 == 3)) * abs_dev_dist_in_child3_chauf2,coef_abs_dev_distance +util_chauf1_abs_dev_rs_child12,Chauffer 1 Absolute deviation ride share - child 1 & 2,((bundle1 == bundle2) & (bundle1 != bundle3) & (chauf1 == 1)) * abs_dev_dist_in_child12_chauf1,coef_abs_dev_distance +util_chauf2_abs_dev_rs_child12,Chauffer 2 Absolute deviation ride share - child 1 & 2,((bundle1 == bundle2) & (bundle1 != bundle3) & (chauf1 == 3)) * abs_dev_dist_in_child12_chauf2,coef_abs_dev_distance +util_chauf1_abs_dev_rs_child13,Chauffer 1 Absolute deviation ride share - child 1 & 3,((bundle1 != bundle2) & (bundle1 == bundle3) & (chauf1 == 1)) * abs_dev_dist_in_child13_chauf1,coef_abs_dev_distance +util_chauf2_abs_dev_rs_child13,Chauffer 2 Absolute deviation ride share - child 1 & 3,((bundle1 != bundle2) & (bundle1 == bundle3) & (chauf1 == 3)) * abs_dev_dist_in_child13_chauf2,coef_abs_dev_distance +util_chauf1_abs_dev_rs_child23,Chauffer 1 Absolute deviation ride share - child 2 & 3,((bundle1 != bundle2) & (bundle2 == bundle3) & (chauf2 == 1)) * abs_dev_dist_in_child23_chauf1,coef_abs_dev_distance +util_chauf2_abs_dev_rs_child23,Chauffer 2 Absolute deviation ride share - child 2 & 3,((bundle1 != bundle2) & (bundle2 == bundle3) & (chauf2 == 3)) * abs_dev_dist_in_child23_chauf2,coef_abs_dev_distance +util_chauf1_abs_dev_rs_child123,Chauffer 1 Absolute deviation ride share - child 1 & 2 & 3,((bundle1 == bundle2) & (bundle2 == bundle3) & (chauf1 == 1)) * abs_dev_dist_in_child123_chauf1,coef_abs_dev_distance +util_chauf2_abs_dev_rs_child123,Chauffer 2 Absolute deviation ride share - child 1 & 2 & 3,((bundle1 == bundle2) & (bundle2 == bundle3) & (chauf1 == 3)) * abs_dev_dist_in_child123_chauf2,coef_abs_dev_distance +util_chauf1_rel_dev_rs_child1only,Chauffer 1 Relative deviation ride share - child 1 only,((bundle1 != bundle2) & (bundle1 != bundle3) & (chauf1 == 1)) * abs_dev_dist_in_child1_chauf1 / (dist_home_to_mand1 + 0.001),coef_rel_dev_distance +util_chauf2_rel_dev_rs_child1only,Chauffer 2 Relative deviation ride share - child 1 only,((bundle1 != bundle2) & (bundle1 != bundle3) & (chauf1 == 3)) * abs_dev_dist_in_child1_chauf2 / (dist_home_to_mand2 + 0.001),coef_rel_dev_distance +util_chauf1_rel_dev_rs_child2only,Chauffer 1 Relative deviation ride share - child 2 only,((bundle1 != bundle2) & (bundle2 != bundle3) & (chauf2 == 1)) * abs_dev_dist_in_child2_chauf1 / (dist_home_to_mand1 + 0.001),coef_rel_dev_distance +util_chauf2_rel_dev_rs_child2only,Chauffer 2 Relative deviation ride share - child 2 only,((bundle1 != bundle2) & (bundle2 != bundle3) & (chauf2 == 3)) * abs_dev_dist_in_child2_chauf2 / (dist_home_to_mand2 + 0.001),coef_rel_dev_distance +util_chauf1_rel_dev_rs_child3only,Chauffer 1 Relative deviation ride share - child 3 only,((bundle1 != bundle3) & (bundle2 != bundle3) & (chauf3 == 1)) * abs_dev_dist_in_child3_chauf1 / (dist_home_to_mand1 + 0.001),coef_rel_dev_distance +util_chauf2_rel_dev_rs_child3only,Chauffer 2 Relative deviation ride share - child 3 only,((bundle1 != bundle3) & (bundle2 != bundle3) & (chauf3 == 3)) * abs_dev_dist_in_child3_chauf2 / (dist_home_to_mand2 + 0.001),coef_rel_dev_distance +util_chauf1_rel_dev_rs_child12,Chauffer 1 Relative deviation ride share - child 1 & 2,((bundle1 == bundle2) & (bundle1 != bundle3) & (chauf1 == 1)) * abs_dev_dist_in_child12_chauf1 / (dist_home_to_mand1 + 0.001),coef_rel_dev_distance +util_chauf2_rel_dev_rs_child12,Chauffer 2 Relative deviation ride share - child 1 & 2,((bundle1 == bundle2) & (bundle1 != bundle3) & (chauf1 == 3)) * abs_dev_dist_in_child12_chauf2 / (dist_home_to_mand2 + 0.001),coef_rel_dev_distance +util_chauf1_rel_dev_rs_child13,Chauffer 1 Relative deviation ride share - child 1 & 3,((bundle1 != bundle2) & (bundle1 == bundle3) & (chauf1 == 1)) * abs_dev_dist_in_child13_chauf1 / (dist_home_to_mand1 + 0.001),coef_rel_dev_distance +util_chauf2_rel_dev_rs_child13,Chauffer 2 Relative deviation ride share - child 1 & 3,((bundle1 != bundle2) & (bundle1 == bundle3) & (chauf1 == 3)) * abs_dev_dist_in_child13_chauf2 / (dist_home_to_mand2 + 0.001),coef_rel_dev_distance +util_chauf1_rel_dev_rs_child23,Chauffer 1 Relative deviation ride share - child 2 & 3,((bundle1 != bundle2) & (bundle2 == bundle3) & (chauf2 == 1)) * abs_dev_dist_in_child23_chauf1 / (dist_home_to_mand1 + 0.001),coef_rel_dev_distance +util_chauf2_rel_dev_rs_child23,Chauffer 2 Relative deviation ride share - child 2 & 3,((bundle1 != bundle2) & (bundle2 == bundle3) & (chauf2 == 3)) * abs_dev_dist_in_child23_chauf2 / (dist_home_to_mand2 + 0.001),coef_rel_dev_distance +util_chauf1_rel_dev_rs_child123,Chauffer 1 Relative deviation ride share - child 1 & 2 & 3,((bundle1 == bundle2) & (bundle2 == bundle3) & (chauf1 == 1)) * abs_dev_dist_in_child123_chauf1 / (dist_home_to_mand1 + 0.001),coef_rel_dev_distance +util_chauf2_rel_dev_rs_child123,Chauffer 2 Relative deviation ride share - child 1 & 2 & 3,((bundle1 == bundle2) & (bundle2 == bundle3) & (chauf1 == 3)) * abs_dev_dist_in_child123_chauf2 / (dist_home_to_mand2 + 0.001),coef_rel_dev_distance +# ,Same TAZ,, +util_same_taz_child12_escort,Same school TAZ for child 1 and 2 - Escorting,(bundle1 == bundle2) & (bundle1 > 0) & (school_location_child1 == school_location_child2) & (school_location_child1 > 0),coef_same_taz_escort +util_same_taz_child13_escort,Same school TAZ for child 1 and 3 - Escorting,(bundle1 == bundle3) & (bundle1 > 0) & (school_location_child1 == school_location_child3) & (school_location_child1 > 0),coef_same_taz_escort +util_same_taz_child32_escort,Same school TAZ for child 3 and 2 - Escorting,(bundle3 == bundle2) & (bundle2 > 0) & (school_location_child3 == school_location_child2) & (school_location_child2 > 0),coef_same_taz_escort +util_same_taz_child12_no_escort,Same school TAZ for child 1 and 2 - No Escorting,(bundle1 == 0) & (bundle2 == 0) & (child_id1 > 0) & (child_id2 > 0) & (school_location_child1 == school_location_child2) & (school_location_child1 > 0),coef_same_taz_no_escort +util_same_taz_child13_no_escort,Same school TAZ for child 1 and 3 - No Escorting,(bundle1 == 0) & (bundle3 == 0) & (child_id1 > 0) & (child_id3 > 0) & (school_location_child1 == school_location_child3) & (school_location_child1 > 0),coef_same_taz_no_escort +util_same_taz_child32_no_escort,Same school TAZ for child 3 and 2 - No Escorting,(bundle3 == 0) & (bundle2 == 0) & (child_id3 > 0) & (child_id2 > 0) & (school_location_child3 == school_location_child2) & (school_location_child2 > 0),coef_same_taz_no_escort +# ,Outbound Terms,, +util_no_escort_outbound_child1,No escorting in outbound direction - Child 1,(bundle1 == 0) & (child_id1 > 0) & (bundle1_outbound == 0),coef_no_escort_outbound +util_no_escort_outbound_child2,No escorting in outbound direction - Child 2,(bundle2 == 0) & (child_id2 > 0) & (bundle2_outbound == 0),coef_no_escort_outbound +util_no_escort_outbound_child3,No escorting in outbound direction - Child 3,(bundle3 == 0) & (child_id3 > 0) & (bundle3_outbound == 0),coef_no_escort_outbound +util_outbound_rs_child1,Ride sharing in the outbound direction - Child 1,((chauf1 == 1) | (chauf1 == 3)) & ((chauf1_outbound == 1) | (chauf1_outbound == 3)),coef_outbound_rs +util_outbound_rs_child2,Ride sharing in the outbound direction - Child 2,((chauf2 == 1) | (chauf2 == 3)) & ((chauf2_outbound == 1) | (chauf2_outbound == 3)),coef_outbound_rs +util_outbound_rs_child3,Ride sharing in the outbound direction - Child 3,((chauf3 == 1) | (chauf3 == 3)) & ((chauf3_outbound == 1) | (chauf3_outbound == 3)),coef_outbound_rs +util_same_chauf1,Same chauffeur in both directions (not child specific) - chauf 1 inbound & outbound,((chauf1 == 1) | (chauf1 == 2) | (chauf2 == 1) | (chauf2 == 2) | (chauf3 == 1) | (chauf3 == 2)) & ((nbund1_outbound > 0)),coef_same_chauf +util_same_chauf2,Same chauffeur in both directions (not child specific) - chauf 2 inbound & outbound,((chauf1 == 3) | (chauf1 == 4) | (chauf2 == 3) | (chauf2 == 4) | (chauf3 == 3) | (chauf3 == 4)) & ((nbund2_outbound > 0)),coef_same_chauf +# ,Calibration Constants,, +util_calib_child1_age_u6_rs,Child 1 age under 6 - Calibration constant - Ride Share,((chauf1 == 1) | (chauf1 == 3)) & (age_child1 < 6),coef_calib_child_age_u6_rs +util_calib_child2_age_u6_rs,Child 2 age under 6 - Calibration constant - Ride Share,((chauf2 == 1) | (chauf2 == 3)) & (age_child2 < 6),coef_calib_child_age_u6_rs +util_calib_child3_age_u6_rs,Child 3 age under 6 - Calibration constant - Ride Share,((chauf3 == 1) | (chauf3 == 3)) & (age_child3 < 6),coef_calib_child_age_u6_rs +util_calib_child1_age_16p_rs,Child 1 age 16 years or older - Calibration constant - Ride Share,((chauf1 == 1) | (chauf1 == 3)) & (age_child1 > 15),coef_calib_child_age_16p_rs +util_calib_child2_age_16p_rs,Child 2 age 16 years or older - Calibration constant - Ride Share,((chauf2 == 1) | (chauf2 == 3)) & (age_child2 > 15),coef_calib_child_age_16p_rs +util_calib_child3_age_16p_rs,Child 3 age 16 years or older - Calibration constant - Ride Share,((chauf3 == 1) | (chauf3 == 3)) & (age_child3 > 15),coef_calib_child_age_16p_rs +util_calib_child1_age_6to15_noes,Child 1 age 6 to 15 years - Calibration constant - No Escort,(bundle1 == 0) & (child_id1 > 0) & (age_child1 > 5) & (age_child1 < 16),coef_calib_child_age_6to15_noes +util_calib_child2_age_6to15_noes,Child 2 age 6 to 15 years - Calibration constant - No Escort,(bundle2 == 0) & (child_id2 > 0) & (age_child2 > 5) & (age_child2 < 16),coef_calib_child_age_6to15_noes +util_calib_child3_age_6to15_noes,Child 3 age 6 to 15 years - Calibration constant - No Escort,(bundle3 == 0) & (child_id3 > 0) & (age_child3 > 5) & (age_child3 < 16),coef_calib_child_age_6to15_noes +util_calib_child1_age_u6_noes,Child 1 age 5 years or younger - Calibration constant - No escort,(bundle1 == 0) & (child_id1 > 0) & (age_child1 < 6),coef_calib_child_age_u6_noes +util_calib_child2_age_u6_noes,Child 2 age 5 years or younger - Calibration constant - No escort,(bundle2 == 0) & (child_id2 > 0) & (age_child2 < 6),coef_calib_child_age_u6_noes +util_calib_child3_age_u6_noes,Child 3 age 5 years or younger - Calibration constant - No escort,(bundle3 == 0) & (child_id3 > 0) & (age_child3 < 6),coef_calib_child_age_u6_noes +util_calib_child1_age_6to15_rs,Child 1 age 6 to 15 years - Calibration constant - Ride Share,((chauf1 == 1) | (chauf1 == 3)) & (age_child1 > 5) & (age_child1 < 16),coef_calib_child_age_6to15_rs +util_calib_child2_age_6to15_rs,Child 2 age 6 to 15 years - Calibration constant - Ride Share,((chauf2 == 1) | (chauf2 == 3)) & (age_child2 > 5) & (age_child2 < 16),coef_calib_child_age_6to15_rs +util_calib_child3_age_6to15_rs,Child 3 age 6 to 15 years - Calibration constant - Ride Share,((chauf3 == 1) | (chauf3 == 3)) & (age_child3 > 5) & (age_child3 < 16),coef_calib_child_age_6to15_rs diff --git a/resident/configs/school_escorting_outbound.csv b/resident/configs/school_escorting_outbound.csv new file mode 100644 index 0000000..82a1c66 --- /dev/null +++ b/resident/configs/school_escorting_outbound.csv @@ -0,0 +1,163 @@ +Label,Description,Expression,Coefficient +# ,Availability Conditions,, +util_one_child_to_school,Availability based on number of eligible children,((bundle2 + bundle3) > 0) & (num_children_going_to_school==1),coef_unavail +util_two_children_to_school,Availability based on number of eligible children,(bundle3 > 0) & (num_children_going_to_school == 2),coef_unavail +util_one_potential_chauffeur,Availability based on number of eligible chauffeurs,((bundle1 + bundle2 + bundle3) > 0) & (num_potential_chauffeurs == 0),coef_unavail +util_two_potential_chauffeurs,Availability based on number of eligible chauffeurs,((chauf1 > 2) | (chauf2 > 2) | (chauf3 > 2)) & (num_potential_chauffeurs == 1),coef_unavail +util_avail_rs_cdap_child1_chauf1,Availability for RideSharing by daily pattern - Child 1 and Chauffeur 1,(bundle1 > 0) & (chauf1 == 1) & (cdap_chauf1 != 'M'),coef_unavail +util_avail_rs_cdap_child1_chauf2,Availability for RideSharing by daily pattern - Child 1 and Chauffeur 2,(bundle1 > 0) & (chauf1 == 3) & (cdap_chauf2 != 'M'),coef_unavail +util_avail_rs_cdap_child2_chauf1,Availability for RideSharing by daily pattern - Child 2 and Chauffeur 1,(bundle2 > 0) & (chauf2 == 1) & (cdap_chauf1 != 'M'),coef_unavail +util_avail_rs_cdap_child2_chauf2,Availability for RideSharing by daily pattern - Child 2 and Chauffeur 2,(bundle2 > 0) & (chauf2 == 3) & (cdap_chauf2 != 'M'),coef_unavail +util_avail_rs_cdap_child3_chauf1,Availability for RideSharing by daily pattern - Child 3 and Chauffeur 1,(bundle3 > 0) & (chauf3 == 1) & (cdap_chauf1 != 'M'),coef_unavail +util_avail_rs_cdap_child3_chauf2,Availability for RideSharing by daily pattern - Child 3 and Chauffeur 2,(bundle3 > 0) & (chauf3 == 3) & (cdap_chauf2 != 'M'),coef_unavail +util_avail_rs_sync_child1_chauf1,Availability for RideSharing by synchronization - Child 1 and Chauffeur 1,@(df.bundle1 > 0) & (df.chauf1 == 1) & (df.pref_depart_time_chauf1 > 0) & (np.abs(df.pref_depart_time_chauf1 - df.pref_depart_time_school1) > max_bin_difference_between_departure_times),coef_unavail +util_avail_rs_sync_child1_chauf2,Availability for RideSharing by synchronization - Child 1 and Chauffeur 2,@(df.bundle1 > 0) & (df.chauf1 == 3) & (df.pref_depart_time_chauf2 > 0) & (np.abs(df.pref_depart_time_chauf2 - df.pref_depart_time_school1) > max_bin_difference_between_departure_times),coef_unavail +util_avail_rs_sync_child2_chauf1,Availability for RideSharing by synchronization - Child 2 and Chauffeur 1,@(df.bundle2 > 0) & (df.chauf2 == 1) & (df.pref_depart_time_chauf1 > 0) & (np.abs(df.pref_depart_time_chauf1 - df.pref_depart_time_school2) > max_bin_difference_between_departure_times),coef_unavail +util_avail_rs_sync_child2_chauf2,Availability for RideSharing by synchronization - Child 2 and Chauffeur 2,@(df.bundle2 > 0) & (df.chauf2 == 3) & (df.pref_depart_time_chauf2 > 0) & (np.abs(df.pref_depart_time_chauf2 - df.pref_depart_time_school2) > max_bin_difference_between_departure_times),coef_unavail +util_avail_rs_sync_child3_chauf1,Availability for RideSharing by synchronization - Child 3 and Chauffeur 1,@(df.bundle3 > 0) & (df.chauf3 == 1) & (df.pref_depart_time_chauf1 > 0) & (np.abs(df.pref_depart_time_chauf1 - df.pref_depart_time_school3) > max_bin_difference_between_departure_times),coef_unavail +util_avail_rs_sync_child3_chauf2,Availability for RideSharing by synchronization - Child 3 and Chauffeur 2,@(df.bundle3 > 0) & (df.chauf3 == 3) & (df.pref_depart_time_chauf2 > 0) & (np.abs(df.pref_depart_time_chauf2 - df.pref_depart_time_school3) > max_bin_difference_between_departure_times),coef_unavail +util_avail_pe_cdap_child1_chauf1,Availability for Pure-Escorting by daily pattern - Child 1 and Chauffeur 1,(bundle1 > 0) & (chauf1 == 2) & (ptype_chauf1 < 6) & (cdap_chauf1 == 'H'),coef_unavail +util_avail_pe_cdap_child1_chauf2,Availability for Pure-Escorting by daily pattern - Child 1 and Chauffeur 2,(bundle1 > 0) & (chauf1 == 4) & (ptype_chauf2 < 6) & (cdap_chauf2 == 'H'),coef_unavail +util_avail_pe_cdap_child2_chauf1,Availability for Pure-Escorting by daily pattern - Child 2 and Chauffeur 1,(bundle2 > 0) & (chauf2 == 2) & (ptype_chauf1 < 6) & (cdap_chauf1 == 'H'),coef_unavail +util_avail_pe_cdap_child2_chauf2,Availability for Pure-Escorting by daily pattern - Child 2 and Chauffeur 2,(bundle2 > 0) & (chauf2 == 4) & (ptype_chauf2 < 6) & (cdap_chauf2 == 'H'),coef_unavail +util_avail_pe_cdap_child3_chauf1,Availability for Pure-Escorting by daily pattern - Child 3 and Chauffeur 1,(bundle3 > 0) & (chauf3 == 2) & (ptype_chauf1 < 6) & (cdap_chauf1 == 'H'),coef_unavail +util_avail_pe_cdap_child3_chauf2,Availability for Pure-Escorting by daily pattern - Child 3 and Chauffeur 2,(bundle3 > 0) & (chauf3 == 4) & (ptype_chauf2 < 6) & (cdap_chauf2 == 'H'),coef_unavail +util_avail_pe_sync_child1_chauf1,Availability for Pure-Escorting by synchronization - Child 1 and Chauffeur 1,@(df.bundle1 > 0) & (df.chauf1 == 2) & (df.ptype_chauf1 < 4) & (df.cdap_chauf1 == 'H') & (np.abs(df.pref_depart_time_school1 + ((df.time_home_to_school1 + df.time_school_to_home1) / mins_per_time_bin) - df.pref_depart_time_chauf1) > max_bin_difference_between_departure_times),coef_unavail +util_avail_pe_sync_child1_chauf2,Availability for Pure-Escorting by synchronization - Child 1 and Chauffeur 2,@(df.bundle1 > 0) & (df.chauf1 == 4) & (df.ptype_chauf2 < 4) & (df.cdap_chauf2 == 'H') & (np.abs(df.pref_depart_time_school1 + ((df.time_home_to_school1 + df.time_school_to_home1) / mins_per_time_bin) - df.pref_depart_time_chauf2) > max_bin_difference_between_departure_times),coef_unavail +util_avail_pe_sync_child2_chauf1,Availability for Pure-Escorting by synchronization - Child 2 and Chauffeur 1,@(df.bundle2 > 0) & (df.chauf2 == 2) & (df.ptype_chauf1 < 4) & (df.cdap_chauf1 == 'H') & (np.abs(df.pref_depart_time_school2 + ((df.time_home_to_school2 + df.time_school_to_home2) / mins_per_time_bin) - df.pref_depart_time_chauf1) > max_bin_difference_between_departure_times),coef_unavail +util_avail_pe_sync_child2_chauf2,Availability for Pure-Escorting by synchronization - Child 2 and Chauffeur 2,@(df.bundle2 > 0) & (df.chauf2 == 4) & (df.ptype_chauf2 < 4) & (df.cdap_chauf2 == 'H') & (np.abs(df.pref_depart_time_school2 + ((df.time_home_to_school2 + df.time_school_to_home2) / mins_per_time_bin) - df.pref_depart_time_chauf2) > max_bin_difference_between_departure_times),coef_unavail +util_avail_pe_sync_child3_chauf1,Availability for Pure-Escorting by synchronization - Child 3 and Chauffeur 1,@(df.bundle3 > 0) & (df.chauf3 == 2) & (df.ptype_chauf1 < 4) & (df.cdap_chauf1 == 'H') & (np.abs(df.pref_depart_time_school3 + ((df.time_home_to_school3 + df.time_school_to_home3) / mins_per_time_bin) - df.pref_depart_time_chauf1) > max_bin_difference_between_departure_times),coef_unavail +util_avail_pe_sync_child3_chauf2,Availability for Pure-Escorting by synchronization - Child 3 and Chauffeur 2,@(df.bundle3 > 0) & (df.chauf3 == 4) & (df.ptype_chauf2 < 4) & (df.cdap_chauf2 == 'H') & (np.abs(df.pref_depart_time_school3 + ((df.time_home_to_school3 + df.time_school_to_home3) / mins_per_time_bin) - df.pref_depart_time_chauf2) > max_bin_difference_between_departure_times),coef_unavail +util_avail_bundle_child_1and2,Availability for bundling child 1 and child 2,@(df.bundle1 == df.bundle2) & (df.bundle1 > 0) & (np.abs(df.pref_depart_time_school1 - df.pref_depart_time_school2) > max_bin_difference_between_departure_times),coef_unavail +util_avail_bundle_child_1and3,Availability for bundling child 1 and child 3,@(df.bundle1 == df.bundle3) & (df.bundle1 > 0) & (np.abs(df.pref_depart_time_school1 - df.pref_depart_time_school3) > max_bin_difference_between_departure_times),coef_unavail +util_avail_bundle_child_2and3,Availability for bundling child 2 and child 3,@(df.bundle2 == df.bundle3) & (df.bundle2 > 0) & (np.abs(df.pref_depart_time_school2 - df.pref_depart_time_school3) > max_bin_difference_between_departure_times),coef_unavail +util_avail_mult_bundles_outbound1,Availability Chauffeur 1 - Expected arrival from previous pure escort must be before departure for subsequent escort,((nrs1 + npe1) > 1) & ~avail_multiple_bundles,coef_unavail +util_avail_mult_bundles_outbound2,Availability Chauffeur 2 - Expected arrival from previous pure escort must be before departure for subsequent escort,((nrs2 + npe2) > 1) & ~avail_multiple_bundles,coef_unavail +util_avail_pe_during_mand_child1_chauf1,Availability pure escort tour must take place before mandatory tour - Child 1 and Chauffeur 1,@(df.bundle1 > 0) & (df.chauf1 == 2) & (df.cdap_chauf1 == 'M') & ((df.pref_depart_time_chauf1 - max_bin_difference_between_departure_times) <= df.pref_depart_time_school1),coef_unavail +util_avail_pe_during_mand_child1_chauf2,Availability pure escort tour must take place before mandatory tour - Child 1 and Chauffeur 2,@(df.bundle1 > 0) & (df.chauf1 == 4) & (df.cdap_chauf2 == 'M') & ((df.pref_depart_time_chauf2 - max_bin_difference_between_departure_times) <= df.pref_depart_time_school1),coef_unavail +util_avail_pe_during_mand_child2_chauf1,Availability pure escort tour must take place before mandatory tour - Child 2 and Chauffeur 1,@(df.bundle2 > 0) & (df.chauf2 == 2) & (df.cdap_chauf1 == 'M') & ((df.pref_depart_time_chauf1 - max_bin_difference_between_departure_times) <= df.pref_depart_time_school2),coef_unavail +util_avail_pe_during_mand_child2_chauf2,Availability pure escort tour must take place before mandatory tour - Child 2 and Chauffeur 2,@(df.bundle2 > 0) & (df.chauf2 == 4) & (df.cdap_chauf2 == 'M') & ((df.pref_depart_time_chauf2 - max_bin_difference_between_departure_times) <= df.pref_depart_time_school2),coef_unavail +util_avail_pe_during_mand_child3_chauf1,Availability pure escort tour must take place before mandatory tour - Child 3 and Chauffeur 1,@(df.bundle3 > 0) & (df.chauf3 == 2) & (df.cdap_chauf1 == 'M') & ((df.pref_depart_time_chauf1 - max_bin_difference_between_departure_times) <= df.pref_depart_time_school3),coef_unavail +util_avail_pe_during_mand_child3_chauf2,Availability pure escort tour must take place before mandatory tour - Child 3 and Chauffeur 2,@(df.bundle3 > 0) & (df.chauf3 == 4) & (df.cdap_chauf2 == 'M') & ((df.pref_depart_time_chauf2 - max_bin_difference_between_departure_times) <= df.pref_depart_time_school3),coef_unavail +# ,No escorting,, +util_child1_age_16p_noes,Child 1 age 16 years or older - No escort,(bundle1 == 0) & (child_id1 > 0) & (age_child1 > 15),coef_child_age_16p_noes +util_child2_age_16p_noes,Child 2 age 16 years or older - No escort,(bundle2 == 0) & (child_id2 > 0) & (age_child2 > 15),coef_child_age_16p_noes +util_child3_age_16p_noes,Child 3 age 16 years or older - No escort,(bundle3 == 0) & (child_id3 > 0) & (age_child3 > 15),coef_child_age_16p_noes +util_child1_age_10_to_15_noes,Child 1 age 10 to 15 years - No escort,(bundle1 == 0) & (child_id1 > 0) & (age_child1 > 9) & (age_child1 < 16),coef_child_age_10_to_15_noes +util_child2_age_10_to_15_noes,Child 2 age 10 to 15 years - No escort,(bundle2 == 0) & (child_id2 > 0) & (age_child2 > 9) & (age_child2 < 16),coef_child_age_10_to_15_noes +util_child3_age_10_to_15_noes,Child 3 age 10 to 15 years - No escort,(bundle3 == 0) & (child_id3 > 0) & (age_child3 > 9) & (age_child3 < 16),coef_child_age_10_to_15_noes +util_child1_age_u9_noes,Child 1 age 9 years or younger - No escort,(bundle1 == 0) & (child_id1 > 0) & (age_child1 < 10),coef_child_age_u9_noes +util_child2_age_u9_noes,Child 2 age 9 years or younger - No escort,(bundle2 == 0) & (child_id2 > 0) & (age_child2 < 10),coef_child_age_u9_noes +util_child3_age_u9_noes,Child 3 age 9 years or younger - No escort,(bundle3 == 0) & (child_id3 > 0) & (age_child3 < 10),coef_child_age_u9_noes +util_ln_dist_to_school_child1_noes,Logged distance to school for Child 1 - No escort,@(df.bundle1 == 0) * (df.child_id1 > 0) * np.log(1 + df.dist_home_to_school1),coef_ln_dist_to_school_noes +util_ln_dist_to_school_child2_noes,Logged distance to school for Child 2 - No escort,@(df.bundle2 == 0) * (df.child_id2 > 0) * np.log(1 + df.dist_home_to_school2),coef_ln_dist_to_school_noes +util_ln_dist_to_school_child3_noes,Logged distance to school for Child 3 - No escort,@(df.bundle3 == 0) * (df.child_id3 > 0) * np.log(1 + df.dist_home_to_school3),coef_ln_dist_to_school_noes +util_ln_dist_to_school_child1_u6_noes,Logged distance to school for Child 1 under 6 years old - No escort,@(df.bundle1 == 0) * (df.child_id1 > 0) * (df.age_child1 < 6) * np.log(1 + df.dist_home_to_school1),coef_ln_dist_to_school_u6_noes +util_ln_dist_to_school_child2_u6_noes,Logged distance to school for Child 2 under 6 years old - No escort,@(df.bundle2 == 0) * (df.child_id2 > 0) * (df.age_child2 < 6) * np.log(1 + df.dist_home_to_school2),coef_ln_dist_to_school_u6_noes +util_ln_dist_to_school_child3_u6_noes,Logged distance to school for Child 3 under 6 years old - No escort,@(df.bundle3 == 0) * (df.child_id3 > 0) * (df.age_child3 < 6) * np.log(1 + df.dist_home_to_school3),coef_ln_dist_to_school_u6_noes +util_ln_dist_to_school_child1_6to9_noes,Logged distance to school for Child 1 6 to 9 years old - No escort,@(df.bundle1 == 0) * (df.child_id1 > 0) * (df.age_child1 > 5) * (df.age_child1 < 10) * np.log(1 + df.dist_home_to_school1),coef_ln_dist_to_school_6to9_noes +util_ln_dist_to_school_child2_6to9_noes,Logged distance to school for Child 2 6 to 9 years old - No escort,@(df.bundle2 == 0) * (df.child_id2 > 0) * (df.age_child2 > 5) * (df.age_child2 < 10) * np.log(1 + df.dist_home_to_school2),coef_ln_dist_to_school_6to9_noes +util_ln_dist_to_school_child3_6to9_noes,Logged distance to school for Child 3 6 to 9 years old - No escort,@(df.bundle3 == 0) * (df.child_id3 > 0) * (df.age_child3 > 5) * (df.age_child3 < 10) * np.log(1 + df.dist_home_to_school3),coef_ln_dist_to_school_6to9_noes +# ,Ride Sharing,, +util_child1_age_16p_rs,Child 1 age 16 years or older - Ride Share,((chauf1 == 1) | (chauf1 == 3)) & (age_child1 > 15),coef_child_age_16p_rs +util_child2_age_16p_rs,Child 2 age 16 years or older - Ride Share,((chauf2 == 1) | (chauf2 == 3)) & (age_child2 > 15),coef_child_age_16p_rs +util_child3_age_16p_rs,Child 3 age 16 years or older - Ride Share,((chauf3 == 1) | (chauf3 == 3)) & (age_child3 > 15),coef_child_age_16p_rs +util_child1_age_10to15_rs,Child 1 age 10 to 15 years - Ride Share,((chauf1 == 1) | (chauf1 == 3)) & (age_child1 > 9) & (age_child1 < 16),coef_child_age_10to15_rs +util_child2_age_10to15_rs,Child 2 age 10 to 15 years - Ride Share,((chauf2 == 1) | (chauf2 == 3)) & (age_child2 > 9) & (age_child2 < 16),coef_child_age_10to15_rs +util_child3_age_10to15_rs,Child 3 age 10 to 15 years - Ride Share,((chauf3 == 1) | (chauf3 == 3)) & (age_child3 > 9) & (age_child3 < 16),coef_child_age_10to15_rs +util_child1_age_6to9_rs,Child 1 age 6 to 9 years - Ride Share,((chauf1 == 1) | (chauf1 == 3)) & (age_child1 > 5) & (age_child1 < 10),coef_child_age_6to9_rs +util_child2_age_6to9_rs,Child 2 age 6 to 9 years - Ride Share,((chauf2 == 1) | (chauf2 == 3)) & (age_child2 > 5) & (age_child2 < 10),coef_child_age_6to9_rs +util_child3_age_6to9_rs,Child 3 age 6 to 9 years - Ride Share,((chauf3 == 1) | (chauf3 == 3)) & (age_child3 > 5) & (age_child3 < 10),coef_child_age_6to9_rs +util_child1_age_u6_rs,Child 1 age under 6 years old - Ride Share,((chauf1 == 1) | (chauf1 == 3)) & (age_child1 < 6),coef_child_age_u6_rs +util_child2_age_u6_rs,Child 2 age under 6 years old - Ride Share,((chauf2 == 1) | (chauf2 == 3)) & (age_child2 < 6),coef_child_age_u6_rs +util_child3_age_u6_rs,Child 3 age under 6 years old - Ride Share,((chauf3 == 1) | (chauf3 == 3)) & (age_child3 < 6),coef_child_age_u6_rs +# ,Pure Escort Distance,, +util_child1_dist_pe,Child 1 distance to school - Pure escorting,((chauf1 == 2) | (chauf1 == 4)) * dist_home_to_school1,coef_child_dist_pe +util_child2_dist_pe,Child 2 distance to school - Pure escorting,((chauf2 == 2) | (chauf2 == 4)) * dist_home_to_school2,coef_child_dist_pe +util_child3_dist_pe,Child 3 distance to school - Pure escorting,((chauf3 == 2) | (chauf3 == 4)) * dist_home_to_school3,coef_child_dist_pe +util_child1_dist_pe_over30miles,Child 1 Pure escorting not allowed if over 30 miles,((chauf1 == 2) | (chauf1 == 4)) & (dist_home_to_school1 > 30),coef_unavail +util_child2_dist_pe_over30miles,Child 2 Pure escorting not allowed if over 30 miles,((chauf2 == 2) | (chauf2 == 4)) & (dist_home_to_school2 > 30),coef_unavail +util_child3_dist_pe_over30miles,Child 3 Pure escorting not allowed if over 30 miles,((chauf3 == 2) | (chauf3 == 4)) & (dist_home_to_school3 > 30),coef_unavail +# ,Household Interactions,, +util_hh_inc_u25k_noes,Household income less than 25k - No Escorting,(income <= 25000) & ((bundle1 == 0) | ((bundle2 == 0) & (num_children_going_to_school > 1)) | ((bundle3 == 0) & (num_children_going_to_school > 2))),coef_hh_inc_u25k_noes +util_hh_inc_25to50k_noes,Household income between 25 and 50k - No Escorting,((income > 25000) & (income <= 50000)) & ((bundle1 == 0) | ((bundle2 == 0) & (num_children_going_to_school > 1)) | ((bundle3 == 0) & (num_children_going_to_school > 2))),coef_hh_inc_25to50k_noes +util_zero_auto_hh_noes,Zero cars in the household - No Escorting,(auto_ownership == 0) & ((bundle1 == 0) | ((bundle2 == 0) & (num_children_going_to_school > 1)) | ((bundle3 == 0) & (num_children_going_to_school > 2))),coef_zero_auto_hh_noes +util_cars_lt_workers_rs,Cars fewer than household workers - Ride Share,(auto_ownership < num_workers) & ((chauf1 % 2 == 1) | (chauf2 % 2 == 1) | (chauf3 % 2 == 1)),coef_cars_lt_workers_rs +util_cars_lt_workers_pe,Cars fewer than household workers - Pure escort,(auto_ownership < num_workers) & ((chauf1 % 2 == 0) | (chauf2 % 2 == 0) | (chauf3 % 2 == 0)),coef_cars_lt_workers_pe +# ,Chauffer Interactions,, +util_chauf1_female_rs,Chauffeur 1 Female - Ride share,(gender_chauf1 == 2) & ((chauf1 == 1) | (chauf2 == 1) | (chauf3 == 1)),coef_chauf_female_rs +util_chauf1_male_rs,Chauffeur 1 Male - Ride share,(gender_chauf1 == 1) & ((chauf1 == 1) | (chauf2 == 1) | (chauf3 == 1)),coef_chauf_male_rs +util_chauf1_female_pe,Chauffeur 1 Female - Pure Escort,(gender_chauf1 == 2) & ((chauf1 == 2) | (chauf2 == 2) | (chauf3 == 2)),coef_chauf_female_pe +util_chauf1_male_pe,Chauffeur 1 Male - Pure Escort,(gender_chauf1 == 1) & ((chauf1 == 2) | (chauf2 == 2) | (chauf3 == 2)),coef_chauf_male_pe +util_chauf2_female_rs,Chauffeur 2 Female - Ride share,(gender_chauf2 == 2) & ((chauf1 == 3) | (chauf2 == 3) | (chauf3 == 3)),coef_chauf_female_rs +util_chauf2_male_rs,Chauffeur 2 Male - Ride share,(gender_chauf2 == 1) & ((chauf1 == 3) | (chauf2 == 3) | (chauf3 == 3)),coef_chauf_male_rs +util_chauf2_female_pe,Chauffeur 2 Female - Pure Escort,(gender_chauf2 == 2) & ((chauf1 == 4) | (chauf2 == 4) | (chauf3 == 4)),coef_chauf_female_pe +util_chauf2_male_pe,Chauffeur 2 Male - Pure Escort,(gender_chauf2 == 1) & ((chauf1 == 4) | (chauf2 == 4) | (chauf3 == 4)),coef_chauf_male_pe +util_chauf1_pt_worker_rs,Chauffer 1 part time worker - Ride share,(ptype_chauf1 == 2) & ((chauf1 == 1) | (chauf2 == 1) | (chauf3 == 1)),coef_chauf_pt_worker_rs +util_chauf1_pt_worker_pe,Chauffer 1 part time worker - Pure escort,(ptype_chauf1 == 2) & ((chauf1 == 2) | (chauf2 == 2) | (chauf3 == 2)),coef_chauf_pt_worker_pe +util_chauf2_pt_worker_rs,Chauffer 2 part time worker - Ride share,(ptype_chauf2 == 2) & ((chauf1 == 3) | (chauf2 == 3) | (chauf3 == 3)),coef_chauf_pt_worker_rs +util_chauf2_pt_worker_pe,Chauffer 2 part time worker - Pure escort,(ptype_chauf2 == 2) & ((chauf1 == 4) | (chauf2 == 4) | (chauf3 == 4)),coef_chauf_pt_worker_pe +util_chauf1_non_worker_pe,Chauffer 1 non worker - Pure escort,(ptype_chauf1 == 4) & ((chauf1 == 2) | (chauf2 == 2) | (chauf3 == 2)),coef_chauf_non_worker_pe +util_chauf2_non_worker_pe,Chauffer 2 non worker - Pure escort,(ptype_chauf2 == 4) & ((chauf1 == 4) | (chauf2 == 4) | (chauf3 == 4)),coef_chauf_non_worker_pe +util_chauf1_univ_stud_re,Chauffer 1 university student - Ride Share,(ptype_chauf1 == 3) & ((chauf1 == 1) | (chauf2 == 1) | (chauf3 == 1)),coef_chauf_univ_stud_re +util_chauf2_univ_stud_re,Chauffer 2 university student - Ride Share,(ptype_chauf2 == 3) & ((chauf1 == 3) | (chauf2 == 3) | (chauf3 == 3)),coef_chauf_univ_stud_re +util_chauf1_age_u35_pe,Chauffer 1 Age 35 years or younger - Pure escort,(ptype_chauf1 == 4) & ((chauf1 == 2) | (chauf2 == 2) | (chauf3 == 2)),coef_chauf_age_u35_pe +util_chauf2_age_u35_pe,Chauffer 2 Age 35 years or younger - Pure escort,(ptype_chauf2 == 4) & ((chauf1 == 4) | (chauf2 == 4) | (chauf3 == 4)),coef_chauf_age_u35_pe +util_chauf1_time_to_work_or_univ_rs,Chauffer 1 Auto time to work or university - Ride Share,time_home_to_mand1 * ((chauf1 == 1) | (chauf2 == 1) | (chauf3 == 1)),coef_chauf_time_to_work_or_univ_rs +util_chauf2_time_to_work_or_univ_rs,Chauffer 2 Auto time to work or university - Ride Share,time_home_to_mand2 * ((chauf1 == 3) | (chauf2 == 3) | (chauf3 == 3)),coef_chauf_time_to_work_or_univ_rs +util_chauf1_walk_dist_to_work_or_univ_rs,Chauffer 1 Walk dist to work or university - Ride Share,(dist_home_to_mand1 < 3) & ((chauf1 == 1) | (chauf2 == 1) | (chauf3 == 1)),coef_chauf_walk_dist_to_work_or_univ_rs +util_chauf2_walk_dist_to_work_or_univ_rs,Chauffer 2 Walk dist to work or university - Ride Share,(dist_home_to_mand2 < 3) & ((chauf1 == 3) | (chauf2 == 3) | (chauf3 == 3)),coef_chauf_walk_dist_to_work_or_univ_rs +util_chauf1_walk_dist_to_work_or_univ_pe,Chauffer 1 Walk dist to work or university - Pure Escort,(dist_home_to_mand1 < 3) & ((chauf1 == 2) | (chauf2 == 2) | (chauf3 == 2)),coef_chauf_walk_dist_to_work_or_univ_pe +util_chauf2_walk_dist_to_work_or_univ_pe,Chauffer 2 Walk dist to work or university - Pure Escort,(dist_home_to_mand2 < 3) & ((chauf1 == 4) | (chauf2 == 4) | (chauf3 == 4)),coef_chauf_walk_dist_to_work_or_univ_pe +# ,Chauffer deviation,, +util_chauf1_abs_dev_rs_child1only,Chauffer 1 Absolute deviation ride share - child 1 only,((bundle1 != bundle2) & (bundle1 != bundle3) & (chauf1 == 1)) * abs_dev_dist_out_child1_chauf1,coef_abs_dev_distance +util_chauf2_abs_dev_rs_child1only,Chauffer 2 Absolute deviation ride share - child 1 only,((bundle1 != bundle2) & (bundle1 != bundle3) & (chauf1 == 3)) * abs_dev_dist_out_child1_chauf2,coef_abs_dev_distance +util_chauf1_abs_dev_rs_child2only,Chauffer 1 Absolute deviation ride share - child 2 only,((bundle1 != bundle2) & (bundle2 != bundle3) & (chauf2 == 1)) * abs_dev_dist_out_child2_chauf1,coef_abs_dev_distance +util_chauf2_abs_dev_rs_child2only,Chauffer 2 Absolute deviation ride share - child 2 only,((bundle1 != bundle2) & (bundle2 != bundle3) & (chauf2 == 3)) * abs_dev_dist_out_child2_chauf2,coef_abs_dev_distance +util_chauf1_abs_dev_rs_child3only,Chauffer 1 Absolute deviation ride share - child 3 only,((bundle1 != bundle3) & (bundle2 != bundle3) & (chauf3 == 1)) * abs_dev_dist_out_child3_chauf1,coef_abs_dev_distance +util_chauf2_abs_dev_rs_child3only,Chauffer 2 Absolute deviation ride share - child 3 only,((bundle1 != bundle3) & (bundle2 != bundle3) & (chauf3 == 3)) * abs_dev_dist_out_child3_chauf2,coef_abs_dev_distance +util_chauf1_abs_dev_rs_child12,Chauffer 1 Absolute deviation ride share - child 1 & 2,((bundle1 == bundle2) & (bundle1 != bundle3) & (chauf1 == 1)) * abs_dev_dist_out_child12_chauf1,coef_abs_dev_distance +util_chauf2_abs_dev_rs_child12,Chauffer 2 Absolute deviation ride share - child 1 & 2,((bundle1 == bundle2) & (bundle1 != bundle3) & (chauf1 == 3)) * abs_dev_dist_out_child12_chauf2,coef_abs_dev_distance +util_chauf1_abs_dev_rs_child13,Chauffer 1 Absolute deviation ride share - child 1 & 3,((bundle1 != bundle2) & (bundle1 == bundle3) & (chauf1 == 1)) * abs_dev_dist_out_child13_chauf1,coef_abs_dev_distance +util_chauf2_abs_dev_rs_child13,Chauffer 2 Absolute deviation ride share - child 1 & 3,((bundle1 != bundle2) & (bundle1 == bundle3) & (chauf1 == 3)) * abs_dev_dist_out_child13_chauf2,coef_abs_dev_distance +util_chauf1_abs_dev_rs_child23,Chauffer 1 Absolute deviation ride share - child 2 & 3,((bundle1 != bundle2) & (bundle2 == bundle3) & (chauf2 == 1)) * abs_dev_dist_out_child23_chauf1,coef_abs_dev_distance +util_chauf2_abs_dev_rs_child23,Chauffer 2 Absolute deviation ride share - child 2 & 3,((bundle1 != bundle2) & (bundle2 == bundle3) & (chauf2 == 3)) * abs_dev_dist_out_child23_chauf2,coef_abs_dev_distance +util_chauf1_abs_dev_rs_child123,Chauffer 1 Absolute deviation ride share - child 1 & 2 & 3,((bundle1 == bundle2) & (bundle2 == bundle3) & (chauf1 == 1)) * abs_dev_dist_out_child123_chauf1,coef_abs_dev_distance +util_chauf2_abs_dev_rs_child123,Chauffer 2 Absolute deviation ride share - child 1 & 2 & 3,((bundle1 == bundle2) & (bundle2 == bundle3) & (chauf1 == 3)) * abs_dev_dist_out_child123_chauf2,coef_abs_dev_distance +util_chauf1_rel_dev_rs_child1only,Chauffer 1 Relative deviation ride share - child 1 only,((bundle1 != bundle2) & (bundle1 != bundle3) & (chauf1 == 1)) * abs_dev_dist_out_child1_chauf1 / (dist_home_to_mand1 + 0.001),coef_rel_dev_distance +util_chauf2_rel_dev_rs_child1only,Chauffer 2 Relative deviation ride share - child 1 only,((bundle1 != bundle2) & (bundle1 != bundle3) & (chauf1 == 3)) * abs_dev_dist_out_child1_chauf2 / (dist_home_to_mand2 + 0.001),coef_rel_dev_distance +util_chauf1_rel_dev_rs_child2only,Chauffer 1 Relative deviation ride share - child 2 only,((bundle1 != bundle2) & (bundle2 != bundle3) & (chauf2 == 1)) * abs_dev_dist_out_child2_chauf1 / (dist_home_to_mand1 + 0.001),coef_rel_dev_distance +util_chauf2_rel_dev_rs_child2only,Chauffer 2 Relative deviation ride share - child 2 only,((bundle1 != bundle2) & (bundle2 != bundle3) & (chauf2 == 3)) * abs_dev_dist_out_child2_chauf2 / (dist_home_to_mand2 + 0.001),coef_rel_dev_distance +util_chauf1_rel_dev_rs_child3only,Chauffer 1 Relative deviation ride share - child 3 only,((bundle1 != bundle3) & (bundle2 != bundle3) & (chauf3 == 1)) * abs_dev_dist_out_child3_chauf1 / (dist_home_to_mand1 + 0.001),coef_rel_dev_distance +util_chauf2_rel_dev_rs_child3only,Chauffer 2 Relative deviation ride share - child 3 only,((bundle1 != bundle3) & (bundle2 != bundle3) & (chauf3 == 3)) * abs_dev_dist_out_child3_chauf2 / (dist_home_to_mand2 + 0.001),coef_rel_dev_distance +util_chauf1_rel_dev_rs_child12,Chauffer 1 Relative deviation ride share - child 1 & 2,((bundle1 == bundle2) & (bundle1 != bundle3) & (chauf1 == 1)) * abs_dev_dist_out_child12_chauf1 / (dist_home_to_mand1 + 0.001),coef_rel_dev_distance +util_chauf2_rel_dev_rs_child12,Chauffer 2 Relative deviation ride share - child 1 & 2,((bundle1 == bundle2) & (bundle1 != bundle3) & (chauf1 == 3)) * abs_dev_dist_out_child12_chauf2 / (dist_home_to_mand2 + 0.001),coef_rel_dev_distance +util_chauf1_rel_dev_rs_child13,Chauffer 1 Relative deviation ride share - child 1 & 3,((bundle1 != bundle2) & (bundle1 == bundle3) & (chauf1 == 1)) * abs_dev_dist_out_child13_chauf1 / (dist_home_to_mand1 + 0.001),coef_rel_dev_distance +util_chauf2_rel_dev_rs_child13,Chauffer 2 Relative deviation ride share - child 1 & 3,((bundle1 != bundle2) & (bundle1 == bundle3) & (chauf1 == 3)) * abs_dev_dist_out_child13_chauf2 / (dist_home_to_mand2 + 0.001),coef_rel_dev_distance +util_chauf1_rel_dev_rs_child23,Chauffer 1 Relative deviation ride share - child 2 & 3,((bundle1 != bundle2) & (bundle2 == bundle3) & (chauf2 == 1)) * abs_dev_dist_out_child23_chauf1 / (dist_home_to_mand1 + 0.001),coef_rel_dev_distance +util_chauf2_rel_dev_rs_child23,Chauffer 2 Relative deviation ride share - child 2 & 3,((bundle1 != bundle2) & (bundle2 == bundle3) & (chauf2 == 3)) * abs_dev_dist_out_child23_chauf2 / (dist_home_to_mand2 + 0.001),coef_rel_dev_distance +util_chauf1_rel_dev_rs_child123,Chauffer 1 Relative deviation ride share - child 1 & 2 & 3,((bundle1 == bundle2) & (bundle2 == bundle3) & (chauf1 == 1)) * abs_dev_dist_out_child123_chauf1 / (dist_home_to_mand1 + 0.001),coef_rel_dev_distance +util_chauf2_rel_dev_rs_child123,Chauffer 2 Relative deviation ride share - child 1 & 2 & 3,((bundle1 == bundle2) & (bundle2 == bundle3) & (chauf1 == 3)) * abs_dev_dist_out_child123_chauf2 / (dist_home_to_mand2 + 0.001),coef_rel_dev_distance +# ,Same TAZ,, +util_same_taz_child12_escort,Same school TAZ for child 1 and 2 - Escorting,(bundle1 == bundle2) & (bundle1 > 0) & (school_location_child1 == school_location_child2) & (school_location_child1 > 0),coef_same_taz_escort +util_same_taz_child13_escort,Same school TAZ for child 1 and 3 - Escorting,(bundle1 == bundle3) & (bundle1 > 0) & (school_location_child1 == school_location_child3) & (school_location_child1 > 0),coef_same_taz_escort +util_same_taz_child32_escort,Same school TAZ for child 3 and 2 - Escorting,(bundle3 == bundle2) & (bundle2 > 0) & (school_location_child3 == school_location_child2) & (school_location_child2 > 0),coef_same_taz_escort +util_same_taz_child12_no_escort,Same school TAZ for child 1 and 2 - No Escorting,(bundle1 == 0) & (bundle2 == 0) & (child_id1 > 0) & (child_id2 > 0) & (school_location_child1 == school_location_child2) & (school_location_child1 > 0),coef_same_taz_no_escort +util_same_taz_child13_no_escort,Same school TAZ for child 1 and 3 - No Escorting,(bundle1 == 0) & (bundle3 == 0) & (child_id1 > 0) & (child_id3 > 0) & (school_location_child1 == school_location_child3) & (school_location_child1 > 0),coef_same_taz_no_escort +util_same_taz_child32_no_escort,Same school TAZ for child 3 and 2 - No Escorting,(bundle3 == 0) & (bundle2 == 0) & (child_id3 > 0) & (child_id2 > 0) & (school_location_child3 == school_location_child2) & (school_location_child2 > 0),coef_same_taz_no_escort_23 +# ,Calibration constants,, +util_calib_child1_age_u6_rs,Child 1 age under 6 - Calibration constant - Ride Share,((chauf1 == 1) | (chauf1 == 3)) & (age_child1 < 6),coef_calib_child_age_u6_rs +util_calib_child2_age_u6_rs,Child 2 age under 6 - Calibration constant - Ride Share,((chauf2 == 1) | (chauf2 == 3)) & (age_child2 < 6),coef_calib_child_age_u6_rs +util_calib_child3_age_u6_rs,Child 3 age under 6 - Calibration constant - Ride Share,((chauf3 == 1) | (chauf3 == 3)) & (age_child3 < 6),coef_calib_child_age_u6_rs +util_calib_child1_age_16p_rs,Child 1 age 16 years or older - Calibration constant - Ride Share,((chauf1 == 1) | (chauf1 == 3)) & (age_child1 > 15),coef_calib_child_age_16p_rs +util_calib_child2_age_16p_rs,Child 2 age 16 years or older - Calibration constant - Ride Share,((chauf2 == 1) | (chauf2 == 3)) & (age_child2 > 15),coef_calib_child_age_16p_rs +util_calib_child3_age_16p_rs,Child 3 age 16 years or older - Calibration constant - Ride Share,((chauf3 == 1) | (chauf3 == 3)) & (age_child3 > 15),coef_calib_child_age_16p_rs +util_calib_child1_age_6to15_noes,Child 1 age 6 to 15 years - Calibration constant - No Escort,(bundle1 == 0) & (child_id1 > 0) & (age_child1 > 5) & (age_child1 < 16),coef_calib_child_age_6to15_noes +util_calib_child2_age_6to15_noes,Child 2 age 6 to 15 years - Calibration constant - No Escort,(bundle2 == 0) & (child_id2 > 0) & (age_child2 > 5) & (age_child2 < 16),coef_calib_child_age_6to15_noes +util_calib_child3_age_6to15_noes,Child 3 age 6 to 15 years - Calibration constant - No Escort,(bundle3 == 0) & (child_id3 > 0) & (age_child3 > 5) & (age_child3 < 16),coef_calib_child_age_6to15_noes +util_calib_child1_age_6to15_rs,Child 1 age 6 to 15 years - Calibration constant - Ride Share,((chauf1 == 1) | (chauf1 == 3)) & (age_child1 > 5) & (age_child1 < 16),coef_calib_child_age_6to15_rs +util_calib_child2_age_6to15_rs,Child 2 age 6 to 15 years - Calibration constant - Ride Share,((chauf2 == 1) | (chauf2 == 3)) & (age_child2 > 5) & (age_child2 < 16),coef_calib_child_age_6to15_rs +util_calib_child3_age_6to15_rs,Child 3 age 6 to 15 years - Calibration constant - Ride Share,((chauf3 == 1) | (chauf3 == 3)) & (age_child3 > 5) & (age_child3 < 16),coef_calib_child_age_6to15_rs +util_calib_child1_age_u6_noes,Child 1 age 6 years or younger - Calibration constant - No escort,(bundle1 == 0) & (child_id1 > 0) & (age_child1 < 6),coef_calib_child_age_u6_noes +util_calib_child2_age_u6_noes,Child 2 age 6 years or younger - Calibration constant - No escort,(bundle2 == 0) & (child_id2 > 0) & (age_child2 < 6),coef_calib_child_age_u6_noes +util_calib_child3_age_u6_noes,Child 3 age 6 years or younger - Calibration constant - No escort,(bundle3 == 0) & (child_id3 > 0) & (age_child3 < 6),coef_calib_child_age_u6_noes diff --git a/resident/configs/school_escorting_outbound_cond.csv b/resident/configs/school_escorting_outbound_cond.csv new file mode 100644 index 0000000..5d890c7 --- /dev/null +++ b/resident/configs/school_escorting_outbound_cond.csv @@ -0,0 +1,169 @@ +Label,Description,Expression,Coefficient +# ,Availability Conditions,, +util_one_child_to_school,Availability based on number of eligible children,((bundle2 + bundle3) > 0) & (num_children_going_to_school==1),coef_unavail +util_two_children_to_school,Availability based on number of eligible children,(bundle3 > 0) & (num_children_going_to_school == 2),coef_unavail +util_one_potential_chauffeur,Availability based on number of eligible chauffeurs,((bundle1 + bundle2 + bundle3) > 0) & (num_potential_chauffeurs == 0),coef_unavail +util_two_potential_chauffeurs,Availability based on number of eligible chauffeurs,((chauf1 > 2) | (chauf2 > 2) | (chauf3 > 2)) & (num_potential_chauffeurs == 1),coef_unavail +util_avail_rs_cdap_child1_chauf1,Availability for RideSharing by daily pattern - Child 1 and Chauffeur 1,(bundle1 > 0) & (chauf1 == 1) & (cdap_chauf1 != 'M'),coef_unavail +util_avail_rs_cdap_child1_chauf2,Availability for RideSharing by daily pattern - Child 1 and Chauffeur 2,(bundle1 > 0) & (chauf1 == 3) & (cdap_chauf2 != 'M'),coef_unavail +util_avail_rs_cdap_child2_chauf1,Availability for RideSharing by daily pattern - Child 2 and Chauffeur 1,(bundle2 > 0) & (chauf2 == 1) & (cdap_chauf1 != 'M'),coef_unavail +util_avail_rs_cdap_child2_chauf2,Availability for RideSharing by daily pattern - Child 2 and Chauffeur 2,(bundle2 > 0) & (chauf2 == 3) & (cdap_chauf2 != 'M'),coef_unavail +util_avail_rs_cdap_child3_chauf1,Availability for RideSharing by daily pattern - Child 3 and Chauffeur 1,(bundle3 > 0) & (chauf3 == 1) & (cdap_chauf1 != 'M'),coef_unavail +util_avail_rs_cdap_child3_chauf2,Availability for RideSharing by daily pattern - Child 3 and Chauffeur 2,(bundle3 > 0) & (chauf3 == 3) & (cdap_chauf2 != 'M'),coef_unavail +util_avail_rs_sync_child1_chauf1,Availability for RideSharing by synchronization - Child 1 and Chauffeur 1,@(df.bundle1 > 0) & (df.chauf1 == 1) & (np.abs(df.pref_depart_time_chauf1 - df.pref_depart_time_school1) > max_bin_difference_between_departure_times),coef_unavail +util_avail_rs_sync_child1_chauf2,Availability for RideSharing by synchronization - Child 1 and Chauffeur 2,@(df.bundle1 > 0) & (df.chauf1 == 3) & (np.abs(df.pref_depart_time_chauf2 - df.pref_depart_time_school1) > max_bin_difference_between_departure_times),coef_unavail +util_avail_rs_sync_child2_chauf1,Availability for RideSharing by synchronization - Child 2 and Chauffeur 1,@(df.bundle2 > 0) & (df.chauf2 == 1) & (np.abs(df.pref_depart_time_chauf1 - df.pref_depart_time_school2) > max_bin_difference_between_departure_times),coef_unavail +util_avail_rs_sync_child2_chauf2,Availability for RideSharing by synchronization - Child 2 and Chauffeur 2,@(df.bundle2 > 0) & (df.chauf2 == 3) & (np.abs(df.pref_depart_time_chauf2 - df.pref_depart_time_school2) > max_bin_difference_between_departure_times),coef_unavail +util_avail_rs_sync_child3_chauf1,Availability for RideSharing by synchronization - Child 3 and Chauffeur 1,@(df.bundle3 > 0) & (df.chauf3 == 1) & (np.abs(df.pref_depart_time_chauf1 - df.pref_depart_time_school3) > max_bin_difference_between_departure_times),coef_unavail +util_avail_rs_sync_child3_chauf2,Availability for RideSharing by synchronization - Child 3 and Chauffeur 2,@(df.bundle3 > 0) & (df.chauf3 == 3) & (np.abs(df.pref_depart_time_chauf2 - df.pref_depart_time_school3) > max_bin_difference_between_departure_times),coef_unavail +util_avail_pe_cdap_child1_chauf1,Availability for Pure-Escorting by daily pattern - Child 1 and Chauffeur 1,(bundle1 > 0) & (chauf1 == 2) & (ptype_chauf1 < 6) & (cdap_chauf1 == 'H'),coef_unavail +util_avail_pe_cdap_child1_chauf2,Availability for Pure-Escorting by daily pattern - Child 1 and Chauffeur 2,(bundle1 > 0) & (chauf1 == 4) & (ptype_chauf2 < 6) & (cdap_chauf2 == 'H'),coef_unavail +util_avail_pe_cdap_child2_chauf1,Availability for Pure-Escorting by daily pattern - Child 2 and Chauffeur 1,(bundle2 > 0) & (chauf2 == 2) & (ptype_chauf1 < 6) & (cdap_chauf1 == 'H'),coef_unavail +util_avail_pe_cdap_child2_chauf2,Availability for Pure-Escorting by daily pattern - Child 2 and Chauffeur 2,(bundle2 > 0) & (chauf2 == 4) & (ptype_chauf2 < 6) & (cdap_chauf2 == 'H'),coef_unavail +util_avail_pe_cdap_child3_chauf1,Availability for Pure-Escorting by daily pattern - Child 3 and Chauffeur 1,(bundle3 > 0) & (chauf3 == 2) & (ptype_chauf1 < 6) & (cdap_chauf1 == 'H'),coef_unavail +util_avail_pe_cdap_child3_chauf2,Availability for Pure-Escorting by daily pattern - Child 3 and Chauffeur 2,(bundle3 > 0) & (chauf3 == 4) & (ptype_chauf2 < 6) & (cdap_chauf2 == 'H'),coef_unavail +util_avail_pe_sync_child1_chauf1,Availability for Pure-Escorting by synchronization - Child 1 and Chauffeur 1,@(df.bundle1 > 0) & (df.chauf1 == 2) & (df.ptype_chauf1 < 4) & (df.cdap_chauf1 == 'H') & (np.abs(df.pref_depart_time_school1 + ((df.time_home_to_school1 + df.time_school_to_home1) / mins_per_time_bin) - df.pref_depart_time_chauf1) > max_bin_difference_between_departure_times),coef_unavail +util_avail_pe_sync_child1_chauf2,Availability for Pure-Escorting by synchronization - Child 1 and Chauffeur 2,@(df.bundle1 > 0) & (df.chauf1 == 4) & (df.ptype_chauf2 < 4) & (df.cdap_chauf2 == 'H') & (np.abs(df.pref_depart_time_school1 + ((df.time_home_to_school1 + df.time_school_to_home1) / mins_per_time_bin) - df.pref_depart_time_chauf2) > max_bin_difference_between_departure_times),coef_unavail +util_avail_pe_sync_child2_chauf1,Availability for Pure-Escorting by synchronization - Child 2 and Chauffeur 1,@(df.bundle2 > 0) & (df.chauf2 == 2) & (df.ptype_chauf1 < 4) & (df.cdap_chauf1 == 'H') & (np.abs(df.pref_depart_time_school2 + ((df.time_home_to_school2 + df.time_school_to_home2) / mins_per_time_bin) - df.pref_depart_time_chauf1) > max_bin_difference_between_departure_times),coef_unavail +util_avail_pe_sync_child2_chauf2,Availability for Pure-Escorting by synchronization - Child 2 and Chauffeur 2,@(df.bundle2 > 0) & (df.chauf2 == 4) & (df.ptype_chauf2 < 4) & (df.cdap_chauf2 == 'H') & (np.abs(df.pref_depart_time_school2 + ((df.time_home_to_school2 + df.time_school_to_home2) / mins_per_time_bin) - df.pref_depart_time_chauf2) > max_bin_difference_between_departure_times),coef_unavail +util_avail_pe_sync_child3_chauf1,Availability for Pure-Escorting by synchronization - Child 3 and Chauffeur 1,@(df.bundle3 > 0) & (df.chauf3 == 2) & (df.ptype_chauf1 < 4) & (df.cdap_chauf1 == 'H') & (np.abs(df.pref_depart_time_school3 + ((df.time_home_to_school3 + df.time_school_to_home3) / mins_per_time_bin) - df.pref_depart_time_chauf1) > max_bin_difference_between_departure_times),coef_unavail +util_avail_pe_sync_child3_chauf2,Availability for Pure-Escorting by synchronization - Child 3 and Chauffeur 2,@(df.bundle3 > 0) & (df.chauf3 == 4) & (df.ptype_chauf2 < 4) & (df.cdap_chauf2 == 'H') & (np.abs(df.pref_depart_time_school3 + ((df.time_home_to_school3 + df.time_school_to_home3) / mins_per_time_bin) - df.pref_depart_time_chauf2) > max_bin_difference_between_departure_times),coef_unavail +util_avail_bundle_child_1and2,Availability for bundling child 1 and child 2,@(df.bundle1 == df.bundle2) & (df.bundle1 > 0) & (np.abs(df.pref_depart_time_school1 - df.pref_depart_time_school2) > max_bin_difference_between_departure_times),coef_unavail +util_avail_bundle_child_1and3,Availability for bundling child 1 and child 3,@(df.bundle1 == df.bundle3) & (df.bundle1 > 0) & (np.abs(df.pref_depart_time_school1 - df.pref_depart_time_school3) > max_bin_difference_between_departure_times),coef_unavail +util_avail_bundle_child_2and3,Availability for bundling child 2 and child 3,@(df.bundle2 == df.bundle3) & (df.bundle2 > 0) & (np.abs(df.pref_depart_time_school2 - df.pref_depart_time_school3) > max_bin_difference_between_departure_times),coef_unavail +util_avail_mult_bundles_outbound1,Availability Chauffeur 1 - Expected arrival from previous pure escort must be before departure for subsequent escort,((nrs1 + npe1) > 1) & ~avail_multiple_bundles,coef_unavail +util_avail_mult_bundles_outbound2,Availability Chauffeur 2 - Expected arrival from previous pure escort must be before departure for subsequent escort,((nrs2 + npe2) > 1) & ~avail_multiple_bundles,coef_unavail +util_avail_pe_during_mand_child1_chauf1,Availability pure escort tour must take place before mandatory tour - Child 1 and Chauffeur 1,@(df.bundle1 > 0) & (df.chauf1 == 2) & (df.cdap_chauf1 == 'M') & ((df.pref_depart_time_chauf1 - max_bin_difference_between_departure_times) <= df.pref_depart_time_school1),coef_unavail +util_avail_pe_during_mand_child1_chauf2,Availability pure escort tour must take place before mandatory tour - Child 1 and Chauffeur 2,@(df.bundle1 > 0) & (df.chauf1 == 4) & (df.cdap_chauf2 == 'M') & ((df.pref_depart_time_chauf2 - max_bin_difference_between_departure_times) <= df.pref_depart_time_school1),coef_unavail +util_avail_pe_during_mand_child2_chauf1,Availability pure escort tour must take place before mandatory tour - Child 2 and Chauffeur 1,@(df.bundle2 > 0) & (df.chauf2 == 2) & (df.cdap_chauf1 == 'M') & ((df.pref_depart_time_chauf1 - max_bin_difference_between_departure_times) <= df.pref_depart_time_school2),coef_unavail +util_avail_pe_during_mand_child2_chauf2,Availability pure escort tour must take place before mandatory tour - Child 2 and Chauffeur 2,@(df.bundle2 > 0) & (df.chauf2 == 4) & (df.cdap_chauf2 == 'M') & ((df.pref_depart_time_chauf2 - max_bin_difference_between_departure_times) <= df.pref_depart_time_school2),coef_unavail +util_avail_pe_during_mand_child3_chauf1,Availability pure escort tour must take place before mandatory tour - Child 3 and Chauffeur 1,@(df.bundle3 > 0) & (df.chauf3 == 2) & (df.cdap_chauf1 == 'M') & ((df.pref_depart_time_chauf1 - max_bin_difference_between_departure_times) <= df.pref_depart_time_school3),coef_unavail +util_avail_pe_during_mand_child3_chauf2,Availability pure escort tour must take place before mandatory tour - Child 3 and Chauffeur 2,@(df.bundle3 > 0) & (df.chauf3 == 4) & (df.cdap_chauf2 == 'M') & ((df.pref_depart_time_chauf2 - max_bin_difference_between_departure_times) <= df.pref_depart_time_school3),coef_unavail +# ,No escorting,, +util_child1_age_16p_noes,Child 1 age 16 years or older - No escort,(bundle1 == 0) & (child_id1 > 0) & (age_child1 > 15),coef_child_age_16p_noes +util_child2_age_16p_noes,Child 2 age 16 years or older - No escort,(bundle2 == 0) & (child_id2 > 0) & (age_child2 > 15),coef_child_age_16p_noes +util_child3_age_16p_noes,Child 3 age 16 years or older - No escort,(bundle3 == 0) & (child_id3 > 0) & (age_child3 > 15),coef_child_age_16p_noes +util_child1_age_10_to_15_noes,Child 1 age 10 to 15 years - No escort,(bundle1 == 0) & (child_id1 > 0) & (age_child1 > 9) & (age_child1 < 16),coef_child_age_10_to_15_noes +util_child2_age_10_to_15_noes,Child 2 age 10 to 15 years - No escort,(bundle2 == 0) & (child_id2 > 0) & (age_child2 > 9) & (age_child2 < 16),coef_child_age_10_to_15_noes +util_child3_age_10_to_15_noes,Child 3 age 10 to 15 years - No escort,(bundle3 == 0) & (child_id3 > 0) & (age_child3 > 9) & (age_child3 < 16),coef_child_age_10_to_15_noes +util_child1_age_u9_noes,Child 1 age 9 years or younger - No escort,(bundle1 == 0) & (child_id1 > 0) & (age_child1 < 10),coef_child_age_u9_noes +util_child2_age_u9_noes,Child 2 age 9 years or younger - No escort,(bundle2 == 0) & (child_id2 > 0) & (age_child2 < 10),coef_child_age_u9_noes +util_child3_age_u9_noes,Child 3 age 9 years or younger - No escort,(bundle3 == 0) & (child_id3 > 0) & (age_child3 < 10),coef_child_age_u9_noes +util_ln_dist_to_school_child1_noes,Logged distance to school for Child 1 - No escort,@(df.bundle1 == 0) * (df.child_id1 > 0) * np.log(1 + df.dist_home_to_school1),coef_ln_dist_to_school_noes +util_ln_dist_to_school_child2_noes,Logged distance to school for Child 2 - No escort,@(df.bundle2 == 0) * (df.child_id2 > 0) * np.log(1 + df.dist_home_to_school2),coef_ln_dist_to_school_noes +util_ln_dist_to_school_child3_noes,Logged distance to school for Child 3 - No escort,@(df.bundle3 == 0) * (df.child_id3 > 0) * np.log(1 + df.dist_home_to_school3),coef_ln_dist_to_school_noes +util_ln_dist_to_school_child1_u6_noes,Logged distance to school for Child 1 under 6 years old - No escort,@(df.bundle1 == 0) * (df.child_id1 > 0) * (df.age_child1 < 6) * np.log(1 + df.dist_home_to_school1),coef_ln_dist_to_school_u6_noes +util_ln_dist_to_school_child2_u6_noes,Logged distance to school for Child 2 under 6 years old - No escort,@(df.bundle2 == 0) * (df.child_id2 > 0) * (df.age_child2 < 6) * np.log(1 + df.dist_home_to_school2),coef_ln_dist_to_school_u6_noes +util_ln_dist_to_school_child3_u6_noes,Logged distance to school for Child 3 under 6 years old - No escort,@(df.bundle3 == 0) * (df.child_id3 > 0) * (df.age_child3 < 6) * np.log(1 + df.dist_home_to_school3),coef_ln_dist_to_school_u6_noes +util_ln_dist_to_school_child1_6to9_noes,Logged distance to school for Child 1 6 to 9 years old - No escort,@(df.bundle1 == 0) * (df.child_id1 > 0) * (df.age_child1 > 5) * (df.age_child1 < 10) * np.log(1 + df.dist_home_to_school1),coef_ln_dist_to_school_6to9_noes +util_ln_dist_to_school_child2_6to9_noes,Logged distance to school for Child 2 6 to 9 years old - No escort,@(df.bundle2 == 0) * (df.child_id2 > 0) * (df.age_child2 > 5) * (df.age_child2 < 10) * np.log(1 + df.dist_home_to_school2),coef_ln_dist_to_school_6to9_noes +util_ln_dist_to_school_child3_6to9_noes,Logged distance to school for Child 3 6 to 9 years old - No escort,@(df.bundle3 == 0) * (df.child_id3 > 0) * (df.age_child3 > 5) * (df.age_child3 < 10) * np.log(1 + df.dist_home_to_school3),coef_ln_dist_to_school_6to9_noes +# ,Ride Sharing,, +util_child1_age_16p_rs,Child 1 age 16 years or older - Ride Share,((chauf1 == 1) | (chauf1 == 3)) & (age_child1 > 15),coef_child_age_16p_rs +util_child2_age_16p_rs,Child 2 age 16 years or older - Ride Share,((chauf2 == 1) | (chauf2 == 3)) & (age_child2 > 15),coef_child_age_16p_rs +util_child3_age_16p_rs,Child 3 age 16 years or older - Ride Share,((chauf3 == 1) | (chauf3 == 3)) & (age_child3 > 15),coef_child_age_16p_rs +util_child1_age_10to15_rs,Child 1 age 10 to 15 years - Ride Share,((chauf1 == 1) | (chauf1 == 3)) & (age_child1 > 9) & (age_child1 < 16),coef_child_age_10to15_rs +util_child2_age_10to15_rs,Child 2 age 10 to 15 years - Ride Share,((chauf2 == 1) | (chauf2 == 3)) & (age_child2 > 9) & (age_child2 < 16),coef_child_age_10to15_rs +util_child3_age_10to15_rs,Child 3 age 10 to 15 years - Ride Share,((chauf3 == 1) | (chauf3 == 3)) & (age_child3 > 9) & (age_child3 < 16),coef_child_age_10to15_rs +util_child1_age_6to9_rs,Child 1 age 6 to 9 years - Ride Share,((chauf1 == 1) | (chauf1 == 3)) & (age_child1 > 5) & (age_child1 < 10),coef_child_age_6to9_rs +util_child2_age_6to9_rs,Child 2 age 6 to 9 years - Ride Share,((chauf2 == 1) | (chauf2 == 3)) & (age_child2 > 5) & (age_child2 < 10),coef_child_age_6to9_rs +util_child3_age_6to9_rs,Child 3 age 6 to 9 years - Ride Share,((chauf3 == 1) | (chauf3 == 3)) & (age_child3 > 5) & (age_child3 < 10),coef_child_age_6to9_rs +util_child1_age_u6_rs,Child 1 age under 6 years old - Ride Share,((chauf1 == 1) | (chauf1 == 3)) & (age_child1 < 6),coef_child_age_u6_rs +util_child2_age_u6_rs,Child 2 age under 6 years old - Ride Share,((chauf2 == 1) | (chauf2 == 3)) & (age_child2 < 6),coef_child_age_u6_rs +util_child3_age_u6_rs,Child 3 age under 6 years old - Ride Share,((chauf3 == 1) | (chauf3 == 3)) & (age_child3 < 6),coef_child_age_u6_rs +# ,Pure Escort Distance,, +util_child1_dist_pe,Child 1 distance to school - Pure escorting,((chauf1 == 2) | (chauf1 == 4)) * dist_home_to_school1,coef_child_dist_pe +util_child2_dist_pe,Child 2 distance to school - Pure escorting,((chauf2 == 2) | (chauf2 == 4)) * dist_home_to_school2,coef_child_dist_pe +util_child3_dist_pe,Child 3 distance to school - Pure escorting,((chauf3 == 2) | (chauf3 == 4)) * dist_home_to_school3,coef_child_dist_pe +util_child1_dist_pe_over30miles,Child 1 Pure escorting not allowed if over 30 miles,((chauf1 == 2) | (chauf1 == 4)) & (dist_home_to_school1 > 30),coef_unavail +util_child2_dist_pe_over30miles,Child 2 Pure escorting not allowed if over 30 miles,((chauf2 == 2) | (chauf2 == 4)) & (dist_home_to_school2 > 30),coef_unavail +util_child3_dist_pe_over30miles,Child 3 Pure escorting not allowed if over 30 miles,((chauf3 == 2) | (chauf3 == 4)) & (dist_home_to_school3 > 30),coef_unavail +# ,Household Interactions,, +util_hh_inc_u25k_noes,Household income less than 25k - No Escorting,(income <= 25000) & ((bundle1 == 0) | ((bundle2 == 0) & (num_children_going_to_school > 1)) | ((bundle3 == 0) & (num_children_going_to_school > 2))),coef_hh_inc_u25k_noes +util_hh_inc_25to50k_noes,Household income between 25 and 50k - No Escorting,((income > 25000) & (income <= 50000)) & ((bundle1 == 0) | ((bundle2 == 0) & (num_children_going_to_school > 1)) | ((bundle3 == 0) & (num_children_going_to_school > 2))),coef_hh_inc_25to50k_noes +util_zero_auto_hh_noes,Zero cars in the household - No Escorting,(auto_ownership == 0) & ((bundle1 == 0) | ((bundle2 == 0) & (num_children_going_to_school > 1)) | ((bundle3 == 0) & (num_children_going_to_school > 2))),coef_zero_auto_hh_noes +util_cars_lt_workers_rs,Cars fewer than household workers - Ride Share,(auto_ownership < num_workers) & ((chauf1 % 2 == 1) | (chauf2 % 2 == 1) | (chauf3 % 2 == 1)),coef_cars_lt_workers_rs +util_cars_lt_workers_pe,Cars fewer than household workers - Pure escort,(auto_ownership < num_workers) & ((chauf1 % 2 == 0) | (chauf2 % 2 == 0) | (chauf3 % 2 == 0)),coef_cars_lt_workers_pe +# ,Chauffer Interactions,, +util_chauf1_female_rs,Chauffeur 1 Female - Ride share,(gender_chauf1 == 2) & ((chauf1 == 1) | (chauf2 == 1) | (chauf3 == 1)),coef_chauf_female_rs +util_chauf1_male_rs,Chauffeur 1 Male - Ride share,(gender_chauf1 == 1) & ((chauf1 == 1) | (chauf2 == 1) | (chauf3 == 1)),coef_chauf_male_rs +util_chauf1_female_pe,Chauffeur 1 Female - Pure Escort,(gender_chauf1 == 2) & ((chauf1 == 2) | (chauf2 == 2) | (chauf3 == 2)),coef_chauf_female_pe +util_chauf1_male_pe,Chauffeur 1 Male - Pure Escort,(gender_chauf1 == 1) & ((chauf1 == 2) | (chauf2 == 2) | (chauf3 == 2)),coef_chauf_male_pe +util_chauf2_female_rs,Chauffeur 2 Female - Ride share,(gender_chauf2 == 2) & ((chauf1 == 3) | (chauf2 == 3) | (chauf3 == 3)),coef_chauf_female_rs +util_chauf2_male_rs,Chauffeur 2 Male - Ride share,(gender_chauf2 == 1) & ((chauf1 == 3) | (chauf2 == 3) | (chauf3 == 3)),coef_chauf_male_rs +util_chauf2_female_pe,Chauffeur 2 Female - Pure Escort,(gender_chauf2 == 2) & ((chauf1 == 4) | (chauf2 == 4) | (chauf3 == 4)),coef_chauf_female_pe +util_chauf2_male_pe,Chauffeur 2 Male - Pure Escort,(gender_chauf2 == 1) & ((chauf1 == 4) | (chauf2 == 4) | (chauf3 == 4)),coef_chauf_male_pe +util_chauf1_pt_worker_rs,Chauffer 1 part time worker - Ride share,(ptype_chauf1 == 2) & ((chauf1 == 1) | (chauf2 == 1) | (chauf3 == 1)),coef_chauf_pt_worker_rs +util_chauf1_pt_worker_pe,Chauffer 1 part time worker - Pure escort,(ptype_chauf1 == 2) & ((chauf1 == 2) | (chauf2 == 2) | (chauf3 == 2)),coef_chauf_pt_worker_pe +util_chauf2_pt_worker_rs,Chauffer 2 part time worker - Ride share,(ptype_chauf2 == 2) & ((chauf1 == 3) | (chauf2 == 3) | (chauf3 == 3)),coef_chauf_pt_worker_rs +util_chauf2_pt_worker_pe,Chauffer 2 part time worker - Pure escort,(ptype_chauf2 == 2) & ((chauf1 == 4) | (chauf2 == 4) | (chauf3 == 4)),coef_chauf_pt_worker_pe +util_chauf1_non_worker_pe,Chauffer 1 non worker - Pure escort,(ptype_chauf1 == 4) & ((chauf1 == 2) | (chauf2 == 2) | (chauf3 == 2)),coef_chauf_non_worker_pe +util_chauf2_non_worker_pe,Chauffer 2 non worker - Pure escort,(ptype_chauf2 == 4) & ((chauf1 == 4) | (chauf2 == 4) | (chauf3 == 4)),coef_chauf_non_worker_pe +util_chauf1_univ_stud_re,Chauffer 1 university student - Ride Share,(ptype_chauf1 == 3) & ((chauf1 == 1) | (chauf2 == 1) | (chauf3 == 1)),coef_chauf_univ_stud_re +util_chauf2_univ_stud_re,Chauffer 2 university student - Ride Share,(ptype_chauf2 == 3) & ((chauf1 == 3) | (chauf2 == 3) | (chauf3 == 3)),coef_chauf_univ_stud_re +util_chauf1_age_u35_pe,Chauffer 1 Age 35 years or younger - Pure escort,(ptype_chauf1 == 4) & ((chauf1 == 2) | (chauf2 == 2) | (chauf3 == 2)),coef_chauf_age_u35_pe +util_chauf2_age_u35_pe,Chauffer 2 Age 35 years or younger - Pure escort,(ptype_chauf2 == 4) & ((chauf1 == 4) | (chauf2 == 4) | (chauf3 == 4)),coef_chauf_age_u35_pe +util_chauf1_time_to_work_or_univ_rs,Chauffer 1 Auto time to work or university - Ride Share,time_home_to_mand1 * ((chauf1 == 1) | (chauf2 == 1) | (chauf3 == 1)),coef_chauf_time_to_work_or_univ_rs +util_chauf2_time_to_work_or_univ_rs,Chauffer 2 Auto time to work or university - Ride Share,time_home_to_mand2 * ((chauf1 == 3) | (chauf2 == 3) | (chauf3 == 3)),coef_chauf_time_to_work_or_univ_rs +util_chauf1_walk_dist_to_work_or_univ_rs,Chauffer 1 Walk dist to work or university - Ride Share,(dist_home_to_mand1 < 3) & ((chauf1 == 1) | (chauf2 == 1) | (chauf3 == 1)),coef_chauf_walk_dist_to_work_or_univ_rs +util_chauf2_walk_dist_to_work_or_univ_rs,Chauffer 2 Walk dist to work or university - Ride Share,(dist_home_to_mand2 < 3) & ((chauf1 == 3) | (chauf2 == 3) | (chauf3 == 3)),coef_chauf_walk_dist_to_work_or_univ_rs +util_chauf1_walk_dist_to_work_or_univ_pe,Chauffer 1 Walk dist to work or university - Pure Escort,(dist_home_to_mand1 < 3) & ((chauf1 == 2) | (chauf2 == 2) | (chauf3 == 2)),coef_chauf_walk_dist_to_work_or_univ_pe +util_chauf2_walk_dist_to_work_or_univ_pe,Chauffer 2 Walk dist to work or university - Pure Escort,(dist_home_to_mand2 < 3) & ((chauf1 == 4) | (chauf2 == 4) | (chauf3 == 4)),coef_chauf_walk_dist_to_work_or_univ_pe +# ,Chauffer deviation,, +util_chauf1_abs_dev_rs_child1only,Chauffer 1 Absolute deviation ride share - child 1 only,((bundle1 != bundle2) & (bundle1 != bundle3) & (chauf1 == 1)) * abs_dev_dist_out_child1_chauf1,coef_abs_dev_distance +util_chauf2_abs_dev_rs_child1only,Chauffer 2 Absolute deviation ride share - child 1 only,((bundle1 != bundle2) & (bundle1 != bundle3) & (chauf1 == 3)) * abs_dev_dist_out_child1_chauf2,coef_abs_dev_distance +util_chauf1_abs_dev_rs_child2only,Chauffer 1 Absolute deviation ride share - child 2 only,((bundle1 != bundle2) & (bundle2 != bundle3) & (chauf2 == 1)) * abs_dev_dist_out_child2_chauf1,coef_abs_dev_distance +util_chauf2_abs_dev_rs_child2only,Chauffer 2 Absolute deviation ride share - child 2 only,((bundle1 != bundle2) & (bundle2 != bundle3) & (chauf2 == 3)) * abs_dev_dist_out_child2_chauf2,coef_abs_dev_distance +util_chauf1_abs_dev_rs_child3only,Chauffer 1 Absolute deviation ride share - child 3 only,((bundle1 != bundle3) & (bundle2 != bundle3) & (chauf3 == 1)) * abs_dev_dist_out_child3_chauf1,coef_abs_dev_distance +util_chauf2_abs_dev_rs_child3only,Chauffer 2 Absolute deviation ride share - child 3 only,((bundle1 != bundle3) & (bundle2 != bundle3) & (chauf3 == 3)) * abs_dev_dist_out_child3_chauf2,coef_abs_dev_distance +util_chauf1_abs_dev_rs_child12,Chauffer 1 Absolute deviation ride share - child 1 & 2,((bundle1 == bundle2) & (bundle1 != bundle3) & (chauf1 == 1)) * abs_dev_dist_out_child12_chauf1,coef_abs_dev_distance +util_chauf2_abs_dev_rs_child12,Chauffer 2 Absolute deviation ride share - child 1 & 2,((bundle1 == bundle2) & (bundle1 != bundle3) & (chauf1 == 3)) * abs_dev_dist_out_child12_chauf2,coef_abs_dev_distance +util_chauf1_abs_dev_rs_child13,Chauffer 1 Absolute deviation ride share - child 1 & 3,((bundle1 != bundle2) & (bundle1 == bundle3) & (chauf1 == 1)) * abs_dev_dist_out_child13_chauf1,coef_abs_dev_distance +util_chauf2_abs_dev_rs_child13,Chauffer 2 Absolute deviation ride share - child 1 & 3,((bundle1 != bundle2) & (bundle1 == bundle3) & (chauf1 == 3)) * abs_dev_dist_out_child13_chauf2,coef_abs_dev_distance +util_chauf1_abs_dev_rs_child23,Chauffer 1 Absolute deviation ride share - child 2 & 3,((bundle1 != bundle2) & (bundle2 == bundle3) & (chauf2 == 1)) * abs_dev_dist_out_child23_chauf1,coef_abs_dev_distance +util_chauf2_abs_dev_rs_child23,Chauffer 2 Absolute deviation ride share - child 2 & 3,((bundle1 != bundle2) & (bundle2 == bundle3) & (chauf2 == 3)) * abs_dev_dist_out_child23_chauf2,coef_abs_dev_distance +util_chauf1_abs_dev_rs_child123,Chauffer 1 Absolute deviation ride share - child 1 & 2 & 3,((bundle1 == bundle2) & (bundle2 == bundle3) & (chauf1 == 1)) * abs_dev_dist_out_child123_chauf1,coef_abs_dev_distance +util_chauf2_abs_dev_rs_child123,Chauffer 2 Absolute deviation ride share - child 1 & 2 & 3,((bundle1 == bundle2) & (bundle2 == bundle3) & (chauf1 == 3)) * abs_dev_dist_out_child123_chauf2,coef_abs_dev_distance +util_chauf1_rel_dev_rs_child1only,Chauffer 1 Relative deviation ride share - child 1 only,((bundle1 != bundle2) & (bundle1 != bundle3) & (chauf1 == 1)) * abs_dev_dist_out_child1_chauf1 / (dist_home_to_mand1 + 0.001),coef_rel_dev_distance +util_chauf2_rel_dev_rs_child1only,Chauffer 2 Relative deviation ride share - child 1 only,((bundle1 != bundle2) & (bundle1 != bundle3) & (chauf1 == 3)) * abs_dev_dist_out_child1_chauf2 / (dist_home_to_mand2 + 0.001),coef_rel_dev_distance +util_chauf1_rel_dev_rs_child2only,Chauffer 1 Relative deviation ride share - child 2 only,((bundle1 != bundle2) & (bundle2 != bundle3) & (chauf2 == 1)) * abs_dev_dist_out_child2_chauf1 / (dist_home_to_mand1 + 0.001),coef_rel_dev_distance +util_chauf2_rel_dev_rs_child2only,Chauffer 2 Relative deviation ride share - child 2 only,((bundle1 != bundle2) & (bundle2 != bundle3) & (chauf2 == 3)) * abs_dev_dist_out_child2_chauf2 / (dist_home_to_mand2 + 0.001),coef_rel_dev_distance +util_chauf1_rel_dev_rs_child3only,Chauffer 1 Relative deviation ride share - child 3 only,((bundle1 != bundle3) & (bundle2 != bundle3) & (chauf3 == 1)) * abs_dev_dist_out_child3_chauf1 / (dist_home_to_mand1 + 0.001),coef_rel_dev_distance +util_chauf2_rel_dev_rs_child3only,Chauffer 2 Relative deviation ride share - child 3 only,((bundle1 != bundle3) & (bundle2 != bundle3) & (chauf3 == 3)) * abs_dev_dist_out_child3_chauf2 / (dist_home_to_mand2 + 0.001),coef_rel_dev_distance +util_chauf1_rel_dev_rs_child12,Chauffer 1 Relative deviation ride share - child 1 & 2,((bundle1 == bundle2) & (bundle1 != bundle3) & (chauf1 == 1)) * abs_dev_dist_out_child12_chauf1 / (dist_home_to_mand1 + 0.001),coef_rel_dev_distance +util_chauf2_rel_dev_rs_child12,Chauffer 2 Relative deviation ride share - child 1 & 2,((bundle1 == bundle2) & (bundle1 != bundle3) & (chauf1 == 3)) * abs_dev_dist_out_child12_chauf2 / (dist_home_to_mand2 + 0.001),coef_rel_dev_distance +util_chauf1_rel_dev_rs_child13,Chauffer 1 Relative deviation ride share - child 1 & 3,((bundle1 != bundle2) & (bundle1 == bundle3) & (chauf1 == 1)) * abs_dev_dist_out_child13_chauf1 / (dist_home_to_mand1 + 0.001),coef_rel_dev_distance +util_chauf2_rel_dev_rs_child13,Chauffer 2 Relative deviation ride share - child 1 & 3,((bundle1 != bundle2) & (bundle1 == bundle3) & (chauf1 == 3)) * abs_dev_dist_out_child13_chauf2 / (dist_home_to_mand2 + 0.001),coef_rel_dev_distance +util_chauf1_rel_dev_rs_child23,Chauffer 1 Relative deviation ride share - child 2 & 3,((bundle1 != bundle2) & (bundle2 == bundle3) & (chauf2 == 1)) * abs_dev_dist_out_child23_chauf1 / (dist_home_to_mand1 + 0.001),coef_rel_dev_distance +util_chauf2_rel_dev_rs_child23,Chauffer 2 Relative deviation ride share - child 2 & 3,((bundle1 != bundle2) & (bundle2 == bundle3) & (chauf2 == 3)) * abs_dev_dist_out_child23_chauf2 / (dist_home_to_mand2 + 0.001),coef_rel_dev_distance +util_chauf1_rel_dev_rs_child123,Chauffer 1 Relative deviation ride share - child 1 & 2 & 3,((bundle1 == bundle2) & (bundle2 == bundle3) & (chauf1 == 1)) * abs_dev_dist_out_child123_chauf1 / (dist_home_to_mand1 + 0.001),coef_rel_dev_distance +util_chauf2_rel_dev_rs_child123,Chauffer 2 Relative deviation ride share - child 1 & 2 & 3,((bundle1 == bundle2) & (bundle2 == bundle3) & (chauf1 == 3)) * abs_dev_dist_out_child123_chauf2 / (dist_home_to_mand2 + 0.001),coef_rel_dev_distance +# ,Same TAZ,, +util_same_taz_child12_escort,Same school TAZ for child 1 and 2 - Escorting,(bundle1 == bundle2) & (bundle1 > 0) & (school_location_child1 == school_location_child2) & (school_location_child1 > 0),coef_same_taz_escort +util_same_taz_child13_escort,Same school TAZ for child 1 and 3 - Escorting,(bundle1 == bundle3) & (bundle1 > 0) & (school_location_child1 == school_location_child3) & (school_location_child1 > 0),coef_same_taz_escort +util_same_taz_child32_escort,Same school TAZ for child 3 and 2 - Escorting,(bundle3 == bundle2) & (bundle2 > 0) & (school_location_child3 == school_location_child2) & (school_location_child2 > 0),coef_same_taz_escort +util_same_taz_child12_no_escort,Same school TAZ for child 1 and 2 - No Escorting,(bundle1 == 0) & (bundle2 == 0) & (child_id1 > 0) & (child_id2 > 0) & (school_location_child1 == school_location_child2) & (school_location_child1 > 0),coef_same_taz_no_escort +util_same_taz_child13_no_escort,Same school TAZ for child 1 and 3 - No Escorting,(bundle1 == 0) & (bundle3 == 0) & (child_id1 > 0) & (child_id3 > 0) & (school_location_child1 == school_location_child3) & (school_location_child1 > 0),coef_same_taz_no_escort +util_same_taz_child32_no_escort,Same school TAZ for child 3 and 2 - No Escorting,(bundle3 == 0) & (bundle2 == 0) & (child_id3 > 0) & (child_id2 > 0) & (school_location_child3 == school_location_child2) & (school_location_child2 > 0),coef_same_taz_no_escort +# ,Constants related to inbound choice,, +util_no_escort_inbound_child1,No escorting in inbound direction - Child 1,(bundle1 == 0) & (child_id1 > 0) & (bundle1_inbound == 0),coef_no_escort_inbound +util_no_escort_inbound_child2,No escorting in inbound direction - Child 2,(bundle2 == 0) & (child_id2 > 0) & (bundle2_inbound == 0),coef_no_escort_inbound +util_no_escort_inbound_child3,No escorting in inbound direction - Child 3,(bundle3 == 0) & (child_id3 > 0) & (bundle3_inbound == 0),coef_no_escort_inbound +util_same_chauf1,Same chauffeur in both directions (not child specific) - chauf 1 inbound & inbound,((chauf1 == 1) | (chauf1 == 2) | (chauf2 == 1) | (chauf2 == 2) | (chauf3 == 1) | (chauf3 == 2)) & ((nbund1_inbound > 0)),coef_same_chauf +util_same_chauf2,Same chauffeur in both directions (not child specific) - chauf 2 inbound & inbound,((chauf1 == 3) | (chauf1 == 4) | (chauf2 == 3) | (chauf2 == 4) | (chauf3 == 3) | (chauf3 == 4)) & ((nbund2_inbound > 0)),coef_same_chauf +# ,Calibration constants,, +util_calib_child1_age_u6_rs,Child 1 age under 6 - Calibration constant - Ride Share,((chauf1 == 1) | (chauf1 == 3)) & (age_child1 < 6),coef_calib_child_age_u6_rs +util_calib_child2_age_u6_rs,Child 2 age under 6 - Calibration constant - Ride Share,((chauf2 == 1) | (chauf2 == 3)) & (age_child2 < 6),coef_calib_child_age_u6_rs +util_calib_child3_age_u6_rs,Child 3 age under 6 - Calibration constant - Ride Share,((chauf3 == 1) | (chauf3 == 3)) & (age_child3 < 6),coef_calib_child_age_u6_rs +util_calib_child1_age_16p_rs,Child 1 age 16 years or older - Calibration constant - Ride Share,((chauf1 == 1) | (chauf1 == 3)) & (age_child1 > 15),coef_calib_child_age_16p_rs +util_calib_child2_age_16p_rs,Child 2 age 16 years or older - Calibration constant - Ride Share,((chauf2 == 1) | (chauf2 == 3)) & (age_child2 > 15),coef_calib_child_age_16p_rs +util_calib_child3_age_16p_rs,Child 3 age 16 years or older - Calibration constant - Ride Share,((chauf3 == 1) | (chauf3 == 3)) & (age_child3 > 15),coef_calib_child_age_16p_rs +util_calib_child1_age_6to15_noes,Child 1 age 6 to 15 years - Calibration constant - No Escort,(bundle1 == 0) & (child_id1 > 0) & (age_child1 > 5) & (age_child1 < 16),coef_calib_child_age_6to15_noes +util_calib_child2_age_6to15_noes,Child 2 age 6 to 15 years - Calibration constant - No Escort,(bundle2 == 0) & (child_id2 > 0) & (age_child2 > 5) & (age_child2 < 16),coef_calib_child_age_6to15_noes +util_calib_child3_age_6to15_noes,Child 3 age 6 to 15 years - Calibration constant - No Escort,(bundle3 == 0) & (child_id3 > 0) & (age_child3 > 5) & (age_child3 < 16),coef_calib_child_age_6to15_noes +util_calib_child1_age_6to15_rs,Child 1 age 6 to 15 years - Calibration constant - Ride Share,((chauf1 == 1) | (chauf1 == 3)) & (age_child1 > 5) & (age_child1 < 16),coef_calib_child_age_6to15_rs +util_calib_child2_age_6to15_rs,Child 2 age 6 to 15 years - Calibration constant - Ride Share,((chauf2 == 1) | (chauf2 == 3)) & (age_child2 > 5) & (age_child2 < 16),coef_calib_child_age_6to15_rs +util_calib_child3_age_6to15_rs,Child 3 age 6 to 15 years - Calibration constant - Ride Share,((chauf3 == 1) | (chauf3 == 3)) & (age_child3 > 5) & (age_child3 < 16),coef_calib_child_age_6to15_rs +util_calib_child1_age_u6_noes,Child 1 age 6 years or younger - Calibration constant - No escort,(bundle1 == 0) & (child_id1 > 0) & (age_child1 < 6),coef_calib_child_age_u6_noes +util_calib_child2_age_u6_noes,Child 2 age 6 years or younger - Calibration constant - No escort,(bundle2 == 0) & (child_id2 > 0) & (age_child2 < 6),coef_calib_child_age_u6_noes +util_calib_child3_age_u6_noes,Child 3 age 6 years or younger - Calibration constant - No escort,(bundle3 == 0) & (child_id3 > 0) & (age_child3 < 6),coef_calib_child_age_u6_noes diff --git a/resident/configs/school_escorting_preprocessor_inbound.csv b/resident/configs/school_escorting_preprocessor_inbound.csv new file mode 100644 index 0000000..4c174fc --- /dev/null +++ b/resident/configs/school_escorting_preprocessor_inbound.csv @@ -0,0 +1,148 @@ +Description,Target,Expression +Number of children going to school,num_children_going_to_school,"np.where(df['child_id1'] > 0, 1, 0) + np.where(df['child_id2'] > 0, 1, 0) + np.where(df['child_id3'] > 0,1,0)" +Number of potential chauffeurs,num_potential_chauffeurs,"np.where(df['chauf_id1'] > 0, 1, 0) + np.where(df['chauf_id2'] > 0, 1, 0)" +Person Type - chauffer 1,ptype_chauf1,"reindex(persons.ptype, df.chauf_id1)" +Person Type - chauffer 2,ptype_chauf2,"reindex(persons.ptype, df.chauf_id2)" +Gender - chauffer 1,gender_chauf1,"reindex(persons.SEX, df.chauf_id1)" +Gender - chauffer 2,gender_chauf2,"reindex(persons.SEX, df.chauf_id2)" +Age - chauffer 1,age_chauf1,"reindex(persons.age, df.chauf_id1)" +Age - chauffer 2,age_chauf2,"reindex(persons.age, df.chauf_id2)" +Daily activity pattern - chauffer 1,cdap_chauf1,"reindex(persons.cdap_activity, df.chauf_id1)" +Daily activity pattern - chauffer 2,cdap_chauf2,"reindex(persons.cdap_activity, df.chauf_id2)" +Age - child 1,age_child1,"reindex(persons.age, df.child_id1)" +Age - child 2,age_child2,"reindex(persons.age, df.child_id2)" +Age - child 3,age_child3,"reindex(persons.age, df.child_id3)" +# Departure times from school and work ,, +Preferred departure time from school - child 1,pref_depart_time_school1,"reindex(tours[(tours.tour_type == 'school') & (tours.tour_num == 1)].set_index('person_id').end, df.child_id1)" +Preferred departure time from school - child 2,pref_depart_time_school2,"reindex(tours[(tours.tour_type == 'school') & (tours.tour_num == 1)].set_index('person_id').end, df.child_id2)" +Preferred departure time from school - child 3,pref_depart_time_school3,"reindex(tours[(tours.tour_type == 'school') & (tours.tour_num == 1)].set_index('person_id').end, df.child_id3)" +# setting preffered departure time for chauffer to the last mandatory tour of the day for inbound escorting ,, +Preferred departure time from work / univ - chauffer 1 - tour 1,pref_depart_time_chauf1_tour1,"reindex(tours[(tours.tour_category == 'mandatory') & (tours.tour_num == 1)].set_index('person_id').end, df.chauf_id1)" +Preferred departure time from work / univ - chauffer 2 - tour 1,pref_depart_time_chauf2_tour1,"reindex(tours[(tours.tour_category == 'mandatory') & (tours.tour_num == 1)].set_index('person_id').end, df.chauf_id2)" +Preferred departure time from work / univ - chauffer 1 - tour 2,pref_depart_time_chauf1_tour2,"reindex(tours[(tours.tour_category == 'mandatory') & (tours.tour_num == 2)].set_index('person_id').end, df.chauf_id1)" +Preferred departure time from work / univ - chauffer 2 - tour 2,pref_depart_time_chauf2_tour2,"reindex(tours[(tours.tour_category == 'mandatory') & (tours.tour_num == 2)].set_index('person_id').end, df.chauf_id2)" +Preferred departure time from work / univ - chauffer 1,pref_depart_time_chauf1,"np.where(pref_depart_time_chauf1_tour2 > pref_depart_time_chauf1_tour1, pref_depart_time_chauf1_tour2, pref_depart_time_chauf1_tour1)" +Preferred departure time from work / univ - chauffer 1,pref_depart_time_chauf2,"np.where(pref_depart_time_chauf2_tour2 > pref_depart_time_chauf2_tour1, pref_depart_time_chauf2_tour2, pref_depart_time_chauf2_tour1)" +# Distances and times to school and work ,, +School location - child 1,school_location_child1,"reindex(persons.school_zone_id, df.child_id1)" +School location - child 2,school_location_child2,"reindex(persons.school_zone_id, df.child_id2)" +School location - child 3,school_location_child3,"reindex(persons.school_zone_id, df.child_id3)" +School location - chauffer 1,_school_location_chauf1,"reindex(persons.workplace_zone_id, df.chauf_id1)" +School location - chauffer 2,_school_location_chauf2,"reindex(persons.workplace_zone_id, df.chauf_id2)" +Work location - chauffer 1,_work_location_chauf1,"reindex(persons.workplace_zone_id, df.chauf_id1)" +Work location - chauffer 2,_work_location_chauf2,"reindex(persons.workplace_zone_id, df.chauf_id2)" +Mandatory tour location - chauffer 1,_mandatory_location_chauf1,"_school_location_chauf1.where(ptype_chauf1 == 3, _work_location_chauf1)" +Mandatory tour location - chauffer 2,_mandatory_location_chauf2,"_school_location_chauf1.where(ptype_chauf2 == 3, _work_location_chauf2)" +# creating valid school locations to pass to skim_dict,, +,_valid_school_location_child1,school_location_child1.fillna(persons[persons.school_zone_id > 0].school_zone_id.mode()[0]) +,_valid_school_location_child2,school_location_child2.fillna(persons[persons.school_zone_id > 0].school_zone_id.mode()[0]) +,_valid_school_location_child3,school_location_child3.fillna(persons[persons.school_zone_id > 0].school_zone_id.mode()[0]) +,_valid_mandatory_location_chauf1,_mandatory_location_chauf1.fillna(persons[persons.workplace_zone_id > 0].workplace_zone_id.mode()[0]) +,_valid_mandatory_location_chauf2,_mandatory_location_chauf2.fillna(persons[persons.workplace_zone_id > 0].workplace_zone_id.mode()[0]) +Auto time home to school - child 1,time_home_to_school1,"np.where(school_location_child1 > 0, skim_dict.lookup(df.home_zone_id, _valid_school_location_child1, ('SR2_M_TIME', 'PM')), 0)" +Auto time home to school - child 2,time_home_to_school2,"np.where(school_location_child2 > 0, skim_dict.lookup(df.home_zone_id, _valid_school_location_child2, ('SR2_M_TIME', 'PM')), 0)" +Auto time home to school - child 3,time_home_to_school3,"np.where(school_location_child3 > 0, skim_dict.lookup(df.home_zone_id, _valid_school_location_child3, ('SR2_M_TIME', 'PM')), 0)" +Auto time school to home - child 1,time_school_to_home1,"np.where(school_location_child1 > 0, skim_dict.lookup(_valid_school_location_child1, df.home_zone_id, ('SR2_M_TIME', 'PM')), 0)" +Auto time school to home - child 2,time_school_to_home2,"np.where(school_location_child2 > 0, skim_dict.lookup(_valid_school_location_child2, df.home_zone_id, ('SR2_M_TIME', 'PM')), 0)" +Auto time school to home - child 3,time_school_to_home3,"np.where(school_location_child3 > 0, skim_dict.lookup(_valid_school_location_child3, df.home_zone_id, ('SR2_M_TIME', 'PM')), 0)" +Auto dist home to school - child 1,dist_school_to_home1,"np.where(school_location_child1 > 0, skim_dict.lookup(_valid_school_location_child1, df.home_zone_id, ('SR2_M_DIST', 'PM')), 0)" +Auto dist home to school - child 2,dist_school_to_home2,"np.where(school_location_child2 > 0, skim_dict.lookup(_valid_school_location_child2, df.home_zone_id, ('SR2_M_DIST', 'PM')), 0)" +Auto dist home to school - child 3,dist_school_to_home3,"np.where(school_location_child3 > 0, skim_dict.lookup(_valid_school_location_child3, df.home_zone_id, ('SR2_M_DIST', 'PM')), 0)" +Auto dist intra school taz - child 1,dist_intra_school1,"np.where(school_location_child1 > 0, skim_dict.lookup(df.home_zone_id, _valid_school_location_child1, ('SR2_M_DIST', 'PM')), 0)" +Auto dist intra school taz - child 2,dist_intra_school2,"np.where(school_location_child2 > 0, skim_dict.lookup(df.home_zone_id, _valid_school_location_child2, ('SR2_M_DIST', 'PM')), 0)" +Auto dist intra school taz - child 3,dist_intra_school3,"np.where(school_location_child3 > 0, skim_dict.lookup(df.home_zone_id, _valid_school_location_child3, ('SR2_M_DIST', 'PM')), 0)" +Auto time home to work or university - chauffer 1,time_home_to_mand1,"np.where(_mandatory_location_chauf1 > 0, skim_dict.lookup(df.home_zone_id, _valid_mandatory_location_chauf1, ('SR2_M_TIME', 'PM')), 0)" +Auto time home to work or university - chauffer 2,time_home_to_mand2,"np.where(_mandatory_location_chauf2 > 0, skim_dict.lookup(df.home_zone_id, _valid_mandatory_location_chauf2, ('SR2_M_TIME', 'PM')), 0)" +Auto time work or university to home - chauffer 1,time_mand_to_home1,"np.where(_mandatory_location_chauf1 > 0, skim_dict.lookup(_valid_mandatory_location_chauf1, df.home_zone_id, ('SR2_M_TIME', 'PM')), 0)" +Auto time work or university to home - chauffer 2,time_mand_to_home2,"np.where(_mandatory_location_chauf2 > 0, skim_dict.lookup(_valid_mandatory_location_chauf2, df.home_zone_id, ('SR2_M_TIME', 'PM')), 0)" +Auto dist home to work or university - chauffer 1,dist_home_to_mand1,"np.where(_mandatory_location_chauf1 > 0, skim_dict.lookup(df.home_zone_id, _valid_mandatory_location_chauf1, ('SR2_M_DIST', 'PM')), 0)" +Auto dist home to work or university - chauffer 2,dist_home_to_mand2,"np.where(_mandatory_location_chauf2 > 0, skim_dict.lookup(df.home_zone_id, _valid_mandatory_location_chauf2, ('SR2_M_DIST', 'PM')), 0)" +Auto dist work or university to home - chauffer 1,dist_mand1_to_home,"np.where(_mandatory_location_chauf1 > 0, skim_dict.lookup(_valid_mandatory_location_chauf1, df.home_zone_id, ('SR2_M_DIST', 'PM')), 0)" +Auto dist work or university to home - chauffer 2,dist_mand2_to_home,"np.where(_mandatory_location_chauf2 > 0, skim_dict.lookup(_valid_mandatory_location_chauf2, df.home_zone_id, ('SR2_M_DIST', 'PM')), 0)" +# inbound distance combinations between chauffeurs and children,, +Distance from chauffeur 1 mandatory location to child 1 school,time_mand1_to_school1,"np.where((school_location_child1 > 0) & (_mandatory_location_chauf1 > 0), skim_dict.lookup(_valid_school_location_child1, _valid_mandatory_location_chauf1, ('SR2_M_TIME', 'PM')), 0)" +Distance from chauffeur 1 mandatory location to child 2 school,time_mand1_to_school2,"np.where((school_location_child2 > 0) & (_mandatory_location_chauf1 > 0), skim_dict.lookup(_valid_school_location_child2, _valid_mandatory_location_chauf1, ('SR2_M_TIME', 'PM')), 0)" +Distance from chauffeur 1 mandatory location to child 3 school,time_mand1_to_school3,"np.where((school_location_child3 > 0) & (_mandatory_location_chauf1 > 0), skim_dict.lookup(_valid_school_location_child3, _valid_mandatory_location_chauf1, ('SR2_M_TIME', 'PM')), 0)" +Distance from chauffeur 2 mandatory location to child 1 school,time_mand2_to_school1,"np.where((school_location_child1 > 0) & (_mandatory_location_chauf2 > 0), skim_dict.lookup(_valid_school_location_child1, _valid_mandatory_location_chauf2, ('SR2_M_TIME', 'PM')), 0)" +Distance from chauffeur 2 mandatory location to child 2 school,time_mand2_to_school2,"np.where((school_location_child2 > 0) & (_mandatory_location_chauf2 > 0), skim_dict.lookup(_valid_school_location_child2, _valid_mandatory_location_chauf2, ('SR2_M_TIME', 'PM')), 0)" +Distance from chauffeur 2 mandatory location to child 3 school,time_mand2_to_school3,"np.where((school_location_child3 > 0) & (_mandatory_location_chauf2 > 0), skim_dict.lookup(_valid_school_location_child3, _valid_mandatory_location_chauf2, ('SR2_M_TIME', 'PM')), 0)" +Distance from chauffeur 1 mandatory location to child 1 school,_dist_mand1_to_school1,"np.where((school_location_child1 > 0) & (_mandatory_location_chauf1 > 0), skim_dict.lookup(_valid_mandatory_location_chauf1, _valid_school_location_child1, ('SR2_M_TIME', 'PM')), 0)" +Distance from chauffeur 2 mandatory location to child 1 school,_dist_mand2_to_school1,"np.where((school_location_child1 > 0) & (_mandatory_location_chauf2 > 0), skim_dict.lookup(_valid_mandatory_location_chauf2, _valid_school_location_child1, ('SR2_M_TIME', 'PM')), 0)" +Distance from chauffeur 1 mandatory location to child 2 school,_dist_mand1_to_school2,"np.where((school_location_child2 > 0) & (_mandatory_location_chauf1 > 0), skim_dict.lookup(_valid_mandatory_location_chauf1, _valid_school_location_child2, ('SR2_M_TIME', 'PM')), 0)" +Distance from chauffeur 2 mandatory location to child 2 school,_dist_mand2_to_school2,"np.where((school_location_child2 > 0) & (_mandatory_location_chauf2 > 0), skim_dict.lookup(_valid_mandatory_location_chauf2, _valid_school_location_child2, ('SR2_M_TIME', 'PM')), 0)" +Distance from chauffeur 1 mandatory location to child 3 school,_dist_mand1_to_school3,"np.where((school_location_child3 > 0) & (_mandatory_location_chauf1 > 0), skim_dict.lookup(_valid_mandatory_location_chauf1, _valid_school_location_child3, ('SR2_M_TIME', 'PM')), 0)" +Distance from chauffeur 2 mandatory location to child 3 school,_dist_mand2_to_school3,"np.where((school_location_child3 > 0) & (_mandatory_location_chauf2 > 0), skim_dict.lookup(_valid_mandatory_location_chauf2, _valid_school_location_child3, ('SR2_M_TIME', 'PM')), 0)" +Distance from child 1 school to child 2 school,_dist_school1_to_school2,"np.where((school_location_child1 > 0) & (school_location_child2 > 0), skim_dict.lookup(_valid_school_location_child1, _valid_school_location_child2, ('SR2_M_TIME', 'PM')), 0)" +Distance from child 1 school to child 3 school,_dist_school1_to_school3,"np.where((school_location_child1 > 0) & (school_location_child3 > 0), skim_dict.lookup(_valid_school_location_child1, _valid_school_location_child3, ('SR2_M_TIME', 'PM')), 0)" +Distance from child 2 school to child 3 school,_dist_school2_to_school3,"np.where((school_location_child2 > 0) & (school_location_child3 > 0), skim_dict.lookup(_valid_school_location_child2, _valid_school_location_child3, ('SR2_M_TIME', 'PM')), 0)" +Distance from child 2 school to child 1 school,_dist_school2_to_school1,"np.where((school_location_child2 > 0) & (school_location_child1 > 0), skim_dict.lookup(_valid_school_location_child2, _valid_school_location_child1, ('SR2_M_TIME', 'PM')), 0)" +Distance from child 3 school to child 1 school,_dist_school3_to_school1,"np.where((school_location_child3 > 0) & (school_location_child1 > 0), skim_dict.lookup(_valid_school_location_child3, _valid_school_location_child1, ('SR2_M_TIME', 'PM')), 0)" +Distance from child 3 school to child 2 school,_dist_school3_to_school2,"np.where((school_location_child3 > 0) & (school_location_child2 > 0), skim_dict.lookup(_valid_school_location_child3, _valid_school_location_child2, ('SR2_M_TIME', 'PM')), 0)" +,_return_min_taking_child1,"(pref_depart_time_school1 * mins_per_time_bin) + time_home_to_school1 + time_school_to_home1" +,_return_min_taking_child2,"(pref_depart_time_school2 * mins_per_time_bin) + time_home_to_school2 + time_school_to_home2" +,_return_min_taking_child3,"(pref_depart_time_school3 * mins_per_time_bin) + time_home_to_school3 + time_school_to_home3" +Availability of taking child 1 and then 2,_avail_child1_then_child2,"(_return_min_taking_child1 < (pref_depart_time_school2 * mins_per_time_bin))" +Availability of taking child 2 and then 1,_avail_child2_then_child1,"(_return_min_taking_child2 < (pref_depart_time_school1 * mins_per_time_bin))" +Availability of taking child 1 and then 3,_avail_child1_then_child3,"(_return_min_taking_child1 < (pref_depart_time_school3 * mins_per_time_bin))" +Availability of taking child 3 and then 1,_avail_child3_then_child1,"(_return_min_taking_child3 < (pref_depart_time_school1 * mins_per_time_bin))" +Availability of taking child 2 and then 3,_avail_child2_then_child3,"(_return_min_taking_child2 < (pref_depart_time_school3 * mins_per_time_bin))" +Availability of taking child 3 and then 2,_avail_child3_then_child2,"(_return_min_taking_child3 < (pref_depart_time_school2 * mins_per_time_bin))" +multiple_bundle_availability,avail_multiple_bundles,(_avail_child1_then_child2 | _avail_child2_then_child1 | _avail_child1_then_child3 | _avail_child3_then_child1 | _avail_child2_then_child3 | _avail_child3_then_child2) +# absolute deviation distance inbound,, +Absolute deviation inbound distance Child 1 Chauffer 1,abs_dev_dist_in_child1_chauf1,"np.maximum(_dist_mand1_to_school1 + dist_school_to_home1 - dist_mand1_to_home,0)" +Absolute deviation inbound distance Child 1 Chauffer 2,abs_dev_dist_in_child1_chauf2,"np.maximum(_dist_mand2_to_school1 + dist_school_to_home1 - dist_mand2_to_home,0)" +Absolute deviation inbound distance Child 2 Chauffer 1,abs_dev_dist_in_child2_chauf1,"np.maximum(_dist_mand1_to_school2 + dist_school_to_home2 - dist_mand1_to_home,0)" +Absolute deviation inbound distance Child 2 Chauffer 2,abs_dev_dist_in_child2_chauf2,"np.maximum(_dist_mand2_to_school2 + dist_school_to_home2 - dist_mand2_to_home,0)" +Absolute deviation inbound distance Child 3 Chauffer 1,abs_dev_dist_in_child3_chauf1,"np.maximum(_dist_mand1_to_school3 + dist_school_to_home3 - dist_mand1_to_home,0)" +Absolute deviation inbound distance Child 3 Chauffer 2,abs_dev_dist_in_child3_chauf2,"np.maximum(_dist_mand2_to_school3 + dist_school_to_home3 - dist_mand2_to_home,0)" +Absolute deviation inbound distance child12 Chauffer 1,abs_dev_dist_in_child12_chauf1,"np.maximum(np.minimum(_dist_mand1_to_school1 + _dist_school1_to_school2 + dist_school_to_home2, _dist_mand1_to_school2 + _dist_school2_to_school1 + dist_school_to_home1) - dist_mand1_to_home, 0)" +Absolute deviation inbound distance child12 Chauffer 2,abs_dev_dist_in_child12_chauf2,"np.maximum(np.minimum(_dist_mand2_to_school1 + _dist_school1_to_school2 + dist_school_to_home2, _dist_mand2_to_school2 + _dist_school2_to_school1 + dist_school_to_home1) - dist_mand2_to_home, 0)" +Absolute deviation inbound distance child13 Chauffer 1,abs_dev_dist_in_child13_chauf1,"np.maximum(np.minimum(_dist_mand1_to_school1 + _dist_school1_to_school3 + dist_school_to_home2, _dist_mand1_to_school3 + _dist_school3_to_school1 + dist_school_to_home3) - dist_mand1_to_home, 0)" +Absolute deviation inbound distance child13 Chauffer 2,abs_dev_dist_in_child13_chauf2,"np.maximum(np.minimum(_dist_mand2_to_school1 + _dist_school1_to_school3 + dist_school_to_home2, _dist_mand2_to_school3 + _dist_school3_to_school1 + dist_school_to_home3) - dist_mand2_to_home, 0)" +Absolute deviation inbound distance child23 Chauffer 1,abs_dev_dist_in_child23_chauf1,"np.maximum(np.minimum(_dist_mand1_to_school1 + _dist_school2_to_school3 + dist_school_to_home3, _dist_mand1_to_school3 + _dist_school3_to_school2 + dist_school_to_home2) - dist_mand1_to_home, 0)" +Absolute deviation inbound distance child23 Chauffer 2,abs_dev_dist_in_child23_chauf2,"np.maximum(np.minimum(_dist_mand2_to_school1 + _dist_school2_to_school3 + dist_school_to_home3, _dist_mand2_to_school3 + _dist_school3_to_school2 + dist_school_to_home2) - dist_mand2_to_home, 0)" +,_dist_mand1_school1_school2_school3,"_dist_mand1_to_school1 + _dist_school1_to_school2 + _dist_school2_to_school3 + dist_school_to_home1" +,_dist_mand1_school1_school3_school2,"_dist_mand1_to_school1 + _dist_school1_to_school3 + _dist_school3_to_school2 + dist_school_to_home1" +,_dist_mand1_school2_school1_school3,"_dist_mand1_to_school2 + _dist_school2_to_school1 + _dist_school1_to_school3 + dist_school_to_home2" +,_dist_mand1_school2_school3_school1,"_dist_mand1_to_school2 + _dist_school2_to_school3 + _dist_school3_to_school1 + dist_school_to_home2" +,_dist_mand1_school3_school1_school2,"_dist_mand1_to_school3 + _dist_school3_to_school1 + _dist_school1_to_school2 + dist_school_to_home3" +,_dist_mand1_school3_school2_school1,"_dist_mand1_to_school3 + _dist_school3_to_school2 + _dist_school2_to_school1 + dist_school_to_home3" +,_min_dist_dropoff_order_in_child123_chauf1,_dist_mand1_school1_school2_school3 +,_min_dist_dropoff_order_in_child123_chauf1,"np.where((_dist_mand1_school1_school3_school2 > 0) & (_dist_mand1_school1_school3_school2 < _min_dist_dropoff_order_in_child123_chauf1), _dist_mand1_school1_school3_school2, _min_dist_dropoff_order_in_child123_chauf1)" +,_min_dist_dropoff_order_in_child123_chauf1,"np.where((_dist_mand1_school2_school1_school3 > 0) & (_dist_mand1_school2_school1_school3 < _min_dist_dropoff_order_in_child123_chauf1), _dist_mand1_school2_school1_school3, _min_dist_dropoff_order_in_child123_chauf1)" +,_min_dist_dropoff_order_in_child123_chauf1,"np.where((_dist_mand1_school2_school3_school1 > 0) & (_dist_mand1_school2_school3_school1 < _min_dist_dropoff_order_in_child123_chauf1), _dist_mand1_school2_school3_school1, _min_dist_dropoff_order_in_child123_chauf1)" +,_min_dist_dropoff_order_in_child123_chauf1,"np.where((_dist_mand1_school3_school1_school2 > 0) & (_dist_mand1_school3_school1_school2 < _min_dist_dropoff_order_in_child123_chauf1), _dist_mand1_school3_school1_school2, _min_dist_dropoff_order_in_child123_chauf1)" +,_min_dist_dropoff_order_in_child123_chauf1,"np.where((_dist_mand1_school3_school2_school1 > 0) & (_dist_mand1_school3_school2_school1 < _min_dist_dropoff_order_in_child123_chauf1), _dist_mand1_school3_school2_school1, _min_dist_dropoff_order_in_child123_chauf1)" +Absolute deviation inbound distance child123 Chauffer 1,abs_dev_dist_in_child123_chauf1,"np.maximum(_min_dist_dropoff_order_in_child123_chauf1 - dist_home_to_mand1, 0)" +,_dist_mand2_school1_school2_school3,"_dist_mand2_to_school1 + _dist_school1_to_school2 + _dist_school2_to_school3 + dist_school_to_home1" +,_dist_mand2_school1_school3_school2,"_dist_mand2_to_school1 + _dist_school1_to_school3 + _dist_school3_to_school2 + dist_school_to_home1" +,_dist_mand2_school2_school1_school3,"_dist_mand2_to_school2 + _dist_school2_to_school1 + _dist_school1_to_school3 + dist_school_to_home2" +,_dist_mand2_school2_school3_school1,"_dist_mand2_to_school2 + _dist_school2_to_school3 + _dist_school3_to_school1 + dist_school_to_home2" +,_dist_mand2_school3_school1_school2,"_dist_mand2_to_school3 + _dist_school3_to_school1 + _dist_school1_to_school2 + dist_school_to_home3" +,_dist_mand2_school3_school2_school1,"_dist_mand2_to_school3 + _dist_school3_to_school2 + _dist_school2_to_school1 + dist_school_to_home3" +,_min_dist_dropoff_order_in_child123_chauf2,_dist_mand2_school1_school2_school3 +,_min_dist_dropoff_order_in_child123_chauf2,"np.where((_dist_mand2_school1_school3_school2 > 0) & (_dist_mand2_school1_school3_school2 < _min_dist_dropoff_order_in_child123_chauf2), _dist_mand2_school1_school3_school2, _min_dist_dropoff_order_in_child123_chauf2)" +,_min_dist_dropoff_order_in_child123_chauf2,"np.where((_dist_mand2_school2_school1_school3 > 0) & (_dist_mand2_school2_school1_school3 < _min_dist_dropoff_order_in_child123_chauf2), _dist_mand2_school2_school1_school3, _min_dist_dropoff_order_in_child123_chauf2)" +,_min_dist_dropoff_order_in_child123_chauf2,"np.where((_dist_mand2_school2_school3_school1 > 0) & (_dist_mand2_school2_school3_school1 < _min_dist_dropoff_order_in_child123_chauf2), _dist_mand2_school2_school3_school1, _min_dist_dropoff_order_in_child123_chauf2)" +,_min_dist_dropoff_order_in_child123_chauf2,"np.where((_dist_mand2_school3_school1_school2 > 0) & (_dist_mand2_school3_school1_school2 < _min_dist_dropoff_order_in_child123_chauf2), _dist_mand2_school3_school1_school2, _min_dist_dropoff_order_in_child123_chauf2)" +,_min_dist_dropoff_order_in_child123_chauf2,"np.where((_dist_mand2_school3_school2_school1 > 0) & (_dist_mand2_school3_school2_school1 < _min_dist_dropoff_order_in_child123_chauf2), _dist_mand2_school3_school2_school1, _min_dist_dropoff_order_in_child123_chauf2)" +Absolute deviation inbound distance child123 Chauffer 2,abs_dev_dist_in_child123_chauf2,"np.maximum(_min_dist_dropoff_order_in_child123_chauf2 - dist_home_to_mand1, 0)" +# overlapping time windows from outbound escorting,, +Preferred departure time to school - child 1,_pref_depart_time_to_school1,"reindex(tours[(tours.tour_type == 'school') & (tours.tour_num == 1)].set_index('person_id').start, df.child_id1)" +Preferred departure time to school - child 2,_pref_depart_time_to_school2,"reindex(tours[(tours.tour_type == 'school') & (tours.tour_num == 1)].set_index('person_id').start, df.child_id2)" +Preferred departure time to school - child 3,_pref_depart_time_to_school3,"reindex(tours[(tours.tour_type == 'school') & (tours.tour_num == 1)].set_index('person_id').start, df.child_id3)" +,return_min_taking_outbound_child1_pe,"(_pref_depart_time_to_school1 * mins_per_time_bin) + time_home_to_school1 + time_school_to_home1" +,return_min_taking_outbound_child2_pe,"(_pref_depart_time_to_school2 * mins_per_time_bin) + time_home_to_school2 + time_school_to_home2" +,return_min_taking_outbound_child3_pe,"(_pref_depart_time_to_school3 * mins_per_time_bin) + time_home_to_school3 + time_school_to_home3" +finding latest time chauffer 1 returns from outbound pure escort tour,outbound_pe_return_time_home_chauf1,-1 +,outbound_pe_return_time_home_chauf1,"np.where((df.chauf1_outbound == 2) & (return_min_taking_outbound_child1_pe > outbound_pe_return_time_home_chauf1), return_min_taking_outbound_child1_pe, outbound_pe_return_time_home_chauf1)" +,outbound_pe_return_time_home_chauf1,"np.where((df.chauf2_outbound == 2) & (return_min_taking_outbound_child2_pe > outbound_pe_return_time_home_chauf1), return_min_taking_outbound_child2_pe, outbound_pe_return_time_home_chauf1)" +,outbound_pe_return_time_home_chauf1,"np.where((df.chauf3_outbound == 2) & (return_min_taking_outbound_child3_pe > outbound_pe_return_time_home_chauf1), return_min_taking_outbound_child3_pe, outbound_pe_return_time_home_chauf1)" +finding latest time chauffer 2 returns from outbound pure escort tour,outbound_pe_return_time_home_chauf2,-1 +,outbound_pe_return_time_home_chauf2,"np.where((df.chauf1_outbound == 4) & (return_min_taking_outbound_child1_pe > outbound_pe_return_time_home_chauf2), return_min_taking_outbound_child1_pe, outbound_pe_return_time_home_chauf2)" +,outbound_pe_return_time_home_chauf2,"np.where((df.chauf2_outbound == 4) & (return_min_taking_outbound_child2_pe > outbound_pe_return_time_home_chauf2), return_min_taking_outbound_child2_pe, outbound_pe_return_time_home_chauf2)" +,outbound_pe_return_time_home_chauf2,"np.where((df.chauf3_outbound == 4) & (return_min_taking_outbound_child3_pe > outbound_pe_return_time_home_chauf2), return_min_taking_outbound_child3_pe, outbound_pe_return_time_home_chauf2)" +finding latest time chauffer 1 returns from outbound ride share tour,outbound_rs_return_time_home_chauf1,pref_depart_time_chauf1 +finding latest time chauffer 2 returns from outbound ride share tour,outbound_rs_return_time_home_chauf2,pref_depart_time_chauf2 +return time of outbound school escoring tour - chauffeur 1,return_bin_outbound_school_escorting1,"np.where(df.nrs1_outbound > 0, pref_depart_time_chauf1, outbound_pe_return_time_home_chauf1 / mins_per_time_bin)" +return time of outbound school escoring tour - chauffeur 2,return_bin_outbound_school_escorting2,"np.where(df.nrs2_outbound > 0, pref_depart_time_chauf2, outbound_pe_return_time_home_chauf2 / mins_per_time_bin)" diff --git a/resident/configs/school_escorting_preprocessor_outbound.csv b/resident/configs/school_escorting_preprocessor_outbound.csv new file mode 100644 index 0000000..8997826 --- /dev/null +++ b/resident/configs/school_escorting_preprocessor_outbound.csv @@ -0,0 +1,122 @@ +Description,Target,Expression +Number of children going to school,num_children_going_to_school,"np.where(df['child_id1'] > 0, 1, 0) + np.where(df['child_id2'] > 0, 1, 0) + np.where(df['child_id3'] > 0,1,0)" +Number of potential chauffeurs,num_potential_chauffeurs,"np.where(df['chauf_id1'] > 0, 1, 0) + np.where(df['chauf_id2'] > 0, 1, 0)" +Person Type - chauffer 1,ptype_chauf1,"reindex(persons.ptype, df.chauf_id1)" +Person Type - chauffer 2,ptype_chauf2,"reindex(persons.ptype, df.chauf_id2)" +Gender - chauffer 1,gender_chauf1,"reindex(persons.SEX, df.chauf_id1)" +Gender - chauffer 2,gender_chauf2,"reindex(persons.SEX, df.chauf_id2)" +Age - chauffer 1,age_chauf1,"reindex(persons.age, df.chauf_id1)" +Age - chauffer 2,age_chauf2,"reindex(persons.age, df.chauf_id2)" +Daily activity pattern - chauffer 1,cdap_chauf1,"reindex(persons.cdap_activity, df.chauf_id1)" +Daily activity pattern - chauffer 2,cdap_chauf2,"reindex(persons.cdap_activity, df.chauf_id2)" +Age - child 1,age_child1,"reindex(persons.age, df.child_id1)" +Age - child 2,age_child2,"reindex(persons.age, df.child_id2)" +Age - child 3,age_child3,"reindex(persons.age, df.child_id3)" +# Departure times to school and work +Preferred departure time to school - child 1,pref_depart_time_school1,"reindex(tours[(tours.tour_type == 'school') & (tours.tour_num == 1)].set_index('person_id').start, df.child_id1)" +Preferred departure time to school - child 2,pref_depart_time_school2,"reindex(tours[(tours.tour_type == 'school') & (tours.tour_num == 1)].set_index('person_id').start, df.child_id2)" +Preferred departure time to school - child 3,pref_depart_time_school3,"reindex(tours[(tours.tour_type == 'school') & (tours.tour_num == 1)].set_index('person_id').start, df.child_id3)" +Preferred departure time to work / univ - chauffer 1,pref_depart_time_chauf1,"reindex(tours[(tours.tour_category == 'mandatory') & (tours.tour_num == 1)].set_index('person_id').start, df.chauf_id1)" +Preferred departure time to work / univ - chauffer 2,pref_depart_time_chauf2,"reindex(tours[(tours.tour_category == 'mandatory') & (tours.tour_num == 1)].set_index('person_id').start, df.chauf_id2)" +# Distances and times to school and work +School location - child 1,school_location_child1,"reindex(persons.school_zone_id, df.child_id1)" +School location - child 2,school_location_child2,"reindex(persons.school_zone_id, df.child_id2)" +School location - child 3,school_location_child3,"reindex(persons.school_zone_id, df.child_id3)" +School location - chauffer 1,_school_location_chauf1,"reindex(persons.workplace_zone_id, df.chauf_id1)" +School location - chauffer 2,_school_location_chauf2,"reindex(persons.workplace_zone_id, df.chauf_id2)" +Work location - chauffer 1,_work_location_chauf1,"reindex(persons.workplace_zone_id, df.chauf_id1)" +Work location - chauffer 2,_work_location_chauf2,"reindex(persons.workplace_zone_id, df.chauf_id2)" +Mandatory tour location - chauffer 1,_mandatory_location_chauf1,"_school_location_chauf1.where(ptype_chauf1 == 3, _work_location_chauf1)" +Mandatory tour location - chauffer 2,_mandatory_location_chauf2,"_school_location_chauf1.where(ptype_chauf2 == 3, _work_location_chauf2)" +# creating valid school locations to pass to skim_dict,, +,_valid_school_location_child1,school_location_child1.fillna(persons[persons.school_zone_id > 0].school_zone_id.mode()[0]) +,_valid_school_location_child2,school_location_child2.fillna(persons[persons.school_zone_id > 0].school_zone_id.mode()[0]) +,_valid_school_location_child3,school_location_child3.fillna(persons[persons.school_zone_id > 0].school_zone_id.mode()[0]) +,_valid_mandatory_location_chauf1,_mandatory_location_chauf1.fillna(persons[persons.workplace_zone_id > 0].workplace_zone_id.mode()[0]) +,_valid_mandatory_location_chauf2,_mandatory_location_chauf2.fillna(persons[persons.workplace_zone_id > 0].workplace_zone_id.mode()[0]) +Auto time home to school - child 1,time_home_to_school1,"np.where(school_location_child1 > 0, skim_dict.lookup(df.home_zone_id, _valid_school_location_child1, ('SR2_M_TIME', 'AM')), 0)" +Auto time home to school - child 2,time_home_to_school2,"np.where(school_location_child2 > 0, skim_dict.lookup(df.home_zone_id, _valid_school_location_child2, ('SR2_M_TIME', 'AM')), 0)" +Auto time home to school - child 3,time_home_to_school3,"np.where(school_location_child3 > 0, skim_dict.lookup(df.home_zone_id, _valid_school_location_child3, ('SR2_M_TIME', 'AM')), 0)" +Auto time school to home - child 1,time_school_to_home1,"np.where(school_location_child1 > 0, skim_dict.lookup(_valid_school_location_child1, df.home_zone_id, ('SR2_M_TIME', 'AM')), 0)" +Auto time school to home - child 2,time_school_to_home2,"np.where(school_location_child2 > 0, skim_dict.lookup(_valid_school_location_child2, df.home_zone_id, ('SR2_M_TIME', 'AM')), 0)" +Auto time school to home - child 3,time_school_to_home3,"np.where(school_location_child3 > 0, skim_dict.lookup(_valid_school_location_child3, df.home_zone_id, ('SR2_M_TIME', 'AM')), 0)" +Auto dist home to school - child 1,dist_home_to_school1,"np.where(school_location_child1 > 0, skim_dict.lookup(df.home_zone_id, _valid_school_location_child1, ('SR2_M_DIST', 'AM')), 0)" +Auto dist home to school - child 2,dist_home_to_school2,"np.where(school_location_child2 > 0, skim_dict.lookup(df.home_zone_id, _valid_school_location_child2, ('SR2_M_DIST', 'AM')), 0)" +Auto dist home to school - child 3,dist_home_to_school3,"np.where(school_location_child3 > 0, skim_dict.lookup(df.home_zone_id, _valid_school_location_child3, ('SR2_M_DIST', 'AM')), 0)" +Auto dist intra school taz - child 1,dist_intra_school1,"np.where(school_location_child1 > 0, skim_dict.lookup(df.home_zone_id, _valid_school_location_child1, ('SR2_M_DIST', 'AM')), 0)" +Auto dist intra school taz - child 2,dist_intra_school2,"np.where(school_location_child2 > 0, skim_dict.lookup(df.home_zone_id, _valid_school_location_child2, ('SR2_M_DIST', 'AM')), 0)" +Auto dist intra school taz - child 3,dist_intra_school3,"np.where(school_location_child3 > 0, skim_dict.lookup(df.home_zone_id, _valid_school_location_child3, ('SR2_M_DIST', 'AM')), 0)" +Auto time home to work or university - chauffer 1,time_home_to_mand1,"np.where(_mandatory_location_chauf1 > 0, skim_dict.lookup(df.home_zone_id, _valid_mandatory_location_chauf1, ('SR2_M_TIME', 'AM')), 0)" +Auto time home to work or university - chauffer 2,time_home_to_mand2,"np.where(_mandatory_location_chauf2 > 0, skim_dict.lookup(df.home_zone_id, _valid_mandatory_location_chauf2, ('SR2_M_TIME', 'AM')), 0)" +Auto dist home to work or university - chauffer 1,dist_home_to_mand1,"np.where(_mandatory_location_chauf1 > 0, skim_dict.lookup(df.home_zone_id, _valid_mandatory_location_chauf1, ('SR2_M_DIST', 'AM')), 0)" +Auto dist home to work or university - chauffer 2,dist_home_to_mand2,"np.where(_mandatory_location_chauf2 > 0, skim_dict.lookup(df.home_zone_id, _valid_mandatory_location_chauf2, ('SR2_M_DIST', 'AM')), 0)" +# outbound distance combinations between chauffeurs and children,, +Distance from child 1 school to chauffeur 1 mandatory location,_dist_school1_to_mand1,"np.where((school_location_child1 > 0) & (_mandatory_location_chauf1 > 0), skim_dict.lookup(_valid_school_location_child1, _valid_mandatory_location_chauf1, ('SR2_M_TIME', 'AM')), 0)" +Distance from child 1 school to chauffeur 2 mandatory location,_dist_school1_to_mand2,"np.where((school_location_child1 > 0) & (_mandatory_location_chauf2 > 0), skim_dict.lookup(_valid_school_location_child1, _valid_mandatory_location_chauf2, ('SR2_M_TIME', 'AM')), 0)" +Distance from child 2 school to chauffeur 1 mandatory location,_dist_school2_to_mand1,"np.where((school_location_child2 > 0) & (_mandatory_location_chauf1 > 0), skim_dict.lookup(_valid_school_location_child2, _valid_mandatory_location_chauf1, ('SR2_M_TIME', 'AM')), 0)" +Distance from child 2 school to chauffeur 2 mandatory location,_dist_school2_to_mand2,"np.where((school_location_child2 > 0) & (_mandatory_location_chauf2 > 0), skim_dict.lookup(_valid_school_location_child2, _valid_mandatory_location_chauf2, ('SR2_M_TIME', 'AM')), 0)" +Distance from child 3 school to chauffeur 1 mandatory location,_dist_school3_to_mand1,"np.where((school_location_child3 > 0) & (_mandatory_location_chauf1 > 0), skim_dict.lookup(_valid_school_location_child3, _valid_mandatory_location_chauf1, ('SR2_M_TIME', 'AM')), 0)" +Distance from child 3 school to chauffeur 2 mandatory location,_dist_school3_to_mand2,"np.where((school_location_child3 > 0) & (_mandatory_location_chauf2 > 0), skim_dict.lookup(_valid_school_location_child3, _valid_mandatory_location_chauf2, ('SR2_M_TIME', 'AM')), 0)" +Distance from child 1 school to child 2 school,_dist_school1_to_school2,"np.where((school_location_child1 > 0) & (school_location_child2 > 0), skim_dict.lookup(_valid_school_location_child1, _valid_school_location_child2, ('SR2_M_TIME', 'AM')), 0)" +Distance from child 1 school to child 3 school,_dist_school1_to_school3,"np.where((school_location_child1 > 0) & (school_location_child3 > 0), skim_dict.lookup(_valid_school_location_child1, _valid_school_location_child3, ('SR2_M_TIME', 'AM')), 0)" +Distance from child 2 school to child 3 school,_dist_school2_to_school3,"np.where((school_location_child2 > 0) & (school_location_child3 > 0), skim_dict.lookup(_valid_school_location_child2, _valid_school_location_child3, ('SR2_M_TIME', 'AM')), 0)" +Distance from child 2 school to child 1 school,_dist_school2_to_school1,"np.where((school_location_child2 > 0) & (school_location_child1 > 0), skim_dict.lookup(_valid_school_location_child2, _valid_school_location_child1, ('SR2_M_TIME', 'AM')), 0)" +Distance from child 3 school to child 1 school,_dist_school3_to_school1,"np.where((school_location_child3 > 0) & (school_location_child1 > 0), skim_dict.lookup(_valid_school_location_child3, _valid_school_location_child1, ('SR2_M_TIME', 'AM')), 0)" +Distance from child 3 school to child 2 school,_dist_school3_to_school2,"np.where((school_location_child3 > 0) & (school_location_child2 > 0), skim_dict.lookup(_valid_school_location_child3, _valid_school_location_child2, ('SR2_M_TIME', 'AM')), 0)" +# absolute deviation distance outbound,, +Absolute deviation outbound distance Child 1 Chauffer 1,abs_dev_dist_out_child1_chauf1,"np.maximum(dist_home_to_school1 + _dist_school1_to_mand1 - dist_home_to_mand1,0)" +Absolute deviation outbound distance Child 1 Chauffer 2,abs_dev_dist_out_child1_chauf2,"np.maximum(dist_home_to_school1 + _dist_school1_to_mand2 - dist_home_to_mand2,0)" +Absolute deviation outbound distance Child 2 Chauffer 1,abs_dev_dist_out_child2_chauf1,"np.maximum(dist_home_to_school2 + _dist_school2_to_mand1 - dist_home_to_mand1,0)" +Absolute deviation outbound distance Child 2 Chauffer 2,abs_dev_dist_out_child2_chauf2,"np.maximum(dist_home_to_school2 + _dist_school2_to_mand2 - dist_home_to_mand2,0)" +Absolute deviation outbound distance Child 3 Chauffer 1,abs_dev_dist_out_child3_chauf1,"np.maximum(dist_home_to_school3 + _dist_school3_to_mand1 - dist_home_to_mand1,0)" +Absolute deviation outbound distance Child 3 Chauffer 2,abs_dev_dist_out_child3_chauf2,"np.maximum(dist_home_to_school3 + _dist_school3_to_mand2 - dist_home_to_mand2,0)" +Absolute deviation outbound distance child12 Chauffer 1,abs_dev_dist_out_child12_chauf1,"np.maximum(np.minimum(dist_home_to_school1 + _dist_school1_to_school2 + _dist_school2_to_mand1, dist_home_to_school2 + _dist_school2_to_school1 + _dist_school1_to_mand1) - dist_home_to_mand1, 0)" +Absolute deviation outbound distance child12 Chauffer 2,abs_dev_dist_out_child12_chauf2,"np.maximum(np.minimum(dist_home_to_school1 + _dist_school1_to_school2 + _dist_school2_to_mand2, dist_home_to_school2 + _dist_school2_to_school1 + _dist_school1_to_mand2) - dist_home_to_mand2, 0)" +Absolute deviation outbound distance child13 Chauffer 1,abs_dev_dist_out_child13_chauf1,"np.maximum(np.minimum(dist_home_to_school1 + _dist_school1_to_school3 + _dist_school3_to_mand1, dist_home_to_school3 + _dist_school3_to_school1 + _dist_school1_to_mand1) - dist_home_to_mand1, 0)" +Absolute deviation outbound distance child13 Chauffer 2,abs_dev_dist_out_child13_chauf2,"np.maximum(np.minimum(dist_home_to_school1 + _dist_school1_to_school3 + _dist_school3_to_mand2, dist_home_to_school3 + _dist_school3_to_school1 + _dist_school1_to_mand2) - dist_home_to_mand2, 0)" +Absolute deviation outbound distance child23 Chauffer 1,abs_dev_dist_out_child23_chauf1,"np.maximum(np.minimum(dist_home_to_school2 + _dist_school2_to_school3 + _dist_school3_to_mand1, dist_home_to_school3 + _dist_school3_to_school2 + _dist_school2_to_mand1) - dist_home_to_mand1, 0)" +Absolute deviation outbound distance child23 Chauffer 2,abs_dev_dist_out_child23_chauf2,"np.maximum(np.minimum(dist_home_to_school2 + _dist_school2_to_school3 + _dist_school3_to_mand2, dist_home_to_school3 + _dist_school3_to_school2 + _dist_school2_to_mand2) - dist_home_to_mand2, 0)" +,_dist_school1_school2_school3_mand1,"dist_home_to_school1 + _dist_school1_to_school2 + _dist_school2_to_school3 + _dist_school3_to_mand1" +,_dist_school1_school3_school2_mand1,"dist_home_to_school1 + _dist_school1_to_school3 + _dist_school3_to_school2 + _dist_school2_to_mand1" +,_dist_school2_school1_school3_mand1,"dist_home_to_school2 + _dist_school2_to_school1 + _dist_school1_to_school3 + _dist_school3_to_mand1" +,_dist_school2_school3_school1_mand1,"dist_home_to_school2 + _dist_school2_to_school3 + _dist_school3_to_school1 + _dist_school1_to_mand1" +,_dist_school3_school1_school2_mand1,"dist_home_to_school3 + _dist_school3_to_school1 + _dist_school1_to_school2 + _dist_school2_to_mand1" +,_dist_school3_school2_school1_mand1,"dist_home_to_school3 + _dist_school3_to_school2 + _dist_school2_to_school1 + _dist_school1_to_mand1" +,_min_dist_dropoff_order_out_child123_chauf1,_dist_school1_school2_school3_mand1 +,_min_dist_dropoff_order_out_child123_chauf1,"np.where((_dist_school1_school3_school2_mand1 > 0) & (_dist_school1_school3_school2_mand1 < _min_dist_dropoff_order_out_child123_chauf1), _dist_school1_school3_school2_mand1, _min_dist_dropoff_order_out_child123_chauf1)" +,_min_dist_dropoff_order_out_child123_chauf1,"np.where((_dist_school2_school1_school3_mand1 > 0) & (_dist_school2_school1_school3_mand1 < _min_dist_dropoff_order_out_child123_chauf1), _dist_school2_school1_school3_mand1, _min_dist_dropoff_order_out_child123_chauf1)" +,_min_dist_dropoff_order_out_child123_chauf1,"np.where((_dist_school2_school3_school1_mand1 > 0) & (_dist_school2_school3_school1_mand1 < _min_dist_dropoff_order_out_child123_chauf1), _dist_school2_school3_school1_mand1, _min_dist_dropoff_order_out_child123_chauf1)" +,_min_dist_dropoff_order_out_child123_chauf1,"np.where((_dist_school3_school1_school2_mand1 > 0) & (_dist_school3_school1_school2_mand1 < _min_dist_dropoff_order_out_child123_chauf1), _dist_school3_school1_school2_mand1, _min_dist_dropoff_order_out_child123_chauf1)" +,_min_dist_dropoff_order_out_child123_chauf1,"np.where((_dist_school3_school2_school1_mand1 > 0) & (_dist_school3_school2_school1_mand1 < _min_dist_dropoff_order_out_child123_chauf1), _dist_school3_school2_school1_mand1, _min_dist_dropoff_order_out_child123_chauf1)" +Absolute deviation outbound distance child123 Chauffer 1,abs_dev_dist_out_child123_chauf1,"np.maximum(_min_dist_dropoff_order_out_child123_chauf1 - dist_home_to_mand1, 0)" +,_dist_school1_school2_school3_mand2,"dist_home_to_school1 + _dist_school1_to_school2 + _dist_school2_to_school3 + _dist_school3_to_mand2" +,_dist_school1_school3_school2_mand2,"dist_home_to_school1 + _dist_school1_to_school3 + _dist_school3_to_school2 + _dist_school2_to_mand2" +,_dist_school2_school1_school3_mand2,"dist_home_to_school2 + _dist_school2_to_school1 + _dist_school1_to_school3 + _dist_school3_to_mand2" +,_dist_school2_school3_school1_mand2,"dist_home_to_school2 + _dist_school2_to_school3 + _dist_school3_to_school1 + _dist_school1_to_mand2" +,_dist_school3_school1_school2_mand2,"dist_home_to_school3 + _dist_school3_to_school1 + _dist_school1_to_school2 + _dist_school2_to_mand2" +,_dist_school3_school2_school1_mand2,"dist_home_to_school3 + _dist_school3_to_school2 + _dist_school2_to_school1 + _dist_school1_to_mand2" +,_min_dist_dropoff_order_out_child123_chauf2,_dist_school1_school2_school3_mand2 +,_min_dist_dropoff_order_out_child123_chauf2,"np.where((_dist_school1_school3_school2_mand2 > 0) & (_dist_school1_school3_school2_mand2 < _min_dist_dropoff_order_out_child123_chauf2), _dist_school1_school3_school2_mand2, _min_dist_dropoff_order_out_child123_chauf2)" +,_min_dist_dropoff_order_out_child123_chauf2,"np.where((_dist_school2_school1_school3_mand2 > 0) & (_dist_school2_school1_school3_mand2 < _min_dist_dropoff_order_out_child123_chauf2), _dist_school2_school1_school3_mand2, _min_dist_dropoff_order_out_child123_chauf2)" +,_min_dist_dropoff_order_out_child123_chauf2,"np.where((_dist_school2_school3_school1_mand2 > 0) & (_dist_school2_school3_school1_mand2 < _min_dist_dropoff_order_out_child123_chauf2), _dist_school2_school3_school1_mand2, _min_dist_dropoff_order_out_child123_chauf2)" +,_min_dist_dropoff_order_out_child123_chauf2,"np.where((_dist_school3_school1_school2_mand2 > 0) & (_dist_school3_school1_school2_mand2 < _min_dist_dropoff_order_out_child123_chauf2), _dist_school3_school1_school2_mand2, _min_dist_dropoff_order_out_child123_chauf2)" +,_min_dist_dropoff_order_out_child123_chauf2,"np.where((_dist_school3_school2_school1_mand2 > 0) & (_dist_school3_school2_school1_mand2 < _min_dist_dropoff_order_out_child123_chauf2), _dist_school3_school2_school1_mand2, _min_dist_dropoff_order_out_child123_chauf2)" +Absolute deviation outbound distance child123 Chauffer 1,abs_dev_dist_out_child123_chauf2,"np.maximum(_min_dist_dropoff_order_out_child123_chauf2 - dist_home_to_mand2, 0)" +# Availability for multiple bundles,, +,_return_min_taking_child1,"(pref_depart_time_school1 * mins_per_time_bin) + time_home_to_school1 + time_school_to_home1" +,_return_min_taking_child2,"(pref_depart_time_school2 * mins_per_time_bin) + time_home_to_school2 + time_school_to_home2" +,_return_min_taking_child3,"(pref_depart_time_school3 * mins_per_time_bin) + time_home_to_school3 + time_school_to_home3" +Availability of taking child 1 and then 2,_avail_child1_then_child2,"(_return_min_taking_child1 < (pref_depart_time_school2 * mins_per_time_bin))" +Availability of taking child 2 and then 1,_avail_child2_then_child1,"(_return_min_taking_child2 < (pref_depart_time_school1 * mins_per_time_bin))" +Availability of taking child 1 and then 3,_avail_child1_then_child3,"(_return_min_taking_child1 < (pref_depart_time_school3 * mins_per_time_bin))" +Availability of taking child 3 and then 1,_avail_child3_then_child1,"(_return_min_taking_child3 < (pref_depart_time_school1 * mins_per_time_bin))" +Availability of taking child 2 and then 3,_avail_child2_then_child3,"(_return_min_taking_child2 < (pref_depart_time_school3 * mins_per_time_bin))" +Availability of taking child 3 and then 2,_avail_child3_then_child2,"(_return_min_taking_child3 < (pref_depart_time_school2 * mins_per_time_bin))" +multiple_bundle_availability,avail_multiple_bundles,(_avail_child1_then_child2 | _avail_child2_then_child1 | _avail_child1_then_child3 | _avail_child3_then_child1 | _avail_child2_then_child3 | _avail_child3_then_child2) +# ,, +# Inbound specific terms ,, +Preferred return time from school - child 1,pref_return_time_school1,"reindex(tours[(tours.tour_type == 'school') & (tours.tour_num == 1)].set_index('person_id').end, df.child_id1)" +Preferred return time from school - child 2,pref_return_time_school2,"reindex(tours[(tours.tour_type == 'school') & (tours.tour_num == 1)].set_index('person_id').end, df.child_id2)" +Preferred return time from school - child 3,pref_return_time_school3,"reindex(tours[(tours.tour_type == 'school') & (tours.tour_num == 1)].set_index('person_id').end, df.child_id3)" +Preferred return time from work / univ - chauffer 1,pref_return_time_chauf1,"reindex(tours[(tours.tour_category == 'mandatory') & (tours.tour_num == 1)].set_index('person_id').end, df.chauf_id1)" +Preferred return time from work / univ - chauffer 2,pref_return_time_chauf2,"reindex(tours[(tours.tour_category == 'mandatory') & (tours.tour_num == 1)].set_index('person_id').end, df.chauf_id2)" diff --git a/resident/configs/school_escorting_preprocessor_outbound_cond.csv b/resident/configs/school_escorting_preprocessor_outbound_cond.csv new file mode 100644 index 0000000..10a9811 --- /dev/null +++ b/resident/configs/school_escorting_preprocessor_outbound_cond.csv @@ -0,0 +1,138 @@ +Description,Target,Expression +Number of children going to school,num_children_going_to_school,"np.where(df['child_id1'] > 0, 1, 0) + np.where(df['child_id2'] > 0, 1, 0) + np.where(df['child_id3'] > 0,1,0)" +Number of potential chauffeurs,num_potential_chauffeurs,"np.where(df['chauf_id1'] > 0, 1, 0) + np.where(df['chauf_id2'] > 0, 1, 0)" +Person Type - chauffer 1,ptype_chauf1,"reindex(persons.ptype, df.chauf_id1)" +Person Type - chauffer 2,ptype_chauf2,"reindex(persons.ptype, df.chauf_id2)" +Gender - chauffer 1,gender_chauf1,"reindex(persons.SEX, df.chauf_id1)" +Gender - chauffer 2,gender_chauf2,"reindex(persons.SEX, df.chauf_id2)" +Age - chauffer 1,age_chauf1,"reindex(persons.age, df.chauf_id1)" +Age - chauffer 2,age_chauf2,"reindex(persons.age, df.chauf_id2)" +Daily activity pattern - chauffer 1,cdap_chauf1,"reindex(persons.cdap_activity, df.chauf_id1)" +Daily activity pattern - chauffer 2,cdap_chauf2,"reindex(persons.cdap_activity, df.chauf_id2)" +Age - child 1,age_child1,"reindex(persons.age, df.child_id1)" +Age - child 2,age_child2,"reindex(persons.age, df.child_id2)" +Age - child 3,age_child3,"reindex(persons.age, df.child_id3)" +# Departure times to school and work +Preferred departure time to school - child 1,pref_depart_time_school1,"reindex(tours[(tours.tour_type == 'school') & (tours.tour_num == 1)].set_index('person_id').start, df.child_id1)" +Preferred departure time to school - child 2,pref_depart_time_school2,"reindex(tours[(tours.tour_type == 'school') & (tours.tour_num == 1)].set_index('person_id').start, df.child_id2)" +Preferred departure time to school - child 3,pref_depart_time_school3,"reindex(tours[(tours.tour_type == 'school') & (tours.tour_num == 1)].set_index('person_id').start, df.child_id3)" +Preferred departure time to work / univ - chauffer 1,pref_depart_time_chauf1,"reindex(tours[(tours.tour_category == 'mandatory') & (tours.tour_num == 1)].set_index('person_id').start, df.chauf_id1)" +Preferred departure time to work / univ - chauffer 2,pref_depart_time_chauf2,"reindex(tours[(tours.tour_category == 'mandatory') & (tours.tour_num == 1)].set_index('person_id').start, df.chauf_id2)" +# Distances and times to school and work +School location - child 1,school_location_child1,"reindex(persons.school_zone_id, df.child_id1)" +School location - child 2,school_location_child2,"reindex(persons.school_zone_id, df.child_id2)" +School location - child 3,school_location_child3,"reindex(persons.school_zone_id, df.child_id3)" +School location - chauffer 1,_school_location_chauf1,"reindex(persons.workplace_zone_id, df.chauf_id1)" +School location - chauffer 2,_school_location_chauf2,"reindex(persons.workplace_zone_id, df.chauf_id2)" +Work location - chauffer 1,_work_location_chauf1,"reindex(persons.workplace_zone_id, df.chauf_id1)" +Work location - chauffer 2,_work_location_chauf2,"reindex(persons.workplace_zone_id, df.chauf_id2)" +Mandatory tour location - chauffer 1,_mandatory_location_chauf1,"_school_location_chauf1.where(ptype_chauf1 == 3, _work_location_chauf1)" +Mandatory tour location - chauffer 2,_mandatory_location_chauf2,"_school_location_chauf1.where(ptype_chauf2 == 3, _work_location_chauf2)" +# creating valid school locations to pass to skim_dict,, +,_valid_school_location_child1,school_location_child1.fillna(persons[persons.school_zone_id > 0].school_zone_id.mode()[0]) +,_valid_school_location_child2,school_location_child2.fillna(persons[persons.school_zone_id > 0].school_zone_id.mode()[0]) +,_valid_school_location_child3,school_location_child3.fillna(persons[persons.school_zone_id > 0].school_zone_id.mode()[0]) +,_valid_mandatory_location_chauf1,_mandatory_location_chauf1.fillna(persons[persons.workplace_zone_id > 0].workplace_zone_id.mode()[0]) +,_valid_mandatory_location_chauf2,_mandatory_location_chauf2.fillna(persons[persons.workplace_zone_id > 0].workplace_zone_id.mode()[0]) +Auto time home to school - child 1,time_home_to_school1,"np.where(school_location_child1 > 0, skim_dict.lookup(df.home_zone_id, _valid_school_location_child1, ('SR2_M_TIME', 'AM')), 0)" +Auto time home to school - child 2,time_home_to_school2,"np.where(school_location_child2 > 0, skim_dict.lookup(df.home_zone_id, _valid_school_location_child2, ('SR2_M_TIME', 'AM')), 0)" +Auto time home to school - child 3,time_home_to_school3,"np.where(school_location_child3 > 0, skim_dict.lookup(df.home_zone_id, _valid_school_location_child3, ('SR2_M_TIME', 'AM')), 0)" +Auto time school to home - child 1,time_school_to_home1,"np.where(school_location_child1 > 0, skim_dict.lookup(_valid_school_location_child1, df.home_zone_id, ('SR2_M_TIME', 'AM')), 0)" +Auto time school to home - child 2,time_school_to_home2,"np.where(school_location_child2 > 0, skim_dict.lookup(_valid_school_location_child2, df.home_zone_id, ('SR2_M_TIME', 'AM')), 0)" +Auto time school to home - child 3,time_school_to_home3,"np.where(school_location_child3 > 0, skim_dict.lookup(_valid_school_location_child3, df.home_zone_id, ('SR2_M_TIME', 'AM')), 0)" +Auto dist home to school - child 1,dist_home_to_school1,"np.where(school_location_child1 > 0, skim_dict.lookup(df.home_zone_id, _valid_school_location_child1, ('SR2_M_DIST', 'AM')), 0)" +Auto dist home to school - child 2,dist_home_to_school2,"np.where(school_location_child2 > 0, skim_dict.lookup(df.home_zone_id, _valid_school_location_child2, ('SR2_M_DIST', 'AM')), 0)" +Auto dist home to school - child 3,dist_home_to_school3,"np.where(school_location_child3 > 0, skim_dict.lookup(df.home_zone_id, _valid_school_location_child3, ('SR2_M_DIST', 'AM')), 0)" +Auto dist intra school taz - child 1,dist_intra_school1,"np.where(school_location_child1 > 0, skim_dict.lookup(df.home_zone_id, _valid_school_location_child1, ('SR2_M_DIST', 'AM')), 0)" +Auto dist intra school taz - child 2,dist_intra_school2,"np.where(school_location_child2 > 0, skim_dict.lookup(df.home_zone_id, _valid_school_location_child2, ('SR2_M_DIST', 'AM')), 0)" +Auto dist intra school taz - child 3,dist_intra_school3,"np.where(school_location_child3 > 0, skim_dict.lookup(df.home_zone_id, _valid_school_location_child3, ('SR2_M_DIST', 'AM')), 0)" +Auto time home to work or university - chauffer 1,time_home_to_mand1,"np.where(_mandatory_location_chauf1 > 0, skim_dict.lookup(df.home_zone_id, _valid_mandatory_location_chauf1, ('SR2_M_TIME', 'AM')), 0)" +Auto time home to work or university - chauffer 2,time_home_to_mand2,"np.where(_mandatory_location_chauf2 > 0, skim_dict.lookup(df.home_zone_id, _valid_mandatory_location_chauf2, ('SR2_M_TIME', 'AM')), 0)" +Auto dist home to work or university - chauffer 1,dist_home_to_mand1,"np.where(_mandatory_location_chauf1 > 0, skim_dict.lookup(df.home_zone_id, _valid_mandatory_location_chauf1, ('SR2_M_DIST', 'AM')), 0)" +Auto dist home to work or university - chauffer 2,dist_home_to_mand2,"np.where(_mandatory_location_chauf2 > 0, skim_dict.lookup(df.home_zone_id, _valid_mandatory_location_chauf2, ('SR2_M_DIST', 'AM')), 0)" +# outbound distance combinations between chauffeurs and children,, +Distance from child 1 school to chauffeur 1 mandatory location,_dist_school1_to_mand1,"np.where((school_location_child1 > 0) & (_mandatory_location_chauf1 > 0), skim_dict.lookup(_valid_school_location_child1, _valid_mandatory_location_chauf1, ('SR2_M_TIME', 'AM')), 0)" +Distance from child 1 school to chauffeur 2 mandatory location,_dist_school1_to_mand2,"np.where((school_location_child1 > 0) & (_mandatory_location_chauf2 > 0), skim_dict.lookup(_valid_school_location_child1, _valid_mandatory_location_chauf2, ('SR2_M_TIME', 'AM')), 0)" +Distance from child 2 school to chauffeur 1 mandatory location,_dist_school2_to_mand1,"np.where((school_location_child2 > 0) & (_mandatory_location_chauf1 > 0), skim_dict.lookup(_valid_school_location_child2, _valid_mandatory_location_chauf1, ('SR2_M_TIME', 'AM')), 0)" +Distance from child 2 school to chauffeur 2 mandatory location,_dist_school2_to_mand2,"np.where((school_location_child2 > 0) & (_mandatory_location_chauf2 > 0), skim_dict.lookup(_valid_school_location_child2, _valid_mandatory_location_chauf2, ('SR2_M_TIME', 'AM')), 0)" +Distance from child 3 school to chauffeur 1 mandatory location,_dist_school3_to_mand1,"np.where((school_location_child3 > 0) & (_mandatory_location_chauf1 > 0), skim_dict.lookup(_valid_school_location_child3, _valid_mandatory_location_chauf1, ('SR2_M_TIME', 'AM')), 0)" +Distance from child 3 school to chauffeur 2 mandatory location,_dist_school3_to_mand2,"np.where((school_location_child3 > 0) & (_mandatory_location_chauf2 > 0), skim_dict.lookup(_valid_school_location_child3, _valid_mandatory_location_chauf2, ('SR2_M_TIME', 'AM')), 0)" +Distance from child 1 school to child 2 school,_dist_school1_to_school2,"np.where((school_location_child1 > 0) & (school_location_child2 > 0), skim_dict.lookup(_valid_school_location_child1, _valid_school_location_child2, ('SR2_M_TIME', 'AM')), 0)" +Distance from child 1 school to child 3 school,_dist_school1_to_school3,"np.where((school_location_child1 > 0) & (school_location_child3 > 0), skim_dict.lookup(_valid_school_location_child1, _valid_school_location_child3, ('SR2_M_TIME', 'AM')), 0)" +Distance from child 2 school to child 3 school,_dist_school2_to_school3,"np.where((school_location_child2 > 0) & (school_location_child3 > 0), skim_dict.lookup(_valid_school_location_child2, _valid_school_location_child3, ('SR2_M_TIME', 'AM')), 0)" +Distance from child 2 school to child 1 school,_dist_school2_to_school1,"np.where((school_location_child2 > 0) & (school_location_child1 > 0), skim_dict.lookup(_valid_school_location_child2, _valid_school_location_child1, ('SR2_M_TIME', 'AM')), 0)" +Distance from child 3 school to child 1 school,_dist_school3_to_school1,"np.where((school_location_child3 > 0) & (school_location_child1 > 0), skim_dict.lookup(_valid_school_location_child3, _valid_school_location_child1, ('SR2_M_TIME', 'AM')), 0)" +Distance from child 3 school to child 2 school,_dist_school3_to_school2,"np.where((school_location_child3 > 0) & (school_location_child2 > 0), skim_dict.lookup(_valid_school_location_child3, _valid_school_location_child2, ('SR2_M_TIME', 'AM')), 0)" +# absolute deviation distance outbound,, +Absolute deviation outbound distance Child 1 Chauffer 1,abs_dev_dist_out_child1_chauf1,"np.maximum(dist_home_to_school1 + _dist_school1_to_mand1 - dist_home_to_mand1,0)" +Absolute deviation outbound distance Child 1 Chauffer 2,abs_dev_dist_out_child1_chauf2,"np.maximum(dist_home_to_school1 + _dist_school1_to_mand2 - dist_home_to_mand2,0)" +Absolute deviation outbound distance Child 2 Chauffer 1,abs_dev_dist_out_child2_chauf1,"np.maximum(dist_home_to_school2 + _dist_school2_to_mand1 - dist_home_to_mand1,0)" +Absolute deviation outbound distance Child 2 Chauffer 2,abs_dev_dist_out_child2_chauf2,"np.maximum(dist_home_to_school2 + _dist_school2_to_mand2 - dist_home_to_mand2,0)" +Absolute deviation outbound distance Child 3 Chauffer 1,abs_dev_dist_out_child3_chauf1,"np.maximum(dist_home_to_school3 + _dist_school3_to_mand1 - dist_home_to_mand1,0)" +Absolute deviation outbound distance Child 3 Chauffer 2,abs_dev_dist_out_child3_chauf2,"np.maximum(dist_home_to_school3 + _dist_school3_to_mand2 - dist_home_to_mand2,0)" +Absolute deviation outbound distance child12 Chauffer 1,abs_dev_dist_out_child12_chauf1,"np.maximum(np.minimum(dist_home_to_school1 + _dist_school1_to_school2 + _dist_school2_to_mand1, dist_home_to_school2 + _dist_school2_to_school1 + _dist_school1_to_mand1) - dist_home_to_mand1, 0)" +Absolute deviation outbound distance child12 Chauffer 2,abs_dev_dist_out_child12_chauf2,"np.maximum(np.minimum(dist_home_to_school1 + _dist_school1_to_school2 + _dist_school2_to_mand2, dist_home_to_school2 + _dist_school2_to_school1 + _dist_school1_to_mand2) - dist_home_to_mand2, 0)" +Absolute deviation outbound distance child13 Chauffer 1,abs_dev_dist_out_child13_chauf1,"np.maximum(np.minimum(dist_home_to_school1 + _dist_school1_to_school3 + _dist_school3_to_mand1, dist_home_to_school3 + _dist_school3_to_school1 + _dist_school1_to_mand1) - dist_home_to_mand1, 0)" +Absolute deviation outbound distance child13 Chauffer 2,abs_dev_dist_out_child13_chauf2,"np.maximum(np.minimum(dist_home_to_school1 + _dist_school1_to_school3 + _dist_school3_to_mand2, dist_home_to_school3 + _dist_school3_to_school1 + _dist_school1_to_mand2) - dist_home_to_mand2, 0)" +Absolute deviation outbound distance child23 Chauffer 1,abs_dev_dist_out_child23_chauf1,"np.maximum(np.minimum(dist_home_to_school2 + _dist_school2_to_school3 + _dist_school3_to_mand1, dist_home_to_school3 + _dist_school3_to_school2 + _dist_school2_to_mand1) - dist_home_to_mand1, 0)" +Absolute deviation outbound distance child23 Chauffer 2,abs_dev_dist_out_child23_chauf2,"np.maximum(np.minimum(dist_home_to_school2 + _dist_school2_to_school3 + _dist_school3_to_mand2, dist_home_to_school3 + _dist_school3_to_school2 + _dist_school2_to_mand2) - dist_home_to_mand2, 0)" +,_dist_school1_school2_school3_mand1,"dist_home_to_school1 + _dist_school1_to_school2 + _dist_school2_to_school3 + _dist_school3_to_mand1" +,_dist_school1_school3_school2_mand1,"dist_home_to_school1 + _dist_school1_to_school3 + _dist_school3_to_school2 + _dist_school2_to_mand1" +,_dist_school2_school1_school3_mand1,"dist_home_to_school2 + _dist_school2_to_school1 + _dist_school1_to_school3 + _dist_school3_to_mand1" +,_dist_school2_school3_school1_mand1,"dist_home_to_school2 + _dist_school2_to_school3 + _dist_school3_to_school1 + _dist_school1_to_mand1" +,_dist_school3_school1_school2_mand1,"dist_home_to_school3 + _dist_school3_to_school1 + _dist_school1_to_school2 + _dist_school2_to_mand1" +,_dist_school3_school2_school1_mand1,"dist_home_to_school3 + _dist_school3_to_school2 + _dist_school2_to_school1 + _dist_school1_to_mand1" +,_min_dist_dropoff_order_out_child123_chauf1,_dist_school1_school2_school3_mand1 +,_min_dist_dropoff_order_out_child123_chauf1,"np.where((_dist_school1_school3_school2_mand1 > 0) & (_dist_school1_school3_school2_mand1 < _min_dist_dropoff_order_out_child123_chauf1), _dist_school1_school3_school2_mand1, _min_dist_dropoff_order_out_child123_chauf1)" +,_min_dist_dropoff_order_out_child123_chauf1,"np.where((_dist_school2_school1_school3_mand1 > 0) & (_dist_school2_school1_school3_mand1 < _min_dist_dropoff_order_out_child123_chauf1), _dist_school2_school1_school3_mand1, _min_dist_dropoff_order_out_child123_chauf1)" +,_min_dist_dropoff_order_out_child123_chauf1,"np.where((_dist_school2_school3_school1_mand1 > 0) & (_dist_school2_school3_school1_mand1 < _min_dist_dropoff_order_out_child123_chauf1), _dist_school2_school3_school1_mand1, _min_dist_dropoff_order_out_child123_chauf1)" +,_min_dist_dropoff_order_out_child123_chauf1,"np.where((_dist_school3_school1_school2_mand1 > 0) & (_dist_school3_school1_school2_mand1 < _min_dist_dropoff_order_out_child123_chauf1), _dist_school3_school1_school2_mand1, _min_dist_dropoff_order_out_child123_chauf1)" +,_min_dist_dropoff_order_out_child123_chauf1,"np.where((_dist_school3_school2_school1_mand1 > 0) & (_dist_school3_school2_school1_mand1 < _min_dist_dropoff_order_out_child123_chauf1), _dist_school3_school2_school1_mand1, _min_dist_dropoff_order_out_child123_chauf1)" +Absolute deviation outbound distance child123 Chauffer 1,abs_dev_dist_out_child123_chauf1,"np.maximum(_min_dist_dropoff_order_out_child123_chauf1 - dist_home_to_mand1, 0)" +,_dist_school1_school2_school3_mand2,"dist_home_to_school1 + _dist_school1_to_school2 + _dist_school2_to_school3 + _dist_school3_to_mand2" +,_dist_school1_school3_school2_mand2,"dist_home_to_school1 + _dist_school1_to_school3 + _dist_school3_to_school2 + _dist_school2_to_mand2" +,_dist_school2_school1_school3_mand2,"dist_home_to_school2 + _dist_school2_to_school1 + _dist_school1_to_school3 + _dist_school3_to_mand2" +,_dist_school2_school3_school1_mand2,"dist_home_to_school2 + _dist_school2_to_school3 + _dist_school3_to_school1 + _dist_school1_to_mand2" +,_dist_school3_school1_school2_mand2,"dist_home_to_school3 + _dist_school3_to_school1 + _dist_school1_to_school2 + _dist_school2_to_mand2" +,_dist_school3_school2_school1_mand2,"dist_home_to_school3 + _dist_school3_to_school2 + _dist_school2_to_school1 + _dist_school1_to_mand2" +,_min_dist_dropoff_order_out_child123_chauf2,_dist_school1_school2_school3_mand2 +,_min_dist_dropoff_order_out_child123_chauf2,"np.where((_dist_school1_school3_school2_mand2 > 0) & (_dist_school1_school3_school2_mand2 < _min_dist_dropoff_order_out_child123_chauf2), _dist_school1_school3_school2_mand2, _min_dist_dropoff_order_out_child123_chauf2)" +,_min_dist_dropoff_order_out_child123_chauf2,"np.where((_dist_school2_school1_school3_mand2 > 0) & (_dist_school2_school1_school3_mand2 < _min_dist_dropoff_order_out_child123_chauf2), _dist_school2_school1_school3_mand2, _min_dist_dropoff_order_out_child123_chauf2)" +,_min_dist_dropoff_order_out_child123_chauf2,"np.where((_dist_school2_school3_school1_mand2 > 0) & (_dist_school2_school3_school1_mand2 < _min_dist_dropoff_order_out_child123_chauf2), _dist_school2_school3_school1_mand2, _min_dist_dropoff_order_out_child123_chauf2)" +,_min_dist_dropoff_order_out_child123_chauf2,"np.where((_dist_school3_school1_school2_mand2 > 0) & (_dist_school3_school1_school2_mand2 < _min_dist_dropoff_order_out_child123_chauf2), _dist_school3_school1_school2_mand2, _min_dist_dropoff_order_out_child123_chauf2)" +,_min_dist_dropoff_order_out_child123_chauf2,"np.where((_dist_school3_school2_school1_mand2 > 0) & (_dist_school3_school2_school1_mand2 < _min_dist_dropoff_order_out_child123_chauf2), _dist_school3_school2_school1_mand2, _min_dist_dropoff_order_out_child123_chauf2)" +Absolute deviation outbound distance child123 Chauffer 1,abs_dev_dist_out_child123_chauf2,"np.maximum(_min_dist_dropoff_order_out_child123_chauf2 - dist_home_to_mand2, 0)" +# Availability for multiple bundles,, +,_return_min_taking_child1,"(pref_depart_time_school1 * mins_per_time_bin) + time_home_to_school1 + time_school_to_home1" +,_return_min_taking_child2,"(pref_depart_time_school2 * mins_per_time_bin) + time_home_to_school2 + time_school_to_home2" +,_return_min_taking_child3,"(pref_depart_time_school3 * mins_per_time_bin) + time_home_to_school3 + time_school_to_home3" +Availability of taking child 1 and then 2,_avail_child1_then_child2,"(_return_min_taking_child1 < (pref_depart_time_school2 * mins_per_time_bin))" +Availability of taking child 2 and then 1,_avail_child2_then_child1,"(_return_min_taking_child2 < (pref_depart_time_school1 * mins_per_time_bin))" +Availability of taking child 1 and then 3,_avail_child1_then_child3,"(_return_min_taking_child1 < (pref_depart_time_school3 * mins_per_time_bin))" +Availability of taking child 3 and then 1,_avail_child3_then_child1,"(_return_min_taking_child3 < (pref_depart_time_school1 * mins_per_time_bin))" +Availability of taking child 2 and then 3,_avail_child2_then_child3,"(_return_min_taking_child2 < (pref_depart_time_school3 * mins_per_time_bin))" +Availability of taking child 3 and then 2,_avail_child3_then_child2,"(_return_min_taking_child3 < (pref_depart_time_school2 * mins_per_time_bin))" +multiple_bundle_availability,avail_multiple_bundles,(_avail_child1_then_child2 | _avail_child2_then_child1 | _avail_child1_then_child3 | _avail_child3_then_child1 | _avail_child2_then_child3 | _avail_child3_then_child2) +# ,, +# Inbound specific terms ,, +Preferred return time from school - child 1,pref_return_time_school1,"reindex(tours[(tours.tour_type == 'school') & (tours.tour_num == 1)].set_index('person_id').end, df.child_id1)" +Preferred return time from school - child 2,pref_return_time_school2,"reindex(tours[(tours.tour_type == 'school') & (tours.tour_num == 1)].set_index('person_id').end, df.child_id2)" +Preferred return time from school - child 3,pref_return_time_school3,"reindex(tours[(tours.tour_type == 'school') & (tours.tour_num == 1)].set_index('person_id').end, df.child_id3)" +Preferred return time from work / univ - chauffer 1,pref_return_time_chauf1,"reindex(tours[(tours.tour_category == 'mandatory') & (tours.tour_num == 1)].set_index('person_id').end, df.chauf_id1)" +Preferred return time from work / univ - chauffer 2,pref_return_time_chauf2,"reindex(tours[(tours.tour_category == 'mandatory') & (tours.tour_num == 1)].set_index('person_id').end, df.chauf_id2)" +# overlapping time windows from inbound escorting,, +# ,_return_min_taking_inbound_child1_pe,"(_pref_depart_time_to_school1 * mins_per_time_bin) + time_home_to_school1 + time_school_to_home1" +# ,_return_min_taking_inbound_child2_pe,"(_pref_depart_time_to_school2 * mins_per_time_bin) + time_home_to_school2 + time_school_to_home2" +# ,_return_min_taking_inbound_child3_pe,"(_pref_depart_time_to_school3 * mins_per_time_bin) + time_home_to_school3 + time_school_to_home3" +# finding earliest time chauffer 1 returns from inbound pure escort tour,inbound_pe_return_time_home_chauf1,-1 +# ,inbound_pe_return_time_home_chauf1,"np.where((df.chauf1_inbound == 2) & (_return_min_taking_inbound_child1_pe > inbound_pe_return_time_home_chauf1), _return_min_taking_inbound_child1_pe, inbound_pe_return_time_home_chauf1)" +# ,inbound_pe_return_time_home_chauf1,"np.where((df.chauf2_inbound == 2) & (_return_min_taking_inbound_child2_pe > inbound_pe_return_time_home_chauf1), _return_min_taking_inbound_child2_pe, inbound_pe_return_time_home_chauf1)" +# ,inbound_pe_return_time_home_chauf1,"np.where((df.chauf3_inbound == 2) & (_return_min_taking_inbound_child3_pe > inbound_pe_return_time_home_chauf1), _return_min_taking_inbound_child3_pe, inbound_pe_return_time_home_chauf1)" +# finding earliest time chauffer 2 returns from inbound pure escort tour,inbound_pe_return_time_home_chauf2,-1 +# ,inbound_pe_return_time_home_chauf2,"np.where((df.chauf1_inbound == 2) & (_return_min_taking_inbound_child1_pe > inbound_pe_return_time_home_chauf2), _return_min_taking_inbound_child1_pe, inbound_pe_return_time_home_chauf2)" +# ,inbound_pe_return_time_home_chauf2,"np.where((df.chauf2_inbound == 2) & (_return_min_taking_inbound_child2_pe > inbound_pe_return_time_home_chauf2), _return_min_taking_inbound_child2_pe, inbound_pe_return_time_home_chauf2)" +# ,inbound_pe_return_time_home_chauf2,"np.where((df.chauf3_inbound == 2) & (_return_min_taking_inbound_child3_pe > inbound_pe_return_time_home_chauf2), _return_min_taking_inbound_child3_pe, inbound_pe_return_time_home_chauf2)" +# finding latest time chauffer 1 returns from inbound ride share tour,inbound_rs_return_time_home_chauf1,pref_depart_time_chauf1 +# finding latest time chauffer 2 returns from inbound ride share tour,inbound_rs_return_time_home_chauf2,pref_depart_time_chauf2 +# return time of inbound school escoring tour - chauffeur 1,return_bin_inbound_school_escorting1,"np.where(df.nrs1_inbound > 0, pref_depart_time_chauf1, inbound_pe_return_time_home_chauf1)" +# return time of inbound school escoring tour - chauffeur 2,return_bin_inbound_school_escorting2,"np.where(df.nrs2_inbound > 0, pref_depart_time_chauf2, inbound_pe_return_time_home_chauf2)" diff --git a/resident/configs/school_location.csv b/resident/configs/school_location.csv new file mode 100644 index 0000000..3b07c3d --- /dev/null +++ b/resident/configs/school_location.csv @@ -0,0 +1,31 @@ +Label,Description,Expression,university,highschool,gradeschool,preschool +local_dist,,"_DIST@skims[('SOV_M_DIST', 'MD')]",1,1,1,1 +util_mode_choice_logsum,Mode choice logsum,mode_choice_logsum,coef_mclogsum_univ,coef_mclogsum_hsch,coef_mclogsum_gs,coef_mclogsum_ps +util_Distance,Distance,@_DIST.clip(upper=50),coef_dist_univ,coef_dist_hsch,coef_dist_gs,coef_dist_ps +util_log_Distance,log_Distance,@np.log(_DIST.clip(upper=50)+1),coef_lndist_univ,coef_zero,coef_zero,coef_lndist_ps +util_Distance_squareroot,Squareroot of distance,@_DIST.clip(upper=50)**0.5,coef_sqrtdist_univ,coef_sqrtdist_hsch,coef_sqrtdist_gs,coef_sqrtdist_ps +util_Distance_squared,Distance_squared,@_DIST.clip(upper=50)**2,coef_sqrddist_univ,coef_sqrddist_hsch,coef_sqrddist_gs,coef_sqrddist_ps +util_Distance_cubed,Distance_cubed,@_DIST.clip(upper=50)**3,coef_cubeddist_univ,coef_cubeddist_hsch,coef_cubeddist_gs,coef_cubeddist_ps +util_Distance_worker_univ,Distance for a worker_university specific,"@np.where(df.ptype==1, _DIST.clip(upper=50), 0)",coef_workerdist_univ,coef_zero,coef_zero,coef_zero +# FIXME this ENROLL_COLL variable is looking at the number of enrollment in the origin zone not the destination zone,,,,,, +# need to merge onto the alternative destination,,,,,, +util_Distance_largeuniversity_univ,Distance for large university enrollment,"@np.where(df.COLLEGEENROLL>5000, _DIST.clip(upper=50), 0)",coef_univenrol_dist_univ,coef_zero,coef_zero,coef_zero +util_Distance _lowincome_prek,Distance - low income,"@np.where(df.income<60000, _DIST.clip(upper=50), 0)",coef_zero,coef_zero,coef_zero,coef_lowincdist_ps +util_Distance - age03_prek,Distance - age 0 to 3,"@np.where(df.age<3,_DIST.clip(upper=50),0)",coef_zero,coef_zero,coef_zero,coef_age03dist_ps +util_LoggedSize,Logged Size variable - University specific,@df['size_term'].apply(np.log1p),coef_lnSize,coef_lnSize,coef_lnSize,coef_lnSize +util_no_attractions,no attractions if logged university size is zero,@df['size_term']==0,-999,-999,-999,-999 +util_sample_of_corrections_factor,Sample of alternatives correction factor,"@np.minimum(np.log(df.pick_count/df.prob), 60)",1,1,1,1 +util_sp_utility_adjustment,shadow price utility adjustment,@df['shadow_price_utility_adjustment'],1,1,1 +#,,,,,, +util_ABM2calibration_0-1miles,ABM2 calibration_0-1miles,@(_DIST<1),coef_zero,coef_abmcalib_01miles,coef_abmcalib_01miles,coef_abmcalib_01miles +util_ABM2calibration_1-2miles,ABM2 calibration_1-2miles,@(_DIST<2) * (_DIST>=1),coef_zero,coef_abmcalib_12miles,coef_abmcalib_12miles,coef_abmcalib_12miles +util_ABM2calibration_2-3miles,ABM2 calibration_2-3miles,@(_DIST<3) * (_DIST>=2),coef_zero,coef_abmcalib_23miles,coef_abmcalib_23miles,coef_abmcalib_23miles +util_ABM2calibration_0-20miles,ABM2 calibration_0-20miles,@(_DIST<20) * (_DIST),coef_zero,coef_abmcalib_20miles,coef_abmcalib_20miles,coef_abmcalib_20miles +#,,,,,, +#calibration constants,,, +util_Calibration 0-2 - miles,Calibration 0-2 - miles,@_DIST<=2,,coef_distance_0_2miles,coef_distance_2_5miles,coef_distance_5_10miles +util_Calibration 2-5 - miles,Calibration 2-5 - miles,@(_DIST>2) * (_DIST<=5),,coef_distance_0_2miles,coef_distance_2_5miles,coef_distance_5_10miles +util_Calibration 5-10 - miles,Calibration 5-10 - miles,@(_DIST>5) * (_DIST<=10),,coef_distance_0_2miles,coef_distance_2_5miles,coef_distance_5_10miles +util_ABM2 calibration 10-20 - miles,ABM3 calibration 10-20 - miles,@(_DIST>10) * (_DIST<=20),,coef_distance_0_2miles,coef_distance_2_5miles,coef_distance_5_10miles +util_ABM2 calibration 20-30 - miles,ABM3 calibration 20-30 - miles,@(_DIST>20) * (_DIST<=30),,coef_distance_0_2miles,coef_distance_2_5miles,coef_distance_5_10miles +util_ABM2 calibration >30 - miles,ABM3 calibration >30 - miles,@(_DIST>30),,coef_distance_0_2miles,coef_distance_2_5miles,coef_distance_5_10miles diff --git a/resident/configs/school_location.yaml b/resident/configs/school_location.yaml new file mode 100644 index 0000000..7c3c2b1 --- /dev/null +++ b/resident/configs/school_location.yaml @@ -0,0 +1,74 @@ +SAMPLE_SIZE: 30 +ESTIMATION_SAMPLE_SIZE: 10 + +SIMULATE_CHOOSER_COLUMNS: + - home_zone_id + - school_segment + - household_id + - ptype + - COLLEGEENROLL + - income + - age + +# model-specific logsum-related settings +CHOOSER_ORIG_COL_NAME: home_zone_id +ALT_DEST_COL_NAME: alt_dest +IN_PERIOD: 14 +OUT_PERIOD: 8 + +DEST_CHOICE_COLUMN_NAME: school_zone_id +# comment out DEST_CHOICE_LOGSUM_COLUMN_NAME if not desired in persons table +DEST_CHOICE_LOGSUM_COLUMN_NAME: school_location_logsum +# comment out MODE_CHOICE_LOGSUM_COLUMN_NAME if not desired in persons table +MODE_CHOICE_LOGSUM_COLUMN_NAME: school_modechoice_logsum + +# comment out DEST_CHOICE_LOGSUM_COLUMN_NAME if saved alt logsum table +DEST_CHOICE_SAMPLE_TABLE_NAME: school_location_sample + + +SAMPLE_SPEC: school_location_sample.csv +SPEC: school_location.csv +COEFFICIENTS: school_location_coefficients.csv + +LOGSUM_SETTINGS: tour_mode_choice.yaml +LOGSUM_PREPROCESSOR: nontour_preprocessor + +LOGSUM_TOUR_PURPOSE: + university: univ + highschool: school + gradeschool: school + preschool: school + +annotate_persons: + SPEC: annotate_persons_school + DF: persons + +# - shadow pricing + +# required by initialize_households when creating school_destination_size table +CHOOSER_TABLE_NAME: persons + +# size_terms model_selector +MODEL_SELECTOR: school + +# chooser column with segment_id for this segment type +CHOOSER_SEGMENT_COLUMN_NAME: school_segment + +# boolean column to filter choosers (True means keep) +CHOOSER_FILTER_COLUMN_NAME: is_student + + +# FIXME - these are assigned to persons in annotate_persons. we need a better way to manage this +SEGMENT_IDS: + university: 4 # SCHOOL_SEGMENT_UNIV DEFINED IN constants.yaml + highschool: 3 # SCHOOL_SEGMENT_HIGH + gradeschool: 2 # SCHOOL_SEGMENT_GRADE + preschool: 1 # SCHOOL_SEGMENT_PREK + + +# model adds these tables (informational - not added if commented out) +SHADOW_PRICE_TABLE: school_shadow_prices +MODELED_SIZE_TABLE: school_modeled_size + +# not loaded if commented out +SAVED_SHADOW_PRICE_TABLE_NAME: school_shadow_prices.csv diff --git a/resident/configs/school_location_coefficients.csv b/resident/configs/school_location_coefficients.csv new file mode 100644 index 0000000..06450ed --- /dev/null +++ b/resident/configs/school_location_coefficients.csv @@ -0,0 +1,43 @@ +coefficient_name,value,constrain +coef_zero,0,T +coef_lnSize,1,T +coef_abmcalib_01miles,-0.962352271,T +coef_abmcalib_12miles,-0.6403,T +coef_abmcalib_23miles,-0.1299,T +coef_abmcalib_20miles,0.0307,T +#,, +coef_mclogsum_univ,0.25,F +coef_dist_univ,1.279,F +coef_lndist_univ,10.412,F +coef_sqrtdist_univ,-13.774,F +coef_sqrddist_univ,-0.011,F +coef_cubeddist_univ,0,F +coef_workerdist_univ,0.013,F +coef_univenrol_dist_univ,0.028,F +#,, +coef_mclogsum_hsch,0.25,F +coef_dist_hsch,0.0241,F +coef_sqrtdist_hsch,-2.2566,F +coef_sqrddist_hsch,0.012,F +coef_cubeddist_hsch,-0.0002,F +#,, +coef_mclogsum_gs,0.34935,F +coef_dist_gs,1.4025,F +coef_sqrtdist_gs,-6.6929,F +coef_sqrddist_gs,-0.0245,F +coef_cubeddist_gs,0.0002,F +#,, +coef_mclogsum_ps,0.5,F +coef_dist_ps,0.4333,F +coef_lndist_ps,4.3415,F +coef_sqrtdist_ps,-7.38,F +coef_sqrddist_ps,0.0125,F +coef_cubeddist_ps,-0.0004,F +coef_lowincdist_ps,-0.0956,F +coef_age03dist_ps,-0.007,F +#,, +coef_distance_0_2miles,0.170,F +coef_distance_2_5miles,0.120,F +coef_distance_5_10miles,0.250,F +coef_distance_10_20miles,-0.400,F +coef_distance_20_30miles,-2.900,F diff --git a/resident/configs/school_location_sample.csv b/resident/configs/school_location_sample.csv new file mode 100644 index 0000000..0f6fffc --- /dev/null +++ b/resident/configs/school_location_sample.csv @@ -0,0 +1,16 @@ +Label,Description,Expression,university,highschool,gradeschool,preschool +local_dist,,"_DIST@skims[('SOV_M_DIST', 'MD')]",1,1,1,1 +util_Distance,Distance,@_DIST.clip(upper=50),coef_dist_univ,coef_dist_hsch,coef_dist_gs,coef_dist_ps +util_log_Distance,log_Distance,@np.log(_DIST.clip(upper=50)+1),coef_lndist_univ,coef_zero,coef_zero,coef_lndist_ps +util_Squareroot_distance,Squareroot_distance,@_DIST.clip(upper=50)**0.5,coef_sqrtdist_univ,coef_sqrtdist_hsch,coef_sqrtdist_gs,coef_sqrtdist_ps +util_Distance_squared,Distance_squared,@_DIST.clip(upper=50)**2,coef_sqrddist_univ,coef_sqrddist_hsch,coef_sqrddist_gs,coef_sqrddist_ps +util_Distance_cubed,Distance_cubed,@_DIST.clip(upper=50)**3,coef_cubeddist_univ,coef_cubeddist_hsch,coef_cubeddist_gs,coef_cubeddist_ps +util_Distance_worker_univspecific,Distance for a worker_university specific,"@np.where(df.ptype==1, _DIST.clip(upper=50), 0)",coef_workerdist_univ,coef_zero,coef_zero,coef_zero +# FIXME this ENROLL_COLL variable is looking at the number of enrollment in the origin zone not the destination zone,,,,,, +# need to merge onto the alternative destination,,,,,, +util_Distance_largeuniversity_univ,Distance for large university enrollment,"@np.where(df.COLLEGEENROLL>5000, _DIST.clip(upper=50), 0)",coef_univenrol_dist_univ,coef_zero,coef_zero,coef_zero +util_Distance _lowincome_prek,Distance - low income,"@np.where(df.income<60000, _DIST.clip(upper=50), 0)",coef_zero,coef_zero,coef_zero,coef_lowincdist_ps +util_Distance - age03_prek,Distance - age 0 to 3,"@np.where(df.age<3,_DIST.clip(upper=50),0)",coef_zero,coef_zero,coef_zero,coef_age03dist_ps +util_LoggedSize,Logged Size variable - University specific,@df['size_term'].apply(np.log1p),coef_lnSize,coef_lnSize,coef_lnSize,coef_lnSize +util_no_attractions,no attractions if logged university size is zero,@df['size_term']==0,-999,-999,-999,-999 +util_sp_utility_adjustment,shadow price utility adjustment,@df['shadow_price_utility_adjustment'],1,1,1,1 \ No newline at end of file diff --git a/resident/configs/settings.yaml b/resident/configs/settings.yaml new file mode 100644 index 0000000..3ab1f12 --- /dev/null +++ b/resident/configs/settings.yaml @@ -0,0 +1,165 @@ +model_name: resident + +#inherit_settings: True + +multiprocess: False +fail_fast: True +num_processes: 10 +chunk_training_mode: disabled + +# turn shadow_pricing on and off for all models (e.g. school and work) +# see shadow_pricing.yaml for additional settings +use_shadow_pricing: True + +memory_profile: False + +# Sample TAZ before MAZ in destination choice +want_dest_choice_presampling: True + +# turn writing of sample_tables on and off for all models +# (if True, tables will be written if DEST_CHOICE_SAMPLE_TABLE_NAME is specified in individual model settings) +want_dest_choice_sample_tables: False + +# trace household id; comment out or leave empty for no trace +# households with all tour types +trace_hh_id: #135019 #348740 #732566 #348740 #578678 #348847 + +# trace origin, destination in accessibility calculation; comment out or leave empty for no trace +#trace_od: + +# metro hhs 862973, so 20% = 172595 +households_sample_size: 100 + +# input tables +input_table_list: + - tablename: households + filename: households.csv + index_col: household_id + rename_columns: + MAZ: home_zone_id + HHINCADJ: income + NP: hhsize + WORKERS: num_workers + - tablename: persons + filename: persons.csv + index_col: person_id + rename_columns: + AGEP: age + - tablename: land_use + filename: land_use.csv + index_col: zone_id + rename_columns: + MAZ: zone_id + +cleanup_pipeline_after_run: False + +output_tables: + h5_store: False + action: include + prefix: final_ + sort: True + tables: + - checkpoints + - accessibility + - land_use + - households + - persons + - tours + - trips + - joint_tour_participants + - vehicles + - proto_disaggregate_accessibility + - disaggregate_accessibility + +resume_after: + +models: + ### mp_init_proto_pop (single process) + - transit_lot_connectivity + - initialize_proto_population # Separate step so proto tables can be split for multiprocess. + ### mp_disaggregate_accessibility + - compute_disaggregate_accessibility + ### mp_initialize_hhs (single process) + - initialize_landuse + - initialize_households + ### mp_accessibility + - compute_accessibility + ### mp_households + - license_holding_status + - bike_comfort + - av_ownership + - auto_ownership_simulate + - work_from_home + - external_worker_identification + - external_workplace_location + - school_location + - workplace_location + - vehicle_type_choice + - adjust_auto_operating_cost + - transit_pass_subsidy + - transit_pass_ownership + - free_parking + - telecommute_frequency + - cdap_simulate + - mandatory_tour_frequency + - mandatory_tour_scheduling + - school_escorting + - joint_tour_frequency_composition + - external_joint_tour_identification + - joint_tour_participation + - joint_tour_destination + - external_joint_tour_destination + - joint_tour_scheduling + - non_mandatory_tour_frequency + - external_non_mandatory_identification + - non_mandatory_tour_destination + - external_non_mandatory_destination + - non_mandatory_tour_scheduling + - vehicle_allocation + - park_and_ride_lot_choice + - tour_mode_choice_simulate + - atwork_subtour_frequency + - atwork_subtour_destination + - atwork_subtour_scheduling + - atwork_subtour_mode_choice + - stop_frequency + - trip_purpose + - trip_destination + - trip_purpose_and_destination + - trip_scheduling + - trip_mode_choice + - parking_location + ### mp_summarize (single process) + - write_data_dictionary + - track_skim_usage + - write_trip_matrices + - write_tables + +multiprocess_steps: + - name: mp_init_proto_pop + begin: transit_lot_connectivity + - name: mp_disaggregate_accessibility + num_processes: 10 + begin: compute_disaggregate_accessibility + slice: + tables: + - proto_households + - proto_persons + - proto_tours + - name: mp_initialize_hhs + begin: initialize_landuse + - name: mp_accessibility + begin: compute_accessibility + num_processes: 10 + slice: + tables: + - accessibility + exclude: True # this is needed so landuse (i.e. destinations) doesn't get split + - name: mp_households + begin: license_holding_status + slice: + tables: + - households + - persons + - name: mp_summarize + begin: write_data_dictionary \ No newline at end of file diff --git a/resident/configs/shadow_pricing.yaml b/resident/configs/shadow_pricing.yaml new file mode 100644 index 0000000..6321c95 --- /dev/null +++ b/resident/configs/shadow_pricing.yaml @@ -0,0 +1,50 @@ +shadow_pricing_models: + school: school_location + workplace: workplace_location + external_workplace: external_workplace_location + +# global switch to enable/disable loading of saved shadow prices +# (ignored if global use_shadow_pricing switch is False) +LOAD_SAVED_SHADOW_PRICES: False + +# write out choices by iteration to trace folder +WRITE_ITERATION_CHOICES: False + +# number of shadow price iterations for cold start +MAX_ITERATIONS: 10 + +# number of shadow price iterations for warm start (after loading saved shadow_prices) +MAX_ITERATIONS_SAVED: 1 + +## Shadow pricing method +# SHADOW_PRICE_METHOD: ctramp +# SHADOW_PRICE_METHOD: daysim +SHADOW_PRICE_METHOD: simulation + +# --- simulation method settings +# ignore criteria for zones smaller than size_threshold +SIZE_THRESHOLD: 10 +# ignore criteria for zones smaller than target_threshold (total employmnet or enrollment) +TARGET_THRESHOLD: 20 +# zone passes if modeled is within percent_tolerance of predicted_size +PERCENT_TOLERANCE: 5 +# max percentage of zones allowed to fail +FAIL_THRESHOLD: 1 +# apply different targets for each segment specified in destination_size_terms.csv +school_segmentation_targets: + # format is segment: land_use_column + university: COLLEGEENROLL + highschool: ENROLLGRADE9to12 + gradeschool: ENROLLGRADEKto8 + preschool: preschool_target + +# if target names are the same, they will be combined together +workplace_segmentation_targets: + constr_maint: EMP_TOTAL + health: EMP_TOTAL + military: EMP_TOTAL + mngt_busi_scic_arts: EMP_TOTAL + prod_trans_move: EMP_TOTAL + sales_office: EMP_TOTAL + services: EMP_TOTAL + diff --git a/resident/configs/stop_frequency.yaml b/resident/configs/stop_frequency.yaml new file mode 100644 index 0000000..6118976 --- /dev/null +++ b/resident/configs/stop_frequency.yaml @@ -0,0 +1,77 @@ +# inherit_settings: True +LOGIT_TYPE: MNL + +preprocessor: + SPEC: stop_frequency_annotate_tours_preprocessor + DF: tours_merged + TABLES: + - persons + - land_use + - accessibility + +SEGMENT_COL: primary_purpose + +SPEC_SEGMENTS: + - primary_purpose: work + SPEC: stop_frequency_work.csv + COEFFICIENTS: stop_frequency_coefficients_work.csv + - primary_purpose: school + SPEC: stop_frequency_school.csv + COEFFICIENTS: stop_frequency_coefficients_school.csv + - primary_purpose: univ + SPEC: stop_frequency_univ.csv + COEFFICIENTS: stop_frequency_coefficients_univ.csv + - primary_purpose: social + SPEC: stop_frequency_social.csv + COEFFICIENTS: stop_frequency_coefficients_social.csv + - primary_purpose: shopping + SPEC: stop_frequency_shopping.csv + COEFFICIENTS: stop_frequency_coefficients_shopping.csv + - primary_purpose: eatout + SPEC: stop_frequency_eatout.csv + COEFFICIENTS: stop_frequency_coefficients_eatout.csv + - primary_purpose: escort + SPEC: stop_frequency_escort.csv + COEFFICIENTS: stop_frequency_coefficients_escort.csv + - primary_purpose: othmaint + SPEC: stop_frequency_othmaint.csv + COEFFICIENTS: stop_frequency_coefficients_othmaint.csv + - primary_purpose: othdiscr + SPEC: stop_frequency_othdiscr.csv + COEFFICIENTS: stop_frequency_coefficients_othdiscr.csv + - primary_purpose: atwork + SPEC: stop_frequency_atwork.csv + COEFFICIENTS: stop_frequency_coefficients_atwork.csv + +CONSTANTS: + TRANSIT_MODES: + - WALK_TRANSIT + - PNR_TRANSIT + - KNR_TRANSIT + - TNC_TRANSIT + DRIVE_TO_TRANSIT_MODES: + - PNR_LOC + - KNR_LOC + - TNC_LOC + - PNR_PRM + - KNR_PRM + - TNC_PRM + - PNR_MIX + - KNR_MIX + - TNC_MIX + NONMOTORIZED_MODES: + - WALK + - BIKE + SHOP_TOUR: shopping + MAINT_TOUR: othmaint + SCHOOL_TOUR: school + EATOUT_TOUR: eatout + SOCIAL_TOUR: social + DISCR_TOUR: othdiscr + num_atwork_subtours_map: + no_subtours: 0 + eat: 1 + business1: 1 + maint: 1 + business2: 2 + eat_business: 2 diff --git a/resident/configs/stop_frequency_alternatives.csv b/resident/configs/stop_frequency_alternatives.csv new file mode 100644 index 0000000..72f49a7 --- /dev/null +++ b/resident/configs/stop_frequency_alternatives.csv @@ -0,0 +1,18 @@ +#,,alt file for building tours even though simulation is simple_simulate not interaction_simulate +alt,out,in +0out_0in,0,0 +0out_1in,0,1 +0out_2in,0,2 +0out_3in,0,3 +1out_0in,1,0 +1out_1in,1,1 +1out_2in,1,2 +1out_3in,1,3 +2out_0in,2,0 +2out_1in,2,1 +2out_2in,2,2 +2out_3in,2,3 +3out_0in,3,0 +3out_1in,3,1 +3out_2in,3,2 +3out_3in,3,3 diff --git a/resident/configs/stop_frequency_annotate_tours_preprocessor.csv b/resident/configs/stop_frequency_annotate_tours_preprocessor.csv new file mode 100644 index 0000000..bd2847c --- /dev/null +++ b/resident/configs/stop_frequency_annotate_tours_preprocessor.csv @@ -0,0 +1,60 @@ +Description,Target,Expression +#,, +# define primary_purpose to use for slicing choosers with a value that identifies the spec to be used ,, +# e.g. univ segment means there will be a spec called stop_frequency_univ.csv,, +# so the 'school' tour_type can treat univ and non-univ school tours differently,, +,primary_purpose,"df.tour_type.where((df.tour_type != 'school') | ~(df.pstudent == PSTUDENT_UNIVERSITY), 'univ')" +,primary_purpose,"primary_purpose.where(df.tour_category!='atwork', 'atwork')" +#,, +,distance_in_miles,"od_skims[('SOV_M_DIST', 'MD')]" +#,, +,is_joint,df.tour_category=='joint' +,_HH_PERSON_COUNT,"lambda exp, persons: persons.query(exp).groupby('household_id').size()" +,num_full,"reindex_i(_HH_PERSON_COUNT('ptype == %s' % PEMPLOY_FULL, persons), df.household_id)" +,num_part,"reindex_i(_HH_PERSON_COUNT('ptype == %s' % PEMPLOY_PART, persons), df.household_id)" +,num_student,"reindex_i(_HH_PERSON_COUNT('pstudent != %s' % PSTUDENT_NOT, persons), df.household_id)" +Num Kids between 0 and 4 (including) years old,num_age_0_4,"reindex_i(_HH_PERSON_COUNT('age < 5', persons), df.household_id)" +Num kids between 4 and 15 (including) years old,num_age_5_15,"reindex_i(_HH_PERSON_COUNT('(age >= 5) & (age <16)', persons), df.household_id)" +Number of Adults (>= 16 years old),num_adult,"reindex_i(_HH_PERSON_COUNT('age >= 16', persons), df.household_id)" +,more_cars_than_workers,df.auto_ownership >= (num_full + num_part) +,tour_mode_is_transit,df.tour_mode.isin(TRANSIT_MODES) +,tour_mode_is_drive_transit,df.tour_mode.isin(DRIVE_TO_TRANSIT_MODES) +,tour_mode_is_non_motorized,df.tour_mode.isin(NONMOTORIZED_MODES) +,tour_mode_is_schbus,df.tour_mode.isin(['SCH_BUS']) + +#,, +#num_work_tours already defined,, +,num_total_tours,"reindex_i(df.groupby('person_id').size(), df.person_id)" +school but not university,num_school_tours,"reindex_i(df[primary_purpose==SCHOOL_TOUR].groupby('person_id').size(), df.person_id)" +,num_univ_tours,(df.pstudent == PSTUDENT_UNIVERSITY) * num_school_tours +#num_escort_tours already defined,, +# indiv tour counts should not include joint tours by point_person,, +,num_shop_tours,"reindex_i(df[~is_joint & (df.tour_type==SHOP_TOUR)].groupby('person_id').size(), df.person_id)" +,num_maint_tours,"reindex_i(df[~is_joint & (df.tour_type==MAINT_TOUR)].groupby('person_id').size(), df.person_id)" +,num_eatout_tours,"reindex_i(df[~is_joint & (df.tour_type==EATOUT_TOUR)].groupby('person_id').size(), df.person_id)" +,num_social_tours,"reindex_i(df[~is_joint & (df.tour_type==SOCIAL_TOUR)].groupby('person_id').size(), df.person_id)" +,num_discr_tours,"reindex_i(df[~is_joint & (df.tour_type==DISCR_TOUR)].groupby('person_id').size(), df.person_id)" +#,, +Number of subtours in the tour,num_atwork_subtours,"df.atwork_subtour_frequency.map(num_atwork_subtours_map, na_action='ignore').fillna(0).astype(np.int8)" +#,, +,periods_per_hour,2 +,duration_hours,df.duration / periods_per_hour +Number of hh shop tours including joint,num_hh_shop_tours,"reindex_i(df[df.tour_type==SHOP_TOUR].groupby('household_id').size(), df.person_id)" +Number of hh maint tours including joint,num_hh_maint_tours,"reindex_i(df[df.tour_type==MAINT_TOUR].groupby('household_id').size(), df.person_id)" +tourStartsInPeakPeriod,_tour_starts_in_peak,(network_los.skim_time_period_label(df.start) == 'AM') | (network_los.skim_time_period_label(df.start) == 'PM') +AccesibilityAtOrigin fallback,hhacc,0 +AccesibilityAtOrigin if transit,hhacc,"hhacc.where(~tour_mode_is_transit, df.trPkRetail.where(_tour_starts_in_peak, df.trOpRetail))" +AccesibilityAtOrigin if non_motorized,hhacc,"hhacc.where(~tour_mode_is_non_motorized, df.nmRetail)" +AccesibilityADestination fallback,pracc,0 +AccesibilityADestination peak transit,_dest_trPkRetail,"reindex(accessibility.trPkRetail, df.destination)" +AccesibilityADestination off-peak transit,_dest_trOpRetail,"reindex(accessibility.trOpRetail, df.destination)" +AccesibilityAtDestination if transit,pracc,"pracc.where(~tour_mode_is_transit, _dest_trPkRetail.where(_tour_starts_in_peak, _dest_trOpRetail))" +AccesibilityAtDestination if non_motorized,pracc,"pracc.where(~tour_mode_is_non_motorized, reindex(accessibility.nmRetail, df.destination))" +,destination_area_type,"reindex(land_use.pseudomsa, df.destination)" +,tour_mode_is_sov,df.tour_mode == 'SOV' +,tour_mode_is_hov,"df.tour_mode.isin(['HOV2', 'HOV3'])" +,tour_mode_is_taxi,"df.tour_mode.isin(['TAXI', 'TNC_SINGLE', 'TNC_SHARED'])" +presence of non_worker other than self in household,has_non_worker,"df.person_id.map(other_than(persons.household_id, persons.ptype == PTYPE_NONWORK))" +presence of retiree other than self in household,has_retiree,"df.person_id.map(other_than(persons.household_id, persons.ptype == PTYPE_RETIRED))" +presence of preschooler other than self in household,has_preschool_kid,"df.person_id.map(other_than(persons.household_id, persons.ptype == PTYPE_PRESCHOOL))" +presence of school_kid other than self in household,has_school_kid,"df.person_id.map(other_than(persons.household_id, persons.ptype == PTYPE_SCHOOL))" \ No newline at end of file diff --git a/resident/configs/stop_frequency_atwork.csv b/resident/configs/stop_frequency_atwork.csv new file mode 100644 index 0000000..00880ca --- /dev/null +++ b/resident/configs/stop_frequency_atwork.csv @@ -0,0 +1,9 @@ +Label,Description,Expression,0out_0in,0out_1in,0out_2in,0out_3in,1out_0in,1out_1in,1out_2in,1out_3in,2out_0in,2out_1in,2out_2in,2out_3in,3out_0in,3out_1in,3out_2in,3out_3in +util_alternative_specific_constants,Alternative specific constants,1,,coef_alternative_specific_constants_0out_1in_atwork,coef_alternative_specific_constants_0out_2in_atwork,coef_alternative_specific_constants_0out_3in_atwork,coef_alternative_specific_constants_1out_0in_atwork,coef_alternative_specific_constants_1out_1in_atwork,coef_alternative_specific_constants_1out_2in_atwork,coef_alternative_specific_constants_1out_3in_atwork,coef_alternative_specific_constants_2out_0in_atwork,coef_alternative_specific_constants_2out_1in_atwork,coef_alternative_specific_constants_2out_2in_atwork,coef_alternative_specific_constants_2out_3in_atwork,coef_alternative_specific_constants_3out_0in_atwork,coef_alternative_specific_constants_3out_1in_atwork,coef_alternative_specific_constants_3out_2in_atwork,coef_alternative_specific_constants_3out_3in_atwork +util_is_pre_covid,Tour happened before covid hit,@scenarioYear==2016,,coef_is_pre_covid_1plus_stops_atwork,coef_is_pre_covid_1plus_stops_atwork,coef_is_pre_covid_1plus_stops_atwork,coef_is_pre_covid_1plus_stops_atwork,coef_is_pre_covid_1plus_stops_atwork,coef_is_pre_covid_1plus_stops_atwork,coef_is_pre_covid_1plus_stops_atwork,coef_is_pre_covid_1plus_stops_atwork,coef_is_pre_covid_1plus_stops_atwork,coef_is_pre_covid_1plus_stops_atwork,coef_is_pre_covid_1plus_stops_atwork,coef_is_pre_covid_1plus_stops_atwork,coef_is_pre_covid_1plus_stops_atwork,coef_is_pre_covid_1plus_stops_atwork,coef_is_pre_covid_1plus_stops_atwork +util_num_age_5_15,Number of children in household age 5 to 15,num_age_5_15,,coef_num_age_5_15_1plus_stops_atwork,coef_num_age_5_15_1plus_stops_atwork,coef_num_age_5_15_1plus_stops_atwork,coef_num_age_5_15_1plus_stops_atwork,coef_num_age_5_15_1plus_stops_atwork,coef_num_age_5_15_1plus_stops_atwork,coef_num_age_5_15_1plus_stops_atwork,coef_num_age_5_15_1plus_stops_atwork,coef_num_age_5_15_1plus_stops_atwork,coef_num_age_5_15_1plus_stops_atwork,coef_num_age_5_15_1plus_stops_atwork,coef_num_age_5_15_1plus_stops_atwork,coef_num_age_5_15_1plus_stops_atwork,coef_num_age_5_15_1plus_stops_atwork,coef_num_age_5_15_1plus_stops_atwork +util_tour_mode_non_motorized,Tour mode is non-motorized,tour_mode_is_non_motorized,,coef_tour_mode_non_motorized_1plus_stops_atwork,coef_tour_mode_non_motorized_1plus_stops_atwork,coef_tour_mode_non_motorized_1plus_stops_atwork,coef_tour_mode_non_motorized_1plus_stops_atwork,coef_tour_mode_non_motorized_1plus_stops_atwork,coef_tour_mode_non_motorized_1plus_stops_atwork,coef_tour_mode_non_motorized_1plus_stops_atwork,coef_tour_mode_non_motorized_1plus_stops_atwork,coef_tour_mode_non_motorized_1plus_stops_atwork,coef_tour_mode_non_motorized_1plus_stops_atwork,coef_tour_mode_non_motorized_1plus_stops_atwork,coef_tour_mode_non_motorized_1plus_stops_atwork,coef_tour_mode_non_motorized_1plus_stops_atwork,coef_tour_mode_non_motorized_1plus_stops_atwork,coef_tour_mode_non_motorized_1plus_stops_atwork +util_tour_duration_in_hours,Tour duration in hours,(duration / 2),,coef_tour_duration_in_hours_1plus_stops_atwork,coef_tour_duration_in_hours_1plus_stops_atwork,coef_tour_duration_in_hours_1plus_stops_atwork,coef_tour_duration_in_hours_1plus_stops_atwork,coef_tour_duration_in_hours_1plus_stops_atwork,coef_tour_duration_in_hours_1plus_stops_atwork,coef_tour_duration_in_hours_1plus_stops_atwork,coef_tour_duration_in_hours_1plus_stops_atwork,coef_tour_duration_in_hours_1plus_stops_atwork,coef_tour_duration_in_hours_1plus_stops_atwork,coef_tour_duration_in_hours_1plus_stops_atwork,coef_tour_duration_in_hours_1plus_stops_atwork,coef_tour_duration_in_hours_1plus_stops_atwork,coef_tour_duration_in_hours_1plus_stops_atwork,coef_tour_duration_in_hours_1plus_stops_atwork +util_tour_type_eat,Atwork tour type is eat,(tour_type == 'eat'),,coef_tour_type_eat_1plus_stops_atwork,coef_tour_type_eat_1plus_stops_atwork,coef_tour_type_eat_1plus_stops_atwork,coef_tour_type_eat_1plus_stops_atwork,coef_tour_type_eat_1plus_stops_atwork,coef_tour_type_eat_1plus_stops_atwork,coef_tour_type_eat_1plus_stops_atwork,coef_tour_type_eat_1plus_stops_atwork,coef_tour_type_eat_1plus_stops_atwork,coef_tour_type_eat_1plus_stops_atwork,coef_tour_type_eat_1plus_stops_atwork,coef_tour_type_eat_1plus_stops_atwork,coef_tour_type_eat_1plus_stops_atwork,coef_tour_type_eat_1plus_stops_atwork,coef_tour_type_eat_1plus_stops_atwork +util_nostop_drive_transit,no stops on pnr knr tnc transit tours,"@(df.tour_mode.isin(['PNR_TRANSIT','KNR_TRANSIT']))",,coef_unavailable,coef_unavailable,coef_unavailable,coef_unavailable,coef_unavailable,coef_unavailable,coef_unavailable,coef_unavailable,coef_unavailable,coef_unavailable,coef_unavailable,coef_unavailable,coef_unavailable,coef_unavailable,coef_unavailable +util_abm3_calibration_constants,calibration constants,is_joint==0,-0.3411831667549546,1.5644482135598068,-0.12240711424725262,-1.9592538168463436,1.1518382833625738,2.9268706664109896,-10.0,-0.8904060015251857,-1.2471095726908494,-10.0,-5.0,-0.8005791109300985,-10.0,-10.0,-5.0,2.613748359446448 diff --git a/resident/configs/stop_frequency_coefficients_atwork.csv b/resident/configs/stop_frequency_coefficients_atwork.csv new file mode 100644 index 0000000..141a532 --- /dev/null +++ b/resident/configs/stop_frequency_coefficients_atwork.csv @@ -0,0 +1,22 @@ +coefficient_name,value,constrain +coef_unavailable,-999.0,T +coef_alternative_specific_constants_0out_1in_atwork,-3.092158257967754,F +coef_alternative_specific_constants_0out_2in_atwork,-4.899871765683751,F +coef_alternative_specific_constants_0out_3in_atwork,-5.768845734049241,F +coef_alternative_specific_constants_1out_0in_atwork,-3.546439038144793,F +coef_alternative_specific_constants_1out_1in_atwork,-5.3893910748423615,F +coef_alternative_specific_constants_1out_2in_atwork,-6.542073629348957,F +coef_alternative_specific_constants_1out_3in_atwork,-6.724352088610809,F +coef_alternative_specific_constants_2out_0in_atwork,-5.561245279802787,F +coef_alternative_specific_constants_2out_1in_atwork,-6.254352130392174,F +coef_alternative_specific_constants_2out_2in_atwork,-7.640624157012655,F +coef_alternative_specific_constants_2out_3in_atwork,-7.235168765535057,F +coef_alternative_specific_constants_3out_0in_atwork,-5.768855987819784,F +coef_alternative_specific_constants_3out_1in_atwork,-6.947501714137311,F +coef_alternative_specific_constants_3out_2in_atwork,-8.333869615557205,F +coef_alternative_specific_constants_3out_3in_atwork,-6.542030510182685,F +coef_is_pre_covid_1plus_stops_atwork,0.5756937400481714,F +coef_num_age_5_15_1plus_stops_atwork,0.2843024724548341,F +coef_tour_mode_non_motorized_1plus_stops_atwork,-1.1394252090580002,F +coef_tour_duration_in_hours_1plus_stops_atwork,0.8337318380115536,F +coef_tour_type_eat_1plus_stops_atwork,-1.5126695768515324,F diff --git a/resident/configs/stop_frequency_coefficients_eatout.csv b/resident/configs/stop_frequency_coefficients_eatout.csv new file mode 100644 index 0000000..a32e9fa --- /dev/null +++ b/resident/configs/stop_frequency_coefficients_eatout.csv @@ -0,0 +1,26 @@ +coefficient_name,value,constrain +coef_unavailable,-999.0,T +coef_alternative_specific_constants_0out_1in_eatout,-2.719322587570536,F +coef_alternative_specific_constants_0out_2in_eatout,-4.04271514492949,F +coef_alternative_specific_constants_0out_3in_eatout,-4.838544989216345,F +coef_alternative_specific_constants_1out_0in_eatout,-2.8659262240861816,F +coef_alternative_specific_constants_1out_1in_eatout,-3.916842376183419,F +coef_alternative_specific_constants_1out_2in_eatout,-5.230563088163616,F +coef_alternative_specific_constants_1out_3in_eatout,-5.117238989358759,F +coef_alternative_specific_constants_2out_0in_eatout,-4.067401892944042,F +coef_alternative_specific_constants_2out_1in_eatout,-5.31395707633995,F +coef_alternative_specific_constants_2out_2in_eatout,-5.884510389772713,F +coef_alternative_specific_constants_2out_3in_eatout,-7.063117137546255,F +coef_alternative_specific_constants_3out_0in_eatout,-4.811844812901839,F +coef_alternative_specific_constants_3out_1in_eatout,-6.051577660315912,F +coef_alternative_specific_constants_3out_2in_eatout,-6.370029686002297,F +coef_alternative_specific_constants_3out_3in_eatout,-7.3509005055134,F +coef_is_joint_tour_1plus_stops_eatout,-0.7985681547806562,F +coef_number_of_nonwork_tours_1plus_stops_eatout,-0.1427263606567685,F +coef_age_65_79_1plus_stops_eatout,0.256692320705862,F +coef_age_80_plus_1plus_stops_eatout,0.6133960840207545,F +coef_non_mandatory_accessibility_1plus_stops_eatout,0.0385191271941809,F +coef_tour_mode_non_motorized_1plus_stops_eatout,-0.6439075033171243,F +coef_tour_mode_hov_1plus_stops_eatout,0.7283869214461836,F +coef_tour_duration_in_hours_1plus_stops_eatout,0.0711785217183164,F +coef_distance_to_tour_destination_1plus_stops_eatout,0.0419177818028756,F diff --git a/resident/configs/stop_frequency_coefficients_escort.csv b/resident/configs/stop_frequency_coefficients_escort.csv new file mode 100644 index 0000000..da22c76 --- /dev/null +++ b/resident/configs/stop_frequency_coefficients_escort.csv @@ -0,0 +1,28 @@ +coefficient_name,value,constrain +coef_unavailable,-998.9999999850988,T +coef_alternative_specific_constants_0out_1in_escort,-2.4949660926999164,F +coef_alternative_specific_constants_0out_2in_escort,-3.672015130183568,F +coef_alternative_specific_constants_0out_3in_escort,-4.317384022338293,F +coef_alternative_specific_constants_1out_0in_escort,-2.9339040180046654,F +coef_alternative_specific_constants_1out_1in_escort,-3.948109055444319,F +coef_alternative_specific_constants_1out_2in_escort,-4.78515783271987,F +coef_alternative_specific_constants_1out_3in_escort,-5.303964208013312,F +coef_alternative_specific_constants_2out_0in_escort,-4.186671117779487,F +coef_alternative_specific_constants_2out_1in_escort,-5.057105742551855,F +coef_alternative_specific_constants_2out_2in_escort,-6.576948604682893,F +coef_alternative_specific_constants_2out_3in_escort,-6.443397350305856,F +coef_alternative_specific_constants_3out_0in_escort,-4.421518979725427,F +coef_alternative_specific_constants_3out_1in_escort,-5.088849493880071,F +coef_alternative_specific_constants_3out_2in_escort,-6.124959873262966,F +coef_alternative_specific_constants_3out_3in_escort,-6.220285587804925,F +coef_is_student_1plus_stops_escort,0.2161986201032582,F +coef_age_5_18_1plus_stops_escort,0.3727005469727215,F +coef_age_65_79_1plus_stops_escort,0.389659640407008,F +coef_hhinc_less_15_1plus_stops_escort,-0.3864753297575288,F +coef_hhsize_is_3_1plus_stops_escort,-0.6055607644471578,F +coef_hhsize_is_4p_1plus_stops_escort,-0.5839400121244439,F +coef_non_mandatory_accessibility_1plus_stops_escort,0.0692389635383912,F +coef_tour_mode_non_motorized_1plus_stops_escort,-1.2439664098905787,F +coef_start_time_is_afternoon_1plus_stops_escort,0.1764927977315441,F +coef_distance_to_tour_destination_1plus_stops_escort,0.0353903674319862,F +coef_no_stops_to_school_escorting_1plus_stops_escort,0.5908677898396228,F diff --git a/resident/configs/stop_frequency_coefficients_othdiscr.csv b/resident/configs/stop_frequency_coefficients_othdiscr.csv new file mode 100644 index 0000000..b7e0257 --- /dev/null +++ b/resident/configs/stop_frequency_coefficients_othdiscr.csv @@ -0,0 +1,30 @@ +coefficient_name,value,constrain +coef_unavailable,-999.0,T +coef_alternative_specific_constants_0out_1in_othdiscr,-2.4864652946738204,F +coef_alternative_specific_constants_0out_2in_othdiscr,-3.630516428961318,F +coef_alternative_specific_constants_0out_3in_othdiscr,-4.214343839043677,F +coef_alternative_specific_constants_1out_0in_othdiscr,-2.754048091372477,F +coef_alternative_specific_constants_1out_1in_othdiscr,-3.521188010752997,F +coef_alternative_specific_constants_1out_2in_othdiscr,-4.612681926260593,F +coef_alternative_specific_constants_1out_3in_othdiscr,-4.851923018137574,F +coef_alternative_specific_constants_2out_0in_othdiscr,-4.020178130616265,F +coef_alternative_specific_constants_2out_1in_othdiscr,-4.7492677704752095,F +coef_alternative_specific_constants_2out_2in_othdiscr,-5.897904534647468,F +coef_alternative_specific_constants_2out_3in_othdiscr,-6.020509324357631,F +coef_alternative_specific_constants_3out_0in_othdiscr,-4.428598584691764,F +coef_alternative_specific_constants_3out_1in_othdiscr,-5.2241670713521176,F +coef_alternative_specific_constants_3out_2in_othdiscr,-5.754805074140499,F +coef_alternative_specific_constants_3out_3in_othdiscr,-6.064960698334528,F +coef_is_joint_tour_1plusstops_othdiscr,-0.8985020250009612,F +coef_number_of_nonwork_tours_1plusstops_othdiscr,-0.2643315301718771,F +coef_age_5_18_1plusstops_othdiscr,-0.5660532740496371,F +coef_age_19_35_1plusstops_othdiscr,-0.2845268067760428,F +coef_age_65_79_1plusstops_othdiscr,0.1695756068531331,F +coef_preschool_child_in_hh_1plusstops_othdiscr,0.2693515451224259,F +coef_has_retiree_in_hh_1plusstops_othdiscr,-0.2169572360551797,F +coef_non_mandatory_accessibility_1plusstops_othdiscr,0.0690683633584472,F +coef_tour_mode_non_motorized_1plusstops_othdiscr,-1.508573161584664,F +coef_tour_mode_hov_1plusstops_othdiscr,0.9061054262736302,F +coef_start_time_is_morning_1plusstops_othdiscr,0.2982113854685047,F +coef_start_time_is_afternoon_1plusstops_othdiscr,0.1819176043755117,F +coef_distance_to_tour_destination_1plusstops_othdiscr,0.0375087318106341,F diff --git a/resident/configs/stop_frequency_coefficients_othmaint.csv b/resident/configs/stop_frequency_coefficients_othmaint.csv new file mode 100644 index 0000000..ac32ba7 --- /dev/null +++ b/resident/configs/stop_frequency_coefficients_othmaint.csv @@ -0,0 +1,27 @@ +coefficient_name,value,constrain +coef_unavailable,-999.0,T +coef_alternative_specific_constants_0out_1in_othmaint,-1.5602924404450391,F +coef_alternative_specific_constants_0out_2in_othmaint,-2.384779306812381,F +coef_alternative_specific_constants_0out_3in_othmaint,-2.613794769617,F +coef_alternative_specific_constants_1out_0in_othmaint,-2.307883473311813,F +coef_alternative_specific_constants_1out_1in_othmaint,-2.938077675037544,F +coef_alternative_specific_constants_1out_2in_othmaint,-3.556152461131553,F +coef_alternative_specific_constants_1out_3in_othmaint,-3.3623723352749835,F +coef_alternative_specific_constants_2out_0in_othmaint,-3.1913289639656037,F +coef_alternative_specific_constants_2out_1in_othmaint,-3.8047760031,F +coef_alternative_specific_constants_2out_2in_othmaint,-4.390949985891435,F +coef_alternative_specific_constants_2out_3in_othmaint,-4.092462208845949,F +coef_alternative_specific_constants_3out_0in_othmaint,-3.568806711128329,F +coef_alternative_specific_constants_3out_1in_othmaint,-4.029936250472073,F +coef_alternative_specific_constants_3out_2in_othmaint,-4.599707691979066,F +coef_alternative_specific_constants_3out_3in_othmaint,-4.159153508767797,F +coef_is_joint_tour_1plusstops_othmaint,-1.1415368904713223,F +coef_number_of_children_in_hh_1plusstops_othmaint,0.2355862776646383,F +coef_number_of_nonwork_tours_1plusstops_othmaint,-0.2334086312449428,F +coef_hhsize_is_4p_1plusstops_othmaint,-0.3807340945310137,F +coef_tour_mode_non_motorized_1plusstops_othmaint,-0.6198408634766976,F +coef_tour_mode_hov_1plusstops_othmaint,1.082794990210597,F +coef_tour_duration_in_hours_1plusstops_othmaint,0.0305033478198989,F +coef_start_time_is_morning_1plusstops_othmaint,0.4176418075950055,F +coef_start_time_is_afternoon_1plusstops_othmaint,0.3736907177152264,F +coef_distance_to_tour_destination_1plusstops_othmaint,0.0292475180940679,F diff --git a/resident/configs/stop_frequency_coefficients_school.csv b/resident/configs/stop_frequency_coefficients_school.csv new file mode 100644 index 0000000..946c3a5 --- /dev/null +++ b/resident/configs/stop_frequency_coefficients_school.csv @@ -0,0 +1,32 @@ +coefficient_name,value,constrain +coef_unavailable,-999.0,T +coef_alternative_specific_constants_0out_1in_school,-2.270036976078339,F +coef_alternative_specific_constants_0out_2in_school,-3.145965730364082,F +coef_alternative_specific_constants_0out_3in_school,-3.817363247655941,F +coef_alternative_specific_constants_1out_0in_school,-3.2274948701101858,F +coef_alternative_specific_constants_1out_1in_school,-4.844409089801835,F +coef_alternative_specific_constants_1out_2in_school,-5.650069018317617,F +coef_alternative_specific_constants_1out_3in_school,-6.05550780788901,F +coef_alternative_specific_constants_2out_0in_school,-4.928299261107959,F +coef_alternative_specific_constants_2out_1in_school,-7.308311886880356,F +coef_alternative_specific_constants_2out_2in_school,-7.44173289877394,F +coef_alternative_specific_constants_2out_3in_school,-8.28896797269094,F +coef_alternative_specific_constants_3out_0in_school,-5.984327657398892,F +coef_alternative_specific_constants_3out_1in_school,-8.001275620584247,F +coef_alternative_specific_constants_3out_2in_school,-8.693945624974088,F +coef_alternative_specific_constants_3out_3in_school,-8.694474791173255,F +coef_is_pre_covid_0out_1plusin_school,0.7125702463447188,F +coef_number_of_nonwork_tours_0out_1plusin_school,-0.3498536779228892,F +coef_school_child_in_hh_0out_1plusin_school,0.266793257512214,F +coef_non_mandatory_accessibility_0out_1plusin_school,0.0853214914318699,F +coef_tour_mode_non_motorized_0out_1plusin_school,-1.1253582283051773,F +coef_is_pre_covid_1plusout_1plusin_school,1.002629718374584,F +coef_number_of_nonwork_tours_1plusout_1plusin_school,-0.6057535513686793,F +coef_school_child_in_hh_1plusout_1plusin_school,0.8706807230114831,F +coef_non_worker_in_hh_1plusout_1plusin_school,0.3680982964801776,F +coef_non_mandatory_accessibility_1plusout_1plusin_school,0.1310875479177641,F +coef_tour_mode_non_motorized_1plusout_1plusin_school,-3.1117693140257034,F +coef_is_pre_covid_1plusout_0in_school,0.7698140702438344,F +coef_school_child_in_hh_1plusout_0in_school,0.7989949430082903,F +coef_tour_mode_non_motorized_1plusout_0in_school,-2.226875040638454,F +coef_tour_mode_schbus_0out_1plusin_school,-1.719166651447957,F diff --git a/resident/configs/stop_frequency_coefficients_shopping.csv b/resident/configs/stop_frequency_coefficients_shopping.csv new file mode 100644 index 0000000..84fb051 --- /dev/null +++ b/resident/configs/stop_frequency_coefficients_shopping.csv @@ -0,0 +1,28 @@ +coefficient_name,value,constrain +coef_unavailable,-999.0,T +coef_alternative_specific_constants_0out_1in_shopping,-1.99478486659318,F +coef_alternative_specific_constants_0out_2in_shopping,-3.0029547936212144,F +coef_alternative_specific_constants_0out_3in_shopping,-3.8162472442792046,F +coef_alternative_specific_constants_1out_0in_shopping,-1.9298112667501943,F +coef_alternative_specific_constants_1out_1in_shopping,-3.081424428417236,F +coef_alternative_specific_constants_1out_2in_shopping,-3.952826067704117,F +coef_alternative_specific_constants_1out_3in_shopping,-4.447523137843972,F +coef_alternative_specific_constants_2out_0in_shopping,-2.899949450341273,F +coef_alternative_specific_constants_2out_1in_shopping,-3.882204113550045,F +coef_alternative_specific_constants_2out_2in_shopping,-5.063709523400166,F +coef_alternative_specific_constants_2out_3in_shopping,-5.063705408442639,F +coef_alternative_specific_constants_3out_0in_shopping,-3.4691848835291794,F +coef_alternative_specific_constants_3out_1in_shopping,-4.042050191513082,F +coef_alternative_specific_constants_3out_2in_shopping,-5.363812433746681,F +coef_alternative_specific_constants_3out_3in_shopping,-5.794596035666434,F +coef_is_joint_tour_1plusstops_shopping,-0.9376746632102424,F +coef_is_pre_covid_1plusstops_shopping,0.1880597722233851,F +coef_is_worker_1plusstops_shopping,-0.2984125841057903,F +coef_number_of_nonwork_tours_1plusstops_shopping,-0.2795952331923437,F +coef_school_child_in_hh_1plusstops_shopping,0.2755159915996485,F +coef_non_mandatory_accessibility_1plusstops_shopping,0.0357694082510162,F +coef_tour_mode_non_motorized_1plusstops_shopping,-1.073202207993033,F +coef_tour_mode_hov_1plusstops_shopping,0.8575809701489939,F +coef_tour_mode_transit_1plusstops_shopping,-0.4963693102721252,F +coef_tour_duration_in_hours_1plusstops_shopping,0.091893516673009,F +coef_distance_to_tour_destination_1plusstops_shopping,0.0911619700854451,F diff --git a/resident/configs/stop_frequency_coefficients_social.csv b/resident/configs/stop_frequency_coefficients_social.csv new file mode 100644 index 0000000..cd075f0 --- /dev/null +++ b/resident/configs/stop_frequency_coefficients_social.csv @@ -0,0 +1,24 @@ +coefficient_name,value,constrain +coef_unavailable,-999.0,T +coef_alternative_specific_constants_0out_1in_social,-2.669744941267482,F +coef_alternative_specific_constants_0out_2in_social,-3.948157548888865,F +coef_alternative_specific_constants_0out_3in_social,-5.093324105648871,F +coef_alternative_specific_constants_1out_0in_social,-2.4747854649511694,F +coef_alternative_specific_constants_1out_1in_social,-3.820302803735448,F +coef_alternative_specific_constants_1out_2in_social,-4.842135917765225,F +coef_alternative_specific_constants_1out_3in_social,-5.0934431568988,F +coef_alternative_specific_constants_2out_0in_social,-3.350273969073528,F +coef_alternative_specific_constants_2out_1in_social,-5.429849829581639,F +coef_alternative_specific_constants_2out_2in_social,-6.346124542226189,F +coef_alternative_specific_constants_2out_3in_social,-5.652975455372254,F +coef_alternative_specific_constants_3out_0in_social,-4.84199879613982,F +coef_alternative_specific_constants_3out_1in_social,-5.093366632426675,F +coef_alternative_specific_constants_3out_2in_social,-7.039224828803537,F +coef_alternative_specific_constants_3out_3in_social,-7.039372547075768,F +coef_is_joint_tour_1plus_stops_social,-0.776560245920542,F +coef_number_of_nonwork_tours_1plus_stops_social,-0.2094030767278961,F +coef_age_5_18_1plus_stops_social,-0.7707681157165264,F +coef_non_mandatory_accessibility_1plus_stops_social,0.0399803049577818,F +coef_tour_mode_hov_1plus_stops_social,0.5244598307982651,F +coef_tour_duration_in_hours_1plus_stops_social,0.0731344275787003,F +coef_distance_to_tour_destination_1plus_stops_social,0.0431956613779886,F diff --git a/resident/configs/stop_frequency_coefficients_univ.csv b/resident/configs/stop_frequency_coefficients_univ.csv new file mode 100644 index 0000000..d82f6e1 --- /dev/null +++ b/resident/configs/stop_frequency_coefficients_univ.csv @@ -0,0 +1,20 @@ +coefficient_name,value,constrain +coef_unavailable,-999.0,T +coef_alternative_specific_constants_1out_0in_univ,-2.9606821036239173,F +coef_alternative_specific_constants_1out_1in_univ,-2.994590126632717,F +coef_alternative_specific_constants_1out_2in_univ,-3.964059230970261,F +coef_alternative_specific_constants_1out_3in_univ,-4.110765202022077,F +coef_alternative_specific_constants_2out_0in_univ,-3.653878365193867,F +coef_alternative_specific_constants_2out_1in_univ,-4.570265363130149,F +coef_alternative_specific_constants_2out_2in_univ,-5.109276446867356,F +coef_alternative_specific_constants_2out_3in_univ,-5.109225424254904,F +coef_alternative_specific_constants_3out_0in_univ,-3.836259451583652,F +coef_alternative_specific_constants_3out_1in_univ,-4.416165484960151,F +coef_alternative_specific_constants_3out_2in_univ,-4.975726090153633,F +coef_alternative_specific_constants_3out_3in_univ,-4.858027864893195,F +coef_alternative_specific_constants_0out_1plusin_univ,-2.9441205060555693,F +coef_preschool_child_in_hh_1plusstops_univ,0.7353602959988254,F +coef_tour_mode_non_motorized_1plusstops_univ,-0.7149209302739166,F +coef_tour_mode_hov_1plusstops_univ,0.621498744143073,F +coef_tour_mode_transit_1plusstops_univ,0.4356060774156569,F +coef_tour_duration_in_hours_1plusstops_univ,0.1838966207472205,F diff --git a/resident/configs/stop_frequency_coefficients_work.csv b/resident/configs/stop_frequency_coefficients_work.csv new file mode 100644 index 0000000..2ab4ccf --- /dev/null +++ b/resident/configs/stop_frequency_coefficients_work.csv @@ -0,0 +1,67 @@ +coefficient_name,value,constrain +coef_unavailable,-999.0,T +coef_alternative_specific_constants_0out_1in_work,-2.8447418642103286,F +coef_alternative_specific_constants_0out_2in_work,-5.074469232999513,F +coef_alternative_specific_constants_0out_3in_work,-7.356348978101946,F +coef_alternative_specific_constants_1out_0in_work,-3.998672291447317,F +coef_alternative_specific_constants_1out_1in_work,-5.340917046762127,F +coef_alternative_specific_constants_1out_2in_work,-7.293230160779633,F +coef_alternative_specific_constants_1out_3in_work,-8.644343958007005,F +coef_alternative_specific_constants_2out_0in_work,-5.706113217470375,F +coef_alternative_specific_constants_2out_1in_work,-7.153817173561018,F +coef_alternative_specific_constants_3out_0in_work,-6.7780536846962365,F +coef_alternative_specific_constants_3out_1in_work,-8.192357372172,F +coef_preschool_child_in_hh_1out_0in_work,0.4918316153479145,F +coef_school_child_in_hh_1out_0in_work,0.5231248472492089,F +coef_school_child_in_hh_1out_1in_work,0.281812148150233,F +coef_school_child_in_hh_1out_2in_work,0.5109837440498461,F +coef_school_child_in_hh_1out_3in_work,0.5146518187296193,F +coef_non_worker_in_hh_0out_1in_work,-0.2499539606769114,F +coef_non_worker_in_hh_0out_2in_work,-0.3895812012559245,F +coef_non_worker_in_hh_0out_3in_work,-0.7028004655351838,F +coef_tour_mode_sov_0out_1in_work,0.6663968126601825,F +coef_tour_mode_sov_0out_2in_work,0.8490983927253895,F +coef_tour_mode_sov_0out_3in_work,1.019470744148101,F +coef_tour_mode_hov_0out_1in_work,1.753765085617916,F +coef_tour_mode_hov_0out_2in_work,2.425463128045174,F +coef_tour_mode_hov_0out_3in_work,3.083984260465549,F +coef_tour_mode_hov_1out_0in_work,1.8810986245847765,F +coef_tour_mode_hov_1out_1in_work,2.903102643291588,F +coef_tour_mode_hov_1out_2in_work,3.0104904515059983,F +coef_tour_mode_hov_1out_3in_work,3.4462649672615124,F +coef_tour_mode_hov_2out_0in_work,2.3579589023621974,F +coef_tour_mode_hov_2out_1in_work,2.896537717160822,F +coef_tour_mode_hov_3out_0in_work,2.367639329734867,F +coef_tour_mode_hov_3out_1in_work,3.1434258750404256,F +coef_tour_duration_in_hours_0out_1in_work,0.0968419497302259,F +coef_tour_duration_in_hours_0out_2in_work,0.1904355233462784,F +coef_tour_duration_in_hours_0out_3in_work,0.3367138795904639,F +coef_tour_duration_in_hours_1out_0in_work,0.066650821483439,F +coef_tour_duration_in_hours_1out_1in_work,0.1646547411229061,F +coef_tour_duration_in_hours_1out_2in_work,0.2551530816688599,F +coef_tour_duration_in_hours_1out_3in_work,0.3252548498168742,F +coef_tour_duration_in_hours_2out_1in_work,0.1961700400992169,F +coef_tour_duration_in_hours_3out_1in_work,0.1501823000654714,F +coef_alternative_specific_constants_2plusout_2plusin_work,-9.526330213144162,F +coef_tour_mode_hov_2plusout_2plusin_work,3.7049086577656944,F +coef_tour_duration_in_hours_2plusout_2plusin_work,0.2609768272005556,F +coef_is_parttime_worker_1plusout_1plusin_work,0.3300187195960226,F +coef_is_parttime_worker_0out_1plusin_work,0.2855160872348049,F +coef_non_worker_in_hh_1plusout_1plusin_work,-0.4641234561873718,F +coef_preschool_child_in_hh_2plusout_0in_work,0.5136768446923758,F +coef_school_child_in_hh_2plusout_0in_work,0.715230225338151,F +coef_tour_mode_sov_1plusout_0in_work,0.3208347112170486,F +coef_tour_mode_sov_1plusout_1plusin_work,0.4540272237487077,F +coef_tour_duration_in_hours_2plusout_0in_work,0.0594361573692384,F +coef_preschool_child_in_hh_1plusout_1plusin_work,0.4336604925767433,F +coef_school_child_in_hh_2plusout_1plusin_work,0.3953971625740665,F +coef_age_35_to_44_1plusout_1plusin_work,0.2864843022250448,F +coef_age_55_to_64_0out_1plusin_work,0.173801196295945,F +coef_age_55_to_64_1plusout_0in_work,0.2187960088158339,F +coef_age_45_to_54_1plusout_1plusin_work,0.3495514458182455,F +coef_age_55_to_64_1plusout_1plusin_work,0.3141273930142538,F +coef_non_mandatory_accessibility_1plusout_0in_work,0.0555899473533157,F +coef_distance_to_tour_destination_0out_1plusin_work,0.0075317302289467,F +coef_distance_to_tour_destination_1plusout_1plusin_work,0.0103740905129598,F +coef_distance_to_tour_destination_1plusout_0in_work,0.0122085315777465,F +coef_telecommute_2_4_days_week_1plusout_1plusin_work,0.6007761919718061,F diff --git a/resident/configs/stop_frequency_eatout.csv b/resident/configs/stop_frequency_eatout.csv new file mode 100644 index 0000000..fa9103a --- /dev/null +++ b/resident/configs/stop_frequency_eatout.csv @@ -0,0 +1,14 @@ +Label,Description,Expression,0out_0in,0out_1in,0out_2in,0out_3in,1out_0in,1out_1in,1out_2in,1out_3in,2out_0in,2out_1in,2out_2in,2out_3in,3out_0in,3out_1in,3out_2in,3out_3in +util_alternative_specific_constants,Alternative specific constants,1,,coef_alternative_specific_constants_0out_1in_eatout,coef_alternative_specific_constants_0out_2in_eatout,coef_alternative_specific_constants_0out_3in_eatout,coef_alternative_specific_constants_1out_0in_eatout,coef_alternative_specific_constants_1out_1in_eatout,coef_alternative_specific_constants_1out_2in_eatout,coef_alternative_specific_constants_1out_3in_eatout,coef_alternative_specific_constants_2out_0in_eatout,coef_alternative_specific_constants_2out_1in_eatout,coef_alternative_specific_constants_2out_2in_eatout,coef_alternative_specific_constants_2out_3in_eatout,coef_alternative_specific_constants_3out_0in_eatout,coef_alternative_specific_constants_3out_1in_eatout,coef_alternative_specific_constants_3out_2in_eatout,coef_alternative_specific_constants_3out_3in_eatout +util_is_joint_tour,Is joint tour,is_joint,,coef_is_joint_tour_1plus_stops_eatout,coef_is_joint_tour_1plus_stops_eatout,coef_is_joint_tour_1plus_stops_eatout,coef_is_joint_tour_1plus_stops_eatout,coef_is_joint_tour_1plus_stops_eatout,coef_is_joint_tour_1plus_stops_eatout,coef_is_joint_tour_1plus_stops_eatout,coef_is_joint_tour_1plus_stops_eatout,coef_is_joint_tour_1plus_stops_eatout,coef_is_joint_tour_1plus_stops_eatout,coef_is_joint_tour_1plus_stops_eatout,coef_is_joint_tour_1plus_stops_eatout,coef_is_joint_tour_1plus_stops_eatout,coef_is_joint_tour_1plus_stops_eatout,coef_is_joint_tour_1plus_stops_eatout +util_number_of_nonwork_tours,Number of non-work tours,(num_total_tours - num_work_tours),,coef_number_of_nonwork_tours_1plus_stops_eatout,coef_number_of_nonwork_tours_1plus_stops_eatout,coef_number_of_nonwork_tours_1plus_stops_eatout,coef_number_of_nonwork_tours_1plus_stops_eatout,coef_number_of_nonwork_tours_1plus_stops_eatout,coef_number_of_nonwork_tours_1plus_stops_eatout,coef_number_of_nonwork_tours_1plus_stops_eatout,coef_number_of_nonwork_tours_1plus_stops_eatout,coef_number_of_nonwork_tours_1plus_stops_eatout,coef_number_of_nonwork_tours_1plus_stops_eatout,coef_number_of_nonwork_tours_1plus_stops_eatout,coef_number_of_nonwork_tours_1plus_stops_eatout,coef_number_of_nonwork_tours_1plus_stops_eatout,coef_number_of_nonwork_tours_1plus_stops_eatout,coef_number_of_nonwork_tours_1plus_stops_eatout +util_age_65_79,Age Group - 65 yrs to 79 yrs,(age >= 65) & (age < 80),,coef_age_65_79_1plus_stops_eatout,coef_age_65_79_1plus_stops_eatout,coef_age_65_79_1plus_stops_eatout,coef_age_65_79_1plus_stops_eatout,coef_age_65_79_1plus_stops_eatout,coef_age_65_79_1plus_stops_eatout,coef_age_65_79_1plus_stops_eatout,coef_age_65_79_1plus_stops_eatout,coef_age_65_79_1plus_stops_eatout,coef_age_65_79_1plus_stops_eatout,coef_age_65_79_1plus_stops_eatout,coef_age_65_79_1plus_stops_eatout,coef_age_65_79_1plus_stops_eatout,coef_age_65_79_1plus_stops_eatout,coef_age_65_79_1plus_stops_eatout +util_age_80_plus,Age Group - 80 yrs and older,(age >= 80),,coef_age_80_plus_1plus_stops_eatout,coef_age_80_plus_1plus_stops_eatout,coef_age_80_plus_1plus_stops_eatout,coef_age_80_plus_1plus_stops_eatout,coef_age_80_plus_1plus_stops_eatout,coef_age_80_plus_1plus_stops_eatout,coef_age_80_plus_1plus_stops_eatout,coef_age_80_plus_1plus_stops_eatout,coef_age_80_plus_1plus_stops_eatout,coef_age_80_plus_1plus_stops_eatout,coef_age_80_plus_1plus_stops_eatout,coef_age_80_plus_1plus_stops_eatout,coef_age_80_plus_1plus_stops_eatout,coef_age_80_plus_1plus_stops_eatout,coef_age_80_plus_1plus_stops_eatout +util_non_mandatory_accessibility,Disaggreage accessibility at home location to non-mandatory location,(othdiscr_accessibility + shopping_accessibility) / 2,,coef_non_mandatory_accessibility_1plus_stops_eatout,coef_non_mandatory_accessibility_1plus_stops_eatout,coef_non_mandatory_accessibility_1plus_stops_eatout,coef_non_mandatory_accessibility_1plus_stops_eatout,coef_non_mandatory_accessibility_1plus_stops_eatout,coef_non_mandatory_accessibility_1plus_stops_eatout,coef_non_mandatory_accessibility_1plus_stops_eatout,coef_non_mandatory_accessibility_1plus_stops_eatout,coef_non_mandatory_accessibility_1plus_stops_eatout,coef_non_mandatory_accessibility_1plus_stops_eatout,coef_non_mandatory_accessibility_1plus_stops_eatout,coef_non_mandatory_accessibility_1plus_stops_eatout,coef_non_mandatory_accessibility_1plus_stops_eatout,coef_non_mandatory_accessibility_1plus_stops_eatout,coef_non_mandatory_accessibility_1plus_stops_eatout +util_tour_mode_non_motorized,Tour mode is non-motorized,tour_mode_is_non_motorized,,coef_tour_mode_non_motorized_1plus_stops_eatout,coef_tour_mode_non_motorized_1plus_stops_eatout,coef_tour_mode_non_motorized_1plus_stops_eatout,coef_tour_mode_non_motorized_1plus_stops_eatout,coef_tour_mode_non_motorized_1plus_stops_eatout,coef_tour_mode_non_motorized_1plus_stops_eatout,coef_tour_mode_non_motorized_1plus_stops_eatout,coef_tour_mode_non_motorized_1plus_stops_eatout,coef_tour_mode_non_motorized_1plus_stops_eatout,coef_tour_mode_non_motorized_1plus_stops_eatout,coef_tour_mode_non_motorized_1plus_stops_eatout,coef_tour_mode_non_motorized_1plus_stops_eatout,coef_tour_mode_non_motorized_1plus_stops_eatout,coef_tour_mode_non_motorized_1plus_stops_eatout,coef_tour_mode_non_motorized_1plus_stops_eatout +util_tour_mode_hov,Tour mode is hov,tour_mode_is_hov,,coef_tour_mode_hov_1plus_stops_eatout,coef_tour_mode_hov_1plus_stops_eatout,coef_tour_mode_hov_1plus_stops_eatout,coef_tour_mode_hov_1plus_stops_eatout,coef_tour_mode_hov_1plus_stops_eatout,coef_tour_mode_hov_1plus_stops_eatout,coef_tour_mode_hov_1plus_stops_eatout,coef_tour_mode_hov_1plus_stops_eatout,coef_tour_mode_hov_1plus_stops_eatout,coef_tour_mode_hov_1plus_stops_eatout,coef_tour_mode_hov_1plus_stops_eatout,coef_tour_mode_hov_1plus_stops_eatout,coef_tour_mode_hov_1plus_stops_eatout,coef_tour_mode_hov_1plus_stops_eatout,coef_tour_mode_hov_1plus_stops_eatout +util_tour_duration_in_hours,Tour duration in hours,(duration / 2),,coef_tour_duration_in_hours_1plus_stops_eatout,coef_tour_duration_in_hours_1plus_stops_eatout,coef_tour_duration_in_hours_1plus_stops_eatout,coef_tour_duration_in_hours_1plus_stops_eatout,coef_tour_duration_in_hours_1plus_stops_eatout,coef_tour_duration_in_hours_1plus_stops_eatout,coef_tour_duration_in_hours_1plus_stops_eatout,coef_tour_duration_in_hours_1plus_stops_eatout,coef_tour_duration_in_hours_1plus_stops_eatout,coef_tour_duration_in_hours_1plus_stops_eatout,coef_tour_duration_in_hours_1plus_stops_eatout,coef_tour_duration_in_hours_1plus_stops_eatout,coef_tour_duration_in_hours_1plus_stops_eatout,coef_tour_duration_in_hours_1plus_stops_eatout,coef_tour_duration_in_hours_1plus_stops_eatout +util_distance_to_tour_destination,Distance from origin to tour destination in miles,distance_in_miles,,coef_distance_to_tour_destination_1plus_stops_eatout,coef_distance_to_tour_destination_1plus_stops_eatout,coef_distance_to_tour_destination_1plus_stops_eatout,coef_distance_to_tour_destination_1plus_stops_eatout,coef_distance_to_tour_destination_1plus_stops_eatout,coef_distance_to_tour_destination_1plus_stops_eatout,coef_distance_to_tour_destination_1plus_stops_eatout,coef_distance_to_tour_destination_1plus_stops_eatout,coef_distance_to_tour_destination_1plus_stops_eatout,coef_distance_to_tour_destination_1plus_stops_eatout,coef_distance_to_tour_destination_1plus_stops_eatout,coef_distance_to_tour_destination_1plus_stops_eatout,coef_distance_to_tour_destination_1plus_stops_eatout,coef_distance_to_tour_destination_1plus_stops_eatout,coef_distance_to_tour_destination_1plus_stops_eatout +util_nostop_drive_transit,no stops on pnr knr tnc transit tours,"@(df.tour_mode.isin(['PNR_TRANSIT','KNR_TRANSIT']))",,coef_unavailable,coef_unavailable,coef_unavailable,coef_unavailable,coef_unavailable,coef_unavailable,coef_unavailable,coef_unavailable,coef_unavailable,coef_unavailable,coef_unavailable,coef_unavailable,coef_unavailable,coef_unavailable,coef_unavailable +util_abm3_calibration_constants,calibration constants,is_joint==0,-0.10349,-0.5178,1.3724,-2.4904,0.10798,-0.03633,0.8058,-1.9377,0.5571,-2.5862,-0.7659,-0.1902,0.0788,-1.9865,-10.0,3.70084 +util_abm3_calibration_constants_joint,joint tour calibration constants,is_joint,-0.12758,-0.6326,-1.13646,-1.9196,1.4714,0.7681,-10.0,-0.4150,-0.5229,-5.0,-0.04229,-10.0,-5.0,-5.0,1.3845,-5.0 diff --git a/resident/configs/stop_frequency_escort.csv b/resident/configs/stop_frequency_escort.csv new file mode 100644 index 0000000..8558c1d --- /dev/null +++ b/resident/configs/stop_frequency_escort.csv @@ -0,0 +1,16 @@ +Label,Description,Expression,0out_0in,0out_1in,0out_2in,0out_3in,1out_0in,1out_1in,1out_2in,1out_3in,2out_0in,2out_1in,2out_2in,2out_3in,3out_0in,3out_1in,3out_2in,3out_3in +util_alternative_specific_constants,Alternative specific constants,1,,coef_alternative_specific_constants_0out_1in_escort,coef_alternative_specific_constants_0out_2in_escort,coef_alternative_specific_constants_0out_3in_escort,coef_alternative_specific_constants_1out_0in_escort,coef_alternative_specific_constants_1out_1in_escort,coef_alternative_specific_constants_1out_2in_escort,coef_alternative_specific_constants_1out_3in_escort,coef_alternative_specific_constants_2out_0in_escort,coef_alternative_specific_constants_2out_1in_escort,coef_alternative_specific_constants_2out_2in_escort,coef_alternative_specific_constants_2out_3in_escort,coef_alternative_specific_constants_3out_0in_escort,coef_alternative_specific_constants_3out_1in_escort,coef_alternative_specific_constants_3out_2in_escort,coef_alternative_specific_constants_3out_3in_escort +util_is_student,person is student,is_student,,coef_is_student_1plus_stops_escort,coef_is_student_1plus_stops_escort,coef_is_student_1plus_stops_escort,coef_is_student_1plus_stops_escort,coef_is_student_1plus_stops_escort,coef_is_student_1plus_stops_escort,coef_is_student_1plus_stops_escort,coef_is_student_1plus_stops_escort,coef_is_student_1plus_stops_escort,coef_is_student_1plus_stops_escort,coef_is_student_1plus_stops_escort,coef_is_student_1plus_stops_escort,coef_is_student_1plus_stops_escort,coef_is_student_1plus_stops_escort,coef_is_student_1plus_stops_escort +util_age_5_18,Age Group - 5 yrs to 18 yrs,(age >= 5) & (age < 19),,coef_age_5_18_1plus_stops_escort,coef_age_5_18_1plus_stops_escort,coef_age_5_18_1plus_stops_escort,coef_age_5_18_1plus_stops_escort,coef_age_5_18_1plus_stops_escort,coef_age_5_18_1plus_stops_escort,coef_age_5_18_1plus_stops_escort,coef_age_5_18_1plus_stops_escort,coef_age_5_18_1plus_stops_escort,coef_age_5_18_1plus_stops_escort,coef_age_5_18_1plus_stops_escort,coef_age_5_18_1plus_stops_escort,coef_age_5_18_1plus_stops_escort,coef_age_5_18_1plus_stops_escort,coef_age_5_18_1plus_stops_escort +util_age_65_79,Age Group - 65 yrs to 79 yrs,(age >= 65) & (age < 80),,coef_age_65_79_1plus_stops_escort,coef_age_65_79_1plus_stops_escort,coef_age_65_79_1plus_stops_escort,coef_age_65_79_1plus_stops_escort,coef_age_65_79_1plus_stops_escort,coef_age_65_79_1plus_stops_escort,coef_age_65_79_1plus_stops_escort,coef_age_65_79_1plus_stops_escort,coef_age_65_79_1plus_stops_escort,coef_age_65_79_1plus_stops_escort,coef_age_65_79_1plus_stops_escort,coef_age_65_79_1plus_stops_escort,coef_age_65_79_1plus_stops_escort,coef_age_65_79_1plus_stops_escort,coef_age_65_79_1plus_stops_escort +util_hhinc_less_15,Income less than 15k,(income < 15000),,coef_hhinc_less_15_1plus_stops_escort,coef_hhinc_less_15_1plus_stops_escort,coef_hhinc_less_15_1plus_stops_escort,coef_hhinc_less_15_1plus_stops_escort,coef_hhinc_less_15_1plus_stops_escort,coef_hhinc_less_15_1plus_stops_escort,coef_hhinc_less_15_1plus_stops_escort,coef_hhinc_less_15_1plus_stops_escort,coef_hhinc_less_15_1plus_stops_escort,coef_hhinc_less_15_1plus_stops_escort,coef_hhinc_less_15_1plus_stops_escort,coef_hhinc_less_15_1plus_stops_escort,coef_hhinc_less_15_1plus_stops_escort,coef_hhinc_less_15_1plus_stops_escort,coef_hhinc_less_15_1plus_stops_escort +util_hhsize_is_3,Household size is 3,(hhsize == 3),,coef_hhsize_is_3_1plus_stops_escort,coef_hhsize_is_3_1plus_stops_escort,coef_hhsize_is_3_1plus_stops_escort,coef_hhsize_is_3_1plus_stops_escort,coef_hhsize_is_3_1plus_stops_escort,coef_hhsize_is_3_1plus_stops_escort,coef_hhsize_is_3_1plus_stops_escort,coef_hhsize_is_3_1plus_stops_escort,coef_hhsize_is_3_1plus_stops_escort,coef_hhsize_is_3_1plus_stops_escort,coef_hhsize_is_3_1plus_stops_escort,coef_hhsize_is_3_1plus_stops_escort,coef_hhsize_is_3_1plus_stops_escort,coef_hhsize_is_3_1plus_stops_escort,coef_hhsize_is_3_1plus_stops_escort +util_hhsize_is_4p,Household size is 4p,(hhsize >= 4),,coef_hhsize_is_4p_1plus_stops_escort,coef_hhsize_is_4p_1plus_stops_escort,coef_hhsize_is_4p_1plus_stops_escort,coef_hhsize_is_4p_1plus_stops_escort,coef_hhsize_is_4p_1plus_stops_escort,coef_hhsize_is_4p_1plus_stops_escort,coef_hhsize_is_4p_1plus_stops_escort,coef_hhsize_is_4p_1plus_stops_escort,coef_hhsize_is_4p_1plus_stops_escort,coef_hhsize_is_4p_1plus_stops_escort,coef_hhsize_is_4p_1plus_stops_escort,coef_hhsize_is_4p_1plus_stops_escort,coef_hhsize_is_4p_1plus_stops_escort,coef_hhsize_is_4p_1plus_stops_escort,coef_hhsize_is_4p_1plus_stops_escort +util_non_mandatory_accessibility,Disaggreage accessibility at home location to non-mandatory location,(othdiscr_accessibility + shopping_accessibility) / 2,,coef_non_mandatory_accessibility_1plus_stops_escort,coef_non_mandatory_accessibility_1plus_stops_escort,coef_non_mandatory_accessibility_1plus_stops_escort,coef_non_mandatory_accessibility_1plus_stops_escort,coef_non_mandatory_accessibility_1plus_stops_escort,coef_non_mandatory_accessibility_1plus_stops_escort,coef_non_mandatory_accessibility_1plus_stops_escort,coef_non_mandatory_accessibility_1plus_stops_escort,coef_non_mandatory_accessibility_1plus_stops_escort,coef_non_mandatory_accessibility_1plus_stops_escort,coef_non_mandatory_accessibility_1plus_stops_escort,coef_non_mandatory_accessibility_1plus_stops_escort,coef_non_mandatory_accessibility_1plus_stops_escort,coef_non_mandatory_accessibility_1plus_stops_escort,coef_non_mandatory_accessibility_1plus_stops_escort +util_tour_mode_non_motorized,Tour mode is non-motorized,tour_mode_is_non_motorized,,coef_tour_mode_non_motorized_1plus_stops_escort,coef_tour_mode_non_motorized_1plus_stops_escort,coef_tour_mode_non_motorized_1plus_stops_escort,coef_tour_mode_non_motorized_1plus_stops_escort,coef_tour_mode_non_motorized_1plus_stops_escort,coef_tour_mode_non_motorized_1plus_stops_escort,coef_tour_mode_non_motorized_1plus_stops_escort,coef_tour_mode_non_motorized_1plus_stops_escort,coef_tour_mode_non_motorized_1plus_stops_escort,coef_tour_mode_non_motorized_1plus_stops_escort,coef_tour_mode_non_motorized_1plus_stops_escort,coef_tour_mode_non_motorized_1plus_stops_escort,coef_tour_mode_non_motorized_1plus_stops_escort,coef_tour_mode_non_motorized_1plus_stops_escort,coef_tour_mode_non_motorized_1plus_stops_escort +util_start_time_is_afternoon,Tour start time between noon and 5pm,(start >= 19) & (start < 29),,coef_start_time_is_afternoon_1plus_stops_escort,coef_start_time_is_afternoon_1plus_stops_escort,coef_start_time_is_afternoon_1plus_stops_escort,coef_start_time_is_afternoon_1plus_stops_escort,coef_start_time_is_afternoon_1plus_stops_escort,coef_start_time_is_afternoon_1plus_stops_escort,coef_start_time_is_afternoon_1plus_stops_escort,coef_start_time_is_afternoon_1plus_stops_escort,coef_start_time_is_afternoon_1plus_stops_escort,coef_start_time_is_afternoon_1plus_stops_escort,coef_start_time_is_afternoon_1plus_stops_escort,coef_start_time_is_afternoon_1plus_stops_escort,coef_start_time_is_afternoon_1plus_stops_escort,coef_start_time_is_afternoon_1plus_stops_escort,coef_start_time_is_afternoon_1plus_stops_escort +util_distance_to_tour_destination,Distance from origin to tour destination in miles,distance_in_miles,,coef_distance_to_tour_destination_1plus_stops_escort,coef_distance_to_tour_destination_1plus_stops_escort,coef_distance_to_tour_destination_1plus_stops_escort,coef_distance_to_tour_destination_1plus_stops_escort,coef_distance_to_tour_destination_1plus_stops_escort,coef_distance_to_tour_destination_1plus_stops_escort,coef_distance_to_tour_destination_1plus_stops_escort,coef_distance_to_tour_destination_1plus_stops_escort,coef_distance_to_tour_destination_1plus_stops_escort,coef_distance_to_tour_destination_1plus_stops_escort,coef_distance_to_tour_destination_1plus_stops_escort,coef_distance_to_tour_destination_1plus_stops_escort,coef_distance_to_tour_destination_1plus_stops_escort,coef_distance_to_tour_destination_1plus_stops_escort,coef_distance_to_tour_destination_1plus_stops_escort +util_no_stops_to_school_escorting,Do not allow stops for school escort half-tour -- outbound,"(school_esc_outbound.isin(['ride_share', 'pure_escort']))",,coef_no_stops_to_school_escorting_1plus_stops_escort,coef_no_stops_to_school_escorting_1plus_stops_escort,coef_no_stops_to_school_escorting_1plus_stops_escort,coef_unavailable,coef_unavailable,coef_unavailable,coef_unavailable,coef_unavailable,coef_unavailable,coef_unavailable,coef_unavailable,coef_unavailable,coef_unavailable,coef_unavailable,coef_unavailable +util_no_stops_from_school_escorting,Do not allow stops for school escort half-tour -- inbound,"(school_esc_inbound.isin(['ride_share', 'pure_escort']))",,coef_unavailable,coef_unavailable,coef_unavailable,coef_no_stops_to_school_escorting_1plus_stops_escort,coef_unavailable,coef_unavailable,coef_unavailable,coef_no_stops_to_school_escorting_1plus_stops_escort,coef_unavailable,coef_unavailable,coef_unavailable,coef_no_stops_to_school_escorting_1plus_stops_escort,coef_unavailable,coef_unavailable,coef_unavailable +util_nostop_drive_transit,no stops on pnr knr tnc transit tours,"@(df.tour_mode.isin(['PNR_TRANSIT','KNR_TRANSIT']))",,coef_unavailable,coef_unavailable,coef_unavailable,coef_unavailable,coef_unavailable,coef_unavailable,coef_unavailable,coef_unavailable,coef_unavailable,coef_unavailable,coef_unavailable,coef_unavailable,coef_unavailable,coef_unavailable,coef_unavailable +util_abm3_calibration_constants,calibration constants,is_joint==0,0.01523,0.1329,-0.2217,-0.5655,-1.06194,-0.9030,0.7036,-2.4331,-0.6172,0.7406,1.7755,-4.1892,0.2658,-4.7708,-0.9725,3.5835 diff --git a/resident/configs/stop_frequency_othdiscr.csv b/resident/configs/stop_frequency_othdiscr.csv new file mode 100644 index 0000000..d8164c7 --- /dev/null +++ b/resident/configs/stop_frequency_othdiscr.csv @@ -0,0 +1,18 @@ +Label,Description,Expression,0out_0in,0out_1in,0out_2in,0out_3in,1out_0in,1out_1in,1out_2in,1out_3in,2out_0in,2out_1in,2out_2in,2out_3in,3out_0in,3out_1in,3out_2in,3out_3in +util_alternative_specific_constants,Alternative specific constants,1,,coef_alternative_specific_constants_0out_1in_othdiscr,coef_alternative_specific_constants_0out_2in_othdiscr,coef_alternative_specific_constants_0out_3in_othdiscr,coef_alternative_specific_constants_1out_0in_othdiscr,coef_alternative_specific_constants_1out_1in_othdiscr,coef_alternative_specific_constants_1out_2in_othdiscr,coef_alternative_specific_constants_1out_3in_othdiscr,coef_alternative_specific_constants_2out_0in_othdiscr,coef_alternative_specific_constants_2out_1in_othdiscr,coef_alternative_specific_constants_2out_2in_othdiscr,coef_alternative_specific_constants_2out_3in_othdiscr,coef_alternative_specific_constants_3out_0in_othdiscr,coef_alternative_specific_constants_3out_1in_othdiscr,coef_alternative_specific_constants_3out_2in_othdiscr,coef_alternative_specific_constants_3out_3in_othdiscr +util_is_joint_tour,Is joint tour,is_joint,,coef_is_joint_tour_1plusstops_othdiscr,coef_is_joint_tour_1plusstops_othdiscr,coef_is_joint_tour_1plusstops_othdiscr,coef_is_joint_tour_1plusstops_othdiscr,coef_is_joint_tour_1plusstops_othdiscr,coef_is_joint_tour_1plusstops_othdiscr,coef_is_joint_tour_1plusstops_othdiscr,coef_is_joint_tour_1plusstops_othdiscr,coef_is_joint_tour_1plusstops_othdiscr,coef_is_joint_tour_1plusstops_othdiscr,coef_is_joint_tour_1plusstops_othdiscr,coef_is_joint_tour_1plusstops_othdiscr,coef_is_joint_tour_1plusstops_othdiscr,coef_is_joint_tour_1plusstops_othdiscr,coef_is_joint_tour_1plusstops_othdiscr +util_number_of_nonwork_tours,Number of non-work tours,(num_total_tours - num_work_tours),,coef_number_of_nonwork_tours_1plusstops_othdiscr,coef_number_of_nonwork_tours_1plusstops_othdiscr,coef_number_of_nonwork_tours_1plusstops_othdiscr,coef_number_of_nonwork_tours_1plusstops_othdiscr,coef_number_of_nonwork_tours_1plusstops_othdiscr,coef_number_of_nonwork_tours_1plusstops_othdiscr,coef_number_of_nonwork_tours_1plusstops_othdiscr,coef_number_of_nonwork_tours_1plusstops_othdiscr,coef_number_of_nonwork_tours_1plusstops_othdiscr,coef_number_of_nonwork_tours_1plusstops_othdiscr,coef_number_of_nonwork_tours_1plusstops_othdiscr,coef_number_of_nonwork_tours_1plusstops_othdiscr,coef_number_of_nonwork_tours_1plusstops_othdiscr,coef_number_of_nonwork_tours_1plusstops_othdiscr,coef_number_of_nonwork_tours_1plusstops_othdiscr +util_age_5_18,Age Group - 5 yrs to 18 yrs,(age >= 5) & (age < 19),,coef_age_5_18_1plusstops_othdiscr,coef_age_5_18_1plusstops_othdiscr,coef_age_5_18_1plusstops_othdiscr,coef_age_5_18_1plusstops_othdiscr,coef_age_5_18_1plusstops_othdiscr,coef_age_5_18_1plusstops_othdiscr,coef_age_5_18_1plusstops_othdiscr,coef_age_5_18_1plusstops_othdiscr,coef_age_5_18_1plusstops_othdiscr,coef_age_5_18_1plusstops_othdiscr,coef_age_5_18_1plusstops_othdiscr,coef_age_5_18_1plusstops_othdiscr,coef_age_5_18_1plusstops_othdiscr,coef_age_5_18_1plusstops_othdiscr,coef_age_5_18_1plusstops_othdiscr +util_age_19_35,Age Group - 19 yrs to 34 yrs,(age >= 19) & (age < 35),,coef_age_19_35_1plusstops_othdiscr,coef_age_19_35_1plusstops_othdiscr,coef_age_19_35_1plusstops_othdiscr,coef_age_19_35_1plusstops_othdiscr,coef_age_19_35_1plusstops_othdiscr,coef_age_19_35_1plusstops_othdiscr,coef_age_19_35_1plusstops_othdiscr,coef_age_19_35_1plusstops_othdiscr,coef_age_19_35_1plusstops_othdiscr,coef_age_19_35_1plusstops_othdiscr,coef_age_19_35_1plusstops_othdiscr,coef_age_19_35_1plusstops_othdiscr,coef_age_19_35_1plusstops_othdiscr,coef_age_19_35_1plusstops_othdiscr,coef_age_19_35_1plusstops_othdiscr +util_age_65_79,Age Group - 65 yrs to 79 yrs,(age >= 65) & (age < 80),,coef_age_65_79_1plusstops_othdiscr,coef_age_65_79_1plusstops_othdiscr,coef_age_65_79_1plusstops_othdiscr,coef_age_65_79_1plusstops_othdiscr,coef_age_65_79_1plusstops_othdiscr,coef_age_65_79_1plusstops_othdiscr,coef_age_65_79_1plusstops_othdiscr,coef_age_65_79_1plusstops_othdiscr,coef_age_65_79_1plusstops_othdiscr,coef_age_65_79_1plusstops_othdiscr,coef_age_65_79_1plusstops_othdiscr,coef_age_65_79_1plusstops_othdiscr,coef_age_65_79_1plusstops_othdiscr,coef_age_65_79_1plusstops_othdiscr,coef_age_65_79_1plusstops_othdiscr +util_preschool_child_in_hh,Presence of preschooler in household other than self,has_preschool_kid,,coef_preschool_child_in_hh_1plusstops_othdiscr,coef_preschool_child_in_hh_1plusstops_othdiscr,coef_preschool_child_in_hh_1plusstops_othdiscr,coef_preschool_child_in_hh_1plusstops_othdiscr,coef_preschool_child_in_hh_1plusstops_othdiscr,coef_preschool_child_in_hh_1plusstops_othdiscr,coef_preschool_child_in_hh_1plusstops_othdiscr,coef_preschool_child_in_hh_1plusstops_othdiscr,coef_preschool_child_in_hh_1plusstops_othdiscr,coef_preschool_child_in_hh_1plusstops_othdiscr,coef_preschool_child_in_hh_1plusstops_othdiscr,coef_preschool_child_in_hh_1plusstops_othdiscr,coef_preschool_child_in_hh_1plusstops_othdiscr,coef_preschool_child_in_hh_1plusstops_othdiscr,coef_preschool_child_in_hh_1plusstops_othdiscr +util_has_retiree_in_hh,Presence of reitred peopl in household other than self,has_retiree,,coef_has_retiree_in_hh_1plusstops_othdiscr,coef_has_retiree_in_hh_1plusstops_othdiscr,coef_has_retiree_in_hh_1plusstops_othdiscr,coef_has_retiree_in_hh_1plusstops_othdiscr,coef_has_retiree_in_hh_1plusstops_othdiscr,coef_has_retiree_in_hh_1plusstops_othdiscr,coef_has_retiree_in_hh_1plusstops_othdiscr,coef_has_retiree_in_hh_1plusstops_othdiscr,coef_has_retiree_in_hh_1plusstops_othdiscr,coef_has_retiree_in_hh_1plusstops_othdiscr,coef_has_retiree_in_hh_1plusstops_othdiscr,coef_has_retiree_in_hh_1plusstops_othdiscr,coef_has_retiree_in_hh_1plusstops_othdiscr,coef_has_retiree_in_hh_1plusstops_othdiscr,coef_has_retiree_in_hh_1plusstops_othdiscr +util_non_mandatory_accessibility,Disaggreage accessibility at home location to non-mandatory location,(othdiscr_accessibility + shopping_accessibility) / 2,,coef_non_mandatory_accessibility_1plusstops_othdiscr,coef_non_mandatory_accessibility_1plusstops_othdiscr,coef_non_mandatory_accessibility_1plusstops_othdiscr,coef_non_mandatory_accessibility_1plusstops_othdiscr,coef_non_mandatory_accessibility_1plusstops_othdiscr,coef_non_mandatory_accessibility_1plusstops_othdiscr,coef_non_mandatory_accessibility_1plusstops_othdiscr,coef_non_mandatory_accessibility_1plusstops_othdiscr,coef_non_mandatory_accessibility_1plusstops_othdiscr,coef_non_mandatory_accessibility_1plusstops_othdiscr,coef_non_mandatory_accessibility_1plusstops_othdiscr,coef_non_mandatory_accessibility_1plusstops_othdiscr,coef_non_mandatory_accessibility_1plusstops_othdiscr,coef_non_mandatory_accessibility_1plusstops_othdiscr,coef_non_mandatory_accessibility_1plusstops_othdiscr +util_tour_mode_non_motorized,Tour mode is non-motorized,tour_mode_is_non_motorized,,coef_tour_mode_non_motorized_1plusstops_othdiscr,coef_tour_mode_non_motorized_1plusstops_othdiscr,coef_tour_mode_non_motorized_1plusstops_othdiscr,coef_tour_mode_non_motorized_1plusstops_othdiscr,coef_tour_mode_non_motorized_1plusstops_othdiscr,coef_tour_mode_non_motorized_1plusstops_othdiscr,coef_tour_mode_non_motorized_1plusstops_othdiscr,coef_tour_mode_non_motorized_1plusstops_othdiscr,coef_tour_mode_non_motorized_1plusstops_othdiscr,coef_tour_mode_non_motorized_1plusstops_othdiscr,coef_tour_mode_non_motorized_1plusstops_othdiscr,coef_tour_mode_non_motorized_1plusstops_othdiscr,coef_tour_mode_non_motorized_1plusstops_othdiscr,coef_tour_mode_non_motorized_1plusstops_othdiscr,coef_tour_mode_non_motorized_1plusstops_othdiscr +util_tour_mode_hov,Tour mode is hov,tour_mode_is_hov,,coef_tour_mode_hov_1plusstops_othdiscr,coef_tour_mode_hov_1plusstops_othdiscr,coef_tour_mode_hov_1plusstops_othdiscr,coef_tour_mode_hov_1plusstops_othdiscr,coef_tour_mode_hov_1plusstops_othdiscr,coef_tour_mode_hov_1plusstops_othdiscr,coef_tour_mode_hov_1plusstops_othdiscr,coef_tour_mode_hov_1plusstops_othdiscr,coef_tour_mode_hov_1plusstops_othdiscr,coef_tour_mode_hov_1plusstops_othdiscr,coef_tour_mode_hov_1plusstops_othdiscr,coef_tour_mode_hov_1plusstops_othdiscr,coef_tour_mode_hov_1plusstops_othdiscr,coef_tour_mode_hov_1plusstops_othdiscr,coef_tour_mode_hov_1plusstops_othdiscr +util_start_time_is_morning,Tour start time is before noon,(start < 19),,coef_start_time_is_morning_1plusstops_othdiscr,coef_start_time_is_morning_1plusstops_othdiscr,coef_start_time_is_morning_1plusstops_othdiscr,coef_start_time_is_morning_1plusstops_othdiscr,coef_start_time_is_morning_1plusstops_othdiscr,coef_start_time_is_morning_1plusstops_othdiscr,coef_start_time_is_morning_1plusstops_othdiscr,coef_start_time_is_morning_1plusstops_othdiscr,coef_start_time_is_morning_1plusstops_othdiscr,coef_start_time_is_morning_1plusstops_othdiscr,coef_start_time_is_morning_1plusstops_othdiscr,coef_start_time_is_morning_1plusstops_othdiscr,coef_start_time_is_morning_1plusstops_othdiscr,coef_start_time_is_morning_1plusstops_othdiscr,coef_start_time_is_morning_1plusstops_othdiscr +util_start_time_is_afternoon,Tour start time between noon and 5pm,(start >= 19) & (start < 29),,coef_start_time_is_afternoon_1plusstops_othdiscr,coef_start_time_is_afternoon_1plusstops_othdiscr,coef_start_time_is_afternoon_1plusstops_othdiscr,coef_start_time_is_afternoon_1plusstops_othdiscr,coef_start_time_is_afternoon_1plusstops_othdiscr,coef_start_time_is_afternoon_1plusstops_othdiscr,coef_start_time_is_afternoon_1plusstops_othdiscr,coef_start_time_is_afternoon_1plusstops_othdiscr,coef_start_time_is_afternoon_1plusstops_othdiscr,coef_start_time_is_afternoon_1plusstops_othdiscr,coef_start_time_is_afternoon_1plusstops_othdiscr,coef_start_time_is_afternoon_1plusstops_othdiscr,coef_start_time_is_afternoon_1plusstops_othdiscr,coef_start_time_is_afternoon_1plusstops_othdiscr,coef_start_time_is_afternoon_1plusstops_othdiscr +util_distance_to_tour_destination,Distance from origin to tour destination in miles,distance_in_miles,,coef_distance_to_tour_destination_1plusstops_othdiscr,coef_distance_to_tour_destination_1plusstops_othdiscr,coef_distance_to_tour_destination_1plusstops_othdiscr,coef_distance_to_tour_destination_1plusstops_othdiscr,coef_distance_to_tour_destination_1plusstops_othdiscr,coef_distance_to_tour_destination_1plusstops_othdiscr,coef_distance_to_tour_destination_1plusstops_othdiscr,coef_distance_to_tour_destination_1plusstops_othdiscr,coef_distance_to_tour_destination_1plusstops_othdiscr,coef_distance_to_tour_destination_1plusstops_othdiscr,coef_distance_to_tour_destination_1plusstops_othdiscr,coef_distance_to_tour_destination_1plusstops_othdiscr,coef_distance_to_tour_destination_1plusstops_othdiscr,coef_distance_to_tour_destination_1plusstops_othdiscr,coef_distance_to_tour_destination_1plusstops_othdiscr +util_nostop_drive_transit,no stops on pnr knr tnc transit tours,"@(df.tour_mode.isin(['PNR_TRANSIT','KNR_TRANSIT']))",,coef_unavailable,coef_unavailable,coef_unavailable,coef_unavailable,coef_unavailable,coef_unavailable,coef_unavailable,coef_unavailable,coef_unavailable,coef_unavailable,coef_unavailable,coef_unavailable,coef_unavailable,coef_unavailable,coef_unavailable +util_abm3_calibration_constants,calibration constants,is_joint==0,-0.7203,1.5385,1.3997,1.5604,-0.2187,-1.6251,0.4121,-5.1473,-2.0148,-3.6239,-3.7608,-10.0,-1.2510,-2.3088,-2.5209,2.5864 +util_abm3_calibration_constants_joint,joint tour calibration constants,is_joint,-25.0,-25.0,-25.0,-25.0,-25.0,-25.0,-25.0,-25.0,-25.0,-25.0,-25.0,-25.0,-25.0,-25.0,-25.0,-25.0 diff --git a/resident/configs/stop_frequency_othmaint.csv b/resident/configs/stop_frequency_othmaint.csv new file mode 100644 index 0000000..fa90f39 --- /dev/null +++ b/resident/configs/stop_frequency_othmaint.csv @@ -0,0 +1,15 @@ +Label,Description,Expression,0out_0in,0out_1in,0out_2in,0out_3in,1out_0in,1out_1in,1out_2in,1out_3in,2out_0in,2out_1in,2out_2in,2out_3in,3out_0in,3out_1in,3out_2in,3out_3in +util_alternative_specific_constants,Alternative specific constants,1,,coef_alternative_specific_constants_0out_1in_othmaint,coef_alternative_specific_constants_0out_2in_othmaint,coef_alternative_specific_constants_0out_3in_othmaint,coef_alternative_specific_constants_1out_0in_othmaint,coef_alternative_specific_constants_1out_1in_othmaint,coef_alternative_specific_constants_1out_2in_othmaint,coef_alternative_specific_constants_1out_3in_othmaint,coef_alternative_specific_constants_2out_0in_othmaint,coef_alternative_specific_constants_2out_1in_othmaint,coef_alternative_specific_constants_2out_2in_othmaint,coef_alternative_specific_constants_2out_3in_othmaint,coef_alternative_specific_constants_3out_0in_othmaint,coef_alternative_specific_constants_3out_1in_othmaint,coef_alternative_specific_constants_3out_2in_othmaint,coef_alternative_specific_constants_3out_3in_othmaint +util_is_joint_tour,Is joint tour,is_joint,,coef_is_joint_tour_1plusstops_othmaint,coef_is_joint_tour_1plusstops_othmaint,coef_is_joint_tour_1plusstops_othmaint,coef_is_joint_tour_1plusstops_othmaint,coef_is_joint_tour_1plusstops_othmaint,coef_is_joint_tour_1plusstops_othmaint,coef_is_joint_tour_1plusstops_othmaint,coef_is_joint_tour_1plusstops_othmaint,coef_is_joint_tour_1plusstops_othmaint,coef_is_joint_tour_1plusstops_othmaint,coef_is_joint_tour_1plusstops_othmaint,coef_is_joint_tour_1plusstops_othmaint,coef_is_joint_tour_1plusstops_othmaint,coef_is_joint_tour_1plusstops_othmaint,coef_is_joint_tour_1plusstops_othmaint +util_number_of_children_in_hh,Number of children in HH,num_children,,coef_number_of_children_in_hh_1plusstops_othmaint,coef_number_of_children_in_hh_1plusstops_othmaint,coef_number_of_children_in_hh_1plusstops_othmaint,coef_number_of_children_in_hh_1plusstops_othmaint,coef_number_of_children_in_hh_1plusstops_othmaint,coef_number_of_children_in_hh_1plusstops_othmaint,coef_number_of_children_in_hh_1plusstops_othmaint,coef_number_of_children_in_hh_1plusstops_othmaint,coef_number_of_children_in_hh_1plusstops_othmaint,coef_number_of_children_in_hh_1plusstops_othmaint,coef_number_of_children_in_hh_1plusstops_othmaint,coef_number_of_children_in_hh_1plusstops_othmaint,coef_number_of_children_in_hh_1plusstops_othmaint,coef_number_of_children_in_hh_1plusstops_othmaint,coef_number_of_children_in_hh_1plusstops_othmaint +util_number_of_nonwork_tours,Number of non-work tours,(num_total_tours - num_work_tours),,coef_number_of_nonwork_tours_1plusstops_othmaint,coef_number_of_nonwork_tours_1plusstops_othmaint,coef_number_of_nonwork_tours_1plusstops_othmaint,coef_number_of_nonwork_tours_1plusstops_othmaint,coef_number_of_nonwork_tours_1plusstops_othmaint,coef_number_of_nonwork_tours_1plusstops_othmaint,coef_number_of_nonwork_tours_1plusstops_othmaint,coef_number_of_nonwork_tours_1plusstops_othmaint,coef_number_of_nonwork_tours_1plusstops_othmaint,coef_number_of_nonwork_tours_1plusstops_othmaint,coef_number_of_nonwork_tours_1plusstops_othmaint,coef_number_of_nonwork_tours_1plusstops_othmaint,coef_number_of_nonwork_tours_1plusstops_othmaint,coef_number_of_nonwork_tours_1plusstops_othmaint,coef_number_of_nonwork_tours_1plusstops_othmaint +util_hhsize_is_4p,Household size is 4p,(hhsize >= 4),,coef_hhsize_is_4p_1plusstops_othmaint,coef_hhsize_is_4p_1plusstops_othmaint,coef_hhsize_is_4p_1plusstops_othmaint,coef_hhsize_is_4p_1plusstops_othmaint,coef_hhsize_is_4p_1plusstops_othmaint,coef_hhsize_is_4p_1plusstops_othmaint,coef_hhsize_is_4p_1plusstops_othmaint,coef_hhsize_is_4p_1plusstops_othmaint,coef_hhsize_is_4p_1plusstops_othmaint,coef_hhsize_is_4p_1plusstops_othmaint,coef_hhsize_is_4p_1plusstops_othmaint,coef_hhsize_is_4p_1plusstops_othmaint,coef_hhsize_is_4p_1plusstops_othmaint,coef_hhsize_is_4p_1plusstops_othmaint,coef_hhsize_is_4p_1plusstops_othmaint +util_tour_mode_non_motorized,Tour mode is non-motorized,tour_mode_is_non_motorized,,coef_tour_mode_non_motorized_1plusstops_othmaint,coef_tour_mode_non_motorized_1plusstops_othmaint,coef_tour_mode_non_motorized_1plusstops_othmaint,coef_tour_mode_non_motorized_1plusstops_othmaint,coef_tour_mode_non_motorized_1plusstops_othmaint,coef_tour_mode_non_motorized_1plusstops_othmaint,coef_tour_mode_non_motorized_1plusstops_othmaint,coef_tour_mode_non_motorized_1plusstops_othmaint,coef_tour_mode_non_motorized_1plusstops_othmaint,coef_tour_mode_non_motorized_1plusstops_othmaint,coef_tour_mode_non_motorized_1plusstops_othmaint,coef_tour_mode_non_motorized_1plusstops_othmaint,coef_tour_mode_non_motorized_1plusstops_othmaint,coef_tour_mode_non_motorized_1plusstops_othmaint,coef_tour_mode_non_motorized_1plusstops_othmaint +util_tour_mode_hov,Tour mode is hov,tour_mode_is_hov,,coef_tour_mode_hov_1plusstops_othmaint,coef_tour_mode_hov_1plusstops_othmaint,coef_tour_mode_hov_1plusstops_othmaint,coef_tour_mode_hov_1plusstops_othmaint,coef_tour_mode_hov_1plusstops_othmaint,coef_tour_mode_hov_1plusstops_othmaint,coef_tour_mode_hov_1plusstops_othmaint,coef_tour_mode_hov_1plusstops_othmaint,coef_tour_mode_hov_1plusstops_othmaint,coef_tour_mode_hov_1plusstops_othmaint,coef_tour_mode_hov_1plusstops_othmaint,coef_tour_mode_hov_1plusstops_othmaint,coef_tour_mode_hov_1plusstops_othmaint,coef_tour_mode_hov_1plusstops_othmaint,coef_tour_mode_hov_1plusstops_othmaint +util_tour_duration_in_hours,Tour duration in hours,(duration / 2),,coef_tour_duration_in_hours_1plusstops_othmaint,coef_tour_duration_in_hours_1plusstops_othmaint,coef_tour_duration_in_hours_1plusstops_othmaint,coef_tour_duration_in_hours_1plusstops_othmaint,coef_tour_duration_in_hours_1plusstops_othmaint,coef_tour_duration_in_hours_1plusstops_othmaint,coef_tour_duration_in_hours_1plusstops_othmaint,coef_tour_duration_in_hours_1plusstops_othmaint,coef_tour_duration_in_hours_1plusstops_othmaint,coef_tour_duration_in_hours_1plusstops_othmaint,coef_tour_duration_in_hours_1plusstops_othmaint,coef_tour_duration_in_hours_1plusstops_othmaint,coef_tour_duration_in_hours_1plusstops_othmaint,coef_tour_duration_in_hours_1plusstops_othmaint,coef_tour_duration_in_hours_1plusstops_othmaint +util_start_time_is_morning,Tour start time is before noon,(start < 19),,coef_start_time_is_morning_1plusstops_othmaint,coef_start_time_is_morning_1plusstops_othmaint,coef_start_time_is_morning_1plusstops_othmaint,coef_start_time_is_morning_1plusstops_othmaint,coef_start_time_is_morning_1plusstops_othmaint,coef_start_time_is_morning_1plusstops_othmaint,coef_start_time_is_morning_1plusstops_othmaint,coef_start_time_is_morning_1plusstops_othmaint,coef_start_time_is_morning_1plusstops_othmaint,coef_start_time_is_morning_1plusstops_othmaint,coef_start_time_is_morning_1plusstops_othmaint,coef_start_time_is_morning_1plusstops_othmaint,coef_start_time_is_morning_1plusstops_othmaint,coef_start_time_is_morning_1plusstops_othmaint,coef_start_time_is_morning_1plusstops_othmaint +util_start_time_is_afternoon,Tour start time between noon and 5pm,(start >= 19) & (start < 29),,coef_start_time_is_afternoon_1plusstops_othmaint,coef_start_time_is_afternoon_1plusstops_othmaint,coef_start_time_is_afternoon_1plusstops_othmaint,coef_start_time_is_afternoon_1plusstops_othmaint,coef_start_time_is_afternoon_1plusstops_othmaint,coef_start_time_is_afternoon_1plusstops_othmaint,coef_start_time_is_afternoon_1plusstops_othmaint,coef_start_time_is_afternoon_1plusstops_othmaint,coef_start_time_is_afternoon_1plusstops_othmaint,coef_start_time_is_afternoon_1plusstops_othmaint,coef_start_time_is_afternoon_1plusstops_othmaint,coef_start_time_is_afternoon_1plusstops_othmaint,coef_start_time_is_afternoon_1plusstops_othmaint,coef_start_time_is_afternoon_1plusstops_othmaint,coef_start_time_is_afternoon_1plusstops_othmaint +util_distance_to_tour_destination,Distance from origin to tour destination in miles,distance_in_miles,,coef_distance_to_tour_destination_1plusstops_othmaint,coef_distance_to_tour_destination_1plusstops_othmaint,coef_distance_to_tour_destination_1plusstops_othmaint,coef_distance_to_tour_destination_1plusstops_othmaint,coef_distance_to_tour_destination_1plusstops_othmaint,coef_distance_to_tour_destination_1plusstops_othmaint,coef_distance_to_tour_destination_1plusstops_othmaint,coef_distance_to_tour_destination_1plusstops_othmaint,coef_distance_to_tour_destination_1plusstops_othmaint,coef_distance_to_tour_destination_1plusstops_othmaint,coef_distance_to_tour_destination_1plusstops_othmaint,coef_distance_to_tour_destination_1plusstops_othmaint,coef_distance_to_tour_destination_1plusstops_othmaint,coef_distance_to_tour_destination_1plusstops_othmaint,coef_distance_to_tour_destination_1plusstops_othmaint +util_nostop_drive_transit,no stops on pnr knr tnc transit tours,"@(df.tour_mode.isin(['PNR_TRANSIT','KNR_TRANSIT']))",,coef_unavailable,coef_unavailable,coef_unavailable,coef_unavailable,coef_unavailable,coef_unavailable,coef_unavailable,coef_unavailable,coef_unavailable,coef_unavailable,coef_unavailable,coef_unavailable,coef_unavailable,coef_unavailable,coef_unavailable +util_abm3_calibration_constants,calibration constants,is_joint==0,-0.2656123444029792,0.15348593472541502,-0.3788741163797237,0.123283919762546,0.22954639797160692,0.5755515179272356,-0.21495880583644267,-0.6257626266582341,1.003382155880165,0.28582196724350073,0.0002870161481544698,-1.718504862855789,-2.145372675786179,-2.2250799871962363,1.1522061332946996,1.8970899388634406 +util_abm3_calibration_constants_joint,joint tour calibration constants,is_joint,-1.3355,0.9021,0.5683,-1.7962,1.50029,-0.9014,-1.3765,-10.0,-3.4045,0.7563,-10.0,-1.7586,-0.04717,-0.5280,-10.0,3.9129 diff --git a/resident/configs/stop_frequency_school.csv b/resident/configs/stop_frequency_school.csv new file mode 100644 index 0000000..4142df3 --- /dev/null +++ b/resident/configs/stop_frequency_school.csv @@ -0,0 +1,12 @@ +Label,Description,Expression,0out_0in,0out_1in,0out_2in,0out_3in,1out_0in,1out_1in,1out_2in,1out_3in,2out_0in,2out_1in,2out_2in,2out_3in,3out_0in,3out_1in,3out_2in,3out_3in +util_alternative_specific_constants,Alternative specific constants,1,,coef_alternative_specific_constants_0out_1in_school,coef_alternative_specific_constants_0out_2in_school,coef_alternative_specific_constants_0out_3in_school,coef_alternative_specific_constants_1out_0in_school,coef_alternative_specific_constants_1out_1in_school,coef_alternative_specific_constants_1out_2in_school,coef_alternative_specific_constants_1out_3in_school,coef_alternative_specific_constants_2out_0in_school,coef_alternative_specific_constants_2out_1in_school,coef_alternative_specific_constants_2out_2in_school,coef_alternative_specific_constants_2out_3in_school,coef_alternative_specific_constants_3out_0in_school,coef_alternative_specific_constants_3out_1in_school,coef_alternative_specific_constants_3out_2in_school,coef_alternative_specific_constants_3out_3in_school +util_is_pre_covid,Tour happened before covid hit,@scenarioYear==2016,,coef_is_pre_covid_0out_1plusin_school,coef_is_pre_covid_0out_1plusin_school,coef_is_pre_covid_0out_1plusin_school,coef_is_pre_covid_1plusout_0in_school,coef_is_pre_covid_1plusout_1plusin_school,coef_is_pre_covid_1plusout_1plusin_school,coef_is_pre_covid_1plusout_1plusin_school,coef_is_pre_covid_1plusout_0in_school,coef_is_pre_covid_1plusout_1plusin_school,coef_is_pre_covid_1plusout_1plusin_school,coef_is_pre_covid_1plusout_1plusin_school,coef_is_pre_covid_1plusout_0in_school,coef_is_pre_covid_1plusout_1plusin_school,coef_is_pre_covid_1plusout_1plusin_school,coef_is_pre_covid_1plusout_1plusin_school +util_number_of_nonwork_tours,Number of non-work tours,(num_total_tours - num_work_tours),,coef_number_of_nonwork_tours_0out_1plusin_school,coef_number_of_nonwork_tours_0out_1plusin_school,coef_number_of_nonwork_tours_0out_1plusin_school,,coef_number_of_nonwork_tours_1plusout_1plusin_school,coef_number_of_nonwork_tours_1plusout_1plusin_school,coef_number_of_nonwork_tours_1plusout_1plusin_school,,coef_number_of_nonwork_tours_1plusout_1plusin_school,coef_number_of_nonwork_tours_1plusout_1plusin_school,coef_number_of_nonwork_tours_1plusout_1plusin_school,,coef_number_of_nonwork_tours_1plusout_1plusin_school,coef_number_of_nonwork_tours_1plusout_1plusin_school,coef_number_of_nonwork_tours_1plusout_1plusin_school +util_school_child_in_hh,Presence of school age child in household other than self,has_school_kid,,coef_school_child_in_hh_0out_1plusin_school,coef_school_child_in_hh_0out_1plusin_school,coef_school_child_in_hh_0out_1plusin_school,coef_school_child_in_hh_1plusout_0in_school,coef_school_child_in_hh_1plusout_1plusin_school,coef_school_child_in_hh_1plusout_1plusin_school,coef_school_child_in_hh_1plusout_1plusin_school,coef_school_child_in_hh_1plusout_0in_school,coef_school_child_in_hh_1plusout_1plusin_school,coef_school_child_in_hh_1plusout_1plusin_school,coef_school_child_in_hh_1plusout_1plusin_school,coef_school_child_in_hh_1plusout_0in_school,coef_school_child_in_hh_1plusout_1plusin_school,coef_school_child_in_hh_1plusout_1plusin_school,coef_school_child_in_hh_1plusout_1plusin_school +util_non_worker_in_hh,Presence of non workers in household other than self,has_non_worker,,,,,,coef_non_worker_in_hh_1plusout_1plusin_school,coef_non_worker_in_hh_1plusout_1plusin_school,coef_non_worker_in_hh_1plusout_1plusin_school,,coef_non_worker_in_hh_1plusout_1plusin_school,coef_non_worker_in_hh_1plusout_1plusin_school,coef_non_worker_in_hh_1plusout_1plusin_school,,coef_non_worker_in_hh_1plusout_1plusin_school,coef_non_worker_in_hh_1plusout_1plusin_school,coef_non_worker_in_hh_1plusout_1plusin_school +util_non_mandatory_accessibility,Disaggreage accessibility at home location to non-mandatory location,(othdiscr_accessibility + shopping_accessibility) / 2,,coef_non_mandatory_accessibility_0out_1plusin_school,coef_non_mandatory_accessibility_0out_1plusin_school,coef_non_mandatory_accessibility_0out_1plusin_school,,coef_non_mandatory_accessibility_1plusout_1plusin_school,coef_non_mandatory_accessibility_1plusout_1plusin_school,coef_non_mandatory_accessibility_1plusout_1plusin_school,,coef_non_mandatory_accessibility_1plusout_1plusin_school,coef_non_mandatory_accessibility_1plusout_1plusin_school,coef_non_mandatory_accessibility_1plusout_1plusin_school,,coef_non_mandatory_accessibility_1plusout_1plusin_school,coef_non_mandatory_accessibility_1plusout_1plusin_school,coef_non_mandatory_accessibility_1plusout_1plusin_school +util_tour_mode_non_motorized,Tour mode is non-motorized,tour_mode_is_non_motorized,,coef_tour_mode_non_motorized_0out_1plusin_school,coef_tour_mode_non_motorized_0out_1plusin_school,coef_tour_mode_non_motorized_0out_1plusin_school,coef_tour_mode_non_motorized_1plusout_0in_school,coef_tour_mode_non_motorized_1plusout_1plusin_school,coef_tour_mode_non_motorized_1plusout_1plusin_school,coef_tour_mode_non_motorized_1plusout_1plusin_school,coef_tour_mode_non_motorized_1plusout_0in_school,coef_tour_mode_non_motorized_1plusout_1plusin_school,coef_tour_mode_non_motorized_1plusout_1plusin_school,coef_tour_mode_non_motorized_1plusout_1plusin_school,coef_tour_mode_non_motorized_1plusout_0in_school,coef_tour_mode_non_motorized_1plusout_1plusin_school,coef_tour_mode_non_motorized_1plusout_1plusin_school,coef_tour_mode_non_motorized_1plusout_1plusin_school +util_tour_mode_schoolbus,Tour mode is schoolbus,tour_mode_is_schbus,,coef_tour_mode_schbus_0out_1plusin_school,coef_tour_mode_schbus_0out_1plusin_school,coef_tour_mode_schbus_0out_1plusin_school,,,,,,,,,,,, +util_nostopschooltour,no stops on school tours,tour_mode_is_schbus,,coef_unavailable,coef_unavailable,coef_unavailable,coef_unavailable,coef_unavailable,coef_unavailable,coef_unavailable,coef_unavailable,coef_unavailable,coef_unavailable,coef_unavailable,coef_unavailable,coef_unavailable,coef_unavailable,coef_unavailable +util_nostop_drive_transit,no stops on pnr knr tnc transit tours,"@(df.tour_mode.isin(['PNR_TRANSIT','KNR_TRANSIT']))",,coef_unavailable,coef_unavailable,coef_unavailable,coef_unavailable,coef_unavailable,coef_unavailable,coef_unavailable,coef_unavailable,coef_unavailable,coef_unavailable,coef_unavailable,coef_unavailable,coef_unavailable,coef_unavailable,coef_unavailable +util_abm3_calibration_constants,calibration constants,is_joint==0,-4.1165,1.3926,0.5790,0.9793,4.6426,2.2195,3.4805,2.7292,2.4262,3.10758,-25.0,-25.0,-2.7718,0.2813,-10.0,3.9258 diff --git a/resident/configs/stop_frequency_shopping.csv b/resident/configs/stop_frequency_shopping.csv new file mode 100644 index 0000000..bd0d72f --- /dev/null +++ b/resident/configs/stop_frequency_shopping.csv @@ -0,0 +1,16 @@ +Label,Description,Expression,0out_0in,0out_1in,0out_2in,0out_3in,1out_0in,1out_1in,1out_2in,1out_3in,2out_0in,2out_1in,2out_2in,2out_3in,3out_0in,3out_1in,3out_2in,3out_3in +util_alternative_specific_constants,Alternative specific constants,1,,coef_alternative_specific_constants_0out_1in_shopping,coef_alternative_specific_constants_0out_2in_shopping,coef_alternative_specific_constants_0out_3in_shopping,coef_alternative_specific_constants_1out_0in_shopping,coef_alternative_specific_constants_1out_1in_shopping,coef_alternative_specific_constants_1out_2in_shopping,coef_alternative_specific_constants_1out_3in_shopping,coef_alternative_specific_constants_2out_0in_shopping,coef_alternative_specific_constants_2out_1in_shopping,coef_alternative_specific_constants_2out_2in_shopping,coef_alternative_specific_constants_2out_3in_shopping,coef_alternative_specific_constants_3out_0in_shopping,coef_alternative_specific_constants_3out_1in_shopping,coef_alternative_specific_constants_3out_2in_shopping,coef_alternative_specific_constants_3out_3in_shopping +util_is_joint_tour,Is joint tour,is_joint,,coef_is_joint_tour_1plusstops_shopping,coef_is_joint_tour_1plusstops_shopping,coef_is_joint_tour_1plusstops_shopping,coef_is_joint_tour_1plusstops_shopping,coef_is_joint_tour_1plusstops_shopping,coef_is_joint_tour_1plusstops_shopping,coef_is_joint_tour_1plusstops_shopping,coef_is_joint_tour_1plusstops_shopping,coef_is_joint_tour_1plusstops_shopping,coef_is_joint_tour_1plusstops_shopping,coef_is_joint_tour_1plusstops_shopping,coef_is_joint_tour_1plusstops_shopping,coef_is_joint_tour_1plusstops_shopping,coef_is_joint_tour_1plusstops_shopping,coef_is_joint_tour_1plusstops_shopping +util_is_pre_covid,Tour happened before covid hit,@scenarioYear==2016,,coef_is_pre_covid_1plusstops_shopping,coef_is_pre_covid_1plusstops_shopping,coef_is_pre_covid_1plusstops_shopping,coef_is_pre_covid_1plusstops_shopping,coef_is_pre_covid_1plusstops_shopping,coef_is_pre_covid_1plusstops_shopping,coef_is_pre_covid_1plusstops_shopping,coef_is_pre_covid_1plusstops_shopping,coef_is_pre_covid_1plusstops_shopping,coef_is_pre_covid_1plusstops_shopping,coef_is_pre_covid_1plusstops_shopping,coef_is_pre_covid_1plusstops_shopping,coef_is_pre_covid_1plusstops_shopping,coef_is_pre_covid_1plusstops_shopping,coef_is_pre_covid_1plusstops_shopping +util_is_worker,Person is worker,is_worker,,coef_is_worker_1plusstops_shopping,coef_is_worker_1plusstops_shopping,coef_is_worker_1plusstops_shopping,coef_is_worker_1plusstops_shopping,coef_is_worker_1plusstops_shopping,coef_is_worker_1plusstops_shopping,coef_is_worker_1plusstops_shopping,coef_is_worker_1plusstops_shopping,coef_is_worker_1plusstops_shopping,coef_is_worker_1plusstops_shopping,coef_is_worker_1plusstops_shopping,coef_is_worker_1plusstops_shopping,coef_is_worker_1plusstops_shopping,coef_is_worker_1plusstops_shopping,coef_is_worker_1plusstops_shopping +util_number_of_nonwork_tours,Number of non-work tours,(num_total_tours - num_work_tours),,coef_number_of_nonwork_tours_1plusstops_shopping,coef_number_of_nonwork_tours_1plusstops_shopping,coef_number_of_nonwork_tours_1plusstops_shopping,coef_number_of_nonwork_tours_1plusstops_shopping,coef_number_of_nonwork_tours_1plusstops_shopping,coef_number_of_nonwork_tours_1plusstops_shopping,coef_number_of_nonwork_tours_1plusstops_shopping,coef_number_of_nonwork_tours_1plusstops_shopping,coef_number_of_nonwork_tours_1plusstops_shopping,coef_number_of_nonwork_tours_1plusstops_shopping,coef_number_of_nonwork_tours_1plusstops_shopping,coef_number_of_nonwork_tours_1plusstops_shopping,coef_number_of_nonwork_tours_1plusstops_shopping,coef_number_of_nonwork_tours_1plusstops_shopping,coef_number_of_nonwork_tours_1plusstops_shopping +util_school_child_in_hh,Presence of school age child in household other than self,has_school_kid,,coef_school_child_in_hh_1plusstops_shopping,coef_school_child_in_hh_1plusstops_shopping,coef_school_child_in_hh_1plusstops_shopping,coef_school_child_in_hh_1plusstops_shopping,coef_school_child_in_hh_1plusstops_shopping,coef_school_child_in_hh_1plusstops_shopping,coef_school_child_in_hh_1plusstops_shopping,coef_school_child_in_hh_1plusstops_shopping,coef_school_child_in_hh_1plusstops_shopping,coef_school_child_in_hh_1plusstops_shopping,coef_school_child_in_hh_1plusstops_shopping,coef_school_child_in_hh_1plusstops_shopping,coef_school_child_in_hh_1plusstops_shopping,coef_school_child_in_hh_1plusstops_shopping,coef_school_child_in_hh_1plusstops_shopping +util_non_mandatory_accessibility,Disaggreage accessibility at home location to non-mandatory location,(othdiscr_accessibility + shopping_accessibility) / 2,,coef_non_mandatory_accessibility_1plusstops_shopping,coef_non_mandatory_accessibility_1plusstops_shopping,coef_non_mandatory_accessibility_1plusstops_shopping,coef_non_mandatory_accessibility_1plusstops_shopping,coef_non_mandatory_accessibility_1plusstops_shopping,coef_non_mandatory_accessibility_1plusstops_shopping,coef_non_mandatory_accessibility_1plusstops_shopping,coef_non_mandatory_accessibility_1plusstops_shopping,coef_non_mandatory_accessibility_1plusstops_shopping,coef_non_mandatory_accessibility_1plusstops_shopping,coef_non_mandatory_accessibility_1plusstops_shopping,coef_non_mandatory_accessibility_1plusstops_shopping,coef_non_mandatory_accessibility_1plusstops_shopping,coef_non_mandatory_accessibility_1plusstops_shopping,coef_non_mandatory_accessibility_1plusstops_shopping +util_tour_mode_non_motorized,Tour mode is non-motorized,tour_mode_is_non_motorized,,coef_tour_mode_non_motorized_1plusstops_shopping,coef_tour_mode_non_motorized_1plusstops_shopping,coef_tour_mode_non_motorized_1plusstops_shopping,coef_tour_mode_non_motorized_1plusstops_shopping,coef_tour_mode_non_motorized_1plusstops_shopping,coef_tour_mode_non_motorized_1plusstops_shopping,coef_tour_mode_non_motorized_1plusstops_shopping,coef_tour_mode_non_motorized_1plusstops_shopping,coef_tour_mode_non_motorized_1plusstops_shopping,coef_tour_mode_non_motorized_1plusstops_shopping,coef_tour_mode_non_motorized_1plusstops_shopping,coef_tour_mode_non_motorized_1plusstops_shopping,coef_tour_mode_non_motorized_1plusstops_shopping,coef_tour_mode_non_motorized_1plusstops_shopping,coef_tour_mode_non_motorized_1plusstops_shopping +util_tour_mode_hov,Tour mode is hov,tour_mode_is_hov,,coef_tour_mode_hov_1plusstops_shopping,coef_tour_mode_hov_1plusstops_shopping,coef_tour_mode_hov_1plusstops_shopping,coef_tour_mode_hov_1plusstops_shopping,coef_tour_mode_hov_1plusstops_shopping,coef_tour_mode_hov_1plusstops_shopping,coef_tour_mode_hov_1plusstops_shopping,coef_tour_mode_hov_1plusstops_shopping,coef_tour_mode_hov_1plusstops_shopping,coef_tour_mode_hov_1plusstops_shopping,coef_tour_mode_hov_1plusstops_shopping,coef_tour_mode_hov_1plusstops_shopping,coef_tour_mode_hov_1plusstops_shopping,coef_tour_mode_hov_1plusstops_shopping,coef_tour_mode_hov_1plusstops_shopping +util_tour_mode_transit,Tour mode is transit,tour_mode_is_transit,,coef_tour_mode_transit_1plusstops_shopping,coef_tour_mode_transit_1plusstops_shopping,coef_tour_mode_transit_1plusstops_shopping,coef_tour_mode_transit_1plusstops_shopping,coef_tour_mode_transit_1plusstops_shopping,coef_tour_mode_transit_1plusstops_shopping,coef_tour_mode_transit_1plusstops_shopping,coef_tour_mode_transit_1plusstops_shopping,coef_tour_mode_transit_1plusstops_shopping,coef_tour_mode_transit_1plusstops_shopping,coef_tour_mode_transit_1plusstops_shopping,coef_tour_mode_transit_1plusstops_shopping,coef_tour_mode_transit_1plusstops_shopping,coef_tour_mode_transit_1plusstops_shopping,coef_tour_mode_transit_1plusstops_shopping +util_tour_duration_in_hours,Tour duration in hours,(duration / 2),,coef_tour_duration_in_hours_1plusstops_shopping,coef_tour_duration_in_hours_1plusstops_shopping,coef_tour_duration_in_hours_1plusstops_shopping,coef_tour_duration_in_hours_1plusstops_shopping,coef_tour_duration_in_hours_1plusstops_shopping,coef_tour_duration_in_hours_1plusstops_shopping,coef_tour_duration_in_hours_1plusstops_shopping,coef_tour_duration_in_hours_1plusstops_shopping,coef_tour_duration_in_hours_1plusstops_shopping,coef_tour_duration_in_hours_1plusstops_shopping,coef_tour_duration_in_hours_1plusstops_shopping,coef_tour_duration_in_hours_1plusstops_shopping,coef_tour_duration_in_hours_1plusstops_shopping,coef_tour_duration_in_hours_1plusstops_shopping,coef_tour_duration_in_hours_1plusstops_shopping +util_distance_to_tour_destination,Distance from origin to tour destination in miles,distance_in_miles,,coef_distance_to_tour_destination_1plusstops_shopping,coef_distance_to_tour_destination_1plusstops_shopping,coef_distance_to_tour_destination_1plusstops_shopping,coef_distance_to_tour_destination_1plusstops_shopping,coef_distance_to_tour_destination_1plusstops_shopping,coef_distance_to_tour_destination_1plusstops_shopping,coef_distance_to_tour_destination_1plusstops_shopping,coef_distance_to_tour_destination_1plusstops_shopping,coef_distance_to_tour_destination_1plusstops_shopping,coef_distance_to_tour_destination_1plusstops_shopping,coef_distance_to_tour_destination_1plusstops_shopping,coef_distance_to_tour_destination_1plusstops_shopping,coef_distance_to_tour_destination_1plusstops_shopping,coef_distance_to_tour_destination_1plusstops_shopping,coef_distance_to_tour_destination_1plusstops_shopping +util_nostop_drive_transit,no stops on pnr knr tnc transit tours,"@(df.tour_mode.isin(['PNR_TRANSIT','KNR_TRANSIT']))",,coef_unavailable,coef_unavailable,coef_unavailable,coef_unavailable,coef_unavailable,coef_unavailable,coef_unavailable,coef_unavailable,coef_unavailable,coef_unavailable,coef_unavailable,coef_unavailable,coef_unavailable,coef_unavailable,coef_unavailable +util_abm3_calibration_constants,calibration constants,is_joint==0,-0.2500906598537606,0.057193649152341756,0.4804144838454415,-0.261395884078163,0.22894326452057048,0.5910095505581588,-0.8981616524559722,-4.199029251095443,0.09230772468214696,0.5169317692363429,-0.9915339032679815,-1.1533584002576693,-0.42731015383922333,-2.6985663726233104,1.9854294273438238,3.3854166649272117 +util_abm3_calibration_constants_joint,joint tour calibration constants,is_joint,-0.2533,1.3037,0.7569,-1.6520,-0.7370,0.8157,0.006162,-10.0,-1.1890,-10.0,-10.0,-5.0,-0.0569,-10.0,-5.0,3.9856 diff --git a/resident/configs/stop_frequency_social.csv b/resident/configs/stop_frequency_social.csv new file mode 100644 index 0000000..71fec05 --- /dev/null +++ b/resident/configs/stop_frequency_social.csv @@ -0,0 +1,12 @@ +Label,Description,Expression,0out_0in,0out_1in,0out_2in,0out_3in,1out_0in,1out_1in,1out_2in,1out_3in,2out_0in,2out_1in,2out_2in,2out_3in,3out_0in,3out_1in,3out_2in,3out_3in +util_alternative_specific_constants,Alternative specific constants,1,,coef_alternative_specific_constants_0out_1in_social,coef_alternative_specific_constants_0out_2in_social,coef_alternative_specific_constants_0out_3in_social,coef_alternative_specific_constants_1out_0in_social,coef_alternative_specific_constants_1out_1in_social,coef_alternative_specific_constants_1out_2in_social,coef_alternative_specific_constants_1out_3in_social,coef_alternative_specific_constants_2out_0in_social,coef_alternative_specific_constants_2out_1in_social,coef_alternative_specific_constants_2out_2in_social,coef_alternative_specific_constants_2out_3in_social,coef_alternative_specific_constants_3out_0in_social,coef_alternative_specific_constants_3out_1in_social,coef_alternative_specific_constants_3out_2in_social,coef_alternative_specific_constants_3out_3in_social +util_is_joint_tour,Is joint tour,is_joint,,coef_is_joint_tour_1plus_stops_social,coef_is_joint_tour_1plus_stops_social,coef_is_joint_tour_1plus_stops_social,coef_is_joint_tour_1plus_stops_social,coef_is_joint_tour_1plus_stops_social,coef_is_joint_tour_1plus_stops_social,coef_is_joint_tour_1plus_stops_social,coef_is_joint_tour_1plus_stops_social,coef_is_joint_tour_1plus_stops_social,coef_is_joint_tour_1plus_stops_social,coef_is_joint_tour_1plus_stops_social,coef_is_joint_tour_1plus_stops_social,coef_is_joint_tour_1plus_stops_social,coef_is_joint_tour_1plus_stops_social,coef_is_joint_tour_1plus_stops_social +util_number_of_nonwork_tours,Number of non-work tours,(num_total_tours - num_work_tours),,coef_number_of_nonwork_tours_1plus_stops_social,coef_number_of_nonwork_tours_1plus_stops_social,coef_number_of_nonwork_tours_1plus_stops_social,coef_number_of_nonwork_tours_1plus_stops_social,coef_number_of_nonwork_tours_1plus_stops_social,coef_number_of_nonwork_tours_1plus_stops_social,coef_number_of_nonwork_tours_1plus_stops_social,coef_number_of_nonwork_tours_1plus_stops_social,coef_number_of_nonwork_tours_1plus_stops_social,coef_number_of_nonwork_tours_1plus_stops_social,coef_number_of_nonwork_tours_1plus_stops_social,coef_number_of_nonwork_tours_1plus_stops_social,coef_number_of_nonwork_tours_1plus_stops_social,coef_number_of_nonwork_tours_1plus_stops_social,coef_number_of_nonwork_tours_1plus_stops_social +util_age_5_18,Age Group - 5 yrs to 18 yrs,(age >= 5) & (age < 19),,coef_age_5_18_1plus_stops_social,coef_age_5_18_1plus_stops_social,coef_age_5_18_1plus_stops_social,coef_age_5_18_1plus_stops_social,coef_age_5_18_1plus_stops_social,coef_age_5_18_1plus_stops_social,coef_age_5_18_1plus_stops_social,coef_age_5_18_1plus_stops_social,coef_age_5_18_1plus_stops_social,coef_age_5_18_1plus_stops_social,coef_age_5_18_1plus_stops_social,coef_age_5_18_1plus_stops_social,coef_age_5_18_1plus_stops_social,coef_age_5_18_1plus_stops_social,coef_age_5_18_1plus_stops_social +util_non_mandatory_accessibility,Disaggreage accessibility at home location to non-mandatory location,(othdiscr_accessibility + shopping_accessibility) / 2,,coef_non_mandatory_accessibility_1plus_stops_social,coef_non_mandatory_accessibility_1plus_stops_social,coef_non_mandatory_accessibility_1plus_stops_social,coef_non_mandatory_accessibility_1plus_stops_social,coef_non_mandatory_accessibility_1plus_stops_social,coef_non_mandatory_accessibility_1plus_stops_social,coef_non_mandatory_accessibility_1plus_stops_social,coef_non_mandatory_accessibility_1plus_stops_social,coef_non_mandatory_accessibility_1plus_stops_social,coef_non_mandatory_accessibility_1plus_stops_social,coef_non_mandatory_accessibility_1plus_stops_social,coef_non_mandatory_accessibility_1plus_stops_social,coef_non_mandatory_accessibility_1plus_stops_social,coef_non_mandatory_accessibility_1plus_stops_social,coef_non_mandatory_accessibility_1plus_stops_social +util_tour_mode_hov,Tour mode is hov,tour_mode_is_hov,,coef_tour_mode_hov_1plus_stops_social,coef_tour_mode_hov_1plus_stops_social,coef_tour_mode_hov_1plus_stops_social,coef_tour_mode_hov_1plus_stops_social,coef_tour_mode_hov_1plus_stops_social,coef_tour_mode_hov_1plus_stops_social,coef_tour_mode_hov_1plus_stops_social,coef_tour_mode_hov_1plus_stops_social,coef_tour_mode_hov_1plus_stops_social,coef_tour_mode_hov_1plus_stops_social,coef_tour_mode_hov_1plus_stops_social,coef_tour_mode_hov_1plus_stops_social,coef_tour_mode_hov_1plus_stops_social,coef_tour_mode_hov_1plus_stops_social,coef_tour_mode_hov_1plus_stops_social +util_tour_duration_in_hours,Tour duration in hours,(duration / 2),,coef_tour_duration_in_hours_1plus_stops_social,coef_tour_duration_in_hours_1plus_stops_social,coef_tour_duration_in_hours_1plus_stops_social,coef_tour_duration_in_hours_1plus_stops_social,coef_tour_duration_in_hours_1plus_stops_social,coef_tour_duration_in_hours_1plus_stops_social,coef_tour_duration_in_hours_1plus_stops_social,coef_tour_duration_in_hours_1plus_stops_social,coef_tour_duration_in_hours_1plus_stops_social,coef_tour_duration_in_hours_1plus_stops_social,coef_tour_duration_in_hours_1plus_stops_social,coef_tour_duration_in_hours_1plus_stops_social,coef_tour_duration_in_hours_1plus_stops_social,coef_tour_duration_in_hours_1plus_stops_social,coef_tour_duration_in_hours_1plus_stops_social +util_distance_to_tour_destination,Distance from origin to tour destination in miles,distance_in_miles,,coef_distance_to_tour_destination_1plus_stops_social,coef_distance_to_tour_destination_1plus_stops_social,coef_distance_to_tour_destination_1plus_stops_social,coef_distance_to_tour_destination_1plus_stops_social,coef_distance_to_tour_destination_1plus_stops_social,coef_distance_to_tour_destination_1plus_stops_social,coef_distance_to_tour_destination_1plus_stops_social,coef_distance_to_tour_destination_1plus_stops_social,coef_distance_to_tour_destination_1plus_stops_social,coef_distance_to_tour_destination_1plus_stops_social,coef_distance_to_tour_destination_1plus_stops_social,coef_distance_to_tour_destination_1plus_stops_social,coef_distance_to_tour_destination_1plus_stops_social,coef_distance_to_tour_destination_1plus_stops_social,coef_distance_to_tour_destination_1plus_stops_social +util_nostop_drive_transit,no stops on pnr knr tnc transit tours,"@(df.tour_mode.isin(['PNR_TRANSIT','KNR_TRANSIT']))",,coef_unavailable,coef_unavailable,coef_unavailable,coef_unavailable,coef_unavailable,coef_unavailable,coef_unavailable,coef_unavailable,coef_unavailable,coef_unavailable,coef_unavailable,coef_unavailable,coef_unavailable,coef_unavailable,coef_unavailable +util_abm3_calibration_constants,calibration constants,is_joint==0,0.11096680817187969,-0.5366374837582596,0.49515245428835686,-0.639202818836102,-0.6855014200561323,-0.10103468501475253,-1.2010426433613348,-3.4876529906722045,-0.5781845322079998,-0.7324835700455633,-2.204662008303293,-5.0,-0.21810513280268298,-0.4631769418491418,-4.458065289588038,3.811850876178379 +util_abm3_calibration_constants_joint,joint tour calibration constants,is_joint,-0.1786,0.3422,0.8914,-3.7763,-2.4629,-2.3188,-1.9052,3.2893,-10.0,-10.0,-5.0,-5.0,-0.6356,-5.0,-5.0,-5.0 diff --git a/resident/configs/stop_frequency_univ.csv b/resident/configs/stop_frequency_univ.csv new file mode 100644 index 0000000..3563bb5 --- /dev/null +++ b/resident/configs/stop_frequency_univ.csv @@ -0,0 +1,11 @@ +Label,Description,Expression,0out_0in,0out_1in,0out_2in,0out_3in,1out_0in,1out_1in,1out_2in,1out_3in,2out_0in,2out_1in,2out_2in,2out_3in,3out_0in,3out_1in,3out_2in,3out_3in +util_alternative_specific_constants,Alternative specific constants,1,,coef_alternative_specific_constants_0out_1plusin_univ,coef_alternative_specific_constants_0out_1plusin_univ,coef_alternative_specific_constants_0out_1plusin_univ,coef_alternative_specific_constants_1out_0in_univ,coef_alternative_specific_constants_1out_1in_univ,coef_alternative_specific_constants_1out_2in_univ,coef_alternative_specific_constants_1out_3in_univ,coef_alternative_specific_constants_2out_0in_univ,coef_alternative_specific_constants_2out_1in_univ,coef_alternative_specific_constants_2out_2in_univ,coef_alternative_specific_constants_2out_3in_univ,coef_alternative_specific_constants_3out_0in_univ,coef_alternative_specific_constants_3out_1in_univ,coef_alternative_specific_constants_3out_2in_univ,coef_alternative_specific_constants_3out_3in_univ +util_preschool_child_in_hh,Presence of preschooler in household other than self,has_preschool_kid,,coef_preschool_child_in_hh_1plusstops_univ,coef_preschool_child_in_hh_1plusstops_univ,coef_preschool_child_in_hh_1plusstops_univ,coef_preschool_child_in_hh_1plusstops_univ,coef_preschool_child_in_hh_1plusstops_univ,coef_preschool_child_in_hh_1plusstops_univ,coef_preschool_child_in_hh_1plusstops_univ,coef_preschool_child_in_hh_1plusstops_univ,coef_preschool_child_in_hh_1plusstops_univ,coef_preschool_child_in_hh_1plusstops_univ,coef_preschool_child_in_hh_1plusstops_univ,coef_preschool_child_in_hh_1plusstops_univ,coef_preschool_child_in_hh_1plusstops_univ,coef_preschool_child_in_hh_1plusstops_univ,coef_preschool_child_in_hh_1plusstops_univ +util_tour_mode_non_motorized,Tour mode is non-motorized,tour_mode_is_non_motorized,,coef_tour_mode_non_motorized_1plusstops_univ,coef_tour_mode_non_motorized_1plusstops_univ,coef_tour_mode_non_motorized_1plusstops_univ,coef_tour_mode_non_motorized_1plusstops_univ,coef_tour_mode_non_motorized_1plusstops_univ,coef_tour_mode_non_motorized_1plusstops_univ,coef_tour_mode_non_motorized_1plusstops_univ,coef_tour_mode_non_motorized_1plusstops_univ,coef_tour_mode_non_motorized_1plusstops_univ,coef_tour_mode_non_motorized_1plusstops_univ,coef_tour_mode_non_motorized_1plusstops_univ,coef_tour_mode_non_motorized_1plusstops_univ,coef_tour_mode_non_motorized_1plusstops_univ,coef_tour_mode_non_motorized_1plusstops_univ,coef_tour_mode_non_motorized_1plusstops_univ +util_tour_mode_hov,Tour mode is hov,tour_mode_is_hov,,coef_tour_mode_hov_1plusstops_univ,coef_tour_mode_hov_1plusstops_univ,coef_tour_mode_hov_1plusstops_univ,coef_tour_mode_hov_1plusstops_univ,coef_tour_mode_hov_1plusstops_univ,coef_tour_mode_hov_1plusstops_univ,coef_tour_mode_hov_1plusstops_univ,coef_tour_mode_hov_1plusstops_univ,coef_tour_mode_hov_1plusstops_univ,coef_tour_mode_hov_1plusstops_univ,coef_tour_mode_hov_1plusstops_univ,coef_tour_mode_hov_1plusstops_univ,coef_tour_mode_hov_1plusstops_univ,coef_tour_mode_hov_1plusstops_univ,coef_tour_mode_hov_1plusstops_univ +util_tour_mode_transit,Tour mode is transit,tour_mode_is_transit,,coef_tour_mode_transit_1plusstops_univ,coef_tour_mode_transit_1plusstops_univ,coef_tour_mode_transit_1plusstops_univ,coef_tour_mode_transit_1plusstops_univ,coef_tour_mode_transit_1plusstops_univ,coef_tour_mode_transit_1plusstops_univ,coef_tour_mode_transit_1plusstops_univ,coef_tour_mode_transit_1plusstops_univ,coef_tour_mode_transit_1plusstops_univ,coef_tour_mode_transit_1plusstops_univ,coef_tour_mode_transit_1plusstops_univ,coef_tour_mode_transit_1plusstops_univ,coef_tour_mode_transit_1plusstops_univ,coef_tour_mode_transit_1plusstops_univ,coef_tour_mode_transit_1plusstops_univ +util_tour_duration_in_hours,Tour duration in hours,(duration / 2),,coef_tour_duration_in_hours_1plusstops_univ,coef_tour_duration_in_hours_1plusstops_univ,coef_tour_duration_in_hours_1plusstops_univ,coef_tour_duration_in_hours_1plusstops_univ,coef_tour_duration_in_hours_1plusstops_univ,coef_tour_duration_in_hours_1plusstops_univ,coef_tour_duration_in_hours_1plusstops_univ,coef_tour_duration_in_hours_1plusstops_univ,coef_tour_duration_in_hours_1plusstops_univ,coef_tour_duration_in_hours_1plusstops_univ,coef_tour_duration_in_hours_1plusstops_univ,coef_tour_duration_in_hours_1plusstops_univ,coef_tour_duration_in_hours_1plusstops_univ,coef_tour_duration_in_hours_1plusstops_univ,coef_tour_duration_in_hours_1plusstops_univ +util_no_stops_to_school_escorting,Do not allow stops for school escort half-tour -- outbound,"(school_esc_outbound.isin(['ride_share', 'pure_escort']))",,,,,coef_unavailable,coef_unavailable,coef_unavailable,coef_unavailable,coef_unavailable,coef_unavailable,coef_unavailable,coef_unavailable,coef_unavailable,coef_unavailable,coef_unavailable,coef_unavailable +util_no_stops_from_school_escorting,Do not allow stops for school escort half-tour -- inbound,"(school_esc_inbound.isin(['ride_share', 'pure_escort']))",,coef_unavailable,coef_unavailable,coef_unavailable,,coef_unavailable,coef_unavailable,coef_unavailable,,coef_unavailable,coef_unavailable,coef_unavailable,,coef_unavailable,coef_unavailable,coef_unavailable +util_nostop_drive_transit,no stops on pnr knr tnc transit tours,"@(df.tour_mode.isin(['PNR_TRANSIT','KNR_TRANSIT']))",,coef_unavailable,coef_unavailable,coef_unavailable,coef_unavailable,coef_unavailable,coef_unavailable,coef_unavailable,coef_unavailable,coef_unavailable,coef_unavailable,coef_unavailable,coef_unavailable,coef_unavailable,coef_unavailable,coef_unavailable +util_abm3_calibration_constants,calibration constants,is_joint==0,0.37144,-0.03316,-0.64012,-5.3232,0.6633,-1.8947,-3.6886,-3.3084,0.3413,-15.5376,-20.0,-10.0,-0.2133,-25.0,-25.0,1.4841 diff --git a/resident/configs/stop_frequency_work.csv b/resident/configs/stop_frequency_work.csv new file mode 100644 index 0000000..199bdd7 --- /dev/null +++ b/resident/configs/stop_frequency_work.csv @@ -0,0 +1,19 @@ +Label,Description,Expression,0out_0in,0out_1in,0out_2in,0out_3in,1out_0in,1out_1in,1out_2in,1out_3in,2out_0in,2out_1in,2out_2in,2out_3in,3out_0in,3out_1in,3out_2in,3out_3in +util_alternative_specific_constants,Alternative specific constants,1,,coef_alternative_specific_constants_0out_1in_work,coef_alternative_specific_constants_0out_2in_work,coef_alternative_specific_constants_0out_3in_work,coef_alternative_specific_constants_1out_0in_work,coef_alternative_specific_constants_1out_1in_work,coef_alternative_specific_constants_1out_2in_work,coef_alternative_specific_constants_1out_3in_work,coef_alternative_specific_constants_2out_0in_work,coef_alternative_specific_constants_2out_1in_work,coef_alternative_specific_constants_2plusout_2plusin_work,coef_alternative_specific_constants_2plusout_2plusin_work,coef_alternative_specific_constants_3out_0in_work,coef_alternative_specific_constants_3out_1in_work,coef_alternative_specific_constants_2plusout_2plusin_work,coef_alternative_specific_constants_2plusout_2plusin_work +util_is_parttime_worker,Person is part-time worker,(ptype == 2),,coef_is_parttime_worker_0out_1plusin_work,coef_is_parttime_worker_0out_1plusin_work,coef_is_parttime_worker_0out_1plusin_work,,coef_is_parttime_worker_1plusout_1plusin_work,coef_is_parttime_worker_1plusout_1plusin_work,coef_is_parttime_worker_1plusout_1plusin_work,,coef_is_parttime_worker_1plusout_1plusin_work,coef_is_parttime_worker_1plusout_1plusin_work,coef_is_parttime_worker_1plusout_1plusin_work,,coef_is_parttime_worker_1plusout_1plusin_work,coef_is_parttime_worker_1plusout_1plusin_work,coef_is_parttime_worker_1plusout_1plusin_work +util_age_35_to_44,Age Group - 35 yrs to 44 yrs,(age >= 35) & (age < 45),,,,,,coef_age_35_to_44_1plusout_1plusin_work,coef_age_35_to_44_1plusout_1plusin_work,coef_age_35_to_44_1plusout_1plusin_work,,coef_age_35_to_44_1plusout_1plusin_work,coef_age_35_to_44_1plusout_1plusin_work,coef_age_35_to_44_1plusout_1plusin_work,,coef_age_35_to_44_1plusout_1plusin_work,coef_age_35_to_44_1plusout_1plusin_work,coef_age_35_to_44_1plusout_1plusin_work +util_age_45_to_54,Age Group - 45 yrs to 54 yrs,(age >= 45) & (age < 55),,,,,,coef_age_45_to_54_1plusout_1plusin_work,coef_age_45_to_54_1plusout_1plusin_work,coef_age_45_to_54_1plusout_1plusin_work,,coef_age_45_to_54_1plusout_1plusin_work,coef_age_45_to_54_1plusout_1plusin_work,coef_age_45_to_54_1plusout_1plusin_work,,coef_age_45_to_54_1plusout_1plusin_work,coef_age_45_to_54_1plusout_1plusin_work,coef_age_45_to_54_1plusout_1plusin_work +util_age_55_to_64,Age Group - 55 yrs to 64 yrs,(age >= 55) & (age < 65),,coef_age_55_to_64_0out_1plusin_work,coef_age_55_to_64_0out_1plusin_work,coef_age_55_to_64_0out_1plusin_work,coef_age_55_to_64_1plusout_0in_work,coef_age_55_to_64_1plusout_1plusin_work,coef_age_55_to_64_1plusout_1plusin_work,coef_age_55_to_64_1plusout_1plusin_work,coef_age_55_to_64_1plusout_0in_work,coef_age_55_to_64_1plusout_1plusin_work,coef_age_55_to_64_1plusout_1plusin_work,coef_age_55_to_64_1plusout_1plusin_work,coef_age_55_to_64_1plusout_0in_work,coef_age_55_to_64_1plusout_1plusin_work,coef_age_55_to_64_1plusout_1plusin_work,coef_age_55_to_64_1plusout_1plusin_work +util_preschool_child_in_hh,Presence of preschooler in household other than self,has_preschool_kid,,,,,coef_preschool_child_in_hh_1out_0in_work,coef_preschool_child_in_hh_1plusout_1plusin_work,coef_preschool_child_in_hh_1plusout_1plusin_work,coef_preschool_child_in_hh_1plusout_1plusin_work,coef_preschool_child_in_hh_2plusout_0in_work,coef_preschool_child_in_hh_1plusout_1plusin_work,coef_preschool_child_in_hh_1plusout_1plusin_work,coef_preschool_child_in_hh_1plusout_1plusin_work,coef_preschool_child_in_hh_2plusout_0in_work,coef_preschool_child_in_hh_1plusout_1plusin_work,coef_preschool_child_in_hh_1plusout_1plusin_work,coef_preschool_child_in_hh_1plusout_1plusin_work +util_school_child_in_hh,Presence of school age child in household other than self,has_school_kid,,,,,coef_school_child_in_hh_1out_0in_work,coef_school_child_in_hh_1out_1in_work,coef_school_child_in_hh_1out_2in_work,coef_school_child_in_hh_1out_3in_work,coef_school_child_in_hh_2plusout_0in_work,coef_school_child_in_hh_2plusout_1plusin_work,coef_school_child_in_hh_2plusout_1plusin_work,coef_school_child_in_hh_2plusout_1plusin_work,coef_school_child_in_hh_2plusout_0in_work,coef_school_child_in_hh_2plusout_1plusin_work,coef_school_child_in_hh_2plusout_1plusin_work,coef_school_child_in_hh_2plusout_1plusin_work +util_non_worker_in_hh,Presence of non workers in household other than self,has_non_worker,,coef_non_worker_in_hh_0out_1in_work,coef_non_worker_in_hh_0out_2in_work,coef_non_worker_in_hh_0out_3in_work,,coef_non_worker_in_hh_1plusout_1plusin_work,coef_non_worker_in_hh_1plusout_1plusin_work,coef_non_worker_in_hh_1plusout_1plusin_work,,coef_non_worker_in_hh_1plusout_1plusin_work,coef_non_worker_in_hh_1plusout_1plusin_work,coef_non_worker_in_hh_1plusout_1plusin_work,,coef_non_worker_in_hh_1plusout_1plusin_work,coef_non_worker_in_hh_1plusout_1plusin_work,coef_non_worker_in_hh_1plusout_1plusin_work +util_non_mandatory_accessibility,Disaggreage accessibility at home location to non-mandatory location,(othdiscr_accessibility + shopping_accessibility) / 2,,,,,coef_non_mandatory_accessibility_1plusout_0in_work,,,,coef_non_mandatory_accessibility_1plusout_0in_work,,,,coef_non_mandatory_accessibility_1plusout_0in_work,,, +util_tour_mode_sov,Tour mode is sov,tour_mode_is_sov,,coef_tour_mode_sov_0out_1in_work,coef_tour_mode_sov_0out_2in_work,coef_tour_mode_sov_0out_3in_work,coef_tour_mode_sov_1plusout_0in_work,coef_tour_mode_sov_1plusout_1plusin_work,coef_tour_mode_sov_1plusout_1plusin_work,coef_tour_mode_sov_1plusout_1plusin_work,coef_tour_mode_sov_1plusout_0in_work,coef_tour_mode_sov_1plusout_1plusin_work,coef_tour_mode_sov_1plusout_1plusin_work,coef_tour_mode_sov_1plusout_1plusin_work,coef_tour_mode_sov_1plusout_0in_work,coef_tour_mode_sov_1plusout_1plusin_work,coef_tour_mode_sov_1plusout_1plusin_work,coef_tour_mode_sov_1plusout_1plusin_work +util_tour_mode_hov,Tour mode is hov,tour_mode_is_hov,,coef_tour_mode_hov_0out_1in_work,coef_tour_mode_hov_0out_2in_work,coef_tour_mode_hov_0out_3in_work,coef_tour_mode_hov_1out_0in_work,coef_tour_mode_hov_1out_1in_work,coef_tour_mode_hov_1out_2in_work,coef_tour_mode_hov_1out_3in_work,coef_tour_mode_hov_2out_0in_work,coef_tour_mode_hov_2out_1in_work,coef_tour_mode_hov_2plusout_2plusin_work,coef_tour_mode_hov_2plusout_2plusin_work,coef_tour_mode_hov_3out_0in_work,coef_tour_mode_hov_3out_1in_work,coef_tour_mode_hov_2plusout_2plusin_work,coef_tour_mode_hov_2plusout_2plusin_work +util_tour_duration_in_hours,Tour duration in hours,(duration / 2),,coef_tour_duration_in_hours_0out_1in_work,coef_tour_duration_in_hours_0out_2in_work,coef_tour_duration_in_hours_0out_3in_work,coef_tour_duration_in_hours_1out_0in_work,coef_tour_duration_in_hours_1out_1in_work,coef_tour_duration_in_hours_1out_2in_work,coef_tour_duration_in_hours_1out_3in_work,coef_tour_duration_in_hours_2plusout_0in_work,coef_tour_duration_in_hours_2out_1in_work,coef_tour_duration_in_hours_2plusout_2plusin_work,coef_tour_duration_in_hours_2plusout_2plusin_work,coef_tour_duration_in_hours_2plusout_0in_work,coef_tour_duration_in_hours_3out_1in_work,coef_tour_duration_in_hours_2plusout_2plusin_work,coef_tour_duration_in_hours_2plusout_2plusin_work +util_distance_to_tour_destination,Distance from origin to tour destination in miles,distance_in_miles,,coef_distance_to_tour_destination_0out_1plusin_work,coef_distance_to_tour_destination_0out_1plusin_work,coef_distance_to_tour_destination_0out_1plusin_work,coef_distance_to_tour_destination_1plusout_0in_work,coef_distance_to_tour_destination_1plusout_1plusin_work,coef_distance_to_tour_destination_1plusout_1plusin_work,coef_distance_to_tour_destination_1plusout_1plusin_work,coef_distance_to_tour_destination_1plusout_0in_work,coef_distance_to_tour_destination_1plusout_1plusin_work,coef_distance_to_tour_destination_1plusout_1plusin_work,coef_distance_to_tour_destination_1plusout_1plusin_work,coef_distance_to_tour_destination_1plusout_0in_work,coef_distance_to_tour_destination_1plusout_1plusin_work,coef_distance_to_tour_destination_1plusout_1plusin_work,coef_distance_to_tour_destination_1plusout_1plusin_work +util_telecommute_2_4_days_week,Person telecommutes 2 to 4 days,(telecommute_frequency == '2_4_days_week'),,,,,,coef_telecommute_2_4_days_week_1plusout_1plusin_work,coef_telecommute_2_4_days_week_1plusout_1plusin_work,coef_telecommute_2_4_days_week_1plusout_1plusin_work,,coef_telecommute_2_4_days_week_1plusout_1plusin_work,coef_telecommute_2_4_days_week_1plusout_1plusin_work,coef_telecommute_2_4_days_week_1plusout_1plusin_work,,coef_telecommute_2_4_days_week_1plusout_1plusin_work,coef_telecommute_2_4_days_week_1plusout_1plusin_work,coef_telecommute_2_4_days_week_1plusout_1plusin_work +util_no_stops_to_school_escorting,Do not allow stops for school escort half-tour -- outbound,"(school_esc_outbound.isin(['ride_share', 'pure_escort']))",,,,,coef_unavailable,coef_unavailable,coef_unavailable,coef_unavailable,coef_unavailable,coef_unavailable,coef_unavailable,coef_unavailable,coef_unavailable,coef_unavailable,coef_unavailable,coef_unavailable +util_no_stops_from_school_escorting,Do not allow stops for school escort half-tour -- inbound,"(school_esc_inbound.isin(['ride_share', 'pure_escort']))",,coef_unavailable,coef_unavailable,coef_unavailable,,coef_unavailable,coef_unavailable,coef_unavailable,,coef_unavailable,coef_unavailable,coef_unavailable,,coef_unavailable,coef_unavailable,coef_unavailable +util_nostop_drive_transit,no stops on pnr knr tnc transit tours,"@(df.tour_mode.isin(['PNR_TRANSIT','KNR_TRANSIT']))",,coef_unavailable,coef_unavailable,coef_unavailable,coef_unavailable,coef_unavailable,coef_unavailable,coef_unavailable,coef_unavailable,coef_unavailable,coef_unavailable,coef_unavailable,coef_unavailable,coef_unavailable,coef_unavailable,coef_unavailable +util_abm3_calibration_constants,calibration constants,is_joint==0,-0.1946,0.5187,1.00488,0.29090,-0.46260,-0.40521,0.90284,0.009135,-1.9785,0.6807,2.2795,2.3279,-1.1649,0.7008,-7.2781,2.91850 diff --git a/resident/configs/telecommute_frequency.csv b/resident/configs/telecommute_frequency.csv new file mode 100644 index 0000000..871e065 --- /dev/null +++ b/resident/configs/telecommute_frequency.csv @@ -0,0 +1,32 @@ +Label,Description,Expression,No_Telecommute,1_day_week,2_3_days_week,4_days_week +util_HasChildren0to5,Has children 0 to 5 years old,@df.num_young_children>0,,coef_HasChildren0to5_1day,coef_HasChildren0to5_234day,coef_HasChildren0to5_234day +util_HasChildren6to12,Has children 6 to 12 years old,@df.num_children_6_to_12>0,,coef_HasChildren6to12_1day,coef_HasChildren6to12_23day,coef_HasChildren6to12_4day +util_OneAdultInHH,One adult in hh,@df.num_adults==1,,coef_OneAdultInHH_1day,coef_OneAdultInHH_23day,coef_OneAdultInHH_4day +util_Female,female,@df.female,,coef_Female_1234day,coef_Female_1234day,coef_Female_1234day +util_PartTimeWorker,Part-time worker,@df.pemploy==2,,coef_PartTimeWorker_1234day,coef_PartTimeWorker_1234day,coef_PartTimeWorker_1234day +util_Income60to100k,Income 60-100k,"@df.income.between(60000, 100000)",,coef_Income60to100k_1day,coef_Income60to100k_23day,coef_Income60to100k_4day +util_Income100to150k,Income 100-150k,"@df.income.between(100000, 150000)",,coef_Income100to150k_1day,coef_Income100to150k_234day,coef_Income100to150k_234day +util_Income150kplus,Income 150k+,@df.income > 150000,,coef_Income150kplus_1day,coef_Income150kplus_23day,coef_Income150kplus_4day +util_0Autos,0 Autos,@df.auto_ownership==0,,coef_0Autos_1day,coef_0Autos_234day,coef_0Autos_234day +util_1Auto,1 Auto,@df.auto_ownership==1,,coef_1Auto_1day,coef_1Auto_234day,coef_1Auto_234day +util_3plusAutos,3+ Autos,@df.auto_ownership>=3,,coef_3plusAutos_1day,coef_3plusAutos_23day,coef_3plusAutos_4day +util_DistanceToWork,Distance to work,@df.distance_to_work,,coef_DistanceToWork_1day,coef_DistanceToWork_234day,coef_DistanceToWork_234day +util_calib_2020,scenario year is 2020,@scenarioYear==2020,,coef_calib_2020_1day,coef_calib_2020_23day,coef_calib_2020_4day +util_calib_2025,scenario year is 2025,@scenarioYear==2025,,coef_calib_2025_1day,coef_calib_2025_23day,coef_calib_2025_4day +util_calib_2035,scenario year is 2035,@scenarioYear==2035,,coef_calib_2035_1day,coef_calib_2035_23day,coef_calib_2035_4day +util_calib_2050,scenario year is 2050,@scenarioYear==2050,,coef_calib_2050_1day,coef_calib_2050_23day,coef_calib_2050_4day +util_2016,Model year is 2016,@PRE_COVID,,coef_2016_1day,coef_2016_23day,coef_2016_4day +util_accomodation,Accomodation industry,@df.naics_code==721,,coef_accomodation_1234day,coef_accomodation_1234day,coef_accomodation_1234day +util_agriculture,Agriculture industry,@df.naics_code==11,,coef_agriculture_1234day,coef_agriculture_1234day,coef_agriculture_1234day +util_business_srv,Business services industry,@df.naics_code==54,,coef_business_srv_1day,coef_business_srv_23day,coef_business_srv_4day +util_construction,Construction industry,@df.naics_code==23,,coef_construction_1day,coef_construction_234day,coef_construction_234day +util_education,Education industry,@df.naics_code==61,,coef_education_1234day,coef_education_1234day,coef_education_1234day +util_entertainment,Entertainment industry,@df.naics_code==71,,coef_entertainment_1day,coef_entertainment_23day,coef_entertainment_4day +util_food_srv,Food services industry,@df.naics_code==722,,coef_food_srv_1234day,coef_food_srv_1234day,coef_food_srv_1234day +util_government,Government industry,@df.naics_code==92,,coef_government_1day,coef_government_234day,coef_government_234day +util_healthcare,Healthcare industry,@df.naics_code==62,,coef_healthcare_1234day,coef_healthcare_1234day,coef_healthcare_1234day +util_manufacturing,Manufacturing industry,"@df.naics_code.isin([31,32,33])",,coef_manufacturing_1day,coef_manufacturing_234day,coef_manufacturing_234day +util_mgmt_srv,Management services industry,@df.naics_code==55,,coef_mgmt_srv_1day,coef_mgmt_srv_23day,coef_mgmt_srv_4day +util_military,Miliary industry,@df.naics_code==9000,,coef_military_1day,coef_military_234day,coef_military_234day +util_retail,Retail industry,"@df.naics_code.isin([44,45])",,coef_retail_1day,coef_retail_23day,coef_retail_4day +util_asc,Alternative specific constant,1,,asc_1day,asc_23day,asc_4day diff --git a/resident/configs/telecommute_frequency.yaml b/resident/configs/telecommute_frequency.yaml new file mode 100644 index 0000000..162066e --- /dev/null +++ b/resident/configs/telecommute_frequency.yaml @@ -0,0 +1,9 @@ + +# borrowed from free parking model + +SPEC: telecommute_frequency.csv +COEFFICIENTS: telecommute_frequency_coeffs.csv + +#LOGIT_TYPE: NL +LOGIT_TYPE: MNL + diff --git a/resident/configs/telecommute_frequency_coeffs.csv b/resident/configs/telecommute_frequency_coeffs.csv new file mode 100644 index 0000000..089828f --- /dev/null +++ b/resident/configs/telecommute_frequency_coeffs.csv @@ -0,0 +1,100 @@ +coefficient_name,value,constrain +coef_Services_1day,0.0,F +coef_SalesOffice_1day,0.0,F +coef_ResourceConstruct_1day,0.0,F +coef_TransportMat_1day,0.0,F +coef_HasChildren0to5_1day,0.0,T +coef_HasChildren6to12_1day,0.0,T +coef_OneAdultInHH_1day,0.2307987638695857,F +coef_2plusAdultsInHH_1day,0.0,F +coef_Female_1234day,0.0,T +coef_PartTimeWorker_1234day,-0.1647657054647985,F +coef_PaysToPark_1day,0.0,F +coef_Income60to100k_1day,0.3549582138983352,F +coef_Income100to150k_1day,0.0,T +coef_Income150kplus_1day,0.0,T +coef_0Autos_1day,0.0,T +coef_1Auto_1day,-0.2037126296304744,F +coef_3plusAutos_1day,-0.2366548258122986,F +coef_DistanceToWork_1day,0.0065817284180437,F +coef_Services_23day,0.0,F +coef_SalesOffice_23day,0.0,F +coef_ResourceConstruct_23day,0.0,F +coef_TransportMat_23day,0.0,F +coef_HasChildren0to5_234day,-0.3282697175018726,F +coef_HasChildren6to12_23day,0.0,T +coef_OneAdultInHH_23day,0.0,T +coef_2plusAdultsInHH_23day,0.0,F +coef_PaysToPark_23day,0.0,F +coef_Income60to100k_23day,0.0,T +coef_Income100to150k_234day,0.0,T +coef_Income150kplus_23day,0.0,T +coef_0Autos_234day,0.0,T +coef_1Auto_234day,0.0,T +coef_3plusAutos_23day,-0.2673431713437322,F +coef_DistanceToWork_234day,0.011270292473366,F +coef_HasChildren6to12_4day,0.0,T +coef_OneAdultInHH_4day,0.0,T +coef_2plusAdultsInHH_4day,0.0,F +coef_PaysToPark_4day,0.0,F +coef_Income60to100k_4day,0.0,T +coef_Income100to150k_4day,0.0,T +coef_Income150kplus_4day,0.0,T +coef_3plusAutos_4day,-0.5086281851316395,F +coef_calib_2020_1day,0.0,T +coef_calib_2025_1day,0.0,T +coef_calib_2035_1day,0.0,T +coef_calib_2050_1day,0.0,T +coef_calib_2020_23day,0.0,T +coef_calib_2025_23day,0.0,T +coef_calib_2035_23day,0.0,T +coef_calib_2050_23day,0.0,T +coef_calib_2020_4day,0.0,T +coef_calib_2025_4day,0.0,T +coef_calib_2035_4day,0.0,T +coef_calib_2050_4day,0.0,T +coef_2016_1day,-0.1247850665609524,F +coef_accomodation_1234day,-1.1672033007413072,F +coef_agriculture_1234day,0.0,T +coef_business_srv_1day,0.8048779013830238,F +coef_construction_1day,0.0,T +coef_education_1234day,-0.0387511815872183,F +coef_entertainment_1day,0.0,T +coef_food_srv_1day,0.0,F +coef_government_1day,0.4503818768273094,F +coef_healthcare_1day,0.0,F +coef_manufacturing_1day,0.0,T +coef_mgmt_srv_1day,0.0,T +coef_military_1day,0.0,T +coef_retail_1day,-0.8320341753416274,F +coef_2016_23day,-1.138520687534541,F +coef_accomodation_23day,0.0,F +coef_agriculture_23day,0.0,F +coef_business_srv_23day,0.9228435480770156,F +coef_construction_234day,0.0,T +coef_education_23day,0.0,F +coef_entertainment_23day,0.0,T +coef_food_srv_1234day,-1.6057467385857538,F +coef_government_234day,0.0,T +coef_healthcare_1234day,-0.2529974908266847,F +coef_manufacturing_234day,0.0,T +coef_mgmt_srv_23day,0.5723496481424273,F +coef_military_234day,-0.512646790150494,F +coef_retail_23day,-0.7576291501959187,F +coef_2016_4day,-3.7059529398972826,F +coef_accomodation_4day,0.0,F +coef_agriculture_4day,0.0,F +coef_business_srv_4day,1.117777186998694,F +coef_construction_4day,0.0,F +coef_education_4day,0.0,F +coef_entertainment_4day,0.0,T +coef_food_srv_4day,0.0,F +coef_government_4day,0.0,F +coef_healthcare_4day,0.0,F +coef_manufacturing_4day,0.0,F +coef_mgmt_srv_4day,0.970302573177628,F +coef_military_4day,0.0,F +coef_retail_4day,-2.2338993784519388,F +asc_1day,-2.7588707215613267,F +asc_23day,-1.7439620734289143,F +asc_4day,-2.157902150190833,F diff --git a/resident/configs/tour_departure_and_duration_alternatives.csv b/resident/configs/tour_departure_and_duration_alternatives.csv new file mode 100644 index 0000000..30ff417 --- /dev/null +++ b/resident/configs/tour_departure_and_duration_alternatives.csv @@ -0,0 +1,1177 @@ +start,end +1,1 +1,2 +1,3 +1,4 +1,5 +1,6 +1,7 +1,8 +1,9 +1,10 +1,11 +1,12 +1,13 +1,14 +1,15 +1,16 +1,17 +1,18 +1,19 +1,20 +1,21 +1,22 +1,23 +1,24 +1,25 +1,26 +1,27 +1,28 +1,29 +1,30 +1,31 +1,32 +1,33 +1,34 +1,35 +1,36 +1,37 +1,38 +1,39 +1,40 +1,41 +1,42 +1,43 +1,44 +1,45 +1,46 +1,47 +1,48 +2,2 +2,3 +2,4 +2,5 +2,6 +2,7 +2,8 +2,9 +2,10 +2,11 +2,12 +2,13 +2,14 +2,15 +2,16 +2,17 +2,18 +2,19 +2,20 +2,21 +2,22 +2,23 +2,24 +2,25 +2,26 +2,27 +2,28 +2,29 +2,30 +2,31 +2,32 +2,33 +2,34 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+escort,MD,13,24 +escort,PM,25,27 +escort,EV,33,35 +#,,, +othmaint,EA,1,6 +othmaint,AM,9,10 +othmaint,MD,13,24 +othmaint,PM,25,27 +othmaint,EV,33,35 +#,,, +othdiscr,EA,1,6 +othdiscr,AM,9,10 +othdiscr,MD,13,24 +othdiscr,PM,25,27 +othdiscr,EV,33,35 +#,,, +social,EA,1,6 +social,AM,9,10 +social,MD,13,24 +social,PM,25,27 +social,EV,33,35 +#,,, +eatout,EA,1,6 +eatout,AM,9,10 +eatout,MD,13,24 +eatout,PM,25,27 +eatout,EV,33,35 +#,,, +atwork,EA,1,6 +atwork,AM,9,10 +atwork,MD,13,24 +atwork,PM,25,27 +atwork,EV,33,35 diff --git a/resident/configs/tour_mode_choice.csv b/resident/configs/tour_mode_choice.csv new file mode 100644 index 0000000..726304a --- /dev/null +++ b/resident/configs/tour_mode_choice.csv @@ -0,0 +1,207 @@ +Label,Description,Expression,DRIVEALONE,SHARED2,SHARED3,WALK,BIKE,WALK_TRANSIT,PNR_TRANSIT,KNR_TRANSIT,BIKE_TRANSIT,TAXI,TNC_SINGLE,TNC_SHARED,SCH_BUS,EBIKE,ESCOOTER +#,drivealone,,,,,,,,,,,,,,,, +util_DRIVEALONE_Unavailable,DRIVEALONE - Unavailable,sov_available == False,-999,,,,,,,,,,,,,, +util_Drive alone - In-vehicle time,Drive alone - In-vehicle time,@(df.da_time_skims_inb + df.da_time_skims_out) * df.autoIVTFactor_DA * df.time_factor,coef_ivt,,,,,,,,,,,,,, +util_Drive alone - cost - inb,Drive alone - cost - inbound,@(df.sov_auto_op_cost * df.autoCPMFactor_DA * df.da_dist_skims_inb + df.da_cost_skims_inb)/df.cost_sensitivity,coef_income,,,,,,,,,,,,,, +util_Drive alone - cost - out,Drive alone - cost - outbound,@(df.sov_auto_op_cost * df.autoCPMFactor_DA * df.da_dist_skims_out + df.da_cost_skims_out)/df.cost_sensitivity,coef_income,,,,,,,,,,,,,, +util_Drive alone - Parking cost ,Drive alone - Parking cost ,@(df.parkingCost*df.autoParkingCostFactor_DA)/df.cost_sensitivity,coef_income,,,,,,,,,,,,,, +util_Drive alone - Terminal Time - acc,Drive alone - Terminal Time ,@(df.oTermTime + df.dTermTime) * df.autoTermTimeFactor_DA * df.time_factor,coef_acctime,,,,,,,,,,,,,, +#,Shared ride2,,,,,,,,,,,,,,,, +util_Shared2_Unavailable,Shared ride 2 - Unavailable,sr2_available == False,,-999,,,,,,,,,,,,, +util_Shared ride 2 - In -vehicle time,Shared ride 2 - In -vehicle time,@(df.s2_time_skims_inb + df.s2_time_skims_out) * df.autoIVTFactor_SR2 * df.time_factor,,coef_ivt,,,,,,,,,,,,, +util_Shared ride 2 - cost - inb,Shared ride 2 - cost - inbound,@(df.sr2_auto_op_cost * df.autoCPMFactor_SR2 * df.s2_dist_skims_inb + df.s2_cost_skims_inb)/df.cost_sensitivity,,coef_income,,,,,,,,,,,,, +util_Shared ride 2 - cost - out,Shared ride 2 - cost - outbound,@(df.sr2_auto_op_cost * df.autoCPMFactor_SR2* df.s2_dist_skims_out + df.s2_cost_skims_out)/df.cost_sensitivity,,coef_income,,,,,,,,,,,,, +util_Shared ride 2 - Parking cost ,Shared ride 2 - Parking cost ,"@(df.parkingCost * df.autoParkingCostFactor_SR2 * np.where(df.is_joint,1,df.costFactorS2))/df.cost_sensitivity",,coef_income,,,,,,,,,,,,, +util_Drive alone - Terminal Time - acc,Shared ride 2 - Terminal Time - access,@(df.oTermTime + df.dTermTime) * df.autoTermTimeFactor_SR2 * df.time_factor,,coef_acctime,,,,,,,,,,,,, +util_SHARED2_Two_person_household,Shared ride 2- One person household,@(df.hhsize == 2),,coef_hhsize2_sr2,,,,,,,,,,,,, +util_SHARED RIDE 2_Three_person_household,Shared ride 2 - Two person household,@(df.hhsize == 3),,coef_hhsize3_sr2,,,,,,,,,,,,, +util_SHARED RIDE 2_Three+_person_household,Shared ride 2 - Two person household,@(df.hhsize > 3),,coef_hhsize4p_sr2,,,,,,,,,,,,, +util_SHARED RIDE 2_Person_is_16_24_years_old_or_older,Shared ride 2- Person is 16 to 24 years old or older,@(df.age >= 16) & (df.age <= 24),,coef_age1624_sr2,,,,,,,,,,,,, +util_SHARED RIDE 2_Person_is_41_55_years_old_or_older,Shared ride 2- Person is 41 to 55 years old or older,@(df.age >= 41) & (df.age <= 55),,coef_age4155_sr2,,,,,,,,,,,,, +util_SHARED RIDE 2_Person_is_56_64_years_old_or_older,Shared ride 2- Person is 56 to 64 years old or older,@(df.age >= 56) & (df.age <= 64),,coef_age5664_sr2,,,,,,,,,,,,, +util_SHARED RIDE 2_Person_is_65+_years_old_or_older,Shared ride 2- Person is more than 65 years old or older,@(df.age >= 65),,coef_age65pl_sr2,,,,,,,,,,,,, +util_Shared ride 2 - Female,Shared ride 2 - female,@(df.female == 1),,coef_female_sr2,,,,,,,,,,,,, +#,shared ride 3,,,,,,,,,,,,,,,, +util_Shared ride 3 - In -vehicle time,Shared ride 3 - In -vehicle time,@(df.s3_time_skims_out + df.s3_time_skims_inb) * df.autoIVTFactor_SR3 * df.time_factor,,,coef_ivt,,,,,,,,,,,, +util_Shared ride 3 - cost - inb,Shared ride 3 - cost - inbound,@(df.sr3p_auto_op_cost * df.s3_dist_skims_inb * df.autoCPMFactor_SR3 + df.s3_cost_skims_inb)/df.cost_sensitivity,,,coef_income,,,,,,,,,,,, +util_Shared ride 3 - cost - out,Shared ride 3 - cost - outbound,@(df.sr3p_auto_op_cost * df.s3_dist_skims_out * df.autoCPMFactor_SR3 + df.s3_cost_skims_out)/df.cost_sensitivity,,,coef_income,,,,,,,,,,,, +util_Shared ride 3 - Parking cost ,Shared ride 3 - Parking cost ,"@(df.parkingCost * df.autoParkingCostFactor_SR3 * np.where(df.is_joint,1,df.costFactorS3))/df.cost_sensitivity",,,coef_income,,,,,,,,,,,, +util_Shared ride 3 - Terminal Time - acc,Shared ride 3 - Terminal Time - acc,@(df.oTermTime + df.dTermTime) * df.autoTermTimeFactor_SR3 * df.time_factor,,,coef_acctime,,,,,,,,,,,, +util_SHARED RIDe 3_Two_person_household,Shared ride 3- One person household,@(df.hhsize == 2),,,coef_hhsize2_sr3p,,,,,,,,,,,, +util_SHARED RIDe 3_Three_person_household,Shared ride 3 - Two person household,@(df.hhsize == 3),,,coef_hhsize3_sr3p,,,,,,,,,,,, +util_SHARED RIDe 3_Three+_person_household,Shared ride 3 - Two person household,@(df.hhsize > 3),,,coef_hhsize4p_sr3p,,,,,,,,,,,, +util_SHARED RIDe 3_Person_is_16_24_years_old_or_older,Shared ride 3 - Person is 16 to 24 years old or older,@(df.age >= 16) & (df.age <= 24),,,coef_age1624_sr3p,,,,,,,,,,,, +util_SHARED RIDe 3_Person_is_41_55_years_old_or_older,Shared ride 3 - Person is 41 to 55 years old or older,@(df.age >= 41) & (df.age <= 55),,,coef_age4155_sr3p,,,,,,,,,,,, +util_SHARED RIDe 3_Person_is_56_64_years_old_or_older,Shared ride 3 - Person is 56 to 64 years old or older,@(df.age >= 56) & (df.age <= 64),,,coef_age5664_sr3p,,,,,,,,,,,, +util_SHARED RIDe 3_Person_is_65+_years_old_or_older,Shared ride 3 - Person is more than 65 years old or older,@(df.age >= 65),,,coef_age65pl_sr3p,,,,,,,,,,,, +util_Shared ride 3 - Female,Shared ride 3 - female,@(df.female == 1),,,coef_female_sr3p,,,,,,,,,,,, +#,Walk,,,,,,,,,,,,,,,, +util_Walk - Unavailable,Walk - Unavailable,walkAvailable==0,,,,-999,,,,,,,,,,, +util_Walk - Age under 16,Walk - Age under 16,@(df.age < 16),,,,coef_ageund16_walk,,,,,,,,,,, +util_Walk - Age 16 to 24,Walk - Age 16 to 24,@(df.age > 15) & (df.age < 25) ,,,,coef_age1624_walk,,,,,,,,,,, +util_Walk - Age 41 to 55,Walk - Age 41 to 55,@(df.age > 40) & (df.age < 56),,,,coef_age4155_walk,,,,,,,,,,, +util_Walk - Age 56 to 64,Walk - Age 56 to 64,@(df.age > 55) & (df.age < 65),,,,coef_age5664_walk,,,,,,,,,,, +util_Walk - Age 65 plus,Walk - Age 65 plus,@(df.age > 64),,,,coef_age65pl_walk,,,,,,,,,,, +util_Walk - Female,Walk - Female,@df.female,,,,coef_female_walk,,,,,,,,,,, +util_Walk - Income 60k to 100k,Walk - Income 60k to 100k,@(df.income > 59999) * (df.income < 100000),,,,coef_inc60100_walk,,,,,,,,,,, +util_Walk - Normalized Landuse Variable Sum [Origin Intersection + DU],Walk - Normalized Landuse Variable Sum [Origin Intersection + DU],LUVarsNormalized_walk,,,,coef_LUnorm_walk,,,,,,,,,,, +"util_Walk - Normalized Destination Employment Density, walk","Walk - Normalized Destination Employment Density, walk",dMGRAEmpDenNorm_walk,,,,coef_dEmpNorm_walk,,,,,,,,,,, +util_Walk - time coefficient,Walk - time coefficient,@(df.walk_time_skims_inb + df.walk_time_skims_out) * df.time_factor,,,,coef_walktime,,,,,,,,,,, +#,Bike,,,,,,,,,,,,,,,, +util_BIKE_Mode unavailable,Bike - Mode unavailable,bikeAvailable == 0,,,,,-999,,,,,,,,,, +"util_BIKE_Age under 16, bike","Bike - Age under 16, bike",@(df.age < 16),,,,,coef_ageund16_bike,,,,,,,,,, +"util_BIKE_Age 16 to 24, bike","Bike - Age 16 to 24, bike",@(df.age > 15) & (df.age < 25),,,,,coef_age1624_bike,,,,,,,,,, +"util_BIKE_Age 41 to 55, bike","Bike - Age 41 to 55, bike",@(df.age > 40) & (df.age < 56),,,,,coef_age4155_bike,,,,,,,,,, +"util_BIKE_Age 56 to 64, bike","Bike - Age 56 to 64, bike",@(df.age > 55) & (df.age < 65),,,,,coef_age5664_bike,,,,,,,,,, +"util_BIKE_Age 65 plus, bike","Bike - Age 65 plus, bike",@(df.age > 64),,,,,coef_age65pl_bike,,,,,,,,,, +"util_BIKE_Female, bike","Bike - Female, bike",@df.female,,,,,coef_female_bike,,,,,,,,,, +"util_BIKE_Income 100k plus, bike","Bike - Income 100k plus, bike",@(df.income > 99999),,,,,coef_inc100plus_bike,,,,,,,,,, +util_BIKE_Normalized Landuse Variable [Origin Employment + DU],Bike - - Normalized Landuse Variable [Origin Employment + DU],LUVarsNormalized_bike,,,,,coef_LUnorm_bike,,,,,,,,,, +util_BIKE_Bike - logsum,Bike - logsum,bikeLSI + bikeLSO,,,,,coef_bikeLogsum,,,,,,,,,, +#,Walk_LOC,,,,,,,,,,,,,,,, +util_WALK_Unavailable,WalkTransit_Available,walk_local_available == 0,,,,,,-999,,,,,,,,, +util_WALK_In_vehicle_time,WALK_LOC - In-vehicle time,@(odt_skims['WTW_TIV'] + dot_skims['WTW_TIV']) * df.time_factor,,,,,,coef_ivt,,,,,,,,, +util_WALK_iwait_time,WALK_LOC - wait time,@(odt_skims['WTW_FWT'] + dot_skims['WTW_FWT']) * df.time_factor,,,,,,coef_wait,,,,,,,,, +util_WALK_transfer_wait_time,WALK_LOC - transfer wait time,@(odt_skims['WTW_XWT'] + dot_skims['WTW_XWT'])* df.time_factor,,,,,,coef_xwait,,,,,,,,, +util_WALK_transfer_waLK_time,WALK_LOC - transfer walk time,@(odt_skims['WTW_AUX'] + dot_skims['WTW_AUX'])* df.time_factor,,,,,,coef_xwalk,,,,,,,,, +util_WALK_transfers_penalty,WALK_LOC - number of transfers,"@(-23+23*np.exp(0.414*np.clip(odt_skims['WTW_XFR'] + df.mtnev_egr_xfer, a_min=None,a_max=4))) + (-23+23*np.exp(0.414*np.clip(dot_skims['WTW_XFR'] + df.mtnev_egr_xfer, a_min=None,a_max=4)))*df.time_factor",,,,,,coef_xfer,,,,,,,,, +util_WALK_Walk_access_time,WALK_LOC - Walk access,@2 * df.origin_local_time* df.time_factor,,,,,,coef_acctime,,,,,,,,, +util_WALK_Walk_egress_time,WALK_LOC - egress time,"@np.where(df.nev_local_egress_available, df.nev_local_egress_time, np.where(df.microtransit_local_egress_available, df.microtransit_local_egress_time, 2*df.dest_local_time)) * df.time_factor",,,,,,coef_acctime,,,,,,,,, +util_WALK_wait_egress_time,WALK_LOC - Egress MT/NEV wait time,"@np.where(df.nev_local_egress_available, 2*nevWaitTime, np.where(df.microtransit_local_egress_available, 2*microtransitWaitTime, 0)) * df.time_factor",,,,,,coef_wait,,,,,,,,, +util_WALK_stop_type_constant,WALK_LOC - Stop type constant,@(odt_skims['WTW_RST'] + dot_skims['WTW_RST'])* df.time_factor,,,,,,coef_ivt,,,,,,,,, +util_WALK_vehicle_type_constant,WALK_LOC - Vehicle type constant,@(odt_skims['WTW_VTC'] + dot_skims['WTW_VTC'])* df.time_factor,,,,,,coef_ivt,,,,,,,,, +# FIXME need transit fares,,,,,,,,,,,,,,,,, +#util_WTW_FARE,WALK_LOC - Fare,@df.transitSubsidyPassDiscount*(odt_skims['WTW_FARE'] + dot_skims['WTW_FARE'])*100*df.num_participants/df.cost_sensitivity,,,,,,coef_income,,,,,,,,, +util_WALK_LOC - Age 16 to 24,WALK_LOC - Age 16 to 24,@(df.age > 15) & (df.age < 25),,,,,,coef_age1624_tran,,,,,,,,, +util_WALK_LOC - Age 41 to 55,WALK_LOC - Age 41 to 55,@(df.age > 40) & (df.age < 56),,,,,,coef_age4155_tran,,,,,,,,, +util_WALK_LOC - Age 56 to 64,WALK_LOC - Age 56 to 64,@(df.age > 55) & (df.age < 65),,,,,,coef_age5664_tran,,,,,,,,, +util_WALK_LOC - Age 65+,WALK_LOC - Age 65+,@(df.age > 64),,,,,,coef_age65pl_tran,,,,,,,,, +util_WALK_LOC - Female,WALK_LOC - Female,@(df.female),,,,,,coef_female_tran,,,,,,,,, +util_WALK_LOC - Origin Mix,WALK_LOC - Origin Mix,oMGRAMix,,,,,,coef_oMix_wTran,,,,,,,,, +util_WALK_LOC - Origin Intersection Density,WALK_LOC - Origin Intersection Density,oMGRATotInt,,,,,,coef_oIntDen_wTran,,,,,,,,, +util_WALK_LOC - Destination Employment Density,WALK_LOC - Destination Employment Density,dMGRAEmpDen,,,,,,coef_dEmpDen_wTran,,,,,,,,, +#,PNR_LOC,,,,,,,,,,,,,,,, +util_PNR_Unavailable,PNR_LOC - Unavailable,"@(df.is_valid_pnr == False)|(df.get('num_escortees', 0)>0)",,,,,,,-999,,,,,,,, +util_PNRTransit_0Auto,PNRTransit_0Auto,@(df.auto_ownership==0),,,,,,,-999,,,,,,,, +util_PNR_Unavailable_for_persons_less_than_16,PNR_LOC - Unavailable for persons less than 16,age < 16,,,,,,,-999,,,,,,,, +util_PNR_In_vehicle_time,PNR_LOC - In-vehicle time,@df.transit_time_pnr * df.time_factor,,,,,,,coef_ivt,,,,,,,, +util_PNR_iwait_time,PNR_LOC - First iwait time,@df.iwait_pnr * df.time_factor,,,,,,,coef_wait,,,,,,,, +util_PNR_transfer_wait_time,PNR_LOC - transfer wait time,@df.xfer_wait_pnr * df.time_factor,,,,,,,coef_xwait,,,,,,,, +util_PNR_number_of_transfers,PNR_LOC - number of transfers,"@(-23+23*np.exp(0.414*np.clip(df.xfer_walk_pnr + df.mtnev_egr_xfer, a_min=None,a_max=4))) * df.time_factor",,,,,,,coef_xferdrive,,,,,,,, +util_PNR_PNR_time,PNR_LOC - PNR time,@(df.auto_time_pnr_out + df.auto_time_pnr_inb) * df.time_factor,,,,,,,coef_acctime,,,,,,,, +util_PNR_PNR_cost,PNR_LOC - PNR cost,@df.sov_auto_op_cost * (df.auto_dist_pnr_out + df.auto_dist_pnr_inb) / df.cost_sensitivity,,,,,,,coef_income,,,,,,,, +util_PNR_Walk_egress_time_(at_attraction_end),PNR_LOC - Walk egress time (at attraction end),"@df.walk_at_dest_end_pnr * df.time_factor",,,,,,,coef_acctime,,,,,,,, +util_PNR_wait_egress_time_(at_attraction_end),PNR_LOC - Egress mt/nev wait time (at attraction end),"@np.where(df.nev_local_egress_available, 2*nevWaitTime, np.where(df.microtransit_local_egress_available, 2*microtransitWaitTime, 0)) * df.time_factor",,,,,,,coef_wait,,,,,,,, +util_PNR_Walk_other_time,PNR_LOC - Walk other time,@df.xfer_walk_pnr * df.time_factor,,,,,,,coef_xwalk,,,,,,,, +util_PNR_stop_type_constant,PNR_LOC - Stop type constant,@df.stop_type_pnr * df.time_factor,,,,,,,coef_ivt,,,,,,,, +util_PNR_vehicle_type_constant,PNR_LOC - Vehicle type constant,@df.vehicle_type_pnr * df.time_factor,,,,,,,coef_ivt,,,,,,,, +util_PNR_parking_cost,PNR_LOC - Parking cost,@df.parking_cost_pnr * 100 / df.cost_sensitivity,,,,,,,coef_income,,,,,,,, +util_PNR_Fare_and_operating_cost,PNR_LOC - Fare ,@df.transitSubsidyPassDiscount * df.fare_pnr * 100 * df.num_participants / df.cost_sensitivity,,,,,,,coef_income,,,,,,,, +util_PNR_LOC - Age 16 to 24,PNR_LOC - Age 16 to 24,@(df.age > 15) & (df.age < 25),,,,,,,coef_age1624_tran,,,,,,,, +util_PNR_LOC - Age 41 to 55,PNR_LOC - Age 41 to 55,@(df.age > 40) & (df.age < 56),,,,,,,coef_age4155_tran,,,,,,,, +util_PNR_LOC - Age 56 to 64,PNR_LOC - Age 56 to 64,@(df.age > 55) & (df.age < 65),,,,,,,coef_age5664_tran,,,,,,,, +util_PNR_LOC - Age 65+,PNR_LOC - Age 65+,@(df.age > 65),,,,,,,coef_age65pl_tran,,,,,,,, +util_PNR_LOC - Female,PNR_LOC - Female,@(df.female),,,,,,,coef_female_tran,,,,,,,, +util_PNR_LOC - Destination Employment Density,PNR_LOC - Destination Employment Density,dMGRAEmpDen,,,,,,,coef_dEmpDen_dTran,,,,,,,, +#,,,,,,,,,,,,,,,,, +util_KNR_Unavailable,KNR_LOC - Unavailable,"@(df.knr_local_available == False)|(df.get('num_escortees', 0)>0)",,,,,,,,-999,,,,,,, +util_KNR_In_vehicle_time,KNR_LOC - In-vehicle time,@(odt_skims['KTW_TIV'] + dot_skims['WTK_TIV']) *df.time_factor,,,,,,,,coef_ivt,,,,,,, +util_KNR_iwait_time,KNR_LOC - First iwait time,@(odt_skims['KTW_FWT']) + (dot_skims['WTK_FWT'])*df.time_factor,,,,,,,,coef_wait,,,,,,, +util_KNR_transfer_wait_time,KNR_LOC - transfer wait time,@(odt_skims['KTW_XWT'] + dot_skims['WTK_XWT'])*df.time_factor,,,,,,,,coef_xwait,,,,,,, +util_KNR_number_of_transfers,KNR_LOC - number of transfers,"@(-23+23*np.exp(0.414*np.clip(odt_skims['KTW_XFR'] + df.mtnev_egr_xfer, a_min=None,a_max=4))) + (-23+23*np.exp(0.414*np.clip(dot_skims['WTK_XFR'] + df.mtnev_egr_xfer, a_min=None,a_max=4))) *df.time_factor",,,,,,,,coef_xferdrive,,,,,,, +util_KNR_KNR_time,KNR_LOC - KNR time,@(odt_skims['KTW_ACC'] + dot_skims['WTK_EGR'])*df.time_factor,,,,,,,,coef_acctime,,,,,,, +util_KNR_KNR_cost,KNR_LOC - KNR cost,@(df.sr2_auto_op_cost * ((odt_skims['KTW_ACC'] + dot_skims['WTK_EGR'])/60) *driveSpeed )/df.cost_sensitivity,,,,,,,,coef_income,,,,,,, +util_KNR_Walk_egress_time_(at_attraction_end),KNR_LOC - Walk egress time (at attraction end),"@np.where(df.nev_local_egress_available, df.nev_local_egress_time, np.where(df.microtransit_local_egress_available, df.microtransit_local_egress_time, 2*df.dest_local_time)) * df.time_factor",,,,,,,,coef_acctime,,,,,,, +util_KNR_wait_egress_time_(at_attraction_end),KNR_LOC - Egress mt/nev wait time (at attraction end),"@np.where(df.nev_local_egress_available, 2*nevWaitTime, np.where(df.microtransit_local_egress_available, 2*microtransitWaitTime, 0)) * df.time_factor",,,,,,,,coef_wait,,,,,,, +util_KNR_Walk_other_time,KNR_LOC - Walk other time,@(odt_skims['KTW_AUX'] + dot_skims['WTK_AUX'])*df.time_factor,,,,,,,,coef_xwalk,,,,,,, +util_KNR_stop_type_constant,KNR_LOC - Stop type constant,@(odt_skims['KTW_RST'] + dot_skims['WTK_RST'])* df.time_factor,,,,,,,,coef_ivt,,,,,,, +util_KNR_vehicle_type_constant,KNR_LOC - Vehicle type constant,@(odt_skims['KTW_VTC'] + dot_skims['WTK_VTC'])* df.time_factor,,,,,,,,coef_ivt,,,,,,, +# FIXME need transit fares,,,,,,,,,,,,,,,,, +#util_KNR_Fare_and_operating_cost,KNR_LOC - Fare ,@df.transitSubsidyPassDiscount*(odt_skims['KTW_FARE'] + dot_skims['WTK_FARE'])*100*df.num_participants/df.cost_sensitivity,,,,,,,,coef_income,,,,,,, +util_KNR_LOC - Age 16 to 24,KNR_LOC - Age 16 to 24,@(df.age > 15) & (df.age < 25),,,,,,,,coef_age1624_tran,,,,,,, +util_KNR_LOC - Age 41 to 55,KNR_LOC - Age 41 to 55,@(df.age > 40) & (df.age < 56),,,,,,,,coef_age4155_tran,,,,,,, +util_KNR_LOC - Age 56 to 64,KNR_LOC - Age 56 to 64,@(df.age > 55) & (df.age < 65),,,,,,,,coef_age5664_tran,,,,,,, +util_KNR_LOC - Age 65+,KNR_LOC - Age 65+,@(df.age > 64),,,,,,,,coef_age65pl_tran,,,,,,, +util_KNR_LOC - Female,KNR_LOC - Female,@(df.female),,,,,,,,coef_female_tran,,,,,,, +util_KNR_LOC - Destination Employment Density,KNR_LOC - Destination Employment Density,dMGRAEmpDen,,,,,,,,coef_dEmpDen_dTran,,,,,,, +#,,,,,,,,,,,,,,,,, +util_TNC_Unavailable,TNC_LOC - Unavailable,"@(df.tnc_local_available == False)|(df.get('num_escortees', 0)>0)",,,,,,,,,-999,,,,,, +util_TNC_Unavailable_for_persons_less_than_16,TNC_LOC - Unavailable for persons less than 16,age < 12,,,,,,,,,-999,,,,,, +util_TNC_In_vehicle_time,TNC_LOC - In-vehicle time,@(odt_skims['KTW_TIV'] + dot_skims['WTK_TIV']) *df.time_factor,,,,,,,,,coef_ivt,,,,,, +util_TNC_iwait_time,TNC_LOC - First iwait time,@(odt_skims['KTW_FWT']) + (dot_skims['WTK_FWT'])*df.time_factor,,,,,,,,,coef_wait,,,,,, +util_TNC_transfer_wait_time,TNC_LOC - transfer wait time,@(odt_skims['KTW_XWT'] + dot_skims['WTK_XWT']) *df.time_factor,,,,,,,,,coef_xwait,,,,,, +util_TNC_number_of_transfers,TNC_LOC - number of transfers,"@(-23+23*np.exp(0.414*np.clip(odt_skims['KTW_XFR'] + df.mtnev_acc_xfer + df.mtnev_egr_xfer, a_min=None,a_max=4))) + (-23+23*np.exp(0.414*np.clip(dot_skims['WTK_XFR'] + df.mtnev_acc_xfer + df.mtnev_egr_xfer, a_min=None,a_max=4))) *df.time_factor",,,,,,,,,coef_xferdrive,,,,,, +util_TNC_TNC_time,TNC_LOC - TNC time,"@np.where(df.nev_local_access_available, df.nev_local_access_time, np.where(df.microtransit_local_access_available, df.microtransit_local_access_time, odt_skims['KTW_ACC'] + dot_skims['WTK_EGR'])) *df.time_factor",,,,,,,,,coef_acctime,,,,,, +util_TNC_TNC_wait_time,TNC_LOC - TNC wait time,"@np.where(df.nev_local_access_available, 2*nevWaitTime, np.where(df.microtransit_local_access_available, 2*microtransitWaitTime, 2*df.origSingleTNCWaitTime)) *df.time_factor",,,,,,,,,coef_wait,,,,,, +util_TNC_TNC_fare,TNC_LOC - TNC fare,"@(~df.nev_local_access_available) * (~df.microtransit_local_access_available) * ((np.maximum(TNC_single_baseFare*2 + ((odt_skims['KTW_ACC'] + dot_skims['WTK_EGR'])/60 * driveSpeed) * TNC_single_costPerMile + (odt_skims['KTW_ACC'] + dot_skims['WTK_EGR']) * TNC_single_costPerMinute, TNC_single_costMinimum*2)*100)/df.cost_sensitivity)",,,,,,,,,coef_income,,,,,, +util_TNC_Walk_egress_time_(at_attraction_end),TNC_LOC - Walk egress time (at attraction end),"@np.where(df.nev_local_egress_available, df.nev_local_egress_time, np.where(df.microtransit_local_egress_available, df.microtransit_local_egress_time, 2*df.dest_local_time)) * df.time_factor",,,,,,,,,coef_acctime,,,,,, +util_TNC_wait_egress_time_(at_attraction_end),TNC_LOC - Egress mt/nev wait time (at attraction end),"@np.where(df.nev_local_egress_available, 2*nevWaitTime, np.where(df.microtransit_local_egress_available, 2*microtransitWaitTime, 0)) * df.time_factor",,,,,,,,,coef_wait,,,,,, +util_TNC_Walk_other_time,TNC_LOC - Walk other time,@(odt_skims['KTW_AUX'] + dot_skims['WTK_AUX']) *df.time_factor,,,,,,,,,coef_xwalk,,,,,, +util_TNC_stop_type_constant,TNC_LOC - Stop type constant,@(odt_skims['KTW_RST'] + dot_skims['WTK_RST'])* df.time_factor,,,,,,,,,coef_ivt,,,,,, +util_TNC_vehicle_type_constant,TNC_LOC - Vehicle type constant,@(odt_skims['KTW_VTC'] + dot_skims['WTK_VTC'])* df.time_factor,,,,,,,,,coef_ivt,,,,,, +# FIXME need Fares,,,,,,,,,,,,,,,,, +#util_TNC_Fare_and_operating_cost,TNC_LOC - Fare ,@df.transitSubsidyPassDiscount*(odt_skims['KTW_FARE'] + dot_skims['WTK_FARE'])*100*df.num_participants/df.cost_sensitivity,,,,,,,,,coef_income,,,,,, +util_TNC_LOC - Age 16 to 24,TNC_LOC - Age 16 to 24,@(df.age > 15) & (df.age < 25),,,,,,,,,coef_age1624_tran,,,,,, +util_TNC_LOC - Age 41 to 55,TNC_LOC - Age 41 to 55,@(df.age > 40) & (df.age < 56),,,,,,,,,coef_age4155_tran,,,,,, +util_TNC_LOC - Age 56 to 64,TNC_LOC - Age 56 to 64,@(df.age > 55) & (df.age < 65),,,,,,,,,coef_age5664_tran,,,,,, +util_TNC_LOC - Age 65+,TNC_LOC - Age 65+,@(df.age > 64),,,,,,,,,coef_age65pl_tran,,,,,, +util_TNC_LOC - Female,TNC_LOC - Female,@(df.female),,,,,,,,,coef_female_tran,,,,,, +util_TNC_LOC - Destination Employment Density,TNC_LOC - Destination Employment Density,dMGRAEmpDen,,,,,,,,,coef_dEmpDen_dTran,,,,,, +#,Taxi,,,,,,,,,,,,,,,, +util_Taxi - In-vehicle time,Taxi - In-vehicle time,@(df.s2_time_skims_inb + df.s2_time_skims_out) * df.time_factor,,,,,,,,,,coef_ivt,,,,, +util_Taxi - Wait time,Taxi - Wait time,@df.totalWaitTaxi * df.time_factor,,,,,,,,,,coef_wait,,,,, +util_Taxi - Fare,Taxi - Fare,@(((Taxi_baseFare*2 + (df.s2_dist_skims_out + df.s2_dist_skims_inb) * Taxi_costPerMile + (df.s2_time_skims_out + df.s2_time_skims_inb) * Taxi_costPerMinute)*100 + df.s2_cost_skims_out + df.s2_cost_skims_inb))/df.cost_sensitivity,,,,,,,,,,coef_income,,,,, +#,TNC Single,,,,,,,,,,,,,,,, +util_TNC Single - In-vehicle time,TNC Single - In-vehicle time,@(df.s2_time_skims_out + df.s2_time_skims_inb) * df.time_factor,,,,,,,,,,,coef_ivt,,,, +util_TNC Single - Wait time,TNC Single - Wait time,@df.totalWaitSingleTNC * df.time_factor,,,,,,,,,,,coef_wait,,,, +util_TNC Single - Cost,TNC Single - Cost,"@((np.maximum(TNC_single_baseFare*2 + (df.s2_dist_skims_out + df.s2_dist_skims_inb) * TNC_single_costPerMile + (df.s2_time_skims_out + df.s2_time_skims_inb) * TNC_single_costPerMinute, TNC_single_costMinimum*2)*100 + df.s2_cost_skims_out + df.s2_cost_skims_inb))/df.cost_sensitivity",,,,,,,,,,,coef_income,,,, +#,TNC Shared,,,,,,,,,,,,,,,, +util_TNC Shared_switch,TNC Shared - switch turn-off (depends on data availability),@((~df.nev_available) & (~df.microtransit_available) & (scenarioYear==2022)),,,,,,,,,,,,-999,,, +util_TNC Shared - In-vehicle time,TNC Shared - In-vehicle time,"@(np.where(df.nev_available, df.nev_time, np.where(df.microtransit_available, df.microtransit_time, (df.s3_time_skims_out + df.s3_time_skims_inb) * TNC_shared_IVTFactor))) * df.time_factor",,,,,,,,,,,,coef_ivt,,, +util_TNC Shared - Wait time,TNC Shared - Wait time,"@np.where(df.nev_available, 2*nevWaitTime, np.where(df.microtransit_available, 2*microtransitWaitTime, df.totalWaitSharedTNC)) * df.time_factor",,,,,,,,,,,,coef_wait,,, +util_TNC Shared - Cost,TNC Shared - Cost,"@np.where(df.nev_available, 2*nevCost, np.where(df.microtransit_available, 2*microtransitCost, (np.maximum(TNC_shared_baseFare*2 + (df.s3_dist_skims_out + df.s3_dist_skims_inb) * TNC_shared_costPerMile + (df.s3_time_skims_out + df.s3_time_skims_inb)* TNC_shared_costPerMinute, TNC_shared_costMinimum*2)*100 + df.s3_cost_skims_out + df.s3_cost_skims_inb)))/df.cost_sensitivity",,,,,,,,,,,,coef_income,,, +#,School bus,,,,,,,,,,,,,,,, +util_School Bus - Unavailable,School Bus - Unavailable,SCHBUS_available==0,,,,,,,,,,,,,-999,, +util_School Bus - In-vehicle Time at 20 miles per hour,School Bus - In-vehicle Time at 20 miles per hour,(da_dist_skims_out + da_dist_skims_inb)*3 * time_factor,,,,,,,,,,,,,coef_ivt,, +util_School Bus - Wait Time (asserted),School Bus - Wait Time (asserted) * c_fwt,10 * time_factor,,,,,,,,,,,,,coef_wait,, +util_School Bus - Walk Time (asserted),School Bus - Walk Time (asserted),10 * time_factor,,,,,,,,,,,,,coef_acctime,, +util_School Bus - Age 1 to 5,School Bus - Age 1 to 5,@(df.age > 0) & (df.age < 6),,,,,,,,,,,,,coef_age0105_schb,, +util_School Bus - Age 6 to 12,School Bus - Age 6 to 12,@(df.age > 5) & (df.age < 13),,,,,,,,,,,,,coef_age0612_schb,, +util_School Bus - Age 13 to 15,School Bus - Age 13 to 15,@(df.age > 12) & (df.age < 16),,,,,,,,,,,,,coef_age1315_schb,, +util_School Bus - Female,School Bus - Female,@(df.female),,,,,,,,,,,,,coef_female_schb,, +#calibration,,,,,,,,,,,,,,,,, +util_calib_KNR Transit - Distance Parameter,abm2+ calibration constant,"@np.maximum((10-df.da_dist_skims_out),0)",,,,,,,,coef_calib_distance_KNR_TRANSIT,coef_calib_distance_TNC_TRANSIT,,,,,, +util_calib_Walk-Transit - Distance Parameter,abm2+ calibration constant,"@np.maximum((200+(-133*df.da_dist_skims_out)),0)",,,,,,coef_calib_distance_WALK_TRANSIT,,,,,,,,, +util_calib_PNR Transit - Distance Parameter ,abm2+ calibration constant,"@np.maximum((45+(-2.5*df.da_dist_skims_out)),0)",,,,,,,coef_calib_distance_PNR_TRANSIT,,,,,,,, +util_calib_zeroautohhindiv,abm2+ calibration constant,@(df.is_indiv & (df.auto_ownership == 0)),,coef_calib_zeroautohhindivtou_SHARED2,coef_calib_zeroautohhindivtou_SHARED3,coef_calib_zeroautohhindivtou_WALK,coef_calib_zeroautohhindivtou_BIKE,coef_calib_zeroautohhindivtou_WALK_TRANSIT,coef_calib_zeroautohhindivtou_PNR_TRANSIT,coef_calib_zeroautohhindivtou_KNR_TRANSIT,coef_calib_zeroautohhindivtou_TNC_TRANSIT,coef_calib_zeroautohhindivtou_TAXI,coef_calib_zeroautohhindivtou_TNC_SINGLE,coef_calib_zeroautohhindivtou_TNC_SHARED,coef_calib_zeroautohhindivtou_SCH_BUS,coef_calib_zeroautohhindivtou_EBIKE,coef_calib_zeroautohhindivtou_ESCOOTER +util_calib_autodeficienthh,abm2+ calibration constant,@(df.is_indiv & (df.auto_ownership < df.num_adults) & (df.auto_ownership > 0)),,coef_calib_autodeficienthhind_SHARED2,coef_calib_autodeficienthhind_SHARED3,coef_calib_autodeficienthhind_WALK,coef_calib_autodeficienthhind_BIKE,coef_calib_autodeficienthhind_WALK_TRANSIT,coef_calib_autodeficienthhind_PNR_TRANSIT,coef_calib_autodeficienthhind_KNR_TRANSIT,coef_calib_autodeficienthhind_TNC_TRANSIT,coef_calib_autodeficienthhind_TAXI,coef_calib_autodeficienthhind_TNC_SINGLE,coef_calib_autodeficienthhind_TNC_SHARED,coef_calib_autodeficienthhind_SCH_BUS,coef_calib_autodeficienthhind_EBIKE,coef_calib_autodeficienthhind_ESCOOTER +util_calib_autosufficienth,abm2+ calibration constant,@(df.is_indiv & (df.auto_ownership >= df.num_adults) & (df.auto_ownership > 0)),,coef_calib_autosufficienthhin_SHARED2,coef_calib_autosufficienthhin_SHARED3,coef_calib_autosufficienthhin_WALK,coef_calib_autosufficienthhin_BIKE,coef_calib_autosufficienthhin_WALK_TRANSIT,coef_calib_autosufficienthhin_PNR_TRANSIT,coef_calib_autosufficienthhin_KNR_TRANSIT,coef_calib_autosufficienthhin_TNC_TRANSIT,coef_calib_autosufficienthhin_TAXI,coef_calib_autosufficienthhin_TNC_SINGLE,coef_calib_autosufficienthhin_TNC_SHARED,coef_calib_autosufficienthhin_SCH_BUS,coef_calib_autosufficienthhin_EBIKE,coef_calib_autosufficienthhin_ESCOOTER +util_calib_zeroautohhjoint,abm2+ calibration constant,@(df.is_joint & (df.auto_ownership == 0)),,,coef_calib_zeroautohhjointtou_SHARED3,coef_calib_zeroautohhjointtou_WALK,coef_calib_zeroautohhjointtou_BIKE,coef_calib_zeroautohhjointtou_WALK_TRANSIT,coef_calib_zeroautohhjointtou_PNR_TRANSIT,coef_calib_zeroautohhjointtou_KNR_TRANSIT,coef_calib_zeroautohhjointtou_TNC_TRANSIT,coef_calib_zeroautohhjointtou_TAXI,coef_calib_zeroautohhjointtou_TNC_SINGLE,coef_calib_zeroautohhjointtou_TNC_SHARED,,coef_calib_zeroautohhjointtou_EBIKE,coef_calib_zeroautohhjointtou_ESCOOTER +util_calib_autodeficienthh,abm2+ calibration constant,@(df.is_joint & (df.auto_ownership < df.num_adults) & (df.auto_ownership > 0)),,,coef_calib_autodeficienthhjoi_SHARED3,coef_calib_autodeficienthhjoi_WALK,coef_calib_autodeficienthhjoi_BIKE,coef_calib_autodeficienthhjoi_WALK_TRANSIT,coef_calib_autodeficienthhjoi_PNR_TRANSIT,coef_calib_autodeficienthhjoi_KNR_TRANSIT,coef_calib_autodeficienthhjoi_TNC_TRANSIT,coef_calib_autodeficienthhjoi_TAXI,coef_calib_autodeficienthhjoi_TNC_SINGLE,coef_calib_autodeficienthhjoi_TNC_SHARED,,coef_calib_autodeficienthhjoi_EBIKE,coef_calib_autodeficienthhjoi_ESCOOTER +util_calib_autosufficienth,abm2+ calibration constant,@(df.is_joint & (df.auto_ownership >= df.num_adults) & (df.auto_ownership > 0)),,,coef_calib_autosufficienthhjo_SHARED3,coef_calib_autosufficienthhjo_WALK,coef_calib_autosufficienthhjo_BIKE,coef_calib_autosufficienthhjo_WALK_TRANSIT,coef_calib_autosufficienthhjo_PNR_TRANSIT,coef_calib_autosufficienthhjo_KNR_TRANSIT,coef_calib_autosufficienthhjo_TNC_TRANSIT,coef_calib_autosufficienthhjo_TAXI,coef_calib_autosufficienthhjo_TNC_SINGLE,coef_calib_autosufficienthhjo_TNC_SHARED,,coef_calib_autosufficienthhjo_EBIKE,coef_calib_autosufficienthhjo_ESCOOTER +util_calib_escorttour,abm2+ calibration constant,tour_type == 'escort',,,,coef_calib_escorttour_WALK,coef_calib_escorttour_BIKE,coef_calib_escorttour_WALK_TRANSIT,coef_calib_escorttour_PNR_TRANSIT,coef_calib_escorttour_KNR_TRANSIT,coef_calib_escorttour_TNC_TRANSIT,,,,,, +#, School Escorting eligibility-odd looking where/isnan is to allow this to work with numba fastmath,,,,,,,,,,,,,,,, +util_one_or_more_school_escort,No SOV if on school escort tour,"@(np.where(np.isnan(df.get('num_escortees', 0)), 0 , df.get('num_escortees', 0)) >= 1)",-999,,,,,,,,,,,,,, +util_two_or_more_school_escort,Can't take HOV2 if taking two children and yourself,"@(np.where(np.isnan(df.get('num_escortees', 0)), 0 , df.get('num_escortees', 0)) >= 2)",,-999,,,,,,,,,,,,, +#,Micromobility (e-scooter/e-bike),,,,,,,,,,,,,,,, +util_ebike - Unavailable,ebike - Unavailable,ebike_available==0,,,,,,,,,,,,,,-999, +util_ebike_long_access,Shut off ebike if access time > threshold,"@(((df.micro_access_out > microAccessThreshold) | (df.micro_access_inb > microAccessThreshold)) & (~df.ebike_owner)) | (df.get('num_escortees', 0)>0)",,,,,,,,,,,,,,-999, +util_escooter_long_access,Shut off escooter if access time > threshold,"@((df.micro_access_out > microAccessThreshold) | (df.micro_access_inb > microAccessThreshold) | (df.get('num_escortees', 0)>0))",,,,,,,,,,,,,,,-999 +util_micromobility_long_trip,Shut off ebike if distance > threshold,ebikeMaxDistance,,,,,,,,,,,,,,-999, +util_micromobility_long_trip,Shut off escooter if distance > threshold,escooterMaxDistance,,,,,,,,,,,,,,,-999 +util_ebike_ivt,Ebike utility for in-vehicle time,@(df.ebike_time_inb + df.ebike_time_out)*df.time_factor,,,,,,,,,,,,,,coef_ivt, +util_ebike_access,Ebike utility for access/egress time,"@(np.where(df.ebike_owner, 0, microRentTime + df.micro_access_inb + df.micro_access_out))*df.time_factor",,,,,,,,,,,,,,coef_acctime, +util_ebike_cost_inb,Ebike utility for inbound cost,@((~df.ebike_owner)&((microFixedCost + microVarCost*df.ebike_time_inb)/df.cost_sensitivity)),,,,,,,,,,,,,,coef_income, +util_ebike_cost_out,Ebike utility for outbound cost,@((~df.ebike_owner)&((microFixedCost + microVarCost*df.ebike_time_out)/df.cost_sensitivity)),,,,,,,,,,,,,,coef_income, +util_escooter - Unavailable,escooter - Unavailable,escooter_available==0,,,,,,,,,,,,,,,-999 +util_escooter_ivt,escooter utility for in-vehicle time,@(df.escooter_time_inb + df.escooter_time_out)*df.time_factor,,,,,,,,,,,,,,,coef_ivt +util_escooter_access,escooter utility for in-vehicle time,@(microRentTime + df.micro_access_inb + df.micro_access_out)*df.time_factor,,,,,,,,,,,,,,,coef_acctime +util_escooter_cost_inb,escooter utility for inbound cost,@((microFixedCost + microVarCost*df.escooter_time_inb)/df.cost_sensitivity),,,,,,,,,,,,,,,coef_income +util_escooter_cost_out,escooter utility for outbound cost,@((microFixedCost + microVarCost*df.escooter_time_out)/df.cost_sensitivity),,,,,,,,,,,,,,,coef_income +#,Calibration from on-board survey,,,,,,,,,,,,,,,, +util_calib_onboard,Calibration coefficient to match implied number of tours from on-board survey,1,,,,,,coef_calib_onboard,coef_calib_onboard,coef_calib_onboard,coef_calib_onboard,,,,,, +#,Flexible fleet calibration for zero-auto households,,,,,,,,,,,,,,,, +util_calib_mt_zeroautohh,Calibration coefficient for zero-auto households traveling within microtransit service area,@(df.microtransit_available & (df.auto_ownership == 0)),,,,,,,,,,,,coef_calib_mt_zeroautohh,,, +util_calib_nev_zeroautohh,Calibration coefficient for zero-auto households traveling within NEV service area,@(df.nev_available & (df.auto_ownership == 0)),,,,,,,,,,,,coef_calib_nev_zeroautohh,,, +util_calib_ebike_owner,Calibration coefficient for ebike owners,@(df.ebike_owner),,,,,coef_calib_ebike_owner_BIKE,,,,,,,,,coef_calib_ebike_owner_EBIKE, +util_calib_shared_ebike,Calibration coefficient for shared ebike,@(~df.ebike_owner),,,,,,,,,,,,,,coef_calib_ebike_shared, diff --git a/resident/configs/tour_mode_choice.yaml b/resident/configs/tour_mode_choice.yaml new file mode 100644 index 0000000..326e9de --- /dev/null +++ b/resident/configs/tour_mode_choice.yaml @@ -0,0 +1,96 @@ +LOGIT_TYPE: NL +#LOGIT_TYPE: MNL + +NESTS: + name: root + coefficient: coef_nest_root + alternatives: + - name: AUTO + coefficient: coef_nest_AUTO + alternatives: + - DRIVEALONE + - SHARED2 + - SHARED3 + - name: NONMOTORIZED + coefficient: coef_nest_NONMOTORIZED + alternatives: + - WALK + - BIKE + - name: MICROMOBILITY + coefficient: coef_nest_MICROMOBILITY + alternatives: + - EBIKE + - ESCOOTER + - name: TRANSIT + coefficient: coef_nest_TRANSIT + alternatives: + - WALK_TRANSIT + - PNR_TRANSIT + - KNR_TRANSIT + - BIKE_TRANSIT + - name: RIDEHAIL + coefficient: coef_nest_RIDEHAIL + alternatives: + - TAXI + - TNC_SINGLE + - TNC_SHARED + - name: SCHOOL_BUS + coefficient: coef_nest_SCHOOL_BUS + alternatives: + - SCH_BUS + +SPEC: tour_mode_choice.csv +COEFFICIENTS: tour_mode_choice_coefficients.csv +COEFFICIENT_TEMPLATE: tour_mode_choice_coefficients_template.csv + +run_atwork_pnr_lot_choice: True +include_pnr_for_logsums: True + +# so far, we can use the same spec as for non-joint tours +preprocessor: + SPEC: tour_mode_choice_annotate_choosers_preprocessor + DF: choosers + TABLES: + - land_use + - tours + - vehicles + - households + - persons + +nontour_preprocessor: + SPEC: tour_mode_choice_annotate_choosers_preprocessor + DF: choosers + TABLES: + - land_use + +annotate_tours: + SPEC: annotate_tours_tour_mode_choice + DF: choosers + TABLES: + - tours + + +# to reduce memory needs filter chooser table to these fields +LOGSUM_CHOOSER_COLUMNS: + - tour_type + - hhsize + - density_index + - age + - SEX + - auto_ownership + - num_adults + - ptype + - number_of_participants + - tour_category + - value_of_time + - free_parking_at_work + - income_segment + - income + - time_factor_work + - time_factor_nonwork + - num_escortees + - ebike_owner + - transit_pass_subsidy + - transit_pass_ownership + +MODE_CHOICE_LOGSUM_COLUMN_NAME: mode_choice_logsum diff --git a/resident/configs/tour_mode_choice_annotate_choosers_preprocessor.csv b/resident/configs/tour_mode_choice_annotate_choosers_preprocessor.csv new file mode 100644 index 0000000..eaef019 --- /dev/null +++ b/resident/configs/tour_mode_choice_annotate_choosers_preprocessor.csv @@ -0,0 +1,246 @@ +Description,Target,Expression, +#,,, +local,_DF_IS_TOUR,'tour_type' in df.columns, +,is_atwork_subtour,(df.tour_category == 'atwork') if _DF_IS_TOUR else False, +,_parent_tour_veh,"reindex(tours['selected_vehicle'], df['parent_tour_id']) if 'selected_vehicle' in df.columns else np.nan", +,_parent_tour_mode,"reindex(tours['tour_mode'], df['parent_tour_id']) if 'tour_mode' in df.columns else np.nan", +,sov_veh_option,"np.where(is_atwork_subtour, _parent_tour_veh, df.get('vehicle_occup_1', np.nan))", +,sr2_veh_option,"np.where(is_atwork_subtour, _parent_tour_veh, df.get('vehicle_occup_2', np.nan))", +,sr3p_veh_option,"np.where(is_atwork_subtour, _parent_tour_veh, df.get('vehicle_occup_3.5', np.nan))", +#AV factors,,, +SOV tour Vehicle is AV,sov_tour_uses_av,"np.where(pd.notna(df.get('vehicle_occup_1', np.nan)), pd.Series(sov_veh_option, dtype = 'str').str.contains('AV'), False)" +SR2 tour Vehicle is AV,sr2_tour_uses_av,"np.where(pd.notna(df.get('vehicle_occup_2', np.nan)), pd.Series(sr2_veh_option, dtype = 'str').str.contains('AV'), False)" +SR3 tour Vehicle is AV,sr3_tour_uses_av,"np.where(pd.notna(df.get('vehicle_occup_3.5', np.nan)), pd.Series(sr3p_veh_option, dtype = 'str').str.contains('AV'), False)" +SOV AV IVT Factor Adjustment,autoIVTFactor_DA,"np.where(sov_tour_uses_av, autoIVTFactorAV, 1.0)" +SR2 AV IVT Factor Adjustment,autoIVTFactor_SR2,"np.where(sr2_tour_uses_av, autoIVTFactorAV, 1.0)" +SR3+ AV IVT Factor Adjustment,autoIVTFactor_SR3,"np.where(sr3_tour_uses_av, autoIVTFactorAV, 1.0)" +SOV AV Parking Cost Factor Adjustment,autoParkingCostFactor_DA,"np.where(sov_tour_uses_av, autoParkingCostFactorAV, 1.0)" +SR2 AV Parking Cost Factor Adjustment,autoParkingCostFactor_SR2,"np.where(sr2_tour_uses_av, autoParkingCostFactorAV, 1.0)" +SR3+ AV Parking Cost Factor Adjustment,autoParkingCostFactor_SR3,"np.where(sr3_tour_uses_av, autoParkingCostFactorAV, 1.0)" +SOV AV Auto Terminal Time Factor,autoTermTimeFactor_DA,"np.where(sov_tour_uses_av, autoTerminalTimeFactorAV, 1.0)" +SR2 AV Auto Terminal Time Factor,autoTermTimeFactor_SR2,"np.where(sr2_tour_uses_av, autoTerminalTimeFactorAV, 1.0)" +SR3+ AV Auto Terminal Time Factor,autoTermTimeFactor_SR3,"np.where(sr3_tour_uses_av, autoTerminalTimeFactorAV, 1.0)" +SOV AV Auto Terminal Time Factor,autoCPMFactor_DA,"np.where(sov_tour_uses_av, autoCostPerMileFactorAV, 1.0)" +SR2 AV Auto Terminal Time Factor,autoCPMFactor_SR2,"np.where(sr2_tour_uses_av, autoCostPerMileFactorAV, 1.0)" +SR3+ AV Auto Terminal Time Factor,autoCPMFactor_SR3,"np.where(sr3_tour_uses_av, autoCostPerMileFactorAV, 1.0)" +AV Min Age to Drive Alone,minimumAgeDA,"np.where(sov_tour_uses_av, minAgeDriveAloneAV, 16)" +reimburseProportion placeholder,reimburseProportion,0, +"Tour duration, in hours",tourDuration,df.duration/2, +#,,, +,num_participants,df.number_of_participants if _DF_IS_TOUR else 1, +,is_joint,(df.tour_category=='joint') if _DF_IS_TOUR else False, +treat tours as work if tour_type not yet decided,tour_type,"df.get('tour_type', default='work')", +#,,, +,is_mandatory,(df.tour_category=='mandatory') if 'tour_category' in df.columns else False, +,is_indiv,~is_joint, +,is_escort,(tour_type == 'escort') if _DF_IS_TOUR else False, +# Auto operating costs,,, +,sov_auto_op_cost,"reindex(vehicles.groupby('vehicle_type')['auto_operating_cost'].mean(), pd.Series(sov_veh_option, df.index)) if 'vehicle_occup_1' in df.columns else np.nan", +,sov_auto_op_cost,"np.where(sov_auto_op_cost.isna() | (pd.Series(sov_veh_option, df.index) == 'non_hh_veh'), costPerMile, sov_auto_op_cost)", +,sr2_auto_op_cost,"reindex(vehicles.groupby('vehicle_type')['auto_operating_cost'].mean(), pd.Series(sr2_veh_option, df.index)) if 'vehicle_occup_2' in df.columns else np.nan", +,sr2_auto_op_cost,"np.where(sr2_auto_op_cost.isna() | (pd.Series(sr2_veh_option, df.index) == 'non_hh_veh'), costPerMile, sr2_auto_op_cost)", +,sr3p_auto_op_cost,"reindex(vehicles.groupby('vehicle_type')['auto_operating_cost'].mean(), pd.Series(sr3p_veh_option, df.index)) if 'vehicle_occup_3.5' in df.columns else np.nan", +,sr3p_auto_op_cost,"np.where(sr3p_auto_op_cost.isna() | (pd.Series(sr3p_veh_option, df.index) == 'non_hh_veh'), costPerMile, sr3p_auto_op_cost)", +# RIDEHAIL,,, +# FIXME population_density replaced PopDenPerSqMi which may not be calculated the same,, +population_density calculated in annotate landuse in acres,_origin_density_measure,"reindex(land_use.population_density, df[orig_col_name]) / 640", +population_density calculated in annotate landuse in acres,_dest_density_measure,"reindex(land_use.population_density, df[dest_col_name]) / 640", +,origin_density,"pd.cut(_origin_density_measure, bins=[-np.inf, 500, 2000, 5000, 15000, np.inf], labels=[5, 4, 3, 2, 1]).astype(int)", +,dest_density,"pd.cut(_dest_density_measure, bins=[-np.inf, 500, 2000, 5000, 15000, np.inf], labels=[5, 4, 3, 2, 1]).astype(int)", +,origin_zone_taxi_wait_time_mean,"origin_density.map({k: v for k, v in Taxi_waitTime_mean.items()})", +,origin_zone_taxi_wait_time_sd,"origin_density.map({k: v for k, v in Taxi_waitTime_sd.items()})", +,dest_zone_taxi_wait_time_mean,"dest_density.map({k: v for k, v in Taxi_waitTime_mean.items()})", +,dest_zone_taxi_wait_time_sd,"dest_density.map({k: v for k, v in Taxi_waitTime_sd.items()})", +# ,, Note that the mean and standard deviation are not the values for the distribution itself but of the underlying normal distribution it is derived from +,origTaxiWaitTime,"rng.lognormal_for_df(df, mu=origin_zone_taxi_wait_time_mean, sigma=origin_zone_taxi_wait_time_sd, broadcast=True, scale=True).clip(min_waitTime, max_waitTime)", +,destTaxiWaitTime,"rng.lognormal_for_df(df, mu=dest_zone_taxi_wait_time_mean, sigma=dest_zone_taxi_wait_time_sd, broadcast=True, scale=True).clip(min_waitTime, max_waitTime)", +,origin_zone_singleTNC_wait_time_mean,"origin_density.map({k: v for k, v in TNC_single_waitTime_mean.items()})", +,origin_zone_singleTNC_wait_time_sd,"origin_density.map({k: v for k, v in TNC_single_waitTime_sd.items()})", +,dest_zone_singleTNC_wait_time_mean,"dest_density.map({k: v for k, v in TNC_single_waitTime_mean.items()})", +,dest_zone_singleTNC_wait_time_sd,"dest_density.map({k: v for k, v in TNC_single_waitTime_sd.items()})", +,origSingleTNCWaitTime,"rng.lognormal_for_df(df, mu=origin_zone_singleTNC_wait_time_mean, sigma=origin_zone_singleTNC_wait_time_sd, broadcast=True, scale=True).clip(min_waitTime, max_waitTime)", +,destSingleTNCWaitTime,"rng.lognormal_for_df(df, mu=dest_zone_singleTNC_wait_time_mean, sigma=dest_zone_singleTNC_wait_time_sd, broadcast=True, scale=True).clip(min_waitTime, max_waitTime)", +,origin_zone_sharedTNC_wait_time_mean,"origin_density.map({k: v for k, v in TNC_shared_waitTime_mean.items()})", +,origin_zone_sharedTNC_wait_time_sd,"origin_density.map({k: v for k, v in TNC_shared_waitTime_sd.items()})", +,dest_zone_sharedTNC_wait_time_mean,"dest_density.map({k: v for k, v in TNC_shared_waitTime_mean.items()})", +,dest_zone_sharedTNC_wait_time_sd,"dest_density.map({k: v for k, v in TNC_shared_waitTime_sd.items()})", +,origSharedTNCWaitTime,"rng.lognormal_for_df(df, mu=origin_zone_sharedTNC_wait_time_mean, sigma=origin_zone_sharedTNC_wait_time_sd, broadcast=True, scale=True).clip(min_waitTime, max_waitTime)", +,destSharedTNCWaitTime,"rng.lognormal_for_df(df, mu=dest_zone_sharedTNC_wait_time_mean, sigma=dest_zone_sharedTNC_wait_time_sd, broadcast=True, scale=True).clip(min_waitTime, max_waitTime)", +,totalWaitTaxi,origTaxiWaitTime + destTaxiWaitTime, +,totalWaitSingleTNC,origSingleTNCWaitTime + destSingleTNCWaitTime, +,totalWaitSharedTNC,origSharedTNCWaitTime + destSharedTNCWaitTime, +#parking,,, +,free_parking_available,(tour_type == 'work') & df.free_parking_at_work if _DF_IS_TOUR else False, +person has free on-site parking at workplace,freeOnsite,"(free_parking_available)*np.where(is_indiv,1,0)", +# new reimbursement amount,reimburseProportion,"df.reimburseProportion*np.where(is_indiv,1,0)", +new daily parking cost with reimbursement in cents,parkingCostDay,"reindex(land_use.PRKCST_DAY, df[dest_col_name])", +new hourly parking cost with reimbursement in cents,parkingCostHour,"reindex(land_use.PRKCST_HR, df[dest_col_name])", +new monthly parking cost with reimbursement in cents,parkingCostMonth,"reindex(land_use.PRKCST_MNTH, df[dest_col_name])", +Tour parking cost for full-time workers and university students,_parkingCostBeforeReimb,"df.ptype.isin([1,3]) * is_indiv * np.minimum(parkingCostMonth/22, parkingCostDay)", +Tour parking cost for full-time workers and university students,_parkingCostBeforeReimb,"df.ptype.isin([1,3]) * is_indiv * np.minimum(_parkingCostBeforeReimb, parkingCostHour * tourDuration)", +Tour parking cost for other person types,parkingCostBeforeReimb,"np.where((~df.ptype.isin([1,3]) * is_indiv) | (is_joint), np.minimum(parkingCostDay, parkingCostHour * tourDuration), _parkingCostBeforeReimb)", +Reimbursement applies to this tour purpose,reimbursePurpose,df.tour_type == 'work' if _DF_IS_TOUR else False, +Effective parking cost for free parkers,_parkingCost,"0 * np.where(is_indiv*reimbursePurpose*freeOnsite,1,0)", +Effective parking cost for reimbursed parkers,_parkingCost,"np.where(is_indiv*reimbursePurpose*(1-freeOnsite), np.maximum((1-reimburseProportion) * parkingCostBeforeReimb, 0),_parkingCost)", +Effective parking cost applied to tour purpose,parkingCost,"np.where(is_joint+is_indiv*(1-reimbursePurpose), parkingCostBeforeReimb,_parkingCost)", +# cost coef,,, +,_income_exponent,"np.where(tour_type == 'work', 0.6, 0.5)", +,cost_sensitivity,"np.maximum(df.income,1000).pow(_income_exponent)", +,c_cost,coef_income / cost_sensitivity, +# ivt coef,,, +,time_factor,"np.where(tour_type=='work', df.time_factor_work, df.time_factor_nonwork)", +,c_ivt,coef_ivt * time_factor, +#,,, +,vot_da,c_ivt / c_cost * 0.6, +,vot_s2,vot_da / cost_share_s2, +,vot_s3,vot_da / cost_share_s3, +,_vot_bin_da,"np.where(vot_da < vot_threshold_low, 1, np.where(vot_da < vot_threshold_med, 2, 3))", +,_vot_bin_s2,"np.where(vot_s2 < vot_threshold_low, 1, np.where(vot_s2 < vot_threshold_med, 2, 3))", +,_vot_bin_s3,"np.where(vot_s3 < vot_threshold_low, 1, np.where(vot_s3 < vot_threshold_med, 2, 3))", +# vot-indexed skims,,, +,da_dist_skims_out,"(odt_skims['SOV_L_DIST'] * (_vot_bin_da == 1)) + (odt_skims['SOV_M_DIST'] * (_vot_bin_da == 2)) + (odt_skims['SOV_H_DIST'] * (_vot_bin_da == 3))", +,da_dist_skims_inb,"(dot_skims['SOV_L_DIST'] * (_vot_bin_da == 1)) + (dot_skims['SOV_M_DIST'] * (_vot_bin_da == 2)) + (dot_skims['SOV_H_DIST'] * (_vot_bin_da == 3))", +,da_cost_skims_out,"(odt_skims['SOV_L_COST'] * (_vot_bin_da == 1)) + (odt_skims['SOV_M_COST'] * (_vot_bin_da == 2)) + (odt_skims['SOV_H_COST'] * (_vot_bin_da == 3))", +,da_cost_skims_inb,"(dot_skims['SOV_L_COST'] * (_vot_bin_da == 1)) + (dot_skims['SOV_M_COST'] * (_vot_bin_da == 2)) + (dot_skims['SOV_H_COST'] * (_vot_bin_da == 3))", +,da_time_skims_out,"(odt_skims['SOV_L_TIME'] * (_vot_bin_da == 1)) + (odt_skims['SOV_M_TIME'] * (_vot_bin_da == 2)) + (odt_skims['SOV_H_TIME'] * (_vot_bin_da == 3))", +,da_time_skims_inb,"(dot_skims['SOV_L_TIME'] * (_vot_bin_da == 1)) + (dot_skims['SOV_M_TIME'] * (_vot_bin_da == 2)) + (dot_skims['SOV_H_TIME'] * (_vot_bin_da == 3))", +,s2_dist_skims_out,(((odt_skims['SR2_L_DIST']) * (_vot_bin_s2 == 1)) + ((odt_skims['SR2_M_DIST']) * (_vot_bin_s2 == 2)) + ((odt_skims['SR2_H_DIST']) * (_vot_bin_s2 == 3))), +,s2_dist_skims_inb,(((dot_skims['SR2_L_DIST']) * (_vot_bin_s2 == 1)) + ((dot_skims['SR2_M_DIST']) * (_vot_bin_s2 == 2)) + ((dot_skims['SR2_H_DIST']) * (_vot_bin_s2 == 3))), +,s2_cost_skims_out,(((odt_skims['SR2_L_COST']) * (_vot_bin_s2 == 1)) + ((odt_skims['SR2_M_COST']) * (_vot_bin_s2 == 2)) + ((odt_skims['SR2_H_COST']) * (_vot_bin_s2 == 3))), +,s2_cost_skims_inb,(((dot_skims['SR2_L_COST']) * (_vot_bin_s2 == 1)) + ((dot_skims['SR2_M_COST']) * (_vot_bin_s2 == 2)) + ((dot_skims['SR2_H_COST']) * (_vot_bin_s2 == 3))), +,s2_time_skims_out,(((odt_skims['SR2_L_TIME']) * (_vot_bin_s2 == 1)) + ((odt_skims['SR2_M_TIME']) * (_vot_bin_s2 == 2)) + ((odt_skims['SR2_H_TIME']) * (_vot_bin_s2 == 3))), +,s2_time_skims_inb,(((dot_skims['SR2_L_TIME']) * (_vot_bin_s2 == 1)) + ((dot_skims['SR2_M_TIME']) * (_vot_bin_s2 == 2)) + ((dot_skims['SR2_H_TIME']) * (_vot_bin_s2 == 3))), +,s3_dist_skims_out,(((odt_skims['SR3_L_DIST']) * (_vot_bin_s3 == 1)) + ((odt_skims['SR3_M_DIST']) * (_vot_bin_s3 == 2)) + ((odt_skims['SR3_H_DIST']) * (_vot_bin_s3 == 3))), +,s3_dist_skims_inb,(((dot_skims['SR3_L_DIST']) * (_vot_bin_s3 == 1)) + ((dot_skims['SR3_M_DIST']) * (_vot_bin_s3 == 2)) + ((dot_skims['SR3_H_DIST']) * (_vot_bin_s3 == 3))), +,s3_cost_skims_out,(((odt_skims['SR3_L_COST']) * (_vot_bin_s3 == 1)) + ((odt_skims['SR3_M_COST']) * (_vot_bin_s3 == 2)) + ((odt_skims['SR3_H_COST']) * (_vot_bin_s3 == 3))), +,s3_cost_skims_inb,(((dot_skims['SR3_L_COST']) * (_vot_bin_s3 == 1)) + ((dot_skims['SR3_M_COST']) * (_vot_bin_s3 == 2)) + ((dot_skims['SR3_H_COST']) * (_vot_bin_s3 == 3))), +,s3_time_skims_out,(((odt_skims['SR3_L_TIME']) * (_vot_bin_s3 == 1)) + ((odt_skims['SR3_M_TIME']) * (_vot_bin_s3 == 2)) + ((odt_skims['SR3_H_TIME']) * (_vot_bin_s3 == 3))), +,s3_time_skims_inb,(((dot_skims['SR3_L_TIME']) * (_vot_bin_s3 == 1)) + ((dot_skims['SR3_M_TIME']) * (_vot_bin_s3 == 2)) + ((dot_skims['SR3_H_TIME']) * (_vot_bin_s3 == 3))), +,walk_time_skims_out,od_skims['DISTWALK'] / walkSpeed * 60, +,walk_time_skims_inb,do_skims['DISTWALK'] / walkSpeed * 60, +#,,, +Determining Tour Origin,origin,df.origin if 'origin' in df.columns else df.home_zone_id if 'home_zone_id' in df.columns else df.workplace_zone_id, +#,,, +Person age,age,df.age, +# Household income (in dollars),income,df.incomeInDollars, +#,,, +Joint tour,is_joint,"df.get('tour_category', default=False) == 'joint'", +Determining Tour Destination,destination,df.destination if 'destination' in df.columns else df.alt_dest, +Origin MGRA Dwelling Unit Density,oMGRADUDen,"reindex(land_use.duden,origin)", +Origin MGRA Employment Density,oMGRAEmpDen,"reindex(land_use.empden,origin)", +Origin MGRA Total Intersections,oMGRATotInt,"reindex(land_use.totint,origin)", +Destination MGRA Dwelling Unit Density,dMGRADUDen,"reindex(land_use.duden,destination)", +Destination MGRA Employment Density,dMGRAEmpDen,"reindex(land_use.empden,destination)", +Destination MGRA Total Intersections,dMGRATotInt,"reindex(land_use.totint,destination)", +Origin MGRA Mix,oMGRAMix,"np.where(oMGRADUDen+oMGRAEmpDen > 0, (oMGRADUDen*oMGRAEmpDen)/(oMGRADUDen+oMGRAEmpDen),0)", +Destination MGRA Mix,dMGRAMix,"np.where(dMGRADUDen+dMGRAEmpDen > 0, (dMGRADUDen*dMGRAEmpDen)/(dMGRADUDen+dMGRAEmpDen),0)", +Origin MGRA Dwelling Unit Density Normalized by Average in Bike Estimation Sample,oMGRADUDenNorm_bike,oMGRADUDen/6.77, +Origin MGRA Employment Density Normalized by Average in Bike Estimation Sample,oMGRAEmpDenNorm_bike,oMGRAEmpDen/6.47, +Origin MGRA Dwelling Unit Density Normalized by Average in Walk Estimation Sample,oMGRADUDenNorm_walk,oMGRADUDen/7.18, +Origin MGRA Total Intersections Normalized by Average in Walk Estimation Sample,oMGRATotIntNorm_walk,oMGRATotInt/56.19, +Destination MGRA Employment Density Normalized by Average in Walk Estimation Sample,dMGRAEmpDenNorm_walk,dMGRAEmpDen/13.16, +Normalized Landuse Variable Sum [Origin Employment + DU],LUVarsNormalized_bike,oMGRADUDenNorm_bike + oMGRAEmpDenNorm_bike, +Normalized Landuse Variable Sum [Origin Intersection + DU],LUVarsNormalized_walk,oMGRADUDenNorm_walk + oMGRATotIntNorm_walk, +persontype,personType,"df.ptype*np.where(is_indiv,1,0)", +Origin Terminal Time,oTermTime,"reindex(land_use.TERMINALTIME,origin).fillna(0)", +Destination Terminal Time,dTermTime,"reindex(land_use.TERMINALTIME,destination).fillna(0)", +#,,, +# FIXME need bike logsums and times,,, +Person is female,female,(df.SEX == 2), +#bike logsum inbound,bikeLSI,odt_skims['BIKE_LOGSUM'], +#bike logsum outbound (same as inbound),bikeLSO,dot_skims['BIKE_LOGSUM'], +#bike time inbound,bike_time_inb,dot_skims['BIKE_TIME'], +#bike time outbound,bike_time_out,odt_skims['BIKE_TIME'], +bike logsum inbound,bikeLSI,0, +bike logsum outbound (same as inbound),bikeLSO,0, +bike time inbound,bike_time_inb,do_skims['DISTWALK'] / bikeSpeed * 60, +bike time outbound,bike_time_out,od_skims['DISTWALK'] / bikeSpeed * 60, +bike availability,bikeAvailable,(bikeLSI > -300) & (bikeLSO > -300) & (od_skims['DISTWALK']>0), + +#,,, +"Cost factor for shared 2 tours, 1/(2^0.8)",costFactorS2,0.57, +"Cost factor for shared 3+ tours, 1/(3.5^0.8)",costFactorS3,0.37, +,sov_available,"(age>=minimumAgeDA) * np.where(is_joint,0,1) * np.where(is_atwork_subtour==0,1,np.where(_parent_tour_mode=='DRIVEALONE',1,0))", +,sr2_available,"np.where(is_joint==0,1,np.where(num_participants>=3,0,1))", +,walkAvailable,"np.where(walk_time_skims_out < max_walk_time, 1,0) * np.where(walk_time_skims_inb < max_walk_time, 1,0)", +,SCHBUS_available,(tour_type =='school') & (personType!=3), +#,,, +Determining Tour Origin,origin,df.origin if 'origin' in df.columns else df.home_zone_id if 'home_zone_id' in df.columns else df.workplace_zone_id, +Determining Tour Destination,destination,df.destination if 'destination' in df.columns else df.alt_dest, +#access egress distances,,, +,origin_local_dist,"reindex(land_use.walk_dist_local_bus, origin)", +,origin_micro_local_dist_tncout,"odt_skims['KTW_ACC']/60 * driveSpeed", +,origin_micro_local_dist_tncin,"odt_skims['WTK_ACC']/60 * driveSpeed", +,dest_local_dist,"reindex(land_use.walk_dist_local_bus, destination)", +,dest_micro_local_dist,"reindex(land_use.micro_dist_local_bus, destination)", +#access egress times,,, +,origin_local_time,origin_local_dist * 60/walkSpeed, +,dest_local_time,dest_local_dist * 60/walkSpeed, +#,,, +,walk_local_available,(odt_skims['WTW_TIV']>0) & (dot_skims['WTW_TIV']>0), +,knr_local_available,(odt_skims['KTW_TIV']>0) & (dot_skims['WTK_TIV']>0), +,tnc_local_available,(odt_skims['KTW_TIV']>0) & (dot_skims['WTK_TIV']>0), +# Micromobility times,,, +ebike time inbound,ebike_time_inb,bike_time_inb * bikeSpeed / ebikeSpeed, +ebike time outbound,ebike_time_out,bike_time_out * bikeSpeed / ebikeSpeed, +ebike available,ebike_available,(ebike_time_inb>0) & (ebike_time_out>0), +escooter time inbound,escooter_time_inb,bike_time_inb * bikeSpeed / escooterSpeed, +escooter time outbound,escooter_time_out,bike_time_out * bikeSpeed / escooterSpeed, +escooter available,escooter_available,(escooter_time_inb>0) & (escooter_time_out>0), +Micromobility access time outbound,micro_access_out,"reindex(land_use.ESCOOACCTIME,origin)", +Micromobility access time inbound,micro_access_inb,"reindex(land_use.ESCOOACCTIME,destination)", +ebike max distance availability,ebikeMaxDistance,"(od_skims[('SOV_M_DIST', 'MD')] > ebikeMaxDist)", +escooter max distance availability,escooterMaxDistance,"(od_skims[('SOV_M_DIST', 'MD')] > escooterMaxDist)", +# Microtransit and NEV,,, +microtransit available at origin,microtransit_orig,"reindex(land_use.microtransit, df[orig_col_name])", +microtransit available at destination,microtransit_dest,"reindex(land_use.microtransit, df[dest_col_name])", +microtransit operating at time of tour,microtransit_operating,True, +microtransit available,microtransit_available,(microtransit_orig > 0) & (microtransit_orig == microtransit_dest) & (s3_dist_skims_out < microtransitMaxDist) & (s3_dist_skims_inb < microtransitMaxDist) & microtransit_operating, +microtransit direct time,microtransit_direct_time,"np.maximum(s3_dist_skims_out/microtransitSpeed*60, s3_time_skims_out) + np.maximum(s3_dist_skims_inb/microtransitSpeed*60, s3_time_skims_inb)", +microtransit total time,microtransit_time,"np.maximum(microtransit_direct_time + microtransitDiversionConstant, microtransitDiversionFactor*microtransit_direct_time)", +nev available at origin,nev_orig,"reindex(land_use.nev, df[orig_col_name])", +nev available at destination,nev_dest,"reindex(land_use.nev, df[dest_col_name])", +nev operating at time of tour,nev_operating,True, +nev available,nev_available,(nev_orig > 0) & (nev_orig == nev_dest) & (s3_dist_skims_out < nevMaxDist) & (s3_dist_skims_inb < nevMaxDist) & nev_operating, +nev direct time,nev_direct_time,"np.maximum(s3_dist_skims_out/nevSpeed*60, s3_time_skims_out) + np.maximum(s3_dist_skims_inb/nevSpeed*60, s3_time_skims_inb)", +nev total time,nev_time,"np.maximum(nev_direct_time + nevDiversionConstant, nevDiversionFactor*nev_direct_time)", +# Microtransit and NEV access to transit,,, +microtransit access to local available,microtransit_local_access_available,(microtransit_orig>0) & (origin_micro_local_dist_tncout0) & (origin_micro_local_dist_tncout0) & (dest_micro_local_dist>maxWalkIfMTAccessAvailable) & (dest_micro_local_dist0) & (dest_micro_local_dist>maxWalkIfMTAccessAvailable) & (dest_micro_local_dist 0) & (ldt_skims['WTW_TIV']>0) & (dlt_skims['WTW_TIV']>0)) if _CALC_PNR else False, +outbound auto time for pnr,auto_time_pnr_out,"0 if not _CALC_PNR else np.where(is_valid_pnr, (olt_skims['SOV_L_TIME'] * (_vot_bin_da == 1)) + (olt_skims['SOV_M_TIME'] * (_vot_bin_da == 2)) + (olt_skims['SOV_H_TIME'] * (_vot_bin_da == 3)), 0)", +inbound auto time for pnr,auto_time_pnr_inb,"0 if not _CALC_PNR else np.where(is_valid_pnr, (lot_skims['SOV_L_TIME'] * (_vot_bin_da == 1)) + (lot_skims['SOV_M_TIME'] * (_vot_bin_da == 2)) + (lot_skims['SOV_H_TIME'] * (_vot_bin_da == 3)), 0)", +outbound auto dist for pnr,auto_dist_pnr_out,"0 if not _CALC_PNR else np.where(is_valid_pnr, (olt_skims['SOV_L_DIST'] * (_vot_bin_da == 1)) + (olt_skims['SOV_M_DIST'] * (_vot_bin_da == 2)) + (olt_skims['SOV_H_DIST'] * (_vot_bin_da == 3)), 0)", +inbound auto dist for pnr,auto_dist_pnr_inb,"0 if not _CALC_PNR else np.where(is_valid_pnr, (lot_skims['SOV_L_DIST'] * (_vot_bin_da == 1)) + (lot_skims['SOV_M_DIST'] * (_vot_bin_da == 2)) + (lot_skims['SOV_H_DIST'] * (_vot_bin_da == 3)), 0)", +in vehicle transit time pnr,transit_time_pnr,"0 if not _CALC_PNR else np.where(is_valid_pnr, (ldt_skims['WTW_TIV'] + dlt_skims['WTW_TIV']), 0)", +transfers pnr,transfers_pnr,"0 if not _CALC_PNR else np.where(is_valid_pnr, (ldt_skims['WTW_XFR'] + dlt_skims['WTW_XFR']), 0)", +transfer walk times pnr,xfer_walk_pnr,"0 if not _CALC_PNR else np.where(is_valid_pnr, (ldt_skims['WTW_AUX'] + dlt_skims['WTW_AUX']), 0)", +transfer wait times pnr,xfer_wait_pnr,"0 if not _CALC_PNR else np.where(is_valid_pnr, (ldt_skims['WTW_XWT'] + dlt_skims['WTW_XWT']), 0)", +first wait times pnr,iwait_pnr,"0 if not _CALC_PNR else np.where(is_valid_pnr, (ldt_skims['WTW_FWT'] + dlt_skims['WTW_FWT']), 0)", +stop type constant pnr,stop_type_pnr,"0 if not _CALC_PNR else np.where(is_valid_pnr, (ldt_skims['WTW_RST'] + dlt_skims['WTW_RST']), 0)", +vehicle type constant pnr,vehicle_type_pnr,"0 if not _CALC_PNR else np.where(is_valid_pnr, (ldt_skims['WTW_VTC'] + dlt_skims['WTW_VTC']), 0)", +walking access and egress times at dest end pnr,walk_at_dest_end_pnr,"2 * dest_local_time", +parking cost pnr,parking_cost_pnr,"0 if ((not _CALC_PNR) or ('PNR_PRKCST' not in land_use.columns)) else np.where(is_valid_pnr,reindex(land_use.PNR_PRKCST, df.pnr_zone_id), 0)" +# FIXME need transit fares,,, +#fare pnr,fare_pnr,"0 if not _CALC_PNR else np.where(is_valid_pnr, (ldt_skims['WTW_FARE'] + dlt_skims['WTW_FARE']), 0)", +fare pnr,fare_pnr,0, diff --git a/resident/configs/tour_mode_choice_coefficients.csv b/resident/configs/tour_mode_choice_coefficients.csv new file mode 100644 index 0000000..b65ad3e --- /dev/null +++ b/resident/configs/tour_mode_choice_coefficients.csv @@ -0,0 +1,754 @@ +coefficient_name,value,constrain +coef_zero,0.0,T +coef_one,1.0,T +coef_nest_root,1.0,T +coef_nest_AUTO,0.5,T +coef_nest_NONMOTORIZED,0.5,T +coef_nest_MICROMOBILITY,0.5,T +coef_nest_TRANSIT,0.5,T +coef_nest_RIDEHAIL,0.5,T +coef_nest_SCHOOL_BUS,0.5,T +coef_ivt_work,-0.016,F +coef_ivt_univ,-0.016,F +coef_ivt_school,-0.01,F +coef_ivt_maint,-0.017,F +coef_ivt_disc,-0.015,F +coef_ivt_atwork,-0.032,F +coef_rel_out_work,-0.224,F +coef_rel_out_univ,-0.192,F +coef_rel_out_school,-0.12,F +coef_rel_out_maint,-0.204,F +coef_rel_out_disc,-0.18,F +coef_rel_out_atwork,-0.384,F +coef_rel_inb_work,-0.192,F +coef_rel_inb_univ,-0.192,F +coef_rel_inb_school,-0.12,F +coef_rel_inb_maint,-0.204,F +coef_rel_inb_disc,-0.18,F +coef_rel_inb_atwork,-0.384,F +coef_income_work,-0.625,F +coef_income_univ,-0.262,F +coef_income_school,-0.262,F +coef_income_maint,-0.262,F +coef_income_disc,-0.262,F +coef_income_atwork,-0.262,F +coef_walktime_work,-0.0424,F +coef_walktime_univ,-0.0424,F +coef_walktime_school,-0.068,F +coef_walktime_maint,-0.03995,F +coef_walktime_disc,-0.045,F +coef_walktime_atwork,-0.0848,F +coef_bikels_work,0.134333061,F +coef_bikels_univ,0.134333061,F +coef_bikels_school,0.214932897,F +coef_bikels_maint,0.22,F +coef_bikels_disc,0.22,F +coef_bikels_atwork,0.23,F +coef_wait_work,-0.024,F +coef_wait_univ,-0.024,F +coef_wait_school,-0.015,F +coef_wait_maint,-0.0255,F +coef_wait_disc,-0.0225,F +coef_wait_atwork,-0.048,F +coef_xwalk_work,-0.04,F +coef_xwalk_univ,-0.04,F +coef_xwalk_school,-0.025,F +coef_xwalk_maint,-0.0425,F +coef_xwalk_disc,-0.0375,F +coef_xwalk_atwork,-0.08,F +coef_xwait_work,-0.032,F +coef_xwait_univ,-0.032,F +coef_xwait_school,-0.02,F +coef_xwait_maint,-0.034,F +coef_xwait_disc,-0.03,F +coef_xwait_atwork,-0.064,F +coef_xfer_work,-0.024,F +coef_xfer_univ,-0.024,F +coef_xfer_school,-0.015,F +coef_xfer_maint,-0.0255,F +coef_xfer_disc,-0.0225,F +coef_xfer_atwork,-0.048,F +coef_xferdrive_work,-0.032,F +coef_xferdrive_univ,-0.032,F +coef_xferdrive_school,-0.02,F +coef_xferdrive_maint,-0.034,F +coef_xferdrive_disc,-0.03,F +coef_xferdrive_atwork,-0.064,F +coef_acctime_work,-0.032,F +coef_acctime_univ,-0.032,F +coef_acctime_school,-0.02,F +coef_acctime_maint,-0.034,F +coef_acctime_disc,-0.03,F +coef_acctime_atwork,-0.064,F +coef_oMix_nmot_work,0.210144,F +coef_oMix_wTran_work,0.0,F +coef_oIntDen_nmot_work,0.002998,F +coef_oIntDen_wTran_work,0.0,F +coef_dEmpDen_nmot_work,0.020707,F +coef_dEmpDen_wTran_work,0.0,F +coef_dEmpDen_dTran_work,0.0,F +coef_age1624_sr2_work,-0.21388,F +coef_age1624_sr3p_work,-1.79023,F +coef_age1624_nmot_work,0.303216,F +coef_age1624_tran_work,0.794718,F +coef_age4155_sr2_work,-0.30638,F +coef_age4155_sr3p_work,-0.41025,F +coef_age4155_nmot_work,-0.17752,F +coef_age4155_tran_work,-0.42301,F +coef_age5664_sr2_work,-1.02962,F +coef_age5664_sr3p_work,-0.85641,F +coef_age5664_nmot_work,-0.64534,F +coef_age5664_tran_work,-0.44991,F +coef_age65pl_sr2_work,-0.67111,F +coef_age65pl_sr3p_work,-1.43462,F +coef_age65pl_nmot_work,-1.45334,F +coef_age65pl_tran_work,-1.1231,F +coef_female_sr2_work,0.594728,F +coef_female_sr3p_work,0.848064,F +coef_female_tran_work,0.157786,F +coef_female_nmot_work,0.0,F +coef_ageund16_bike_work,0.0,F +coef_age1624_bike_work,0.0,F +coef_age4155_bike_work,-0.73111,F +coef_age5664_bike_work,-0.64352,F +coef_age65pl_bike_work,-1.54867,F +coef_female_bike_work,-1.19364,F +coef_inc100plus_bike_work,0.641381,F +coef_LUnorm_bike_work,0.083266,F +coef_oMlCoast_bike_work,-1.42018,F +coef_oMlCoast2p_bike_work,1.335076,F +coef_oMlCoast5p_bike_work,0.079563,F +coef_ageund16_walk_work,2.188395,F +coef_age1624_walk_work,1.430593,F +coef_age4155_walk_work,-0.43207,F +coef_age5664_walk_work,-0.51999,F +coef_age65pl_walk_work,-0.83162,F +coef_female_walk_work,0.0,F +coef_inc60100_walk_work,-0.20839,F +coef_LUnorm_walk_work,0.009309,F +coef_dEmpNorm_walk_work,0.099811,F +coef_hhsize2_sr2_work,1.069642353,F +coef_hhsize2_sr3p_work,-0.467357298,F +coef_hhsize3_sr2_work,1.58018418,F +coef_hhsize3_sr3p_work,0.654631352,F +coef_hhsize4p_sr2_work,1.688389262,F +coef_hhsize4p_sr3p_work,1.49870326,F +coef_oMix_nmot_univ,0.122315,F +coef_oMix_wTran_univ,0.0,F +coef_oIntDen_nmot_univ,0.009072,F +coef_oIntDen_wTran_univ,0.0,F +coef_dEmpDen_nmot_univ,0.081786,F +coef_dEmpDen_wTran_univ,0.0,F +coef_dEmpDen_dTran_univ,0.0,F +coef_age1624_sr2_univ,0.0,F +coef_age1624_sr3p_univ,0.0,F +coef_age1624_nmot_univ,0.0,F +coef_age1624_tran_univ,0.461169,F +coef_age4155_sr2_univ,0.0,F +coef_age4155_sr3p_univ,0.0,F +coef_age4155_nmot_univ,0.0,F +coef_age4155_tran_univ,0.0,F +coef_age5664_sr2_univ,0.0,F +coef_age5664_sr3p_univ,0.0,F +coef_age5664_nmot_univ,0.0,F +coef_age5664_tran_univ,0.0,F +coef_age65pl_sr2_univ,0.0,F +coef_age65pl_sr3p_univ,0.0,F +coef_age65pl_nmot_univ,0.0,F +coef_age65pl_tran_univ,0.0,F +coef_female_sr2_univ,0.0,F +coef_female_sr3p_univ,0.0,F +coef_female_tran_univ,0.0,F +coef_female_nmot_univ,0.0,F +coef_ageund16_bike_univ,0.0,F +coef_age1624_bike_univ,0.0,F +coef_age4155_bike_univ,-0.73111,F +coef_age5664_bike_univ,-0.64352,F +coef_age65pl_bike_univ,-1.54867,F +coef_female_bike_univ,-1.19364,F +coef_inc100plus_bike_univ,0.641381,F +coef_LUnorm_bike_univ,0.083266,F +coef_oMlCoast_bike_univ,0.0,F +coef_oMlCoast2p_bike_univ,0.0,F +coef_oMlCoast5p_bike_univ,0.0,F +coef_ageund16_walk_univ,2.188395,F +coef_age1624_walk_univ,1.430593,F +coef_age4155_walk_univ,-0.43207,F +coef_age5664_walk_univ,-0.51999,F +coef_age65pl_walk_univ,-0.83162,F +coef_female_walk_univ,0.0,F +coef_inc60100_walk_univ,-0.20839,F +coef_LUnorm_walk_univ,0.009309,F +coef_dEmpNorm_walk_univ,0.099811,F +coef_hhsize2_sr2_univ,1.871179646,F +coef_hhsize2_sr3p_univ,1.871179646,F +coef_hhsize3_sr2_univ,1.871179646,F +coef_hhsize3_sr3p_univ,1.871179646,F +coef_hhsize4p_sr2_univ,2.426268851,F +coef_hhsize4p_sr3p_univ,2.426268851,F +coef_oMix_nmot_school,0.0,F +coef_oMix_wTran_school,0.0,F +coef_oIntDen_nmot_school,0.002951,F +coef_oIntDen_wTran_school,0.0,F +coef_dEmpDen_nmot_school,0.0,F +coef_dEmpDen_wTran_school,0.0,F +coef_dEmpDen_dTran_school,0.0,F +coef_age0105_schb_school,0.0,F +coef_age0105_nmot_school,-1.16217,F +coef_age0105_tran_school,-6.49996,F +coef_age0612_schb_school,1.448589,F +coef_age0612_nmot_school,-0.57675,F +coef_age0612_tran_school,-4.5987,F +coef_age1315_schb_school,1.296494,F +coef_age1315_nmot_school,0.687165,F +coef_age1315_tran_school,-1.18344,F +coef_female_sr2_school,0.370191,F +coef_female_sr3p_school,0.326161,F +coef_female_tran_school,0.609312,F +coef_female_nmot_school,0.0,F +coef_female_schb_school,0.0,F +coef_ageund16_bike_school,0.0,F +coef_age1624_bike_school,0.0,F +coef_age4155_bike_school,-1.16978,F +coef_age5664_bike_school,-1.02963,F +coef_age65pl_bike_school,-2.47787,F +coef_female_bike_school,-1.90983,F +coef_inc100plus_bike_school,1.026209,F +coef_LUnorm_bike_school,0.133225,F +coef_oMlCoast_bike_school,-2.27229,F +coef_oMlCoast2p_bike_school,2.136121,F +coef_oMlCoast5p_bike_school,0.127301,F +coef_ageund16_walk_school,3.371151,F +coef_age1624_walk_school,2.126636,F +coef_age4155_walk_school,-0.76078,F +coef_age5664_walk_school,-0.90034,F +coef_age65pl_walk_school,-1.38069,F +coef_female_walk_school,0.0,F +coef_inc60100_walk_school,-0.35622,F +coef_LUnorm_walk_school,0.701592,F +coef_dEmpNorm_walk_school,0.191664,F +coef_hhsize2_sr2_school,0.0,F +coef_hhsize2_sr3p_school,0.0,F +coef_hhsize3_sr2_school,0.66814,F +coef_hhsize3_sr3p_school,0.66814,F +coef_hhsize4p_sr2_school,0.41147,F +coef_hhsize4p_sr3p_school,2.09725,F +coef_oMix_nmot_maint,0.145634,F +coef_oMix_wTran_maint,0.0,F +coef_oIntDen_nmot_maint,0.0,F +coef_oIntDen_wTran_maint,0.0,F +coef_dEmpDen_nmot_maint,0.0,F +coef_dEmpDen_wTran_maint,0.0,F +coef_dEmpDen_dTran_maint,0.0,F +coef_age1624_sr2_maint,0.0,F +coef_age1624_sr3p_maint,0.0,F +coef_age1624_nmot_maint,0.0,F +coef_age1624_tran_maint,1.621112,F +coef_age4155_sr2_maint,-0.82262,F +coef_age4155_sr3p_maint,-1.93552,F +coef_age4155_nmot_maint,-1.34146,F +coef_age4155_tran_maint,-1.39312,F +coef_age5664_sr2_maint,-0.95497,F +coef_age5664_sr3p_maint,-2.16777,F +coef_age5664_nmot_maint,-1.3422,F +coef_age5664_tran_maint,-1.46184,F +coef_age65pl_sr2_maint,-1.06222,F +coef_age65pl_sr3p_maint,-2.1471,F +coef_age65pl_nmot_maint,-2.32071,F +coef_age65pl_tran_maint,-2.86495,F +coef_female_sr2_maint,0.323867,F +coef_female_sr3p_maint,0.34258,F +coef_female_tran_maint,0.0,F +coef_female_nmot_maint,0.0,F +coef_ageund16_bike_maint,0.0,F +coef_age1624_bike_maint,0.0,F +coef_age4155_bike_maint,-0.68811,F +coef_age5664_bike_maint,-0.60566,F +coef_age65pl_bike_maint,-1.45757,F +coef_female_bike_maint,-1.12343,F +coef_inc100plus_bike_maint,0.603652,F +coef_LUnorm_bike_maint,0.078368,F +coef_oMlCoast_bike_maint,-1.33664,F +coef_oMlCoast2p_bike_maint,1.256542,F +coef_oMlCoast5p_bike_maint,0.074883,F +coef_ageund16_walk_maint,1.98303,F +coef_age1624_walk_maint,1.250962,F +coef_age4155_walk_maint,-0.44752,F +coef_age5664_walk_maint,-0.52961,F +coef_age65pl_walk_maint,-0.81217,F +coef_female_walk_maint,0.0,F +coef_inc60100_walk_maint,-0.20954,F +coef_LUnorm_walk_maint,0.412701,F +coef_dEmpNorm_walk_maint,0.112744,F +coef_hhsize2_sr2_maint,0.0,F +coef_hhsize2_sr3p_maint,-1.679852226,F +coef_hhsize3_sr2_maint,0.488088852,F +coef_hhsize3_sr3p_maint,-1.335043303,F +coef_hhsize4p_sr2_maint,0.308239564,F +coef_hhsize4p_sr3p_maint,0.585684607,F +coef_oMix_nmot_disc,0.172434,F +coef_oMix_wTran_disc,0.0,F +coef_oIntDen_nmot_disc,0.005711,F +coef_oIntDen_wTran_disc,0.0,F +coef_dEmpDen_nmot_disc,0.0,F +coef_dEmpDen_wTran_disc,0.0,F +coef_dEmpDen_dTran_disc,0.0,F +coef_age1624_sr2_disc,-0.51961,F +coef_age1624_sr3p_disc,-1.31632,F +coef_age1624_nmot_disc,-0.5557,F +coef_age1624_tran_disc,1.063749,F +coef_age4155_sr2_disc,-1.04157,F +coef_age4155_sr3p_disc,-1.21044,F +coef_age4155_nmot_disc,-1.14969,F +coef_age4155_tran_disc,-0.48434,F +coef_age5664_sr2_disc,-0.84295,F +coef_age5664_sr3p_disc,-0.96503,F +coef_age5664_nmot_disc,-0.97814,F +coef_age5664_tran_disc,-1.0845,F +coef_age65pl_sr2_disc,-0.89435,F +coef_age65pl_sr3p_disc,-1.11463,F +coef_age65pl_nmot_disc,-1.69155,F +coef_age65pl_tran_disc,-2.49829,F +coef_female_sr2_disc,0.261989,F +coef_female_sr3p_disc,0.273571,F +coef_female_tran_disc,-0.23309,F +coef_female_nmot_disc,0.0,F +coef_beachParkBikeConstant,0.7,F +coef_ageund16_bike_disc,0.0,F +coef_age1624_bike_disc,0.0,F +coef_age4155_bike_disc,-0.77985,F +coef_age5664_bike_disc,-0.68642,F +coef_age65pl_bike_disc,-1.65192,F +coef_female_bike_disc,-1.27322,F +coef_inc100plus_bike_disc,0.684139,F +coef_LUnorm_bike_disc,0.088817,F +coef_oMlCoast_bike_disc,-1.51486,F +coef_oMlCoast2p_bike_disc,1.424081,F +coef_oMlCoast5p_bike_disc,0.084867,F +coef_ageund16_walk_disc,2.247434,F +coef_age1624_walk_disc,1.417757,F +coef_age4155_walk_disc,-0.50719,F +coef_age5664_walk_disc,-0.60023,F +coef_age65pl_walk_disc,-0.92046,F +coef_female_walk_disc,0.0,F +coef_inc60100_walk_disc,-0.23748,F +coef_LUnorm_walk_disc,0.467728,F +coef_dEmpNorm_walk_disc,0.127776,F +coef_hhsize2_sr2_disc,0.352972249,F +coef_hhsize2_sr3p_disc,-0.936579922,F +coef_hhsize3_sr2_disc,0.412665036,F +coef_hhsize3_sr3p_disc,-0.798959661,F +coef_hhsize4p_sr2_disc,0.761157155,F +coef_hhsize4p_sr3p_disc,0.578130166,F +coef_age1624_sr2_atwork,0.0,F +coef_age1624_sr3p_atwork,0.0,F +coef_age1624_nmot_atwork,0.0,F +coef_age1624_tran_atwork,0.0,F +coef_age4155_sr2_atwork,0.0,F +coef_age4155_sr3p_atwork,0.0,F +coef_age4155_nmot_atwork,0.0,F +coef_age4155_tran_atwork,-1.166,F +coef_age5664_sr2_atwork,0.0,F +coef_age5664_sr3p_atwork,0.0,F +coef_age5664_nmot_atwork,0.0,F +coef_age5664_tran_atwork,-1.263,F +coef_age65pl_sr2_atwork,0.0,F +coef_age65pl_sr3p_atwork,0.0,F +coef_age65pl_nmot_atwork,0.0,F +coef_age65pl_tran_atwork,0.0,F +coef_sr_da_atwork,-0.824,F +coef_sr_sr_atwork,2.435,F +coef_female_sr2_atwork,0.0,F +coef_female_sr3p_atwork,0.0,F +coef_female_tran_atwork,0.0,F +coef_female_nmot_atwork,0.0,F +coef_oMix_nmot_atwork,0.214,F +coef_oMix_wTran_atwork,0.0,F +coef_oIntDen_nmot_atwork,0.0,F +coef_oIntDen_wTran_atwork,0.0,F +coef_dEmpDen_nmot_atwork,0.0,F +coef_dEmpDen_wTran_atwork,0.0,F +coef_dEmpDen_dTran_atwork,0.0,F +coef_hhsize2_sr2_atwork,0.0,F +coef_hhsize2_sr3p_atwork,0.0,F +coef_hhsize3_sr2_atwork,0.0,F +coef_hhsize3_sr3p_atwork,0.0,F +coef_hhsize4p_sr2_atwork,0.0,F +coef_hhsize4p_sr3p_atwork,0.0,F +coef_calib_civtebikeownership_BIKE_school,1.0,F +coef_calib_civtebikeownership_BIKE_atwork,1.0,F +coef_calib_civtebikeownership_BIKE_maint,1.0,F +coef_calib_escorttour_WALK_maint,-1.2579,F +coef_calib_escorttour_PNR_TRANSIT_maint,-5.8388,F +coef_calib_civtebikeownership_BIKE_disc,1.0,F +coef_calib_civtebikeownership_BIKE_univ,1.0,F +coef_calib_escorttour_WALK_TRANSIT_maint,-5.8388,F +coef_calib_escorttour_BIKE_maint,-1.2579,F +coef_calib_escorttour_TNC_TRANSIT_maint,-5.8388,F +coef_calib_escorttour_KNR_TRANSIT_maint,-5.8388,F +coef_calib_civtebikeownership_BIKE_work,1.0,F +coef_calib_parkingconst_WLK_TRANSIT_work,0.64,F +coef_calib_parkingconst_WLK_TRANSIT_univ,0.64,F +coef_calib_parkingconst_WLK_TRANSIT_school,0.4,F 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+coef_calib_autodeficienthhind_KNR_TRANSIT_disc,-6.119248003,F +coef_calib_autodeficienthhind_TNC_TRANSIT_disc,-999.0,F +coef_calib_autodeficienthhind_TAXI_disc,-3.493112839,F +coef_calib_autodeficienthhind_TNC_SINGLE_disc,-4.637538123236864,F +coef_calib_autodeficienthhind_TNC_SHARED_disc,-6.658369,F +coef_calib_autodeficienthhind_EBIKE_disc,-3.553244205201142,F +coef_calib_autodeficienthhind_ESCOOTER_disc,-6.302998778155713,F +coef_calib_autosufficienthhin_SHARED2_disc,-0.6311678896592476,F +coef_calib_autosufficienthhin_SHARED3_disc,0.2541481243589367,F +coef_calib_autosufficienthhin_WALK_disc,-20,F +coef_calib_autosufficienthhin_BIKE_disc,-2.417694133782759,F +coef_calib_autosufficienthhin_WALK_TRANSIT_disc,-2.870,F +coef_calib_autosufficienthhin_PNR_TRANSIT_disc,-7.132,F +coef_calib_autosufficienthhin_KNR_TRANSIT_disc,-7.812,F +coef_calib_autosufficienthhin_TNC_TRANSIT_disc,-8.897372,F +coef_calib_autosufficienthhin_TAXI_disc,-7.985417,F +coef_calib_autosufficienthhin_TNC_SINGLE_disc,-8.227193243,F +coef_calib_autosufficienthhin_TNC_SHARED_disc,-9.985417,F +coef_calib_autosufficienthhin_EBIKE_disc,-4.86785239537406,F +coef_calib_autosufficienthhin_ESCOOTER_disc,-8.74478202515571,F +#coef_calib_zeroautohhjointtou_DRIVEALONE_maint,-999.0,F +#coef_calib_zeroautohhjointtou_SHARED2_maint,8.507773009,F +coef_calib_zeroautohhjointtou_SHARED3_maint,-65.0,F +coef_calib_zeroautohhjointtou_WALK_maint,-42.051551929940764,F +coef_calib_zeroautohhjointtou_BIKE_maint,-999.4773565867827,F +coef_calib_zeroautohhjointtou_WALK_TRANSIT_maint,-62.0,F +coef_calib_zeroautohhjointtou_PNR_TRANSIT_maint,-999.0,F +coef_calib_zeroautohhjointtou_KNR_TRANSIT_maint,-63.0,F +coef_calib_zeroautohhjointtou_TNC_TRANSIT_maint,-999.0,F +coef_calib_zeroautohhjointtou_TAXI_maint,-999.0502454,F +coef_calib_zeroautohhjointtou_TNC_SINGLE_maint,-999.0502454,F +coef_calib_zeroautohhjointtou_TNC_SHARED_maint,-999.0502454,F +coef_calib_zeroautohhjointtou_EBIKE_maint,-60.40469752502022,F +coef_calib_zeroautohhjointtou_ESCOOTER_maint,-58.11200717315571,F +#coef_calib_autodeficienthhjoi_SHARED2_maint,15.5330624,F +coef_calib_autodeficienthhjoi_SHARED3_maint,-48.16957674661429,F +coef_calib_autodeficienthhjoi_WALK_maint,-48.63043930165117,F +coef_calib_autodeficienthhjoi_BIKE_maint,-887.4773565867829,F +coef_calib_autodeficienthhjoi_WALK_TRANSIT_maint,-54.657619847723424,F +coef_calib_autodeficienthhjoi_PNR_TRANSIT_maint,-55.90392356,F +coef_calib_autodeficienthhjoi_KNR_TRANSIT_maint,-56.88159968,F +coef_calib_autodeficienthhjoi_TNC_TRANSIT_maint,-999.0,F +coef_calib_autodeficienthhjoi_TAXI_maint,-54.97066616,F +coef_calib_autodeficienthhjoi_TNC_SINGLE_maint,-56.97066616,F +coef_calib_autodeficienthhjoi_TNC_SHARED_maint,-56.97066616,F +coef_calib_autodeficienthhjoi_EBIKE_maint,-53.834057125020216,F +coef_calib_autodeficienthhjoi_ESCOOTER_maint,-55.11200717315571,F +#coef_calib_autosufficienthhjo_SHARED2_maint,4.253195896,F +coef_calib_autosufficienthhjo_SHARED3_maint,-4.175409902383396,F +coef_calib_autosufficienthhjo_WALK_maint,-3.7539241053813464,F +coef_calib_autosufficienthhjo_BIKE_maint,-17.90445689678276,F +coef_calib_autosufficienthhjo_WALK_TRANSIT_maint,-20.27274185,F +coef_calib_autosufficienthhjo_PNR_TRANSIT_maint,-18.77467449,F +coef_calib_autosufficienthhjo_KNR_TRANSIT_maint,-16.58383844,F +coef_calib_autosufficienthhjo_TNC_TRANSIT_maint,-1003.0,F +coef_calib_autosufficienthhjo_TAXI_maint,-21.31393156,F +coef_calib_autosufficienthhjo_TNC_SINGLE_maint,-23.31393156,F +coef_calib_autosufficienthhjo_TNC_SHARED_maint,-23.31393156,F +coef_calib_autosufficienthhjo_EBIKE_maint,-9.752892614991818,F +coef_calib_autosufficienthhjo_ESCOOTER_maint,-19.112007173155703,F +#coef_calib_zeroautohhjointtou_DRIVEALONE_disc,-999.0,F +#coef_calib_zeroautohhjointtou_SHARED2_disc,8.507773009,F +coef_calib_zeroautohhjointtou_SHARED3_disc,-70.24625539,F +coef_calib_zeroautohhjointtou_WALK_disc,-56.051551929940764,F +coef_calib_zeroautohhjointtou_BIKE_disc,-999.4773565867827,F +coef_calib_zeroautohhjointtou_WALK_TRANSIT_disc,-74.87568752,F +coef_calib_zeroautohhjointtou_PNR_TRANSIT_disc,-999.0,F +coef_calib_zeroautohhjointtou_KNR_TRANSIT_disc,-67.84999945,F +coef_calib_zeroautohhjointtou_TNC_TRANSIT_disc,-999.0,F +coef_calib_zeroautohhjointtou_TAXI_disc,-999.0,F +coef_calib_zeroautohhjointtou_TNC_SINGLE_disc,-999.0,F +coef_calib_zeroautohhjointtou_TNC_SHARED_disc,-999.0,F +coef_calib_zeroautohhjointtou_EBIKE_disc,-60.164697525020216,F +coef_calib_zeroautohhjointtou_ESCOOTER_disc,-57.8720071731557,F +#coef_calib_autodeficienthhjoi_SHARED2_disc,10.5330624,F +coef_calib_autodeficienthhjoi_SHARED3_disc,-48.16957674661429,F +coef_calib_autodeficienthhjoi_WALK_disc,-48.63043930165117,F +coef_calib_autodeficienthhjoi_BIKE_disc,-887.4773565867829,F +coef_calib_autodeficienthhjoi_WALK_TRANSIT_disc,-51.55258834772342,F +coef_calib_autodeficienthhjoi_PNR_TRANSIT_disc,-53.1684399,F +coef_calib_autodeficienthhjoi_KNR_TRANSIT_disc,-53.12678499,F +coef_calib_autodeficienthhjoi_TNC_TRANSIT_disc,-999.0,F +coef_calib_autodeficienthhjoi_TAXI_disc,-52.71598351,F +coef_calib_autodeficienthhjoi_TNC_SINGLE_disc,-54.71598351,F +coef_calib_autodeficienthhjoi_TNC_SHARED_disc,-54.71598351,F +coef_calib_autodeficienthhjoi_EBIKE_disc,-53.59405712502022,F +coef_calib_autodeficienthhjoi_ESCOOTER_disc,-54.8720071731557,F +#coef_calib_autosufficienthhjo_SHARED2_disc,4.253195896,F +coef_calib_autosufficienthhjo_SHARED3_disc,-2.175409902383395,F +coef_calib_autosufficienthhjo_WALK_disc,-7,F +coef_calib_autosufficienthhjo_BIKE_disc,-17.70975822678276,F +coef_calib_autosufficienthhjo_WALK_TRANSIT_disc,-18.918341,F +coef_calib_autosufficienthhjo_PNR_TRANSIT_disc,-15.46037795,F +coef_calib_autosufficienthhjo_KNR_TRANSIT_disc,-22.31188559,F +coef_calib_autosufficienthhjo_TNC_TRANSIT_disc,-19.31188559,F +coef_calib_autosufficienthhjo_TAXI_disc,-18.0336306,F +coef_calib_autosufficienthhjo_TNC_SINGLE_disc,-20.0336306,F +coef_calib_autosufficienthhjo_TNC_SHARED_disc,-20.0336306,F +coef_calib_autosufficienthhjo_EBIKE_disc,-9.51289261499182,F +coef_calib_autosufficienthhjo_ESCOOTER_disc,-18.872007173155698,F +coef_calib_zeroautohhindivtou_ESCOOTER_work,-6.992007173155711,F +coef_calib_zeroautohhindivtou_EBIKE_work,-7.2846975250202135,F +coef_calib_zeroautohhindivtou_ESCOOTER_univ,-6.992007173155711,F +coef_calib_zeroautohhindivtou_EBIKE_univ,-7.2846975250202135,F +coef_calib_zeroautohhindivtou_ESCOOTER_school,-4.2720071731557105,F +coef_calib_zeroautohhindivtou_EBIKE_school,-0.5646975250202154,F +coef_calib_zeroautohhindivtou_ESCOOTER_atwork,-31.912007173155704,F +coef_calib_zeroautohhindivtou_EBIKE_atwork,-88.20469752502021,F +coef_calib_autodeficienthhind_ESCOOTER_work,-8.99200717315571,F +coef_calib_autodeficienthhind_EBIKE_work,-7.2846975250202135,F +coef_calib_autodeficienthhind_ESCOOTER_univ,-4.992007173155711,F +coef_calib_autodeficienthhind_EBIKE_univ,-5.284697525020215,F +coef_calib_autodeficienthhind_ESCOOTER_school,-6.2720071731557105,F +coef_calib_autodeficienthhind_EBIKE_school,-4.564697525020215,F +coef_calib_autodeficienthhind_ESCOOTER_atwork,-10.912007173155711,F +coef_calib_autodeficienthhind_EBIKE_atwork,-11.204697525020217,F +coef_calib_autosufficienthhin_ESCOOTER_work,-8.99200717315571,F +coef_calib_autosufficienthhin_EBIKE_work,-5.606539508020214,F +coef_calib_autosufficienthhin_ESCOOTER_univ,-4.992007173155711,F +coef_calib_autosufficienthhin_EBIKE_univ,-5.284697525020215,F +coef_calib_autosufficienthhin_ESCOOTER_school,-6.2720071731557105,F +coef_calib_autosufficienthhin_EBIKE_school,-2.9282323636347396,F +coef_calib_autosufficienthhin_ESCOOTER_atwork,-10.912007173155711,F +coef_calib_autosufficienthhin_EBIKE_atwork,-5.877228265020214,F +coef_calib_onboard,-0.293475781,F +coef_calib_mt_zeroautohh,4.825,F +coef_calib_nev_zeroautohh,4.825,F +coef_calib_ebike_owner_BIKE,-1.407,F +coef_calib_ebike_owner_EBIKE,0.942,F +coef_calib_ebike_shared,-4.139014765889209,F \ No newline at end of file diff --git a/resident/configs/tour_mode_choice_coefficients_new_coef2.csv b/resident/configs/tour_mode_choice_coefficients_new_coef2.csv new file mode 100644 index 0000000..0005a37 --- /dev/null +++ b/resident/configs/tour_mode_choice_coefficients_new_coef2.csv @@ -0,0 +1,754 @@ +coefficient_name,value,constrain +coef_zero,0.0,T +coef_one,1.0,T +coef_nest_root,1.0,T +coef_nest_AUTO,0.5,T +coef_nest_NONMOTORIZED,0.5,T +coef_nest_MICROMOBILITY,0.5,T +coef_nest_TRANSIT,0.5,T +coef_nest_RIDEHAIL,0.5,T +coef_nest_SCHOOL_BUS,0.5,T +coef_ivt_work,-0.016,F +coef_ivt_univ,-0.016,F +coef_ivt_school,-0.01,F +coef_ivt_maint,-0.017,F +coef_ivt_disc,-0.015,F +coef_ivt_atwork,-0.032,F +coef_rel_out_work,-0.224,F +coef_rel_out_univ,-0.192,F +coef_rel_out_school,-0.12,F +coef_rel_out_maint,-0.204,F +coef_rel_out_disc,-0.18,F +coef_rel_out_atwork,-0.384,F +coef_rel_inb_work,-0.192,F +coef_rel_inb_univ,-0.192,F +coef_rel_inb_school,-0.12,F +coef_rel_inb_maint,-0.204,F +coef_rel_inb_disc,-0.18,F +coef_rel_inb_atwork,-0.384,F +coef_income_work,-0.625,F +coef_income_univ,-0.262,F +coef_income_school,-0.262,F +coef_income_maint,-0.262,F +coef_income_disc,-0.262,F +coef_income_atwork,-0.262,F +coef_walktime_work,-0.0424,F +coef_walktime_univ,-0.0424,F +coef_walktime_school,-0.068,F +coef_walktime_maint,-0.03995,F +coef_walktime_disc,-0.045,F +coef_walktime_atwork,-0.0848,F +coef_bikels_work,0.134333061,F +coef_bikels_univ,0.134333061,F +coef_bikels_school,0.214932897,F +coef_bikels_maint,0.22,F +coef_bikels_disc,0.22,F +coef_bikels_atwork,0.23,F +coef_wait_work,-0.024,F +coef_wait_univ,-0.024,F +coef_wait_school,-0.015,F +coef_wait_maint,-0.0255,F +coef_wait_disc,-0.0225,F +coef_wait_atwork,-0.048,F +coef_xwalk_work,-0.04,F +coef_xwalk_univ,-0.04,F +coef_xwalk_school,-0.025,F +coef_xwalk_maint,-0.0425,F +coef_xwalk_disc,-0.0375,F +coef_xwalk_atwork,-0.08,F +coef_xwait_work,-0.032,F +coef_xwait_univ,-0.032,F +coef_xwait_school,-0.02,F +coef_xwait_maint,-0.034,F +coef_xwait_disc,-0.03,F +coef_xwait_atwork,-0.064,F +coef_xfer_work,-0.024,F +coef_xfer_univ,-0.024,F +coef_xfer_school,-0.015,F +coef_xfer_maint,-0.0255,F +coef_xfer_disc,-0.0225,F +coef_xfer_atwork,-0.048,F +coef_xferdrive_work,-0.032,F +coef_xferdrive_univ,-0.032,F +coef_xferdrive_school,-0.02,F +coef_xferdrive_maint,-0.034,F +coef_xferdrive_disc,-0.03,F +coef_xferdrive_atwork,-0.064,F +coef_acctime_work,-0.032,F +coef_acctime_univ,-0.032,F +coef_acctime_school,-0.02,F +coef_acctime_maint,-0.034,F +coef_acctime_disc,-0.03,F +coef_acctime_atwork,-0.064,F +coef_oMix_nmot_work,0.210144,F +coef_oMix_wTran_work,0.0,F +coef_oIntDen_nmot_work,0.002998,F +coef_oIntDen_wTran_work,0.0,F +coef_dEmpDen_nmot_work,0.020707,F +coef_dEmpDen_wTran_work,0.0,F +coef_dEmpDen_dTran_work,0.0,F +coef_age1624_sr2_work,-0.21388,F +coef_age1624_sr3p_work,-1.79023,F +coef_age1624_nmot_work,0.303216,F +coef_age1624_tran_work,0.794718,F +coef_age4155_sr2_work,-0.30638,F +coef_age4155_sr3p_work,-0.41025,F +coef_age4155_nmot_work,-0.17752,F +coef_age4155_tran_work,-0.42301,F +coef_age5664_sr2_work,-1.02962,F +coef_age5664_sr3p_work,-0.85641,F +coef_age5664_nmot_work,-0.64534,F +coef_age5664_tran_work,-0.44991,F +coef_age65pl_sr2_work,-0.67111,F +coef_age65pl_sr3p_work,-1.43462,F +coef_age65pl_nmot_work,-1.45334,F +coef_age65pl_tran_work,-1.1231,F +coef_female_sr2_work,0.594728,F +coef_female_sr3p_work,0.848064,F +coef_female_tran_work,0.157786,F +coef_female_nmot_work,0.0,F +coef_ageund16_bike_work,0.0,F +coef_age1624_bike_work,0.0,F +coef_age4155_bike_work,-0.73111,F +coef_age5664_bike_work,-0.64352,F +coef_age65pl_bike_work,-1.54867,F +coef_female_bike_work,-1.19364,F +coef_inc100plus_bike_work,0.641381,F +coef_LUnorm_bike_work,0.083266,F +coef_oMlCoast_bike_work,-1.42018,F +coef_oMlCoast2p_bike_work,1.335076,F +coef_oMlCoast5p_bike_work,0.079563,F +coef_ageund16_walk_work,2.188395,F +coef_age1624_walk_work,1.430593,F +coef_age4155_walk_work,-0.43207,F +coef_age5664_walk_work,-0.51999,F +coef_age65pl_walk_work,-0.83162,F +coef_female_walk_work,0.0,F +coef_inc60100_walk_work,-0.20839,F +coef_LUnorm_walk_work,0.009309,F +coef_dEmpNorm_walk_work,0.099811,F +coef_hhsize2_sr2_work,1.069642353,F +coef_hhsize2_sr3p_work,-0.467357298,F +coef_hhsize3_sr2_work,1.58018418,F +coef_hhsize3_sr3p_work,0.654631352,F +coef_hhsize4p_sr2_work,1.688389262,F +coef_hhsize4p_sr3p_work,1.49870326,F +coef_oMix_nmot_univ,0.122315,F +coef_oMix_wTran_univ,0.0,F +coef_oIntDen_nmot_univ,0.009072,F +coef_oIntDen_wTran_univ,0.0,F +coef_dEmpDen_nmot_univ,0.081786,F +coef_dEmpDen_wTran_univ,0.0,F +coef_dEmpDen_dTran_univ,0.0,F +coef_age1624_sr2_univ,0.0,F +coef_age1624_sr3p_univ,0.0,F +coef_age1624_nmot_univ,0.0,F +coef_age1624_tran_univ,0.461169,F +coef_age4155_sr2_univ,0.0,F 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+coef_calib_civtebikeownership_BIKE_atwork,1.0,F +coef_calib_civtebikeownership_BIKE_maint,1.0,F +coef_calib_escorttour_WALK_maint,-1.2579,F +coef_calib_escorttour_PNR_TRANSIT_maint,-5.8388,F +coef_calib_civtebikeownership_BIKE_disc,1.0,F +coef_calib_civtebikeownership_BIKE_univ,1.0,F +coef_calib_escorttour_WALK_TRANSIT_maint,-5.8388,F +coef_calib_escorttour_BIKE_maint,-1.2579,F +coef_calib_escorttour_TNC_TRANSIT_maint,-5.8388,F +coef_calib_escorttour_KNR_TRANSIT_maint,-5.8388,F +coef_calib_civtebikeownership_BIKE_work,1.0,F +coef_calib_parkingconst_WLK_TRANSIT_work,0.64,F +coef_calib_parkingconst_WLK_TRANSIT_univ,0.64,F +coef_calib_parkingconst_WLK_TRANSIT_school,0.4,F +coef_calib_parkingconst_WLK_TRANSIT_maint,0.765,F +coef_calib_parkingconst_WLK_TRANSIT_disc,0.9,F +coef_calib_parkingconst_WLK_TRANSIT_atwork,0.96,F +coef_calib_parkingconst_DRV_TRANSIT_work,1.28,F +coef_calib_parkingconst_DRV_TRANSIT_univ,1.28,F +coef_calib_parkingconst_DRV_TRANSIT_school,0.8,F +coef_calib_parkingconst_DRV_TRANSIT_maint,1.36,F +coef_calib_parkingconst_DRV_TRANSIT_disc,1.2,F +coef_calib_parkingconst_DRV_TRANSIT_atwork,1.92,F +coef_calib_distance_WALK_TRANSIT_work,-0.016,F +coef_calib_distance_WALK_TRANSIT_univ,-0.016,F +coef_calib_distance_WALK_TRANSIT_school,-0.01,F +coef_calib_distance_WALK_TRANSIT_maint,-0.017,F +coef_calib_distance_WALK_TRANSIT_disc,-0.15,F +coef_calib_distance_WALK_TRANSIT_atwork,-0.032,F +coef_calib_distance_PNR_TRANSIT_work,-0.016,F +coef_calib_distance_PNR_TRANSIT_univ,-0.016,F +coef_calib_distance_PNR_TRANSIT_school,-0.01,F +coef_calib_distance_PNR_TRANSIT_maint,-0.017,F +coef_calib_distance_PNR_TRANSIT_disc,-0.015,F +coef_calib_distance_PNR_TRANSIT_atwork,-0.032,F +coef_calib_distance_KNR_TRANSIT_work,-0.08,F +coef_calib_distance_KNR_TRANSIT_univ,-0.08,F +coef_calib_distance_KNR_TRANSIT_school,-0.05,F +coef_calib_distance_KNR_TRANSIT_maint,-0.085,F +coef_calib_distance_KNR_TRANSIT_disc,-0.075,F +coef_calib_distance_KNR_TRANSIT_atwork,-0.16,F +coef_calib_distance_TNC_TRANSIT_work,-0.016,F +coef_calib_distance_TNC_TRANSIT_univ,-0.016,F +coef_calib_distance_TNC_TRANSIT_school,-0.01,F +coef_calib_distance_TNC_TRANSIT_maint,-0.017,F +coef_calib_distance_TNC_TRANSIT_disc,-0.015,F +coef_calib_distance_TNC_TRANSIT_atwork,-0.032,F +#coef_calib_zeroautohhindivtou_DRIVEALONE_work,-0.8236,F +coef_calib_zeroautohhindivtou_SHARED2_work,-1.0611602231451105,F +coef_calib_zeroautohhindivtou_SHARED3_work,-4.1746,F +coef_calib_zeroautohhindivtou_WALK_work,-12,F +coef_calib_zeroautohhindivtou_BIKE_work,-8.079656586782757,F +coef_calib_zeroautohhindivtou_WALK_TRANSIT_work,-1.000,F +coef_calib_zeroautohhindivtou_PNR_TRANSIT_work,10.0,F +coef_calib_zeroautohhindivtou_KNR_TRANSIT_work,-4.075069451,F +coef_calib_zeroautohhindivtou_TNC_TRANSIT_work,-929.0,F +coef_calib_zeroautohhindivtou_TAXI_work,-1.9627,F +coef_calib_zeroautohhindivtou_TNC_SINGLE_work,1.8216385166062703,F 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+coef_calib_zeroautohhindivtou_ESCOOTER_work,-6.992007173155711,F +coef_calib_zeroautohhindivtou_EBIKE_work,-7.2846975250202135,F +coef_calib_zeroautohhindivtou_ESCOOTER_univ,-6.992007173155711,F +coef_calib_zeroautohhindivtou_EBIKE_univ,-7.2846975250202135,F +coef_calib_zeroautohhindivtou_ESCOOTER_school,-4.2720071731557105,F +coef_calib_zeroautohhindivtou_EBIKE_school,-0.5646975250202154,F +coef_calib_zeroautohhindivtou_ESCOOTER_atwork,-31.912007173155704,F +coef_calib_zeroautohhindivtou_EBIKE_atwork,-88.20469752502021,F +coef_calib_autodeficienthhind_ESCOOTER_work,-8.99200717315571,F +coef_calib_autodeficienthhind_EBIKE_work,-7.2846975250202135,F +coef_calib_autodeficienthhind_ESCOOTER_univ,-4.992007173155711,F +coef_calib_autodeficienthhind_EBIKE_univ,-5.284697525020215,F +coef_calib_autodeficienthhind_ESCOOTER_school,-6.2720071731557105,F +coef_calib_autodeficienthhind_EBIKE_school,-4.564697525020215,F +coef_calib_autodeficienthhind_ESCOOTER_atwork,-10.912007173155711,F +coef_calib_autodeficienthhind_EBIKE_atwork,-11.204697525020217,F +coef_calib_autosufficienthhin_ESCOOTER_work,-8.99200717315571,F +coef_calib_autosufficienthhin_EBIKE_work,-5.606539508020214,F +coef_calib_autosufficienthhin_ESCOOTER_univ,-4.992007173155711,F +coef_calib_autosufficienthhin_EBIKE_univ,-5.284697525020215,F +coef_calib_autosufficienthhin_ESCOOTER_school,-6.2720071731557105,F +coef_calib_autosufficienthhin_EBIKE_school,-2.9282323636347396,F +coef_calib_autosufficienthhin_ESCOOTER_atwork,-10.912007173155711,F +coef_calib_autosufficienthhin_EBIKE_atwork,-5.877228265020214,F +coef_calib_onboard,-0.293475781,F +coef_calib_mt_zeroautohh,4.825,F +coef_calib_nev_zeroautohh,4.825,F +coef_calib_ebike_owner_BIKE,-1.407,F +coef_calib_ebike_owner_EBIKE,0.942,F +coef_calib_ebike_shared,-4.139014765889209,F \ No newline at end of file diff --git a/resident/configs/tour_mode_choice_coefficients_new_coef3.csv b/resident/configs/tour_mode_choice_coefficients_new_coef3.csv new file mode 100644 index 0000000..d6c9426 --- /dev/null +++ b/resident/configs/tour_mode_choice_coefficients_new_coef3.csv @@ -0,0 +1,754 @@ +coefficient_name,value,constrain +coef_zero,0.0,T +coef_one,1.0,T +coef_nest_root,1.0,T +coef_nest_AUTO,0.5,T +coef_nest_NONMOTORIZED,0.5,T +coef_nest_MICROMOBILITY,0.5,T +coef_nest_TRANSIT,0.5,T +coef_nest_RIDEHAIL,0.5,T +coef_nest_SCHOOL_BUS,0.5,T +coef_ivt_work,-0.016,F +coef_ivt_univ,-0.016,F +coef_ivt_school,-0.01,F +coef_ivt_maint,-0.017,F +coef_ivt_disc,-0.015,F +coef_ivt_atwork,-0.032,F +coef_rel_out_work,-0.224,F +coef_rel_out_univ,-0.192,F +coef_rel_out_school,-0.12,F +coef_rel_out_maint,-0.204,F +coef_rel_out_disc,-0.18,F +coef_rel_out_atwork,-0.384,F +coef_rel_inb_work,-0.192,F +coef_rel_inb_univ,-0.192,F +coef_rel_inb_school,-0.12,F +coef_rel_inb_maint,-0.204,F +coef_rel_inb_disc,-0.18,F +coef_rel_inb_atwork,-0.384,F +coef_income_work,-0.625,F +coef_income_univ,-0.262,F +coef_income_school,-0.262,F +coef_income_maint,-0.262,F +coef_income_disc,-0.262,F +coef_income_atwork,-0.262,F +coef_walktime_work,-0.0424,F +coef_walktime_univ,-0.0424,F +coef_walktime_school,-0.068,F +coef_walktime_maint,-0.03995,F +coef_walktime_disc,-0.045,F +coef_walktime_atwork,-0.0848,F +coef_bikels_work,0.134333061,F +coef_bikels_univ,0.134333061,F +coef_bikels_school,0.214932897,F +coef_bikels_maint,0.22,F +coef_bikels_disc,0.22,F +coef_bikels_atwork,0.23,F +coef_wait_work,-0.024,F +coef_wait_univ,-0.024,F +coef_wait_school,-0.015,F +coef_wait_maint,-0.0255,F +coef_wait_disc,-0.0225,F +coef_wait_atwork,-0.048,F +coef_xwalk_work,-0.04,F +coef_xwalk_univ,-0.04,F +coef_xwalk_school,-0.025,F +coef_xwalk_maint,-0.0425,F +coef_xwalk_disc,-0.0375,F +coef_xwalk_atwork,-0.08,F +coef_xwait_work,-0.032,F +coef_xwait_univ,-0.032,F +coef_xwait_school,-0.02,F +coef_xwait_maint,-0.034,F +coef_xwait_disc,-0.03,F +coef_xwait_atwork,-0.064,F +coef_xfer_work,-0.024,F +coef_xfer_univ,-0.024,F +coef_xfer_school,-0.015,F +coef_xfer_maint,-0.0255,F +coef_xfer_disc,-0.0225,F +coef_xfer_atwork,-0.048,F +coef_xferdrive_work,-0.032,F +coef_xferdrive_univ,-0.032,F +coef_xferdrive_school,-0.02,F +coef_xferdrive_maint,-0.034,F +coef_xferdrive_disc,-0.03,F +coef_xferdrive_atwork,-0.064,F +coef_acctime_work,-0.032,F +coef_acctime_univ,-0.032,F +coef_acctime_school,-0.02,F +coef_acctime_maint,-0.034,F +coef_acctime_disc,-0.03,F +coef_acctime_atwork,-0.064,F +coef_oMix_nmot_work,0.210144,F +coef_oMix_wTran_work,0.0,F +coef_oIntDen_nmot_work,0.002998,F +coef_oIntDen_wTran_work,0.0,F +coef_dEmpDen_nmot_work,0.020707,F +coef_dEmpDen_wTran_work,0.0,F +coef_dEmpDen_dTran_work,0.0,F +coef_age1624_sr2_work,-0.21388,F +coef_age1624_sr3p_work,-1.79023,F +coef_age1624_nmot_work,0.303216,F +coef_age1624_tran_work,0.794718,F +coef_age4155_sr2_work,-0.30638,F +coef_age4155_sr3p_work,-0.41025,F +coef_age4155_nmot_work,-0.17752,F +coef_age4155_tran_work,-0.42301,F +coef_age5664_sr2_work,-1.02962,F +coef_age5664_sr3p_work,-0.85641,F +coef_age5664_nmot_work,-0.64534,F +coef_age5664_tran_work,-0.44991,F +coef_age65pl_sr2_work,-0.67111,F +coef_age65pl_sr3p_work,-1.43462,F +coef_age65pl_nmot_work,-1.45334,F +coef_age65pl_tran_work,-1.1231,F +coef_female_sr2_work,0.594728,F +coef_female_sr3p_work,0.848064,F +coef_female_tran_work,0.157786,F +coef_female_nmot_work,0.0,F +coef_ageund16_bike_work,0.0,F +coef_age1624_bike_work,0.0,F +coef_age4155_bike_work,-0.73111,F +coef_age5664_bike_work,-0.64352,F +coef_age65pl_bike_work,-1.54867,F +coef_female_bike_work,-1.19364,F +coef_inc100plus_bike_work,0.641381,F +coef_LUnorm_bike_work,0.083266,F +coef_oMlCoast_bike_work,-1.42018,F +coef_oMlCoast2p_bike_work,1.335076,F +coef_oMlCoast5p_bike_work,0.079563,F +coef_ageund16_walk_work,2.188395,F +coef_age1624_walk_work,1.430593,F +coef_age4155_walk_work,-0.43207,F +coef_age5664_walk_work,-0.51999,F +coef_age65pl_walk_work,-0.83162,F +coef_female_walk_work,0.0,F +coef_inc60100_walk_work,-0.20839,F +coef_LUnorm_walk_work,0.009309,F +coef_dEmpNorm_walk_work,0.099811,F +coef_hhsize2_sr2_work,1.069642353,F +coef_hhsize2_sr3p_work,-0.467357298,F +coef_hhsize3_sr2_work,1.58018418,F +coef_hhsize3_sr3p_work,0.654631352,F +coef_hhsize4p_sr2_work,1.688389262,F +coef_hhsize4p_sr3p_work,1.49870326,F +coef_oMix_nmot_univ,0.122315,F +coef_oMix_wTran_univ,0.0,F +coef_oIntDen_nmot_univ,0.009072,F +coef_oIntDen_wTran_univ,0.0,F +coef_dEmpDen_nmot_univ,0.081786,F +coef_dEmpDen_wTran_univ,0.0,F +coef_dEmpDen_dTran_univ,0.0,F +coef_age1624_sr2_univ,0.0,F +coef_age1624_sr3p_univ,0.0,F +coef_age1624_nmot_univ,0.0,F +coef_age1624_tran_univ,0.461169,F +coef_age4155_sr2_univ,0.0,F +coef_age4155_sr3p_univ,0.0,F +coef_age4155_nmot_univ,0.0,F +coef_age4155_tran_univ,0.0,F +coef_age5664_sr2_univ,0.0,F +coef_age5664_sr3p_univ,0.0,F +coef_age5664_nmot_univ,0.0,F +coef_age5664_tran_univ,0.0,F +coef_age65pl_sr2_univ,0.0,F +coef_age65pl_sr3p_univ,0.0,F +coef_age65pl_nmot_univ,0.0,F +coef_age65pl_tran_univ,0.0,F +coef_female_sr2_univ,0.0,F +coef_female_sr3p_univ,0.0,F +coef_female_tran_univ,0.0,F +coef_female_nmot_univ,0.0,F +coef_ageund16_bike_univ,0.0,F +coef_age1624_bike_univ,0.0,F +coef_age4155_bike_univ,-0.73111,F +coef_age5664_bike_univ,-0.64352,F +coef_age65pl_bike_univ,-1.54867,F +coef_female_bike_univ,-1.19364,F +coef_inc100plus_bike_univ,0.641381,F +coef_LUnorm_bike_univ,0.083266,F +coef_oMlCoast_bike_univ,0.0,F +coef_oMlCoast2p_bike_univ,0.0,F +coef_oMlCoast5p_bike_univ,0.0,F +coef_ageund16_walk_univ,2.188395,F +coef_age1624_walk_univ,1.430593,F +coef_age4155_walk_univ,-0.43207,F +coef_age5664_walk_univ,-0.51999,F +coef_age65pl_walk_univ,-0.83162,F +coef_female_walk_univ,0.0,F +coef_inc60100_walk_univ,-0.20839,F +coef_LUnorm_walk_univ,0.009309,F +coef_dEmpNorm_walk_univ,0.099811,F +coef_hhsize2_sr2_univ,1.871179646,F +coef_hhsize2_sr3p_univ,1.871179646,F +coef_hhsize3_sr2_univ,1.871179646,F +coef_hhsize3_sr3p_univ,1.871179646,F +coef_hhsize4p_sr2_univ,2.426268851,F +coef_hhsize4p_sr3p_univ,2.426268851,F +coef_oMix_nmot_school,0.0,F +coef_oMix_wTran_school,0.0,F +coef_oIntDen_nmot_school,0.002951,F +coef_oIntDen_wTran_school,0.0,F +coef_dEmpDen_nmot_school,0.0,F +coef_dEmpDen_wTran_school,0.0,F +coef_dEmpDen_dTran_school,0.0,F +coef_age0105_schb_school,0.0,F +coef_age0105_nmot_school,-1.16217,F +coef_age0105_tran_school,-6.49996,F +coef_age0612_schb_school,1.448589,F +coef_age0612_nmot_school,-0.57675,F +coef_age0612_tran_school,-4.5987,F +coef_age1315_schb_school,1.296494,F +coef_age1315_nmot_school,0.687165,F +coef_age1315_tran_school,-1.18344,F +coef_female_sr2_school,0.370191,F +coef_female_sr3p_school,0.326161,F +coef_female_tran_school,0.609312,F +coef_female_nmot_school,0.0,F +coef_female_schb_school,0.0,F +coef_ageund16_bike_school,0.0,F +coef_age1624_bike_school,0.0,F +coef_age4155_bike_school,-1.16978,F +coef_age5664_bike_school,-1.02963,F +coef_age65pl_bike_school,-2.47787,F +coef_female_bike_school,-1.90983,F +coef_inc100plus_bike_school,1.026209,F +coef_LUnorm_bike_school,0.133225,F +coef_oMlCoast_bike_school,-2.27229,F +coef_oMlCoast2p_bike_school,2.136121,F +coef_oMlCoast5p_bike_school,0.127301,F +coef_ageund16_walk_school,3.371151,F +coef_age1624_walk_school,2.126636,F +coef_age4155_walk_school,-0.76078,F +coef_age5664_walk_school,-0.90034,F +coef_age65pl_walk_school,-1.38069,F +coef_female_walk_school,0.0,F +coef_inc60100_walk_school,-0.35622,F +coef_LUnorm_walk_school,0.701592,F +coef_dEmpNorm_walk_school,0.191664,F +coef_hhsize2_sr2_school,0.0,F +coef_hhsize2_sr3p_school,0.0,F +coef_hhsize3_sr2_school,0.66814,F +coef_hhsize3_sr3p_school,0.66814,F +coef_hhsize4p_sr2_school,0.41147,F +coef_hhsize4p_sr3p_school,2.09725,F +coef_oMix_nmot_maint,0.145634,F +coef_oMix_wTran_maint,0.0,F +coef_oIntDen_nmot_maint,0.0,F +coef_oIntDen_wTran_maint,0.0,F +coef_dEmpDen_nmot_maint,0.0,F +coef_dEmpDen_wTran_maint,0.0,F +coef_dEmpDen_dTran_maint,0.0,F +coef_age1624_sr2_maint,0.0,F +coef_age1624_sr3p_maint,0.0,F +coef_age1624_nmot_maint,0.0,F +coef_age1624_tran_maint,1.621112,F +coef_age4155_sr2_maint,-0.82262,F +coef_age4155_sr3p_maint,-1.93552,F +coef_age4155_nmot_maint,-1.34146,F +coef_age4155_tran_maint,-1.39312,F +coef_age5664_sr2_maint,-0.95497,F +coef_age5664_sr3p_maint,-2.16777,F +coef_age5664_nmot_maint,-1.3422,F +coef_age5664_tran_maint,-1.46184,F +coef_age65pl_sr2_maint,-1.06222,F +coef_age65pl_sr3p_maint,-2.1471,F +coef_age65pl_nmot_maint,-2.32071,F +coef_age65pl_tran_maint,-2.86495,F +coef_female_sr2_maint,0.323867,F +coef_female_sr3p_maint,0.34258,F +coef_female_tran_maint,0.0,F +coef_female_nmot_maint,0.0,F +coef_ageund16_bike_maint,0.0,F +coef_age1624_bike_maint,0.0,F +coef_age4155_bike_maint,-0.68811,F +coef_age5664_bike_maint,-0.60566,F +coef_age65pl_bike_maint,-1.45757,F +coef_female_bike_maint,-1.12343,F +coef_inc100plus_bike_maint,0.603652,F +coef_LUnorm_bike_maint,0.078368,F +coef_oMlCoast_bike_maint,-1.33664,F +coef_oMlCoast2p_bike_maint,1.256542,F +coef_oMlCoast5p_bike_maint,0.074883,F +coef_ageund16_walk_maint,1.98303,F +coef_age1624_walk_maint,1.250962,F +coef_age4155_walk_maint,-0.44752,F +coef_age5664_walk_maint,-0.52961,F +coef_age65pl_walk_maint,-0.81217,F +coef_female_walk_maint,0.0,F +coef_inc60100_walk_maint,-0.20954,F +coef_LUnorm_walk_maint,0.412701,F +coef_dEmpNorm_walk_maint,0.112744,F +coef_hhsize2_sr2_maint,0.0,F +coef_hhsize2_sr3p_maint,-1.679852226,F +coef_hhsize3_sr2_maint,0.488088852,F +coef_hhsize3_sr3p_maint,-1.335043303,F +coef_hhsize4p_sr2_maint,0.308239564,F +coef_hhsize4p_sr3p_maint,0.585684607,F +coef_oMix_nmot_disc,0.172434,F +coef_oMix_wTran_disc,0.0,F +coef_oIntDen_nmot_disc,0.005711,F +coef_oIntDen_wTran_disc,0.0,F +coef_dEmpDen_nmot_disc,0.0,F +coef_dEmpDen_wTran_disc,0.0,F +coef_dEmpDen_dTran_disc,0.0,F +coef_age1624_sr2_disc,-0.51961,F +coef_age1624_sr3p_disc,-1.31632,F +coef_age1624_nmot_disc,-0.5557,F +coef_age1624_tran_disc,1.063749,F +coef_age4155_sr2_disc,-1.04157,F +coef_age4155_sr3p_disc,-1.21044,F +coef_age4155_nmot_disc,-1.14969,F +coef_age4155_tran_disc,-0.48434,F +coef_age5664_sr2_disc,-0.84295,F +coef_age5664_sr3p_disc,-0.96503,F +coef_age5664_nmot_disc,-0.97814,F +coef_age5664_tran_disc,-1.0845,F +coef_age65pl_sr2_disc,-0.89435,F +coef_age65pl_sr3p_disc,-1.11463,F +coef_age65pl_nmot_disc,-1.69155,F +coef_age65pl_tran_disc,-2.49829,F +coef_female_sr2_disc,0.261989,F +coef_female_sr3p_disc,0.273571,F +coef_female_tran_disc,-0.23309,F +coef_female_nmot_disc,0.0,F +coef_beachParkBikeConstant,0.7,F +coef_ageund16_bike_disc,0.0,F +coef_age1624_bike_disc,0.0,F +coef_age4155_bike_disc,-0.77985,F +coef_age5664_bike_disc,-0.68642,F +coef_age65pl_bike_disc,-1.65192,F +coef_female_bike_disc,-1.27322,F +coef_inc100plus_bike_disc,0.684139,F +coef_LUnorm_bike_disc,0.088817,F +coef_oMlCoast_bike_disc,-1.51486,F +coef_oMlCoast2p_bike_disc,1.424081,F +coef_oMlCoast5p_bike_disc,0.084867,F +coef_ageund16_walk_disc,2.247434,F +coef_age1624_walk_disc,1.417757,F 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+coef_calib_autosufficienthhin_ESCOOTER_school,-6.2720071731557105,F +coef_calib_autosufficienthhin_EBIKE_school,-2.9282323636347396,F +coef_calib_autosufficienthhin_ESCOOTER_atwork,-10.912007173155711,F +coef_calib_autosufficienthhin_EBIKE_atwork,-5.877228265020214,F +coef_calib_onboard,-0.293475781,F +coef_calib_mt_zeroautohh,4.825,F +coef_calib_nev_zeroautohh,4.825,F +coef_calib_ebike_owner_BIKE,-1.407,F +coef_calib_ebike_owner_EBIKE,0.942,F +coef_calib_ebike_shared,-4.139014765889209,F \ No newline at end of file diff --git a/resident/configs/tour_mode_choice_coefficients_new_coef4.csv b/resident/configs/tour_mode_choice_coefficients_new_coef4.csv new file mode 100644 index 0000000..1d9130a --- /dev/null +++ b/resident/configs/tour_mode_choice_coefficients_new_coef4.csv @@ -0,0 +1,754 @@ +coefficient_name,value,constrain +coef_zero,0.0,T +coef_one,1.0,T +coef_nest_root,1.0,T +coef_nest_AUTO,0.5,T +coef_nest_NONMOTORIZED,0.5,T +coef_nest_MICROMOBILITY,0.5,T +coef_nest_TRANSIT,0.5,T +coef_nest_RIDEHAIL,0.5,T +coef_nest_SCHOOL_BUS,0.5,T +coef_ivt_work,-0.016,F +coef_ivt_univ,-0.016,F +coef_ivt_school,-0.01,F +coef_ivt_maint,-0.017,F +coef_ivt_disc,-0.015,F +coef_ivt_atwork,-0.032,F +coef_rel_out_work,-0.224,F +coef_rel_out_univ,-0.192,F +coef_rel_out_school,-0.12,F +coef_rel_out_maint,-0.204,F +coef_rel_out_disc,-0.18,F +coef_rel_out_atwork,-0.384,F +coef_rel_inb_work,-0.192,F +coef_rel_inb_univ,-0.192,F +coef_rel_inb_school,-0.12,F +coef_rel_inb_maint,-0.204,F +coef_rel_inb_disc,-0.18,F +coef_rel_inb_atwork,-0.384,F +coef_income_work,-0.625,F +coef_income_univ,-0.262,F +coef_income_school,-0.262,F +coef_income_maint,-0.262,F +coef_income_disc,-0.262,F +coef_income_atwork,-0.262,F +coef_walktime_work,-0.0424,F +coef_walktime_univ,-0.0424,F +coef_walktime_school,-0.068,F +coef_walktime_maint,-0.03995,F +coef_walktime_disc,-0.045,F +coef_walktime_atwork,-0.0848,F +coef_bikels_work,0.134333061,F +coef_bikels_univ,0.134333061,F +coef_bikels_school,0.214932897,F 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+coef_calib_zeroautohhjointtou_TNC_SINGLE_maint,-999.0502454,F +coef_calib_zeroautohhjointtou_TNC_SHARED_maint,-999.0502454,F +coef_calib_zeroautohhjointtou_EBIKE_maint,-60.40469752502022,F +coef_calib_zeroautohhjointtou_ESCOOTER_maint,-58.11200717315571,F +#coef_calib_autodeficienthhjoi_SHARED2_maint,15.5330624,F +coef_calib_autodeficienthhjoi_SHARED3_maint,-48.16957674661429,F +coef_calib_autodeficienthhjoi_WALK_maint,-48.63043930165117,F +coef_calib_autodeficienthhjoi_BIKE_maint,-887.4773565867829,F +coef_calib_autodeficienthhjoi_WALK_TRANSIT_maint,-54.657619847723424,F +coef_calib_autodeficienthhjoi_PNR_TRANSIT_maint,-55.90392356,F +coef_calib_autodeficienthhjoi_KNR_TRANSIT_maint,-56.88159968,F +coef_calib_autodeficienthhjoi_TNC_TRANSIT_maint,-999.0,F +coef_calib_autodeficienthhjoi_TAXI_maint,-54.97066616,F +coef_calib_autodeficienthhjoi_TNC_SINGLE_maint,-56.97066616,F +coef_calib_autodeficienthhjoi_TNC_SHARED_maint,-56.97066616,F +coef_calib_autodeficienthhjoi_EBIKE_maint,-53.834057125020216,F +coef_calib_autodeficienthhjoi_ESCOOTER_maint,-55.11200717315571,F +#coef_calib_autosufficienthhjo_SHARED2_maint,4.253195896,F +coef_calib_autosufficienthhjo_SHARED3_maint,-4.175409902383396,F +coef_calib_autosufficienthhjo_WALK_maint,-3.7539241053813464,F +coef_calib_autosufficienthhjo_BIKE_maint,-17.90445689678276,F +coef_calib_autosufficienthhjo_WALK_TRANSIT_maint,-20.27274185,F +coef_calib_autosufficienthhjo_PNR_TRANSIT_maint,-18.77467449,F +coef_calib_autosufficienthhjo_KNR_TRANSIT_maint,-16.58383844,F +coef_calib_autosufficienthhjo_TNC_TRANSIT_maint,-1003.0,F +coef_calib_autosufficienthhjo_TAXI_maint,-21.31393156,F +coef_calib_autosufficienthhjo_TNC_SINGLE_maint,-23.31393156,F +coef_calib_autosufficienthhjo_TNC_SHARED_maint,-23.31393156,F +coef_calib_autosufficienthhjo_EBIKE_maint,-9.752892614991818,F +coef_calib_autosufficienthhjo_ESCOOTER_maint,-19.112007173155703,F +#coef_calib_zeroautohhjointtou_DRIVEALONE_disc,-999.0,F +#coef_calib_zeroautohhjointtou_SHARED2_disc,8.507773009,F +coef_calib_zeroautohhjointtou_SHARED3_disc,-70.24625539,F +coef_calib_zeroautohhjointtou_WALK_disc,-56.051551929940764,F +coef_calib_zeroautohhjointtou_BIKE_disc,-999.4773565867827,F +coef_calib_zeroautohhjointtou_WALK_TRANSIT_disc,-74.87568752,F +coef_calib_zeroautohhjointtou_PNR_TRANSIT_disc,-999.0,F +coef_calib_zeroautohhjointtou_KNR_TRANSIT_disc,-67.84999945,F +coef_calib_zeroautohhjointtou_TNC_TRANSIT_disc,-999.0,F +coef_calib_zeroautohhjointtou_TAXI_disc,-999.0,F +coef_calib_zeroautohhjointtou_TNC_SINGLE_disc,-999.0,F +coef_calib_zeroautohhjointtou_TNC_SHARED_disc,-999.0,F +coef_calib_zeroautohhjointtou_EBIKE_disc,-60.164697525020216,F +coef_calib_zeroautohhjointtou_ESCOOTER_disc,-57.8720071731557,F +#coef_calib_autodeficienthhjoi_SHARED2_disc,10.5330624,F +coef_calib_autodeficienthhjoi_SHARED3_disc,-48.16957674661429,F +coef_calib_autodeficienthhjoi_WALK_disc,-48.63043930165117,F +coef_calib_autodeficienthhjoi_BIKE_disc,-887.4773565867829,F +coef_calib_autodeficienthhjoi_WALK_TRANSIT_disc,-51.55258834772342,F +coef_calib_autodeficienthhjoi_PNR_TRANSIT_disc,-53.1684399,F +coef_calib_autodeficienthhjoi_KNR_TRANSIT_disc,-53.12678499,F +coef_calib_autodeficienthhjoi_TNC_TRANSIT_disc,-999.0,F +coef_calib_autodeficienthhjoi_TAXI_disc,-52.71598351,F +coef_calib_autodeficienthhjoi_TNC_SINGLE_disc,-54.71598351,F +coef_calib_autodeficienthhjoi_TNC_SHARED_disc,-54.71598351,F +coef_calib_autodeficienthhjoi_EBIKE_disc,-53.59405712502022,F +coef_calib_autodeficienthhjoi_ESCOOTER_disc,-54.8720071731557,F +#coef_calib_autosufficienthhjo_SHARED2_disc,4.253195896,F +coef_calib_autosufficienthhjo_SHARED3_disc,-2.175409902383395,F +coef_calib_autosufficienthhjo_WALK_disc,-7,F +coef_calib_autosufficienthhjo_BIKE_disc,-17.70975822678276,F +coef_calib_autosufficienthhjo_WALK_TRANSIT_disc,-18.918341,F +coef_calib_autosufficienthhjo_PNR_TRANSIT_disc,-15.46037795,F +coef_calib_autosufficienthhjo_KNR_TRANSIT_disc,-22.31188559,F +coef_calib_autosufficienthhjo_TNC_TRANSIT_disc,-19.31188559,F +coef_calib_autosufficienthhjo_TAXI_disc,-18.0336306,F +coef_calib_autosufficienthhjo_TNC_SINGLE_disc,-20.0336306,F +coef_calib_autosufficienthhjo_TNC_SHARED_disc,-20.0336306,F +coef_calib_autosufficienthhjo_EBIKE_disc,-9.51289261499182,F +coef_calib_autosufficienthhjo_ESCOOTER_disc,-18.872007173155698,F +coef_calib_zeroautohhindivtou_ESCOOTER_work,-6.992007173155711,F +coef_calib_zeroautohhindivtou_EBIKE_work,-7.2846975250202135,F +coef_calib_zeroautohhindivtou_ESCOOTER_univ,-6.992007173155711,F +coef_calib_zeroautohhindivtou_EBIKE_univ,-7.2846975250202135,F +coef_calib_zeroautohhindivtou_ESCOOTER_school,-4.2720071731557105,F +coef_calib_zeroautohhindivtou_EBIKE_school,-0.5646975250202154,F +coef_calib_zeroautohhindivtou_ESCOOTER_atwork,-31.912007173155704,F +coef_calib_zeroautohhindivtou_EBIKE_atwork,-88.20469752502021,F +coef_calib_autodeficienthhind_ESCOOTER_work,-8.99200717315571,F +coef_calib_autodeficienthhind_EBIKE_work,-7.2846975250202135,F +coef_calib_autodeficienthhind_ESCOOTER_univ,-4.992007173155711,F +coef_calib_autodeficienthhind_EBIKE_univ,-5.284697525020215,F +coef_calib_autodeficienthhind_ESCOOTER_school,-6.2720071731557105,F +coef_calib_autodeficienthhind_EBIKE_school,-4.564697525020215,F +coef_calib_autodeficienthhind_ESCOOTER_atwork,-10.912007173155711,F +coef_calib_autodeficienthhind_EBIKE_atwork,-11.204697525020217,F +coef_calib_autosufficienthhin_ESCOOTER_work,-8.99200717315571,F +coef_calib_autosufficienthhin_EBIKE_work,-5.606539508020214,F +coef_calib_autosufficienthhin_ESCOOTER_univ,-4.992007173155711,F +coef_calib_autosufficienthhin_EBIKE_univ,-5.284697525020215,F +coef_calib_autosufficienthhin_ESCOOTER_school,-6.2720071731557105,F +coef_calib_autosufficienthhin_EBIKE_school,-2.9282323636347396,F +coef_calib_autosufficienthhin_ESCOOTER_atwork,-10.912007173155711,F +coef_calib_autosufficienthhin_EBIKE_atwork,-5.877228265020214,F +coef_calib_onboard,-0.293475781,F +coef_calib_mt_zeroautohh,4.825,F +coef_calib_nev_zeroautohh,4.825,F +coef_calib_ebike_owner_BIKE,-1.407,F +coef_calib_ebike_owner_EBIKE,0.942,F +coef_calib_ebike_shared,-4.139014765889209,F \ No newline at end of file diff --git a/resident/configs/tour_mode_choice_coefficients_template.csv b/resident/configs/tour_mode_choice_coefficients_template.csv new file mode 100644 index 0000000..e56c5e5 --- /dev/null +++ b/resident/configs/tour_mode_choice_coefficients_template.csv @@ -0,0 +1,191 @@ +coefficient_name,work,univ,school,shopping,escort,othmaint,social,eatout,othdiscr,atwork +#same for all segments,,,,,,,,,, +coef_one,coef_one,coef_one,coef_one,coef_one,coef_one,coef_one,coef_one,coef_one,coef_one,coef_one +coef_nest_root,coef_nest_root,coef_nest_root,coef_nest_root,coef_nest_root,coef_nest_root,coef_nest_root,coef_nest_root,coef_nest_root,coef_nest_root,coef_nest_root +coef_nest_AUTO,coef_nest_AUTO,coef_nest_AUTO,coef_nest_AUTO,coef_nest_AUTO,coef_nest_AUTO,coef_nest_AUTO,coef_nest_AUTO,coef_nest_AUTO,coef_nest_AUTO,coef_nest_AUTO +coef_nest_NONMOTORIZED,coef_nest_NONMOTORIZED,coef_nest_NONMOTORIZED,coef_nest_NONMOTORIZED,coef_nest_NONMOTORIZED,coef_nest_NONMOTORIZED,coef_nest_NONMOTORIZED,coef_nest_NONMOTORIZED,coef_nest_NONMOTORIZED,coef_nest_NONMOTORIZED,coef_nest_NONMOTORIZED +coef_nest_MICROMOBILITY,coef_nest_MICROMOBILITY,coef_nest_MICROMOBILITY,coef_nest_MICROMOBILITY,coef_nest_MICROMOBILITY,coef_nest_MICROMOBILITY,coef_nest_MICROMOBILITY,coef_nest_MICROMOBILITY,coef_nest_MICROMOBILITY,coef_nest_MICROMOBILITY,coef_nest_MICROMOBILITY +coef_nest_TRANSIT,coef_nest_TRANSIT,coef_nest_TRANSIT,coef_nest_TRANSIT,coef_nest_TRANSIT,coef_nest_TRANSIT,coef_nest_TRANSIT,coef_nest_TRANSIT,coef_nest_TRANSIT,coef_nest_TRANSIT,coef_nest_TRANSIT +coef_nest_RIDEHAIL,coef_nest_RIDEHAIL,coef_nest_RIDEHAIL,coef_nest_RIDEHAIL,coef_nest_RIDEHAIL,coef_nest_RIDEHAIL,coef_nest_RIDEHAIL,coef_nest_RIDEHAIL,coef_nest_RIDEHAIL,coef_nest_RIDEHAIL,coef_nest_RIDEHAIL +coef_nest_SCHOOL_BUS,coef_nest_SCHOOL_BUS,coef_nest_SCHOOL_BUS,coef_nest_SCHOOL_BUS,coef_nest_SCHOOL_BUS,coef_nest_SCHOOL_BUS,coef_nest_SCHOOL_BUS,coef_nest_SCHOOL_BUS,coef_nest_SCHOOL_BUS,coef_nest_SCHOOL_BUS,coef_nest_SCHOOL_BUS +coef_ivt,coef_ivt_work,coef_ivt_univ,coef_ivt_school,coef_ivt_maint,coef_ivt_maint,coef_ivt_maint,coef_ivt_disc,coef_ivt_disc,coef_ivt_disc,coef_ivt_atwork +coef_rel_out,coef_rel_out_work,coef_rel_out_univ,coef_rel_out_school,coef_rel_out_maint,coef_rel_out_maint,coef_rel_out_maint,coef_rel_out_disc,coef_rel_out_disc,coef_rel_out_disc,coef_rel_out_atwork +coef_rel_inb,coef_rel_inb_work,coef_rel_inb_univ,coef_rel_inb_school,coef_rel_inb_maint,coef_rel_inb_maint,coef_rel_inb_maint,coef_rel_inb_disc,coef_rel_inb_disc,coef_rel_inb_disc,coef_rel_inb_atwork +coef_income,coef_income_work,coef_income_univ,coef_income_school,coef_income_maint,coef_income_maint,coef_income_maint,coef_income_disc,coef_income_disc,coef_income_disc,coef_income_atwork +coef_walktime,coef_walktime_work,coef_walktime_univ,coef_walktime_school,coef_walktime_maint,coef_walktime_maint,coef_walktime_maint,coef_walktime_disc,coef_walktime_disc,coef_walktime_disc,coef_walktime_atwork +coef_bikeLogsum,coef_bikels_work,coef_bikels_univ,coef_bikels_school,coef_bikels_maint,coef_bikels_maint,coef_bikels_maint,coef_bikels_disc,coef_bikels_disc,coef_bikels_disc,coef_bikels_atwork +#,,,,,,,,,, +coef_wait,coef_wait_work,coef_wait_univ,coef_wait_school,coef_wait_maint,coef_wait_maint,coef_wait_maint,coef_wait_disc,coef_wait_disc,coef_wait_disc,coef_wait_atwork +coef_xwait,coef_xwait_work,coef_xwait_univ,coef_xwait_school,coef_xwait_maint,coef_xwait_maint,coef_xwait_maint,coef_xwait_disc,coef_xwait_disc,coef_xwait_disc,coef_xwait_atwork +coef_acctime,coef_acctime_work,coef_acctime_univ,coef_acctime_school,coef_acctime_maint,coef_acctime_maint,coef_acctime_maint,coef_acctime_disc,coef_acctime_disc,coef_acctime_disc,coef_acctime_atwork +coef_xfer,coef_xfer_work,coef_xfer_univ,coef_xfer_school,coef_xfer_maint,coef_xfer_maint,coef_xfer_maint,coef_xfer_disc,coef_xfer_disc,coef_xfer_disc,coef_xfer_atwork +coef_xferdrive,coef_xferdrive_work,coef_xferdrive_univ,coef_xferdrive_school,coef_xferdrive_maint,coef_xferdrive_maint,coef_xferdrive_maint,coef_xferdrive_disc,coef_xferdrive_disc,coef_xferdrive_disc,coef_xferdrive_atwork +coef_xwalk,coef_xwalk_work,coef_xwalk_univ,coef_xwalk_school,coef_xwalk_maint,coef_xwalk_maint,coef_xwalk_maint,coef_xwalk_disc,coef_xwalk_disc,coef_xwalk_disc,coef_xwalk_atwork +#,,,,,,,,,, +coef_oMix_nmot,coef_oMix_nmot_work,coef_oMix_nmot_univ,coef_oMix_nmot_school,coef_oMix_nmot_maint,coef_oMix_nmot_maint,coef_oMix_nmot_maint,coef_oMix_nmot_disc,coef_oMix_nmot_disc,coef_oMix_nmot_disc,coef_oMix_nmot_atwork +coef_oMix_wTran,coef_oMix_wTran_work,coef_oMix_wTran_univ,coef_oMix_wTran_school,coef_oMix_wTran_maint,coef_oMix_wTran_maint,coef_oMix_wTran_maint,coef_oMix_wTran_disc,coef_oMix_wTran_disc,coef_oMix_wTran_disc,coef_oMix_wTran_atwork +coef_oIntDen_nmot,coef_oIntDen_nmot_work,coef_oIntDen_nmot_univ,coef_oIntDen_nmot_school,coef_oIntDen_nmot_maint,coef_oIntDen_nmot_maint,coef_oIntDen_nmot_maint,coef_oIntDen_nmot_disc,coef_oIntDen_nmot_disc,coef_oIntDen_nmot_disc,coef_oIntDen_nmot_atwork +coef_oIntDen_wTran,coef_oIntDen_wTran_work,coef_oIntDen_wTran_univ,coef_oIntDen_wTran_school,coef_oIntDen_wTran_maint,coef_oIntDen_wTran_maint,coef_oIntDen_wTran_maint,coef_oIntDen_wTran_disc,coef_oIntDen_wTran_disc,coef_oIntDen_wTran_disc,coef_oIntDen_wTran_atwork +coef_dEmpDen_nmot,coef_dEmpDen_nmot_work,coef_dEmpDen_nmot_univ,coef_dEmpDen_nmot_school,coef_dEmpDen_nmot_maint,coef_dEmpDen_nmot_maint,coef_dEmpDen_nmot_maint,coef_dEmpDen_nmot_disc,coef_dEmpDen_nmot_disc,coef_dEmpDen_nmot_disc,coef_dEmpDen_nmot_atwork +coef_dEmpDen_wTran,coef_dEmpDen_wTran_work,coef_dEmpDen_wTran_univ,coef_dEmpDen_wTran_school,coef_dEmpDen_wTran_maint,coef_dEmpDen_wTran_maint,coef_dEmpDen_wTran_maint,coef_dEmpDen_wTran_disc,coef_dEmpDen_wTran_disc,coef_dEmpDen_wTran_disc,coef_dEmpDen_wTran_atwork +coef_dEmpDen_dTran,coef_dEmpDen_dTran_work,coef_dEmpDen_dTran_univ,coef_dEmpDen_dTran_school,coef_dEmpDen_dTran_maint,coef_dEmpDen_dTran_maint,coef_dEmpDen_dTran_maint,coef_dEmpDen_dTran_disc,coef_dEmpDen_dTran_disc,coef_dEmpDen_dTran_disc,coef_dEmpDen_dTran_atwork +coef_age1624_sr2,coef_age1624_sr2_work,coef_age1624_sr2_univ,coef_zero,coef_age1624_sr2_maint,coef_age1624_sr2_maint,coef_age1624_sr2_maint,coef_age1624_sr2_disc,coef_age1624_sr2_disc,coef_age1624_sr2_disc,coef_age1624_sr2_atwork +coef_age1624_sr3p,coef_age1624_sr3p_work,coef_age1624_sr3p_univ,coef_zero,coef_age1624_sr3p_maint,coef_age1624_sr3p_maint,coef_age1624_sr3p_maint,coef_age1624_sr3p_disc,coef_age1624_sr3p_disc,coef_age1624_sr3p_disc,coef_age1624_sr3p_atwork +coef_age1624_nmot,coef_age1624_nmot_work,coef_age1624_nmot_univ,coef_zero,coef_age1624_nmot_maint,coef_age1624_nmot_maint,coef_age1624_nmot_maint,coef_age1624_nmot_disc,coef_age1624_nmot_disc,coef_age1624_nmot_disc,coef_age1624_nmot_atwork +coef_age1624_tran,coef_age1624_tran_work,coef_age1624_tran_univ,coef_zero,coef_age1624_tran_maint,coef_age1624_tran_maint,coef_age1624_tran_maint,coef_age1624_tran_disc,coef_age1624_tran_disc,coef_age1624_tran_disc,coef_age1624_tran_atwork +coef_age4155_sr2,coef_age4155_sr2_work,coef_age4155_sr2_univ,coef_zero,coef_age4155_sr2_maint,coef_age4155_sr2_maint,coef_age4155_sr2_maint,coef_age4155_sr2_disc,coef_age4155_sr2_disc,coef_age4155_sr2_disc,coef_age4155_sr2_atwork +coef_age4155_sr3p,coef_age4155_sr3p_work,coef_age4155_sr3p_univ,coef_zero,coef_age4155_sr3p_maint,coef_age4155_sr3p_maint,coef_age4155_sr3p_maint,coef_age4155_sr3p_disc,coef_age4155_sr3p_disc,coef_age4155_sr3p_disc,coef_age4155_sr3p_atwork +coef_age4155_nmot,coef_age4155_nmot_work,coef_age4155_nmot_univ,coef_zero,coef_age4155_nmot_maint,coef_age4155_nmot_maint,coef_age4155_nmot_maint,coef_age4155_nmot_disc,coef_age4155_nmot_disc,coef_age4155_nmot_disc,coef_age4155_nmot_atwork +coef_age4155_tran,coef_age4155_tran_work,coef_age4155_tran_univ,coef_zero,coef_age4155_tran_maint,coef_age4155_tran_maint,coef_age4155_tran_maint,coef_age4155_tran_disc,coef_age4155_tran_disc,coef_age4155_tran_disc,coef_age4155_tran_atwork +coef_age5664_sr2,coef_age5664_sr2_work,coef_age5664_sr2_univ,coef_zero,coef_age5664_sr2_maint,coef_age5664_sr2_maint,coef_age5664_sr2_maint,coef_age5664_sr2_disc,coef_age5664_sr2_disc,coef_age5664_sr2_disc,coef_age5664_sr2_atwork +coef_age5664_sr3p,coef_age5664_sr3p_work,coef_age5664_sr3p_univ,coef_zero,coef_age5664_sr3p_maint,coef_age5664_sr3p_maint,coef_age5664_sr3p_maint,coef_age5664_sr3p_disc,coef_age5664_sr3p_disc,coef_age5664_sr3p_disc,coef_age5664_sr3p_atwork +coef_age5664_nmot,coef_age5664_nmot_work,coef_age5664_nmot_univ,coef_zero,coef_age5664_nmot_maint,coef_age5664_nmot_maint,coef_age5664_nmot_maint,coef_age5664_nmot_disc,coef_age5664_nmot_disc,coef_age5664_nmot_disc,coef_age5664_nmot_atwork +coef_age5664_tran,coef_age5664_tran_work,coef_age5664_tran_univ,coef_zero,coef_age5664_tran_maint,coef_age5664_tran_maint,coef_age5664_tran_maint,coef_age5664_tran_disc,coef_age5664_tran_disc,coef_age5664_tran_disc,coef_age5664_tran_atwork +coef_age65pl_sr2,coef_age65pl_sr2_work,coef_age65pl_sr2_univ,coef_zero,coef_age65pl_sr2_maint,coef_age65pl_sr2_maint,coef_age65pl_sr2_maint,coef_age65pl_sr2_disc,coef_age65pl_sr2_disc,coef_age65pl_sr2_disc,coef_age65pl_sr2_atwork +coef_age65pl_sr3p,coef_age65pl_sr3p_work,coef_age65pl_sr3p_univ,coef_zero,coef_age65pl_sr3p_maint,coef_age65pl_sr3p_maint,coef_age65pl_sr3p_maint,coef_age65pl_sr3p_disc,coef_age65pl_sr3p_disc,coef_age65pl_sr3p_disc,coef_age65pl_sr3p_atwork +coef_age65pl_nmot,coef_age65pl_nmot_work,coef_age65pl_nmot_univ,coef_zero,coef_age65pl_nmot_maint,coef_age65pl_nmot_maint,coef_age65pl_nmot_maint,coef_age65pl_nmot_disc,coef_age65pl_nmot_disc,coef_age65pl_nmot_disc,coef_age65pl_nmot_atwork +coef_age65pl_tran,coef_age65pl_tran_work,coef_age65pl_tran_univ,coef_zero,coef_age65pl_tran_maint,coef_age65pl_tran_maint,coef_age65pl_tran_maint,coef_age65pl_tran_disc,coef_age65pl_tran_disc,coef_age65pl_tran_disc,coef_age65pl_tran_atwork +coef_female_sr2,coef_female_sr2_work,coef_female_sr2_univ,coef_female_sr2_school,coef_female_sr2_maint,coef_female_sr2_maint,coef_female_sr2_maint,coef_female_sr2_disc,coef_female_sr2_disc,coef_female_sr2_disc,coef_female_sr2_atwork +coef_female_sr3p,coef_female_sr3p_work,coef_female_sr3p_univ,coef_female_sr3p_school,coef_female_sr3p_maint,coef_female_sr3p_maint,coef_female_sr3p_maint,coef_female_sr3p_disc,coef_female_sr3p_disc,coef_female_sr3p_disc,coef_female_sr3p_atwork +coef_female_tran,coef_female_tran_work,coef_female_tran_univ,coef_female_tran_school,coef_female_tran_maint,coef_female_tran_maint,coef_female_tran_maint,coef_female_tran_disc,coef_female_tran_disc,coef_female_tran_disc,coef_female_tran_atwork +coef_female_nmot,coef_female_nmot_work,coef_female_nmot_univ,coef_female_nmot_school,coef_female_nmot_maint,coef_female_nmot_maint,coef_female_nmot_maint,coef_female_nmot_disc,coef_female_nmot_disc,coef_female_nmot_disc,coef_female_nmot_atwork +coef_hhsize2_sr2,coef_hhsize2_sr2_work,coef_hhsize2_sr2_univ,coef_hhsize2_sr2_school,coef_hhsize2_sr2_maint,coef_hhsize2_sr2_maint,coef_hhsize2_sr2_maint,coef_hhsize2_sr2_disc,coef_hhsize2_sr2_disc,coef_hhsize2_sr2_disc,coef_hhsize2_sr2_atwork +coef_hhsize2_sr3p,coef_hhsize2_sr3p_work,coef_hhsize2_sr3p_univ,coef_hhsize2_sr3p_school,coef_hhsize2_sr3p_maint,coef_hhsize2_sr3p_maint,coef_hhsize2_sr3p_maint,coef_hhsize2_sr3p_disc,coef_hhsize2_sr3p_disc,coef_hhsize2_sr3p_disc,coef_hhsize2_sr3p_atwork +coef_hhsize3_sr2,coef_hhsize3_sr2_work,coef_hhsize3_sr2_univ,coef_hhsize3_sr2_school,coef_hhsize3_sr2_maint,coef_hhsize3_sr2_maint,coef_hhsize3_sr2_maint,coef_hhsize3_sr2_disc,coef_hhsize3_sr2_disc,coef_hhsize3_sr2_disc,coef_hhsize3_sr2_atwork +coef_hhsize3_sr3p,coef_hhsize3_sr3p_work,coef_hhsize3_sr3p_univ,coef_hhsize3_sr3p_school,coef_hhsize3_sr3p_maint,coef_hhsize3_sr3p_maint,coef_hhsize3_sr3p_maint,coef_hhsize3_sr3p_disc,coef_hhsize3_sr3p_disc,coef_hhsize3_sr3p_disc,coef_hhsize3_sr3p_atwork +coef_hhsize4p_sr2,coef_hhsize4p_sr2_work,coef_hhsize4p_sr2_univ,coef_hhsize4p_sr2_school,coef_hhsize4p_sr2_maint,coef_hhsize4p_sr2_maint,coef_hhsize4p_sr2_maint,coef_hhsize4p_sr2_disc,coef_hhsize4p_sr2_disc,coef_hhsize4p_sr2_disc,coef_hhsize4p_sr2_atwork +coef_hhsize4p_sr3p,coef_hhsize4p_sr3p_work,coef_hhsize4p_sr3p_univ,coef_hhsize4p_sr3p_school,coef_hhsize4p_sr3p_maint,coef_hhsize4p_sr3p_maint,coef_hhsize4p_sr3p_maint,coef_hhsize4p_sr3p_disc,coef_hhsize4p_sr3p_disc,coef_hhsize4p_sr3p_disc,coef_hhsize4p_sr3p_atwork +coef_ageund16_bike,coef_ageund16_bike_work,coef_ageund16_bike_univ,coef_ageund16_bike_school,coef_ageund16_bike_maint,coef_ageund16_bike_maint,coef_ageund16_bike_maint,coef_ageund16_bike_disc,coef_ageund16_bike_disc,coef_ageund16_bike_disc,coef_zero +coef_age1624_bike,coef_age1624_bike_work,coef_age1624_bike_univ,coef_age1624_bike_school,coef_age1624_bike_maint,coef_age1624_bike_maint,coef_age1624_bike_maint,coef_age1624_bike_disc,coef_age1624_bike_disc,coef_age1624_bike_disc,coef_zero +coef_age4155_bike,coef_age4155_bike_work,coef_age4155_bike_univ,coef_age4155_bike_school,coef_age4155_bike_maint,coef_age4155_bike_maint,coef_age4155_bike_maint,coef_age4155_bike_disc,coef_age4155_bike_disc,coef_age4155_bike_disc,coef_zero +coef_age5664_bike,coef_age5664_bike_work,coef_age5664_bike_univ,coef_age5664_bike_school,coef_age5664_bike_maint,coef_age5664_bike_maint,coef_age5664_bike_maint,coef_age5664_bike_disc,coef_age5664_bike_disc,coef_age5664_bike_disc,coef_zero +coef_age65pl_bike,coef_age65pl_bike_work,coef_age65pl_bike_univ,coef_age65pl_bike_school,coef_age65pl_bike_maint,coef_age65pl_bike_maint,coef_age65pl_bike_maint,coef_age65pl_bike_disc,coef_age65pl_bike_disc,coef_age65pl_bike_disc,coef_zero +coef_female_bike,coef_female_bike_work,coef_female_bike_univ,coef_female_bike_school,coef_female_bike_maint,coef_female_bike_maint,coef_female_bike_maint,coef_female_bike_disc,coef_female_bike_disc,coef_female_bike_disc,coef_zero +coef_inc100plus_bike,coef_inc100plus_bike_work,coef_inc100plus_bike_univ,coef_inc100plus_bike_school,coef_inc100plus_bike_maint,coef_inc100plus_bike_maint,coef_inc100plus_bike_maint,coef_inc100plus_bike_disc,coef_inc100plus_bike_disc,coef_inc100plus_bike_disc,coef_zero +coef_LUnorm_bike,coef_LUnorm_bike_work,coef_LUnorm_bike_univ,coef_LUnorm_bike_school,coef_LUnorm_bike_maint,coef_LUnorm_bike_maint,coef_LUnorm_bike_maint,coef_LUnorm_bike_disc,coef_LUnorm_bike_disc,coef_LUnorm_bike_disc,coef_zero +coef_oMlCoast_bike,coef_oMlCoast_bike_work,coef_oMlCoast_bike_univ,coef_oMlCoast_bike_school,coef_oMlCoast_bike_maint,coef_oMlCoast_bike_maint,coef_oMlCoast_bike_maint,coef_oMlCoast_bike_disc,coef_oMlCoast_bike_disc,coef_oMlCoast_bike_disc,coef_zero +coef_oMlCoast2p_bike,coef_oMlCoast2p_bike_work,coef_oMlCoast2p_bike_univ,coef_oMlCoast2p_bike_school,coef_oMlCoast2p_bike_maint,coef_oMlCoast2p_bike_maint,coef_oMlCoast2p_bike_maint,coef_oMlCoast2p_bike_disc,coef_oMlCoast2p_bike_disc,coef_oMlCoast2p_bike_disc,coef_zero +coef_oMlCoast5p_bike,coef_oMlCoast5p_bike_work,coef_oMlCoast5p_bike_univ,coef_oMlCoast5p_bike_school,coef_oMlCoast5p_bike_maint,coef_oMlCoast5p_bike_maint,coef_oMlCoast5p_bike_maint,coef_oMlCoast5p_bike_disc,coef_oMlCoast5p_bike_disc,coef_oMlCoast5p_bike_disc,coef_zero +coef_ageund16_walk,coef_ageund16_walk_work,coef_ageund16_walk_univ,coef_ageund16_walk_school,coef_ageund16_walk_maint,coef_ageund16_walk_maint,coef_ageund16_walk_maint,coef_ageund16_walk_disc,coef_ageund16_walk_disc,coef_ageund16_walk_disc,coef_zero +coef_age1624_walk,coef_age1624_walk_work,coef_age1624_walk_univ,coef_age1624_walk_school,coef_age1624_walk_maint,coef_age1624_walk_maint,coef_age1624_walk_maint,coef_age1624_walk_disc,coef_age1624_walk_disc,coef_age1624_walk_disc,coef_zero +coef_age4155_walk,coef_age4155_walk_work,coef_age4155_walk_univ,coef_age4155_walk_school,coef_age4155_walk_maint,coef_age4155_walk_maint,coef_age4155_walk_maint,coef_age4155_walk_disc,coef_age4155_walk_disc,coef_age4155_walk_disc,coef_zero +coef_age5664_walk,coef_age5664_walk_work,coef_age5664_walk_univ,coef_age5664_walk_school,coef_age5664_walk_maint,coef_age5664_walk_maint,coef_age5664_walk_maint,coef_age5664_walk_disc,coef_age5664_walk_disc,coef_age5664_walk_disc,coef_zero +coef_age65pl_walk,coef_age65pl_walk_work,coef_age65pl_walk_univ,coef_age65pl_walk_school,coef_age65pl_walk_maint,coef_age65pl_walk_maint,coef_age65pl_walk_maint,coef_age65pl_walk_disc,coef_age65pl_walk_disc,coef_age65pl_walk_disc,coef_zero +coef_female_walk,coef_female_walk_work,coef_female_walk_univ,coef_female_walk_school,coef_female_walk_maint,coef_female_walk_maint,coef_female_walk_maint,coef_female_walk_disc,coef_female_walk_disc,coef_female_walk_disc,coef_zero +coef_inc60100_walk,coef_inc60100_walk_work,coef_inc60100_walk_univ,coef_inc60100_walk_school,coef_inc60100_walk_maint,coef_inc60100_walk_maint,coef_inc60100_walk_maint,coef_inc60100_walk_disc,coef_inc60100_walk_disc,coef_inc60100_walk_disc,coef_zero +coef_LUnorm_walk,coef_LUnorm_walk_work,coef_LUnorm_walk_univ,coef_LUnorm_walk_school,coef_LUnorm_walk_maint,coef_LUnorm_walk_maint,coef_LUnorm_walk_maint,coef_LUnorm_walk_disc,coef_LUnorm_walk_disc,coef_LUnorm_walk_disc,coef_zero +coef_dEmpNorm_walk,coef_dEmpNorm_walk_work,coef_dEmpNorm_walk_univ,coef_dEmpNorm_walk_school,coef_dEmpNorm_walk_maint,coef_dEmpNorm_walk_maint,coef_dEmpNorm_walk_maint,coef_dEmpNorm_walk_disc,coef_dEmpNorm_walk_disc,coef_dEmpNorm_walk_disc,coef_zero +coef_female_schb,coef_zero,coef_zero,coef_female_schb_school,coef_zero,coef_zero,coef_zero,coef_zero,coef_zero,coef_zero,coef_zero +coef_age0105_schb,coef_zero,coef_zero,coef_female_schb_school,coef_zero,coef_zero,coef_zero,coef_zero,coef_zero,coef_zero,coef_zero +coef_age0105_nmot,coef_zero,coef_zero,coef_age0105_schb_school,coef_zero,coef_zero,coef_zero,coef_zero,coef_zero,coef_zero,coef_zero +coef_age0105_tran,coef_zero,coef_zero,coef_age0105_nmot_school,coef_zero,coef_zero,coef_zero,coef_zero,coef_zero,coef_zero,coef_zero +coef_age0612_schb,coef_zero,coef_zero,coef_age0105_tran_school,coef_zero,coef_zero,coef_zero,coef_zero,coef_zero,coef_zero,coef_zero +coef_age0612_nmot,coef_zero,coef_zero,coef_age0612_schb_school,coef_zero,coef_zero,coef_zero,coef_zero,coef_zero,coef_zero,coef_zero +coef_age0612_tran,coef_zero,coef_zero,coef_age0612_nmot_school,coef_zero,coef_zero,coef_zero,coef_zero,coef_zero,coef_zero,coef_zero +coef_age1315_schb,coef_zero,coef_zero,coef_age0612_tran_school,coef_zero,coef_zero,coef_zero,coef_zero,coef_zero,coef_zero,coef_zero +coef_age1315_nmot,coef_zero,coef_zero,coef_age1315_schb_school,coef_zero,coef_zero,coef_zero,coef_zero,coef_zero,coef_zero,coef_zero +coef_age1315_tran,coef_zero,coef_zero,coef_age1315_nmot_school,coef_zero,coef_zero,coef_zero,coef_zero,coef_zero,coef_zero,coef_zero +coef_beachParkBikeConstant,coef_zero,coef_zero,coef_zero,coef_zero,coef_zero,coef_zero,coef_beachParkBikeConstant,coef_beachParkBikeConstant,coef_beachParkBikeConstant,coef_zero +#,,,,,,,,,, +coef_calib_parkingconst_WLK_TRANSIT,coef_calib_parkingconst_WLK_TRANSIT_work,coef_calib_parkingconst_WLK_TRANSIT_univ,coef_calib_parkingconst_WLK_TRANSIT_school,coef_calib_parkingconst_WLK_TRANSIT_maint,coef_calib_parkingconst_WLK_TRANSIT_maint,coef_calib_parkingconst_WLK_TRANSIT_maint,coef_calib_parkingconst_WLK_TRANSIT_disc,coef_calib_parkingconst_WLK_TRANSIT_disc,coef_calib_parkingconst_WLK_TRANSIT_disc,coef_calib_parkingconst_WLK_TRANSIT_atwork +coef_calib_parkingconst_DRV_TRANSIT,coef_calib_parkingconst_DRV_TRANSIT_work,coef_calib_parkingconst_DRV_TRANSIT_univ,coef_calib_parkingconst_DRV_TRANSIT_school,coef_calib_parkingconst_DRV_TRANSIT_maint,coef_calib_parkingconst_DRV_TRANSIT_maint,coef_calib_parkingconst_DRV_TRANSIT_maint,coef_calib_parkingconst_DRV_TRANSIT_disc,coef_calib_parkingconst_DRV_TRANSIT_disc,coef_calib_parkingconst_DRV_TRANSIT_disc,coef_calib_parkingconst_DRV_TRANSIT_atwork +coef_calib_autodeficienthhind_SCH_BUS,coef_zero,coef_zero,coef_calib_autodeficienthhind_SCH_BUS_school,coef_zero,coef_zero,coef_zero,coef_zero,coef_zero,coef_zero,coef_zero +coef_calib_autodeficienthhind_SHARED2,coef_calib_autodeficienthhind_SHARED2_work,coef_calib_autodeficienthhind_SHARED2_univ,coef_calib_autodeficienthhind_SHARED2_school,coef_calib_autodeficienthhind_SHARED2_maint,coef_calib_autodeficienthhind_SHARED2_maint,coef_calib_autodeficienthhind_SHARED2_maint,coef_calib_autodeficienthhind_SHARED2_disc,coef_calib_autodeficienthhind_SHARED2_disc,coef_calib_autodeficienthhind_SHARED2_disc,coef_calib_autodeficienthhind_SHARED2_atwork +coef_calib_autodeficienthhind_SHARED3,coef_calib_autodeficienthhind_SHARED3_work,coef_calib_autodeficienthhind_SHARED3_univ,coef_calib_autodeficienthhind_SHARED3_school,coef_calib_autodeficienthhind_SHARED3_maint,coef_calib_autodeficienthhind_SHARED3_maint,coef_calib_autodeficienthhind_SHARED3_maint,coef_calib_autodeficienthhind_SHARED3_disc,coef_calib_autodeficienthhind_SHARED3_disc,coef_calib_autodeficienthhind_SHARED3_disc,coef_calib_autodeficienthhind_SHARED3_atwork +coef_calib_autodeficienthhind_WALK,coef_calib_autodeficienthhind_WALK_work,coef_calib_autodeficienthhind_WALK_univ,coef_calib_autodeficienthhind_WALK_school,coef_calib_autodeficienthhind_WALK_maint,coef_calib_autodeficienthhind_WALK_maint,coef_calib_autodeficienthhind_WALK_maint,coef_calib_autodeficienthhind_WALK_disc,coef_calib_autodeficienthhind_WALK_disc,coef_calib_autodeficienthhind_WALK_disc,coef_calib_autodeficienthhind_WALK_atwork +coef_calib_autodeficienthhind_BIKE,coef_calib_autodeficienthhind_BIKE_work,coef_calib_autodeficienthhind_BIKE_univ,coef_calib_autodeficienthhind_BIKE_school,coef_calib_autodeficienthhind_BIKE_maint,coef_calib_autodeficienthhind_BIKE_maint,coef_calib_autodeficienthhind_BIKE_maint,coef_calib_autodeficienthhind_BIKE_disc,coef_calib_autodeficienthhind_BIKE_disc,coef_calib_autodeficienthhind_BIKE_disc,coef_calib_autodeficienthhind_BIKE_atwork +coef_calib_autodeficienthhind_WALK_TRANSIT,coef_calib_autodeficienthhind_WALK_TRANSIT_work,coef_calib_autodeficienthhind_WALK_TRANSIT_univ,coef_calib_autodeficienthhind_WALK_TRANSIT_school,coef_calib_autodeficienthhind_WALK_TRANSIT_maint,coef_calib_autodeficienthhind_WALK_TRANSIT_maint,coef_calib_autodeficienthhind_WALK_TRANSIT_maint,coef_calib_autodeficienthhind_WALK_TRANSIT_disc,coef_calib_autodeficienthhind_WALK_TRANSIT_disc,coef_calib_autodeficienthhind_WALK_TRANSIT_disc,coef_calib_autodeficienthhind_WALK_TRANSIT_atwork +coef_calib_autodeficienthhind_PNR_TRANSIT,coef_calib_autodeficienthhind_PNR_TRANSIT_work,coef_calib_autodeficienthhind_PNR_TRANSIT_univ,coef_calib_autodeficienthhind_PNR_TRANSIT_school,coef_calib_autodeficienthhind_PNR_TRANSIT_maint,coef_calib_autodeficienthhind_PNR_TRANSIT_maint,coef_calib_autodeficienthhind_PNR_TRANSIT_maint,coef_calib_autodeficienthhind_PNR_TRANSIT_disc,coef_calib_autodeficienthhind_PNR_TRANSIT_disc,coef_calib_autodeficienthhind_PNR_TRANSIT_disc,coef_calib_autodeficienthhind_PNR_TRANSIT_atwork +coef_calib_autodeficienthhind_KNR_TRANSIT,coef_calib_autodeficienthhind_KNR_TRANSIT_work,coef_calib_autodeficienthhind_KNR_TRANSIT_univ,coef_calib_autodeficienthhind_KNR_TRANSIT_school,coef_calib_autodeficienthhind_KNR_TRANSIT_maint,coef_calib_autodeficienthhind_KNR_TRANSIT_maint,coef_calib_autodeficienthhind_KNR_TRANSIT_maint,coef_calib_autodeficienthhind_KNR_TRANSIT_disc,coef_calib_autodeficienthhind_KNR_TRANSIT_disc,coef_calib_autodeficienthhind_KNR_TRANSIT_disc,coef_calib_autodeficienthhind_KNR_TRANSIT_atwork +coef_calib_autodeficienthhind_TNC_TRANSIT,coef_calib_autodeficienthhind_TNC_TRANSIT_work,coef_calib_autodeficienthhind_TNC_TRANSIT_univ,coef_calib_autodeficienthhind_TNC_TRANSIT_school,coef_calib_autodeficienthhind_TNC_TRANSIT_maint,coef_calib_autodeficienthhind_TNC_TRANSIT_maint,coef_calib_autodeficienthhind_TNC_TRANSIT_maint,coef_calib_autodeficienthhind_TNC_TRANSIT_disc,coef_calib_autodeficienthhind_TNC_TRANSIT_disc,coef_calib_autodeficienthhind_TNC_TRANSIT_disc,coef_calib_autodeficienthhind_TNC_TRANSIT_atwork +coef_calib_autodeficienthhind_TAXI,coef_calib_autodeficienthhind_TAXI_work,coef_calib_autodeficienthhind_TAXI_univ,coef_calib_autodeficienthhind_TAXI_school,coef_calib_autodeficienthhind_TAXI_maint,coef_calib_autodeficienthhind_TAXI_maint,coef_calib_autodeficienthhind_TAXI_maint,coef_calib_autodeficienthhind_TAXI_disc,coef_calib_autodeficienthhind_TAXI_disc,coef_calib_autodeficienthhind_TAXI_disc,coef_calib_autodeficienthhind_TAXI_atwork +coef_calib_autodeficienthhind_TNC_SINGLE,coef_calib_autodeficienthhind_TNC_SINGLE_work,coef_calib_autodeficienthhind_TNC_SINGLE_univ,coef_calib_autodeficienthhind_TNC_SINGLE_school,coef_calib_autodeficienthhind_TNC_SINGLE_maint,coef_calib_autodeficienthhind_TNC_SINGLE_maint,coef_calib_autodeficienthhind_TNC_SINGLE_maint,coef_calib_autodeficienthhind_TNC_SINGLE_disc,coef_calib_autodeficienthhind_TNC_SINGLE_disc,coef_calib_autodeficienthhind_TNC_SINGLE_disc,coef_calib_autodeficienthhind_TNC_SINGLE_atwork +coef_calib_autodeficienthhind_TNC_SHARED,coef_calib_autodeficienthhind_TNC_SHARED_work,coef_calib_autodeficienthhind_TNC_SHARED_univ,coef_calib_autodeficienthhind_TNC_SHARED_school,coef_calib_autodeficienthhind_TNC_SHARED_maint,coef_calib_autodeficienthhind_TNC_SHARED_maint,coef_calib_autodeficienthhind_TNC_SHARED_maint,coef_calib_autodeficienthhind_TNC_SHARED_disc,coef_calib_autodeficienthhind_TNC_SHARED_disc,coef_calib_autodeficienthhind_TNC_SHARED_disc,coef_calib_autodeficienthhind_TNC_SHARED_atwork +coef_calib_autodeficienthhind_EBIKE,coef_calib_autodeficienthhind_EBIKE_work,coef_calib_autodeficienthhind_EBIKE_univ,coef_calib_autodeficienthhind_EBIKE_school,coef_calib_autodeficienthhind_EBIKE_maint,coef_calib_autodeficienthhind_EBIKE_maint,coef_calib_autodeficienthhind_EBIKE_maint,coef_calib_autodeficienthhind_EBIKE_disc,coef_calib_autodeficienthhind_EBIKE_disc,coef_calib_autodeficienthhind_EBIKE_disc,coef_calib_autodeficienthhind_EBIKE_atwork 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+coef_calib_autodeficienthhjoi_WALK_TRANSIT,coef_zero,coef_zero,coef_zero,coef_calib_autodeficienthhjoi_WALK_TRANSIT_maint,coef_calib_autodeficienthhjoi_WALK_TRANSIT_maint,coef_calib_autodeficienthhjoi_WALK_TRANSIT_maint,coef_calib_autodeficienthhjoi_WALK_TRANSIT_disc,coef_calib_autodeficienthhjoi_WALK_TRANSIT_disc,coef_calib_autodeficienthhjoi_WALK_TRANSIT_disc,coef_zero +coef_calib_autodeficienthhjoi_PNR_TRANSIT,coef_zero,coef_zero,coef_zero,coef_calib_autodeficienthhjoi_PNR_TRANSIT_maint,coef_calib_autodeficienthhjoi_PNR_TRANSIT_maint,coef_calib_autodeficienthhjoi_PNR_TRANSIT_maint,coef_calib_autodeficienthhjoi_PNR_TRANSIT_disc,coef_calib_autodeficienthhjoi_PNR_TRANSIT_disc,coef_calib_autodeficienthhjoi_PNR_TRANSIT_disc,coef_zero 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+coef_calib_autosufficienthhin_SHARED3,coef_calib_autosufficienthhin_SHARED3_work,coef_calib_autosufficienthhin_SHARED3_univ,coef_calib_autosufficienthhin_SHARED3_school,coef_calib_autosufficienthhin_SHARED3_maint,coef_calib_autosufficienthhin_SHARED3_maint,coef_calib_autosufficienthhin_SHARED3_maint,coef_calib_autosufficienthhin_SHARED3_disc,coef_calib_autosufficienthhin_SHARED3_disc,coef_calib_autosufficienthhin_SHARED3_disc,coef_calib_autosufficienthhin_SHARED3_atwork +coef_calib_autosufficienthhin_WALK,coef_calib_autosufficienthhin_WALK_work,coef_calib_autosufficienthhin_WALK_univ,coef_calib_autosufficienthhin_WALK_school,coef_calib_autosufficienthhin_WALK_maint,coef_calib_autosufficienthhin_WALK_maint,coef_calib_autosufficienthhin_WALK_maint,coef_calib_autosufficienthhin_WALK_disc,coef_calib_autosufficienthhin_WALK_disc,coef_calib_autosufficienthhin_WALK_disc,coef_calib_autosufficienthhin_WALK_atwork +coef_calib_autosufficienthhin_BIKE,coef_calib_autosufficienthhin_BIKE_work,coef_calib_autosufficienthhin_BIKE_univ,coef_calib_autosufficienthhin_BIKE_school,coef_calib_autosufficienthhin_BIKE_maint,coef_calib_autosufficienthhin_BIKE_maint,coef_calib_autosufficienthhin_BIKE_maint,coef_calib_autosufficienthhin_BIKE_disc,coef_calib_autosufficienthhin_BIKE_disc,coef_calib_autosufficienthhin_BIKE_disc,coef_calib_autosufficienthhin_BIKE_atwork +coef_calib_autosufficienthhin_WALK_TRANSIT,coef_calib_autosufficienthhin_WALK_TRANSIT_work,coef_calib_autosufficienthhin_WALK_TRANSIT_univ,coef_calib_autosufficienthhin_WALK_TRANSIT_school,coef_calib_autosufficienthhin_WALK_TRANSIT_maint,coef_calib_autosufficienthhin_WALK_TRANSIT_maint,coef_calib_autosufficienthhin_WALK_TRANSIT_maint,coef_calib_autosufficienthhin_WALK_TRANSIT_disc,coef_calib_autosufficienthhin_WALK_TRANSIT_disc,coef_calib_autosufficienthhin_WALK_TRANSIT_disc,coef_calib_autosufficienthhin_WALK_TRANSIT_atwork +coef_calib_autosufficienthhin_PNR_TRANSIT,coef_calib_autosufficienthhin_PNR_TRANSIT_work,coef_calib_autosufficienthhin_PNR_TRANSIT_univ,coef_calib_autosufficienthhin_PNR_TRANSIT_school,coef_calib_autosufficienthhin_PNR_TRANSIT_maint,coef_calib_autosufficienthhin_PNR_TRANSIT_maint,coef_calib_autosufficienthhin_PNR_TRANSIT_maint,coef_calib_autosufficienthhin_PNR_TRANSIT_disc,coef_calib_autosufficienthhin_PNR_TRANSIT_disc,coef_calib_autosufficienthhin_PNR_TRANSIT_disc,coef_calib_autosufficienthhin_PNR_TRANSIT_atwork +coef_calib_autosufficienthhin_KNR_TRANSIT,coef_calib_autosufficienthhin_KNR_TRANSIT_work,coef_calib_autosufficienthhin_KNR_TRANSIT_univ,coef_calib_autosufficienthhin_KNR_TRANSIT_school,coef_calib_autosufficienthhin_KNR_TRANSIT_maint,coef_calib_autosufficienthhin_KNR_TRANSIT_maint,coef_calib_autosufficienthhin_KNR_TRANSIT_maint,coef_calib_autosufficienthhin_KNR_TRANSIT_disc,coef_calib_autosufficienthhin_KNR_TRANSIT_disc,coef_calib_autosufficienthhin_KNR_TRANSIT_disc,coef_calib_autosufficienthhin_KNR_TRANSIT_atwork +coef_calib_autosufficienthhin_TNC_TRANSIT,coef_calib_autosufficienthhin_TNC_TRANSIT_work,coef_calib_autosufficienthhin_TNC_TRANSIT_univ,coef_calib_autosufficienthhin_TNC_TRANSIT_school,coef_calib_autosufficienthhin_TNC_TRANSIT_maint,coef_calib_autosufficienthhin_TNC_TRANSIT_maint,coef_calib_autosufficienthhin_TNC_TRANSIT_maint,coef_calib_autosufficienthhin_TNC_TRANSIT_disc,coef_calib_autosufficienthhin_TNC_TRANSIT_disc,coef_calib_autosufficienthhin_TNC_TRANSIT_disc,coef_calib_autosufficienthhin_TNC_TRANSIT_atwork +coef_calib_autosufficienthhin_TAXI,coef_calib_autosufficienthhin_TAXI_work,coef_calib_autosufficienthhin_TAXI_univ,coef_calib_autosufficienthhin_TAXI_school,coef_calib_autosufficienthhin_TAXI_maint,coef_calib_autosufficienthhin_TAXI_maint,coef_calib_autosufficienthhin_TAXI_maint,coef_calib_autosufficienthhin_TAXI_disc,coef_calib_autosufficienthhin_TAXI_disc,coef_calib_autosufficienthhin_TAXI_disc,coef_calib_autosufficienthhin_TAXI_atwork +coef_calib_autosufficienthhin_TNC_SINGLE,coef_calib_autosufficienthhin_TNC_SINGLE_work,coef_calib_autosufficienthhin_TNC_SINGLE_univ,coef_calib_autosufficienthhin_TNC_SINGLE_school,coef_calib_autosufficienthhin_TNC_SINGLE_maint,coef_calib_autosufficienthhin_TNC_SINGLE_maint,coef_calib_autosufficienthhin_TNC_SINGLE_maint,coef_calib_autosufficienthhin_TNC_SINGLE_disc,coef_calib_autosufficienthhin_TNC_SINGLE_disc,coef_calib_autosufficienthhin_TNC_SINGLE_disc,coef_calib_autosufficienthhin_TNC_SINGLE_atwork +coef_calib_autosufficienthhin_TNC_SHARED,coef_calib_autosufficienthhin_TNC_SHARED_work,coef_calib_autosufficienthhin_TNC_SHARED_univ,coef_calib_autosufficienthhin_TNC_SHARED_school,coef_calib_autosufficienthhin_TNC_SHARED_maint,coef_calib_autosufficienthhin_TNC_SHARED_maint,coef_calib_autosufficienthhin_TNC_SHARED_maint,coef_calib_autosufficienthhin_TNC_SHARED_disc,coef_calib_autosufficienthhin_TNC_SHARED_disc,coef_calib_autosufficienthhin_TNC_SHARED_disc,coef_calib_autosufficienthhin_TNC_SHARED_atwork +coef_calib_autosufficienthhin_EBIKE,coef_calib_autosufficienthhin_EBIKE_work,coef_calib_autosufficienthhin_EBIKE_univ,coef_calib_autosufficienthhin_EBIKE_school,coef_calib_autosufficienthhin_EBIKE_maint,coef_calib_autosufficienthhin_EBIKE_maint,coef_calib_autosufficienthhin_EBIKE_maint,coef_calib_autosufficienthhin_EBIKE_disc,coef_calib_autosufficienthhin_EBIKE_disc,coef_calib_autosufficienthhin_EBIKE_disc,coef_calib_autosufficienthhin_EBIKE_atwork 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+coef_calib_zeroautohhjointtou_PNR_TRANSIT,coef_zero,coef_zero,coef_zero,coef_calib_zeroautohhjointtou_PNR_TRANSIT_maint,coef_calib_zeroautohhjointtou_PNR_TRANSIT_maint,coef_calib_zeroautohhjointtou_PNR_TRANSIT_maint,coef_calib_zeroautohhjointtou_PNR_TRANSIT_disc,coef_calib_zeroautohhjointtou_PNR_TRANSIT_disc,coef_calib_zeroautohhjointtou_PNR_TRANSIT_disc,coef_zero +coef_calib_zeroautohhjointtou_KNR_TRANSIT,coef_zero,coef_zero,coef_zero,coef_calib_zeroautohhjointtou_KNR_TRANSIT_maint,coef_calib_zeroautohhjointtou_KNR_TRANSIT_maint,coef_calib_zeroautohhjointtou_KNR_TRANSIT_maint,coef_calib_zeroautohhjointtou_KNR_TRANSIT_disc,coef_calib_zeroautohhjointtou_KNR_TRANSIT_disc,coef_calib_zeroautohhjointtou_KNR_TRANSIT_disc,coef_zero +coef_calib_zeroautohhjointtou_TNC_TRANSIT,coef_zero,coef_zero,coef_zero,coef_calib_zeroautohhjointtou_TNC_TRANSIT_maint,coef_calib_zeroautohhjointtou_TNC_TRANSIT_maint,coef_calib_zeroautohhjointtou_TNC_TRANSIT_maint,coef_calib_zeroautohhjointtou_TNC_TRANSIT_disc,coef_calib_zeroautohhjointtou_TNC_TRANSIT_disc,coef_calib_zeroautohhjointtou_TNC_TRANSIT_disc,coef_zero +coef_calib_zeroautohhjointtou_TAXI,coef_zero,coef_zero,coef_zero,coef_calib_zeroautohhjointtou_TAXI_maint,coef_calib_zeroautohhjointtou_TAXI_maint,coef_calib_zeroautohhjointtou_TAXI_maint,coef_calib_zeroautohhjointtou_TAXI_disc,coef_calib_zeroautohhjointtou_TAXI_disc,coef_calib_zeroautohhjointtou_TAXI_disc,coef_zero +coef_calib_zeroautohhjointtou_TNC_SINGLE,coef_zero,coef_zero,coef_zero,coef_calib_zeroautohhjointtou_TNC_SINGLE_maint,coef_calib_zeroautohhjointtou_TNC_SINGLE_maint,coef_calib_zeroautohhjointtou_TNC_SINGLE_maint,coef_calib_zeroautohhjointtou_TNC_SINGLE_disc,coef_calib_zeroautohhjointtou_TNC_SINGLE_disc,coef_calib_zeroautohhjointtou_TNC_SINGLE_disc,coef_zero +coef_calib_zeroautohhjointtou_TNC_SHARED,coef_zero,coef_zero,coef_zero,coef_calib_zeroautohhjointtou_TNC_SHARED_maint,coef_calib_zeroautohhjointtou_TNC_SHARED_maint,coef_calib_zeroautohhjointtou_TNC_SHARED_maint,coef_calib_zeroautohhjointtou_TNC_SHARED_disc,coef_calib_zeroautohhjointtou_TNC_SHARED_disc,coef_calib_zeroautohhjointtou_TNC_SHARED_disc,coef_zero +coef_calib_zeroautohhjointtou_EBIKE,coef_zero,coef_zero,coef_zero,coef_calib_zeroautohhjointtou_EBIKE_maint,coef_calib_zeroautohhjointtou_EBIKE_maint,coef_calib_zeroautohhjointtou_EBIKE_maint,coef_calib_zeroautohhjointtou_EBIKE_disc,coef_calib_zeroautohhjointtou_EBIKE_disc,coef_calib_zeroautohhjointtou_EBIKE_disc,coef_zero +coef_calib_zeroautohhjointtou_ESCOOTER,coef_zero,coef_zero,coef_zero,coef_calib_zeroautohhjointtou_ESCOOTER_maint,coef_calib_zeroautohhjointtou_ESCOOTER_maint,coef_calib_zeroautohhjointtou_ESCOOTER_maint,coef_calib_zeroautohhjointtou_ESCOOTER_disc,coef_calib_zeroautohhjointtou_ESCOOTER_disc,coef_calib_zeroautohhjointtou_ESCOOTER_disc,coef_zero +coef_calib_onboard,coef_calib_onboard,coef_calib_onboard,coef_calib_onboard,coef_calib_onboard,coef_calib_onboard,coef_calib_onboard,coef_calib_onboard,coef_calib_onboard,coef_calib_onboard,coef_calib_onboard +coef_calib_mt_zeroautohh,coef_calib_mt_zeroautohh,coef_calib_mt_zeroautohh,coef_calib_mt_zeroautohh,coef_calib_mt_zeroautohh,coef_calib_mt_zeroautohh,coef_calib_mt_zeroautohh,coef_calib_mt_zeroautohh,coef_calib_mt_zeroautohh,coef_calib_mt_zeroautohh,coef_calib_mt_zeroautohh +coef_calib_nev_zeroautohh,coef_calib_nev_zeroautohh,coef_calib_nev_zeroautohh,coef_calib_nev_zeroautohh,coef_calib_nev_zeroautohh,coef_calib_nev_zeroautohh,coef_calib_nev_zeroautohh,coef_calib_nev_zeroautohh,coef_calib_nev_zeroautohh,coef_calib_nev_zeroautohh,coef_calib_nev_zeroautohh +coef_calib_ebike_owner_BIKE,coef_calib_ebike_owner_BIKE,coef_calib_ebike_owner_BIKE,coef_calib_ebike_owner_BIKE,coef_calib_ebike_owner_BIKE,coef_calib_ebike_owner_BIKE,coef_calib_ebike_owner_BIKE,coef_calib_ebike_owner_BIKE,coef_calib_ebike_owner_BIKE,coef_calib_ebike_owner_BIKE,coef_calib_ebike_owner_BIKE +coef_calib_ebike_owner_EBIKE,coef_calib_ebike_owner_EBIKE,coef_calib_ebike_owner_EBIKE,coef_calib_ebike_owner_EBIKE,coef_calib_ebike_owner_EBIKE,coef_calib_ebike_owner_EBIKE,coef_calib_ebike_owner_EBIKE,coef_calib_ebike_owner_EBIKE,coef_calib_ebike_owner_EBIKE,coef_calib_ebike_owner_EBIKE,coef_calib_ebike_owner_EBIKE +coef_calib_ebike_shared,coef_calib_ebike_shared,coef_calib_ebike_shared,coef_calib_ebike_shared,coef_calib_ebike_shared,coef_calib_ebike_shared,coef_calib_ebike_shared,coef_calib_ebike_shared,coef_calib_ebike_shared,coef_calib_ebike_shared,coef_calib_ebike_shared \ No newline at end of file diff --git a/resident/configs/tour_scheduling_atwork.csv b/resident/configs/tour_scheduling_atwork.csv new file mode 100644 index 0000000..c1e9adb --- /dev/null +++ b/resident/configs/tour_scheduling_atwork.csv @@ -0,0 +1,50 @@ +Label,Description,Expression,Coefficient +util_Female_Departure_after_1230_pm_Linear,Female - Departure after 12:30 pm - Linear,"@np.where(((df.female) & (df.start>19)), (np.where((df.start<= 19), np.minimum(19 - df.start, 48), 0) + np.where((df.start > 19), np.minimum(df.start - 19, 48), 0)), 0)",coef_Female_Departure_after_1230_pm_Linear +util_Parttime_worker_Departure_after_1230_pm__Linear,Part-time worker - Departure after 12:30 pm - Linear,"@np.where(((df.ptype == 2) & (df.start>19)), (np.where((df.start<= 19), np.minimum(19 - df.start, 48), 0) + np.where((df.start > 19), np.minimum(df.start - 19, 48), 0)), 0)",coef_Parttime_worker_Departure_after_1230_pm__Linear +util_Parttime_worker_Duration_greater_than_0p5_hours_depart_and_arrive_in_the_same_period,Part-time worker - Duration greater than 0.5 hours (depart and arrive in the same period),"@np.where(((df.ptype == 2) & (df.duration>1)), (np.where((df.duration <= 1), np.minimum(1 - df.duration, 47), 0) + np.where((df.duration > 1), np.minimum(df.duration - 1, 47), 0)), 0)",coef_Parttime_worker_Duration_greater_than_0p5_hours_depart_and_arrive_in_the_same_period +util_Low_income_lt25000_Duration_less_than_0p5_hours_depart_and_arrive_in_the_same_period,Low income (<25000) - Duration less than 0.5 hours (depart and arrive in the same period),"@np.where(((df.is_income_less25K) & (df.duration<1)), (np.where((df.duration <= 1), np.minimum(1 - df.duration, 47), 0) + np.where((df.duration > 1), np.minimum(df.duration - 1, 47), 0)), 0)",coef_Low_income_lt25000_Duration_less_than_0p5_hours_depart_and_arrive_in_the_same_period +util_Med_income_25k_to_60k_Duration_less_than_0p5_hours_depart_and_arrive_in_the_same_period,Med income (25k to 60k) - Duration less than 0.5 hours (depart and arrive in the same period),"@np.where(((df.is_income_25K_to_60K) & (df.duration<1)), (np.where((df.duration <= 1), np.minimum(1 - df.duration, 47), 0) + np.where((df.duration > 1), np.minimum(df.duration - 1, 47), 0)), 0)",coef_Med_income_25k_to_60k_Duration_less_than_0p5_hours_depart_and_arrive_in_the_same_period +util_Med_income_25k_to_60k_Duration_greater_than_0p5_hours,Med income (25k to 60k) - Duration greater than 0.5 hours,"@np.where(((df.is_income_25K_to_60K) & (df.duration>1)), (np.where((df.duration <= 1), np.minimum(1 - df.duration, 47), 0) + np.where((df.duration > 1), np.minimum(df.duration - 1, 47), 0)), 0)",coef_Med_income_25k_to_60k_Duration_greater_than_0p5_hours +util_Medhigh_income_60k_to_120k_Duration_greater_than_0p5_hours,Med-high income (60k to 120k) - Duration greater than 0.5 hours,"@np.where(((df.is_income_60K_to_120K) & (df.duration>1)), (np.where((df.duration <= 1), np.minimum(1 - df.duration, 47), 0) + np.where((df.duration > 1), np.minimum(df.duration - 1, 47), 0)), 0)",coef_Medhigh_income_60k_to_120k_Duration_greater_than_0p5_hours +#util_Blue_collar_Duration_less_than_0p5_hours_depart_and_arrive_in_the_same_period,Blue collar - Duration less than 0.5 hours (depart and arrive in the same period),"@np.where(((df.work_segment == 5) & (df.duration<1)), (np.where((df.duration <= 1), np.minimum(1 - df.duration, 47), 0) + np.where((df.duration > 1), np.minimum(df.duration - 1, 47), 0)), 0)",coef_Blue_collar_Duration_less_than_0p5_hours_depart_and_arrive_in_the_same_period +#util_Blue_collar_Duration_greater_than_0p5_hours,Blue collar - Duration greater than 0.5 hours,"@np.where(((df.work_segment == 5) & (df.duration>1)), (np.where((df.duration <= 1), np.minimum(1 - df.duration, 47), 0) + np.where((df.duration > 1), np.minimum(df.duration - 1, 47), 0)), 0)",coef_Blue_collar_Duration_greater_than_0p5_hours +#util_Health_Duration_less_than_0p5_hours_depart_and_arrive_in_the_same_period,Health - Duration less than 0.5 hours (depart and arrive in the same period),"@np.where(((df.work_segment == 3) & (df.duration<1)), (np.where((df.duration <= 1), np.minimum(1 - df.duration, 47), 0) + np.where((df.duration > 1), np.minimum(df.duration - 1, 47), 0)), 0)",coef_Health_Duration_less_than_0p5_hours_depart_and_arrive_in_the_same_period +util_Distance_to_destination_Duration_less_than_0p5_hours_depart_and_arrive_in_the_same_period,Distance to destination - Duration less than 0.5 hours (depart and arrive in the same period),"@np.where((df.duration<1), ((np.where((df.duration <= 1), np.minimum(1 - df.duration, 47), 0) + np.where((df.duration > 1), np.minimum(df.duration - 1, 47), 0))) * df.od_distance, 0)",coef_Distance_to_destination_Duration_less_than_0p5_hours_depart_and_arrive_in_the_same_period +util_Distance_to_destination_Duration_greater_than_0p5_hours,Distance to destination - Duration greater than 0.5 hours,"@np.where((df.duration>1), ((np.where((df.duration <= 1), np.minimum(1 - df.duration, 47), 0) + np.where((df.duration > 1), np.minimum(df.duration - 1, 47), 0))) * df.od_distance, 0)",coef_Distance_to_destination_Duration_greater_than_0p5_hours +util_Subtour_purpose_Business_Departure_before_1200_pm__Linear,Subtour purpose: Business - Departure before 12:00 pm - Linear,"@np.where(((df.tour_type == 'business') & (df.start<19)), (np.where((df.start<= 19), np.minimum(19 - df.start, 48), 0) + np.where((df.start > 19), np.minimum(df.start - 19, 48), 0)), 0)",coef_Subtour_purpose_Business_Departure_before_1200_pm__Linear +util_Subtour_purpose_Business_Departure_after_1230_pm_Linear,Subtour purpose: Business - Departure after 12:30 pm - Linear,"@np.where(((df.tour_type == 'business') & (df.start>19)), (np.where((df.start<= 19), np.minimum(19 - df.start, 48), 0) + np.where((df.start > 19), np.minimum(df.start - 19, 48), 0)), 0)",coef_Subtour_purpose_Business_Departure_after_1230_pm_Linear +util_Subtour_purpose_Business_Duration_greater_than_0p5_hours,Subtour purpose: Business - Duration greater than 0.5 hours,"@np.where(((df.tour_type == 'business') & (df.duration>1)), (np.where((df.duration <= 1), np.minimum(1 - df.duration, 47), 0) + np.where((df.duration > 1), np.minimum(df.duration - 1, 47), 0)), 0)",coef_Subtour_purpose_Business_Duration_greater_than_0p5_hours +util_Subtour_purpose_Eatout_Departure_before_1200_pm__Linear,Subtour purpose: Eat-out - Departure before 12:00 pm - Linear,"@np.where(((df.tour_type == 'eat') & (df.start<19)), (np.where((df.start<= 19), np.minimum(19 - df.start, 48), 0) + np.where((df.start > 19), np.minimum(df.start - 19, 48), 0)), 0)",coef_Subtour_purpose_Eatout_Departure_before_1200_pm__Linear +util_Subtour_purpose_Eatout_Departure_after_1230_pm_Linear,Subtour purpose: Eat-out - Departure after 12:30 pm - Linear,"@np.where(((df.tour_type == 'eat') & (df.start>19)), (np.where((df.start<= 19), np.minimum(19 - df.start, 48), 0) + np.where((df.start > 19), np.minimum(df.start - 19, 48), 0)), 0)",coef_Subtour_purpose_Eatout_Departure_after_1230_pm_Linear +util_Subtour_purpose_Eatout_Duration_less_than_0p5_hours_depart_and_arrive_in_the_same_period,Subtour purpose: Eat-out - Duration less than 0.5 hours (depart and arrive in the same period),"@np.where(((df.tour_type == 'eat') & (df.duration<1)), (np.where((df.duration <= 1), np.minimum(1 - df.duration, 47), 0) + np.where((df.duration > 1), np.minimum(df.duration - 1, 47), 0)), 0)",coef_Subtour_purpose_Eatout_Duration_less_than_0p5_hours_depart_and_arrive_in_the_same_period +util__Departure_constants,# Departure constants,,coef__Departure_constants +util_Shift_for_every_30_minutes_before_1030_am_Linear,Shift for every 30 minutes before 10:30 am - Linear,"@np.where((df.start<16), (np.where((df.start< 16), np.minimum(16 - df.start, 9), 0) + np.where((df.start> 21), np.minimum(df.start - 21, 11), 0)), 0)",coef_Shift_for_every_30_minutes_before_1030_am_Linear +util_Before_1100_AM,Before 11:00 AM,@(df.start<17),coef_start_Before_1100_AM +util_1100_AM_1130_AM_start,11:00 AM - 11:30 AM,@(df.start==17),coef_start_1100_AM_1130_AM +util_1130_AM_1200_PM_start,11:30 AM - 12:00 PM,@(df.start==18),coef_start_1130_AM_1200_PM +util_1200_AM_1230_PM_start,12:00 AM - 12:30 PM,@(df.start==19),coef_start_1200_AM_1230_PM +util_1230_PM_0100_PM_start,12:30 PM - 01:00 PM,@(df.start==20),coef_start_1230_PM_0100_PM +util_After_0100_PM,After 01:00 PM,@(df.start>20),coef_start_After_0100_PM +util_Shift_for_every_30_minutes_after_130_pm_Square_Root,Shift for every 30 minutes after 1:30 pm - Square Root,"@np.where((df.start>21), ((np.where((df.start < 16), np.minimum(16 - df.start, 9), 0) + np.where((df.start > 21), np.minimum(df.start - 21, 11), 0))** 0.5), 0)",coef_Shift_for_every_30_minutes_after_130_pm_Square_Root +util__Arrival_constants,# Arrival constants,,coef__Arrival_constants +util_Shift_for_every_30_minutes_before_1130_am_Linear,Shift for every 30 minutes before 11:30 am - Linear,"@np.where((df.end<18), (np.where((df.end < 14), np.minimum(14 - df.end, 9), 0) + np.where((df.end > 24), np.minimum(df.end - 24, 10), 0)), 0)",coef_Shift_for_every_30_minutes_before_1130_am_Linear +util_Before_1200_PM,Before 12:00 PM,@(df.end<19),coef_end_Before_1200_PM +util_1200_AM_1230_PM_end,12:00 AM - 12:30 PM,@(df.end==19),coef_end_1200_AM_1230_PM +util_1230_PM_0100_PM_end,12:30 PM - 01:00 PM,@(df.end==20),coef_end_1230_PM_0100_PM +util_0100_PM_0130_PM_end,01:00 PM - 01:30 PM,@(df.end==21),coef_end_0100_PM_0130_PM +util_0130_PM_0200_PM_end,01:30 PM - 02:00 PM,@(df.end==22),coef_end_0130_PM_0200_PM +util_0200_PM_0230_PM_end,02:00 PM - 02:30 PM,@(df.end==23),coef_end_0200_PM_0230_PM +util_After_0230_PM,After 02:30 PM,@(df.end>23),coef_end_After_0230_PM +util_Shift_for_every_30_minutes_after_300_pm_Linear,Shift for every 30 minutes after 3:00 pm - Linear,"@np.where((df.end>24), (np.where((df.end < 14), np.minimum(14 - df.end, 9), 0) + np.where((df.end > 24), np.minimum(df.end - 24, 10), 0)), 0)",coef_Shift_for_every_30_minutes_after_300_pm_Linear +util__Duration_constants,# Duration constants,,coef__Duration_constants +util_0_hrs,0 hrs,@(df.duration==0),coef_0_hrs +util_0p5_hrs,0.5 hrs,@(df.duration==1),coef_0p5_hrs +util_1_hrs,1 hrs,@(df.duration==2),coef_1_hrs +util_1p5hrs,1.5hrs,@(df.duration==3),coef_1p5hrs +util_2_hrs,2 hrs,@(df.duration==4),coef_2_hrs +util_Longer_than_2_hrs,Longer than 2 hrs,@(df.duration>4),coef_Longer_than_2_hrs +util_Shift_for_every_30_minutes_more_than_2p5_hrs_Square_Root,Shift for every 30 minutes more than 2.5 hrs - Square Root,"@np.where((df.duration>5), ((np.where((df.duration < 0), np.minimum(0 - df.duration, 47), 0) + np.where((df.duration > 5), np.minimum(df.duration - 5, 13), 0)) ** 0.5), 0)",coef_Shift_for_every_30_minutes_more_than_2p5_hrs_Square_Root +util_Calibration_Constant_Departure_eq_18,Calibration Constant - Departure = 18,@(df.start==18),coef_Calibration_Constant_Departure_eq_18 +util_Calibration_Constant_Departure_eq_19,Calibration Constant - Departure = 19,@(df.start==19),coef_Calibration_Constant_Departure_eq_19 +util_Calibration_Constant_Arrival_eq_20,Calibration Constant - Arrival = 20,@(df.end==20),coef_Calibration_Constant_Arrival_eq_20 +util_Calibration_Constant_Arrival_eq_21,Calibration Constant - Arrival = 21,@(df.end==21),coef_Calibration_Constant_Arrival_eq_21 diff --git a/resident/configs/tour_scheduling_atwork.yaml b/resident/configs/tour_scheduling_atwork.yaml new file mode 100644 index 0000000..f04f93b --- /dev/null +++ b/resident/configs/tour_scheduling_atwork.yaml @@ -0,0 +1,16 @@ + +SPEC: tour_scheduling_atwork.csv +COEFFICIENTS: tour_scheduling_atwork_coeffs.csv + +preprocessor: + SPEC: tour_scheduling_atwork_preprocessor + DF: df +# TABLES: +# - land_use +# - tours + +SIMULATE_CHOOSER_COLUMNS: + - od_distance + +CONSTANTS: + time_cap: 30 \ No newline at end of file diff --git a/resident/configs/tour_scheduling_atwork_coefficients.csv b/resident/configs/tour_scheduling_atwork_coefficients.csv new file mode 100644 index 0000000..dd09d22 --- /dev/null +++ b/resident/configs/tour_scheduling_atwork_coefficients.csv @@ -0,0 +1,50 @@ +coefficient_name,value,constrain +coef_early_start_at_5,-7.765548476,F +coef_am_peak_start_at_6,-6.156717827,F +coef_am_peak_start_at_7,-4.061708142,F +coef_am_peak_start_at_8,-2.330535201,F +coef_am_peak_start_at_9,-1.881593386,F +coef_midday_start_at_10_11_12,0,T +coef_midday_start_at_13_14_15,-0.77502158,F +coef_pm_peak_start_at_16_17_18,-0.227528489,F +coef_evening_start_at_19_20_21,-1.015090023,F +coef_late_start_at_22_23,-0.737570054,F +coef_early_end_at_5_6,-2.928312295,F +coef_am_peak_end,-2.928312295,F +coef_midday_end_at_10_11_12,-2.297264374,F +coef_midday_end_at_13_14,0,T +coef_pm_peak_end_at_15,-0.578344457,F +coef_pm_peak_end_at_16,-1.09408722,F +coef_pm_peak_end_at_17,-1.1658466,F +coef_pm_peak_end_at_18,-1.496131081,F +coef_evening_end_at_19_20_21,-2.31998226,F +coef_late_end_at_22_23,-2.31998226,F +coef_duration_of_0_hours,-0.906681512,F +coef_duration_of_1_hour,0,T +coef_duration_of_2_to_3_hours,-1.362175802,F +coef_duration_of_4_to_5_hours,-0.819617616,F +coef_duration_of_6_to_7_hours,1.088111072,F +coef_duration_of_8_to_10_hours,1.734038505,F +coef_duration_of_11_to_13_hours,0.3,F +coef_duration_of_14_to_18_hours,0,T +coef_start_shift_for_outbound_auto_travel_time_off_peak,0.00065,F +coef_start_shift_for_inbound_auto_travel_time_off_peak,0.00065,F +coef_duration_shift_for_outbound_auto_travel_time_off_peak,0.00981,F +coef_duration_shift_for_inbound_auto_travel_time_off_peak,0.00981,F +coef_start_shift_for_business_related_,-0.1113,F +coef_duration_shift_for_business_related_,0.2646,F +coef_start_shift_for_first_sub_tour_of_same_work_tour,-0.5433,F +coef_duration_shift_for_first_sub_tour_of_same_work_tour,-0.3992,F +coef_start_shift_for_subsequent_sub_tour_of_same_work_tour,-0.1844,F +coef_duration_shift_for_subsequent_sub_tour_of_same_work_tour,-0.1844,F +coef_start_shift_for_number_of_mandatory_tours,-0.0193,F +coef_duration_shift_for_number_of_mandatory_tours,-0.7702,F +coef_start_shift_for_number_of_joint_tours,-0.0206,F +coef_duration_shift_for_number_of_joint_tours,-0.2497,F +coef_start_shift_for_number_of_individual_nonmandatory_tours,-0.0128,F +coef_duration_shift_for_number_of_individual_nonmandatory_tours,-0.0422,F +coef_dummy_for_business_related_purpose_and_duration_from_0_to_1,-1.543,F +coef_dummy_for_eating_out_purpose_and_duration_of_1_hour,0.3999,F +coef_dummy_for_eating_out_purpose_and_departure_at_11,1.511,F +coef_dummy_for_eating_out_purpose_and_departure_at_12,2.721,F +coef_dummy_for_eating_out_purpose_and_departure_at_13,2.122,F diff --git a/resident/configs/tour_scheduling_atwork_coeffs.csv b/resident/configs/tour_scheduling_atwork_coeffs.csv new file mode 100644 index 0000000..0e7ab1a --- /dev/null +++ b/resident/configs/tour_scheduling_atwork_coeffs.csv @@ -0,0 +1,47 @@ +coefficient_name,value,constrain +coef_Female_Departure_after_1230_pm_Linear,0.05574558,F +coef_Parttime_worker_Departure_after_1230_pm__Linear,0.129291333,F +coef_Parttime_worker_Duration_greater_than_0p5_hours_depart_and_arrive_in_the_same_period,0.162008704,F +coef_Low_income_lt25000_Duration_less_than_0p5_hours_depart_and_arrive_in_the_same_period,0.885322446,F +coef_Med_income_25k_to_60k_Duration_less_than_0p5_hours_depart_and_arrive_in_the_same_period,0.526935246,F +coef_Med_income_25k_to_60k_Duration_greater_than_0p5_hours,-0.081917021,F +coef_Medhigh_income_60k_to_120k_Duration_greater_than_0p5_hours,-0.068358924,F +coef_Blue_collar_Duration_less_than_0p5_hours_depart_and_arrive_in_the_same_period,1.191378628,F +coef_Blue_collar_Duration_greater_than_0p5_hours,0.123072852,F +coef_Health_Duration_less_than_0p5_hours_depart_and_arrive_in_the_same_period,0.791205377,F +coef_Distance_to_destination_Duration_less_than_0p5_hours_depart_and_arrive_in_the_same_period,-0.292363361,F +coef_Distance_to_destination_Duration_greater_than_0p5_hours,0.006885922,F +coef_Subtour_purpose_Business_Departure_before_1200_pm__Linear,0.268963895,F +coef_Subtour_purpose_Business_Departure_after_1230_pm_Linear,0.17631122,F +coef_Subtour_purpose_Business_Duration_greater_than_0p5_hours,0.362189199,F +coef_Subtour_purpose_Eatout_Departure_before_1200_pm__Linear,-0.250770206,F +coef_Subtour_purpose_Eatout_Departure_after_1230_pm_Linear,-0.169861029,F +coef_Subtour_purpose_Eatout_Duration_less_than_0p5_hours_depart_and_arrive_in_the_same_period,-0.678939929,F +coef_Shift_for_every_30_minutes_before_1030_am_Linear,-0.731880037,F +coef_start_Before_1100_AM,-2.176744062,F +coef_start_1100_AM_1130_AM,-1.190017952,F +coef_start_1130_AM_1200_PM,-0.198229872,F +coef_start_1200_AM_1230_PM,0,T +coef_start_1230_PM_0100_PM,-0.084950396,F +coef_start_After_0100_PM,-0.205562723,F +coef_Shift_for_every_30_minutes_after_130_pm_Square_Root,0.539088697,F +coef_Shift_for_every_30_minutes_before_1130_am_Linear,0.414546555,F +coef_end_Before_1200_PM,0.279351638,F +coef_end_1200_AM_1230_PM,-0.045281832,F +coef_end_1230_PM_0100_PM,0.214070736,F +coef_end_0100_PM_0130_PM,0,T +coef_end_0130_PM_0200_PM,-0.69742748,F +coef_end_0200_PM_0230_PM,-1.284283533,F +coef_end_After_0230_PM,-2.119733896,F +coef_Shift_for_every_30_minutes_after_300_pm_Linear,-0.508006414,F +coef_0_hrs,-0.969734874,F +coef_0p5_hrs,0,T +coef_1_hrs,0.177457256,F +coef_1p5hrs,-0.171124657,F +coef_2_hrs,-0.4678094,F +coef_Longer_than_2_hrs,-0.523935526,F +coef_Shift_for_every_30_minutes_more_than_2p5_hrs_Square_Root,-0.424301372,F +coef_Calibration_Constant_Departure_eq_18,-0.045958531,F +coef_Calibration_Constant_Departure_eq_19,-0.099009925,F +coef_Calibration_Constant_Arrival_eq_20,-0.0698094,F +coef_Calibration_Constant_Arrival_eq_21,-0.064355276,F diff --git a/resident/configs/tour_scheduling_atwork_preprocessor.csv b/resident/configs/tour_scheduling_atwork_preprocessor.csv new file mode 100644 index 0000000..df2aa14 --- /dev/null +++ b/resident/configs/tour_scheduling_atwork_preprocessor.csv @@ -0,0 +1,3 @@ +Description,Target,Expression +#,, +local scalar distance skim,od_distance,"od_skims[('SOV_M_DIST', 'MD')]" diff --git a/resident/configs/tour_scheduling_joint.csv b/resident/configs/tour_scheduling_joint.csv new file mode 100644 index 0000000..c914769 --- /dev/null +++ b/resident/configs/tour_scheduling_joint.csv @@ -0,0 +1,241 @@ +Label,Description,Expression,Coefficient +#SHOPPING,#SHOPPING,,SHOPPING +util_shop_Joint_Shopping_tours_dummy_Departure_before_10_00_AM_Linear,SHOPPING - Joint Shopping tours dummy: Departure before 10:00 AM - Linear,"@np.where(((df.tour_category == 'joint') & (df.tour_type == 'shopping') & (df.start<15)), ((15-df.start)*(df.start<=15) + (df.start-15)*(df.start>15)), 0)",coef_shop_Joint_Shopping_tours_dummy_Departure_before_10_00_AM_Linear +util_shop_Joint_Shopping_tours_dummy_Departure_after_10_30_AM_Linear,SHOPPING - Joint Shopping tours dummy: Departure after 10:30 AM - Linear,"@np.where(((df.tour_category == 'joint') & (df.tour_type == 'shopping') & (df.start>15)), ((15-df.start)*(df.start<=15) + (df.start-15)*(df.start>15)), 0)",coef_shop_Joint_Shopping_tours_dummy_Departure_after_10_30_AM_Linear +util_shop_Joint_Tours_Party_Size_gt_2_Duration_lt_1p5_hrs,SHOPPING - Joint Tours Party Size > 2: Duration < 1.5 hrs,"@np.where(((df.tour_category == 'joint') & (df.tour_type == 'shopping') & (df.duration<3) & (df.number_of_participants > 2)), ((3-df.duration)*(df.duration<=3) + (df.duration-3)*(df.duration>3)), 0)",coef_shop_Joint_Tours_Party_Size_gt_2_Duration_lt_1p5_hrs +util_shop_Joint_Tours_Party_Size_gt_2_Duration_gt_1p5_hr,SHOPPING - Joint Tours Party Size > 2: Duration > 1.5 hr,"@np.where(((df.tour_category == 'joint') & (df.tour_type == 'shopping') & (df.duration>3) & (df.number_of_participants > 2)), ((3-df.duration)*(df.duration<=3) + (df.duration-3)*(df.duration>3)), 0)",coef_shop_Joint_Tours_Party_Size_gt_2_Duration_gt_1p5_hr +util_shop_Joint_Tour_with_only_adults_Duration_lt_1p5_hrs,SHOPPING - Joint Tour with only adults: Duration < 1.5 hrs,"@np.where(((df.tour_category == 'joint') & (df.tour_type == 'shopping') & (df.duration<3) & (df.composition=='adults')), ((3-df.duration)*(df.duration<=3) + (df.duration-3)*(df.duration>3)), 0)",coef_shop_Joint_Tour_with_only_adults_Duration_lt_1p5_hrs +util_shop_Kids_in_Joint_Tour_Duration_lt_1p5_hrs,SHOPPING - Kids in Joint Tour: Duration < 1.5 hrs,"@np.where(((df.tour_category == 'joint') & (df.tour_type == 'shopping') & (df.duration<3) & ((df.composition=='children')|(df.composition=='mixed'))), ((3-df.duration)*(df.duration<=3) + (df.duration-3)*(df.duration>3)), 0)",coef_shop_Kids_in_Joint_Tour_Duration_lt_1p5_hrs +util_shop_Kids_in_Joint_Tour_Duration_gt_1p5_hr,SHOPPING - Kids in Joint Tour: Duration > 1.5 hr,"@np.where(((df.tour_category == 'joint') & (df.tour_type == 'shopping') & (df.duration>3) & ((df.composition=='children')|(df.composition=='mixed'))), ((3-df.duration)*(df.duration<=3) + (df.duration-3)*(df.duration>3)), 0)",coef_shop_Kids_in_Joint_Tour_Duration_gt_1p5_hr +util_shop_Low_Income_lteq25_000_Duration_gt_1p5_hr,"SHOPPING - Low Income (<=$25,000): Duration > 1.5 hr","@np.where(((df.tour_category == 'joint') & (df.tour_type == 'shopping') & (df.is_income_less25K) & (df.duration>3)), ((3-df.duration)*(df.duration<=3) + (df.duration-3)*(df.duration>3)), 0)",coef_shop_Low_Income_lteq25_000_Duration_gt_1p5_hr +util_shop_Medium_Income_25_001_to_60_000_Duration_lt_1p5_hrs,"SHOPPING - Medium Income ($25,001 to $60,000): Duration < 1.5 hrs","@np.where(((df.tour_category == 'joint') & (df.tour_type == 'shopping') & (df.is_income_25K_to_60K) & (df.duration<3)), ((3-df.duration)*(df.duration<=3) + (df.duration-3)*(df.duration>3)), 0)",coef_shop_Medium_Income_25_001_to_60_000_Duration_lt_1p5_hrs +util_shop_MediumHigh_Income_60_001_to_120_00_Duration_gt_1p5_hr,"SHOPPING - Medium-High Income ($60,001 to $120,00): Duration > 1.5 hr","@np.where(((df.tour_category == 'joint') & (df.tour_type == 'shopping') & (df.is_income_60K_to_120K) & (df.duration>3)), ((3-df.duration)*(df.duration<=3) + (df.duration-3)*(df.duration>3)), 0)",coef_shop_MediumHigh_Income_60_001_to_120_00_Duration_gt_1p5_hr +util_shop_Distance_Duration_lt_1p5_hrs,SHOPPING - Distance: Duration < 1.5 hrs,"@np.where(((df.tour_category == 'joint') & (df.tour_type == 'shopping') & (df.duration<3)), ((3-df.duration)*(df.duration<=3) + (df.duration-3)*(df.duration>3)) * (df.origin_to_destination_distance), 0)",coef_shop_Distance_Duration_lt_1p5_hrs +util_shop_Distance_Duration_gt_1p5_hr,SHOPPING - Distance: Duration > 1.5 hr,"@np.where(((df.tour_category == 'joint') & (df.tour_type == 'shopping') & (df.duration>3)), ((3-df.duration)*(df.duration<=3) + (df.duration-3)*(df.duration>3)) * (df.origin_to_destination_distance), 0)",coef_shop_Distance_Duration_gt_1p5_hr +util_shop_Departure_Constant_Shift_for_every_30_minutes_before_08_30_am_Linear,SHOPPING - Departure Constant: Shift for every 30 minutes before 08:30 am - Linear,"@np.where(((df.tour_category == 'joint') & (df.tour_type == 'shopping') & (df.start<12)), ((12-df.start)*(df.start<=12) + (df.start-12)*(df.start>12)), 0)",coef_shop_Departure_Constant_Shift_for_every_30_minutes_before_08_30_am_Linear +util_shop_Departure_Constant_Shift_for_every_30_minutes_before_08_30_am_Square_root,SHOPPING - Departure Constant: Shift for every 30 minutes before 08:30 am - Square root,"@np.where(((df.tour_category == 'joint') & (df.tour_type == 'shopping') & (df.start<12)), ((12-df.start)*(df.start<=12) + (df.start-12)*(df.start>12))**0.5, 0)",coef_shop_Departure_Constant_Shift_for_every_30_minutes_before_08_30_am_Square_root +util_shop_Departure_Constant_Before_09_00_AM,SHOPPING - Departure Constant: Before 09:00 AM,@((df.tour_category == 'joint') & (df.tour_type == 'shopping') & (df.start<13)),coef_shop_Departure_Constant_Before_09_00_AM +util_shop_Departure_Constant_09_00_AM_09_30_AM,SHOPPING - Departure Constant: 09:00 AM - 09:30 AM,@((df.tour_category == 'joint') & (df.tour_type == 'shopping') & (df.start==13)),coef_shop_Departure_Constant_09_00_AM_09_30_AM +util_shop_Departure_Constant_09_30_AM_10_00_AM,SHOPPING - Departure Constant: 09:30 AM - 10:00 AM,@((df.tour_category == 'joint') & (df.tour_type == 'shopping') & (df.start==14)),coef_shop_Departure_Constant_09_30_AM_10_00_AM +util_shop_Departure_Constant_10_00_AM_10_30_AM,SHOPPING - Departure Constant: 10:00 AM - 10:30 AM,@((df.tour_category == 'joint') & (df.tour_type == 'shopping') & (df.start==15)),coef_shop_Departure_Constant_10_00_AM_10_30_AM +util_shop_Departure_Constant_10_30_AM_11_00_AM,SHOPPING - Departure Constant: 10:30 AM - 11:00 AM,@((df.tour_category == 'joint') & (df.tour_type == 'shopping') & (df.start==16)),coef_shop_Departure_Constant_10_30_AM_11_00_AM +util_shop_Departure_Constant_After_11_00_AM,SHOPPING - Departure Constant: After 11:00 AM,@((df.tour_category == 'joint') & (df.tour_type == 'shopping') & (df.start>16)),coef_shop_Departure_Constant_After_11_00_AM +util_shop_Departure_Constant_Shift_for_every_30_minutes_after_11_30_am_Linear,SHOPPING - Departure Constant: Shift for every 30 minutes after 11:30 am - Linear,"@np.where(((df.tour_category == 'joint') & (df.tour_type == 'shopping') & (df.start>17)), ((17-df.start)*(df.start<=17) + (df.start-17)*(df.start>17)), 0)",coef_shop_Departure_Constant_Shift_for_every_30_minutes_after_11_30_am_Linear +util_shop_Departure_Constant_Shift_for_every_30_minutes_after_11_30_am_Squared,SHOPPING - Departure Constant: Shift for every 30 minutes after 11:30 am - Squared,"@np.where(((df.tour_category == 'joint') & (df.tour_type == 'shopping') & (df.start>17)), (((17-df.start)*(df.start<=17) + (df.start-17)*(df.start>17)) ** 2), 0)",coef_shop_Departure_Constant_Shift_for_every_30_minutes_after_11_30_am_Squared +util_shop_Arrival_Constant_Shift_for_every_30_minutes_before_12_00_pm_Linear,SHOPPING - Arrival Constant: Shift for every 30 minutes before 12:00 pm - Linear,"@np.where(((df.tour_category == 'joint') & (df.tour_type == 'shopping') & (df.end<19)), ((19-df.end)*(df.end<=19) + (df.end-19)*(df.end>19)), 0)",coef_shop_Arrival_Constant_Shift_for_every_30_minutes_before_12_00_pm_Linear +util_shop_Arrival_Constant_Before_12_30_PM,SHOPPING - Arrival Constant: Before 12:30 PM,@((df.tour_category == 'joint') & (df.tour_type == 'shopping') & (df.end<20)),coef_shop_Arrival_Constant_Before_12_30_PM +util_shop_Arrival_Constant_12_30_PM_03_00_PM,SHOPPING - Arrival Constant: 12:30 PM - 03:00 PM,@((df.tour_category == 'joint') & (df.tour_type == 'shopping') & ( df.end>=20) & (df.end<=24)),coef_shop_Arrival_Constant_12_30_PM_03_00_PM +util_shop_Arrival_Constant_03_00_PM_03_30_PM,SHOPPING - Arrival Constant: 03:00 PM - 03:30 PM,@((df.tour_category == 'joint') & (df.tour_type == 'shopping') & (df.end==25)),coef_shop_Arrival_Constant_03_00_PM_03_30_PM +util_shop_Arrival_Constant_03_30_PM_04_00_PM,SHOPPING - Arrival Constant: 03:30 PM - 04:00 PM,@((df.tour_category == 'joint') & (df.tour_type == 'shopping') & (df.end==26)),coef_shop_Arrival_Constant_03_30_PM_04_00_PM +util_shop_Arrival_Constant_04_00_PM_04_30_PM,SHOPPING - Arrival Constant: 04:00 PM - 04:30 PM,@((df.tour_category == 'joint') & (df.tour_type == 'shopping') & (df.end==27)),coef_shop_Arrival_Constant_04_00_PM_04_30_PM +util_shop_Arrival_Constant_04_30_PM_05_00_PM,SHOPPING - Arrival Constant: 04:30 PM - 05:00 PM,@((df.tour_category == 'joint') & (df.tour_type == 'shopping') & (df.end==28)),coef_shop_Arrival_Constant_04_30_PM_05_00_PM +util_shop_Arrival_Constant_05_00_PM_05_30_PM,SHOPPING - Arrival Constant: 05:00 PM - 05:30 PM,@((df.tour_category == 'joint') & (df.tour_type == 'shopping') & (df.end==29)),coef_shop_Arrival_Constant_05_00_PM_05_30_PM +util_shop_Arrival_Constant_05_30_PM_07_00_PM,SHOPPING - Arrival Constant: 05:30 PM - 07:00 PM,@((df.tour_category == 'joint') & (df.tour_type == 'shopping') & (df.end>=30) & (df.end<=32)),coef_shop_Arrival_Constant_05_30_PM_07_00_PM +util_shop_Arrival_Constant_07_00_PM_09_30_PM,SHOPPING - Arrival Constant: 07:00 PM - 09:30 PM,@((df.tour_category == 'joint') & (df.tour_type == 'shopping') & (df.end>=33) & (df.end<=37)),coef_shop_Arrival_Constant_07_00_PM_09_30_PM +util_shop_Arrival_Constant_After_09_30_PM,SHOPPING - Arrival Constant: After 09:30 PM,@((df.tour_category == 'joint') & (df.tour_type == 'shopping') & (df.end>37)),coef_shop_Arrival_Constant_After_09_30_PM +util_shop_Arrival_Constant_Shift_for_every_30_minutes_after_10_00_pm_Linear,SHOPPING - Arrival Constant: Shift for every 30 minutes after 10:00 pm - Linear,"@np.where(((df.tour_category == 'joint') & (df.tour_type == 'shopping') & (df.end>38)), ((38-df.end)*(df.end<=38) + (df.end-38)*(df.end>38)), 0)",coef_shop_Arrival_Constant_Shift_for_every_30_minutes_after_10_00_pm_Linear +util_shop_Duration_Constant_0_hrs,SHOPPING - Duration Constant: 0 hrs,@((df.tour_category == 'joint') & (df.tour_type == 'shopping') & (df.duration==0)),coef_shop_Duration_Constant_0_hrs +util_shop_Duration_Constant_0p5_hrs,SHOPPING - Duration Constant: 0.5 hrs,@((df.tour_category == 'joint') & (df.tour_type == 'shopping') & (df.duration==1)),coef_shop_Duration_Constant_0p5_hrs +util_shop_Duration_Constant_1_hrs,SHOPPING - Duration Constant: 1 hrs,@((df.tour_category == 'joint') & (df.tour_type == 'shopping') & (df.duration==2)),coef_shop_Duration_Constant_1_hrs +util_shop_Duration_Constant_1p5hrs,SHOPPING - Duration Constant: 1.5hrs,@((df.tour_category == 'joint') & (df.tour_type == 'shopping') & (df.duration==3)),coef_shop_Duration_Constant_1p5hrs +util_shop_Duration_Constant_2_hrs,SHOPPING - Duration Constant: 2 hrs,@((df.tour_category == 'joint') & (df.tour_type == 'shopping') & (df.duration==4)),coef_shop_Duration_Constant_2_hrs +util_shop_Duration_Constant_Longer_than_2_hrs,SHOPPING - Duration Constant: Longer than 2 hrs,@((df.tour_category == 'joint') & (df.tour_type == 'shopping') & (df.duration>4)),coef_shop_Duration_Constant_Longer_than_2_hrs +util_shop_Duration_Constant_Duration_gt_2p5_hrs_Linear,SHOPPING - Duration Constant: Duration > 2.5 hrs - Linear,"@np.where(((df.tour_category == 'joint') & (df.tour_type == 'shopping') & (df.duration>5)), ((5-df.duration)*(df.duration<=5) + (df.duration-5)*(df.duration>5)), 0)",coef_shop_Duration_Constant_Duration_gt_2p5_hrs_Linear +util_shop_Duration_Constant_Duration_gt_2p5_hrs_Square_root,SHOPPING - Duration Constant: Duration > 2.5 hrs - Square root,"@np.where(((df.tour_category == 'joint') & (df.tour_type == 'shopping') & (df.duration>5)), (((5-df.duration)*(df.duration<=5) + (df.duration-5)*(df.duration>5)) ** 0.5), 0)",coef_shop_Duration_Constant_Duration_gt_2p5_hrs_Square_root +util_shop_Calibration_Constant_Duration_eq_1,SHOPPING - Calibration Constant - Duration = 1,@((df.tour_category == 'joint') & (df.tour_type == 'shopping') & (df.duration==0)),coef_shop_Calibration_Constant_Duration_eq_1 +util_shop_Calibration_Constant_Duration_eq_2,SHOPPING - Calibration Constant - Duration = 2,@((df.tour_category == 'joint') & (df.tour_type == 'shopping') & (df.duration==1)),coef_shop_Calibration_Constant_Duration_eq_2 +util_shop_Calibration_Constant_Duration_eq_3,SHOPPING - Calibration Constant - Duration = 3,@((df.tour_category == 'joint') & (df.tour_type == 'shopping') & (df.duration==2)),coef_shop_Calibration_Constant_Duration_eq_3 +#MAINTENANCE,#MAINTENANCE,,MAINTENANCE +util_maint_Joint_Maintenance_tours_dummy_Departure_before_10_00_AM_Linear,MAINTENANCE - Joint Maintenance tours dummy: Departure before reference,"@np.where(((df.tour_category == 'joint') & (df.tour_type == 'othmaint') & (df.start<13)), ((13-df.start)*(df.start<=13) + (df.start-13)*(df.start>13)), 0)",coef_maint_Joint_Maintenance_tours_dummy_Departure_before_10_00_AM_Linear +util_maint_Joint_Maintenance_tours_dummy_Departure_after_10_30_AM_Linear,MAINTENANCE - Joint Maintenance tours dummy: Departure after reference,"@np.where(((df.tour_category == 'joint') & (df.tour_type == 'othmaint') & (df.start>13)), ((13-df.start)*(df.start<=13) + (df.start-13)*(df.start>13)), 0)",coef_maint_Joint_Maintenance_tours_dummy_Departure_after_10_30_AM_Linear +util_maint_Joint_Tours_Party_Size_gt_2_Duration_lt_1p5_hrs,MAINTENANCE - Joint Tours Party Size > 2: Duration < reference,"@np.where(((df.tour_category == 'joint') & (df.tour_type == 'othmaint') & (df.duration<4) & (df.number_of_participants > 2)), ((4-df.duration)*(df.duration<=4) + (df.duration-4)*(df.duration>4)), 0)",coef_maint_Joint_Tours_Party_Size_gt_2_Duration_lt_1p5_hrs +util_maint_Joint_Tours_Party_Size_gt_2_Duration_gt_1p5_hr,MAINTENANCE - Joint Tours Party Size > 2: Duration > reference,"@np.where(((df.tour_category == 'joint') & (df.tour_type == 'othmaint') & (df.duration>4) & (df.number_of_participants > 2)), ((4-df.duration)*(df.duration<=4) + (df.duration-4)*(df.duration>4)), 0)",coef_maint_Joint_Tours_Party_Size_gt_2_Duration_gt_1p5_hr +util_maint_Joint_Tour_with_only_adults_Duration_lt_1p5_hrs,MAINTENANCE - Joint Tour with only adults: Duration < reference,"@np.where(((df.tour_category == 'joint') & (df.tour_type == 'othmaint') & (df.duration<4) & (df.composition=='adults')), ((4-df.duration)*(df.duration<=4) + (df.duration-4)*(df.duration>4)), 0)",coef_maint_Joint_Tour_with_only_adults_Duration_lt_1p5_hrs +util_maint_Kids_in_Joint_Tour_Duration_lt_1p5_hrs,MAINTENANCE - Kids in Joint Tour: Duration < reference,"@np.where(((df.tour_category == 'joint') & (df.tour_type == 'othmaint') & (df.duration<4) & ((df.composition=='children')|(df.composition=='mixed'))), ((4-df.duration)*(df.duration<=4) + (df.duration-4)*(df.duration>4)), 0)",coef_maint_Kids_in_Joint_Tour_Duration_lt_1p5_hrs +util_maint_Kids_in_Joint_Tour_Duration_gt_1p5_hr,MAINTENANCE - Kids in Joint Tour: Duration > reference,"@np.where(((df.tour_category == 'joint') & (df.tour_type == 'othmaint') & (df.duration>4) & ((df.composition=='children')|(df.composition=='mixed'))), ((4-df.duration)*(df.duration<=4) + (df.duration-4)*(df.duration>4)), 0)",coef_maint_Kids_in_Joint_Tour_Duration_gt_1p5_hr +util_maint_Low_Income_lteq25_000_Duration_gt_1p5_hr,"MAINTENANCE - Low Income (<=$25,000): Duration > reference","@np.where(((df.tour_category == 'joint') & (df.tour_type == 'othmaint') & (df.is_income_less25K) & (df.duration>4)), ((4-df.duration)*(df.duration<=4) + (df.duration-4)*(df.duration>4)), 0)",coef_maint_Low_Income_lteq25_000_Duration_gt_1p5_hr +util_maint_Medium_Income_25_001_to_60_000_Duration_lt_1p5_hrs,"MAINTENANCE - Medium Income ($25,001 to $60,000): Duration < reference","@np.where(((df.tour_category == 'joint') & (df.tour_type == 'othmaint') & (df.is_income_25K_to_60K) & (df.duration<4)), ((4-df.duration)*(df.duration<=4) + (df.duration-4)*(df.duration>4)), 0)",coef_maint_Medium_Income_25_001_to_60_000_Duration_lt_1p5_hrs +util_maint_Medium_Income_25_001_to_60_000_Duration_gt_1p5_hr,"MAINTENANCE - Medium Income ($25,001 to $60,000): Duration > reference","@np.where(((df.tour_category == 'joint') & (df.tour_type == 'othmaint') & (df.is_income_25K_to_60K) & (df.duration>4)), ((4-df.duration)*(df.duration<=4) + (df.duration-4)*(df.duration>4)), 0)",coef_maint_Medium_Income_25_001_to_60_000_Duration_gt_1p5_hr +util_maint_MediumHigh_Income_60_001_to_120_00_Duration_gt_1p5_hr,"MAINTENANCE - Medium-High Income ($60,001 to $120,00): Duration > reference","@np.where(((df.tour_category == 'joint') & (df.tour_type == 'othmaint') & (df.is_income_60K_to_120K) & (df.duration>4)), ((4-df.duration)*(df.duration<=4) + (df.duration-4)*(df.duration>4)), 0)",coef_maint_MediumHigh_Income_60_001_to_120_00_Duration_gt_1p5_hr +util_maint_Distance_Duration_lt_1p5_hrs,MAINTENANCE - Distance: Duration < reference,"@np.where(((df.tour_category == 'joint') & (df.tour_type == 'othmaint') & (df.duration<4)), ((4-df.duration)*(df.duration<=4) + (df.duration-4)*(df.duration>4)) *(df.origin_to_destination_distance), 0)",coef_maint_Distance_Duration_lt_1p5_hrs +util_maint_Distance_Duration_gt_1p5_hr,MAINTENANCE - Distance: Duration > reference,"@np.where(((df.tour_category == 'joint') & (df.tour_type == 'othmaint') & (df.duration>4)), ((4-df.duration)*(df.duration<=4) + (df.duration-4)*(df.duration>4)) * (df.origin_to_destination_distance), 0)",coef_maint_Distance_Duration_gt_1p5_hr +util_maint_Departure_Constant_Shift_for_every_30_minutes_before_07_30_am_Linear,MAINTENANCE - Departure Constant: Shift for every 30 minutes before 07:30 am - Linear,"@np.where(((df.tour_category == 'joint') & (df.tour_type == 'othmaint') & (df.start<10)), ((10-df.start)*(df.start<=10) + (df.start-10)*(df.start>10)), 0)",coef_maint_Departure_Constant_Shift_for_every_30_minutes_before_07_30_am_Linear +util_maint_Departure_Constant_Shift_for_every_30_minutes_before_07_30_am_Square_Root,MAINTENANCE - Departure Constant: Shift for every 30 minutes before 07:30 am - Square Root,"@np.where(((df.tour_category == 'joint') & (df.tour_type == 'othmaint') & (df.start<10)), (((10-df.start)*(df.start<=10) + (df.start-10)*(df.start>10))** 0.5), 0)",coef_maint_Departure_Constant_Shift_for_every_30_minutes_before_07_30_am_Square_Root +util_maint_Departure_Constant_Before_08_00_AM,MAINTENANCE - Departure Constant: Before 08:00 AM,@((df.tour_category == 'joint') & (df.tour_type == 'othmaint') & (df.start<11)),coef_maint_Departure_Constant_Before_08_00_AM +util_maint_Departure_Constant_08_00_AM_08_30_AM,MAINTENANCE - Departure Constant: 08:00 AM - 08:30 AM,@((df.tour_category == 'joint') & (df.tour_type == 'othmaint') & (df.start==11)),coef_maint_Departure_Constant_08_00_AM_08_30_AM +util_maint_Departure_Constant_08_30_AM_09_00_AM,MAINTENANCE - Departure Constant: 08:30 AM - 09:00 AM,@((df.tour_category == 'joint') & (df.tour_type == 'othmaint') & (df.start==12)),coef_maint_Departure_Constant_08_30_AM_09_00_AM +util_maint_Departure_Constant_09_00_AM_09_30_AM,MAINTENANCE - Departure Constant: 09:00 AM - 09:30 AM,@((df.tour_category == 'joint') & (df.tour_type == 'othmaint') & (df.start==13)),coef_maint_Departure_Constant_09_00_AM_09_30_AM +util_maint_Departure_Constant_09_30_AM_10_00_AM,MAINTENANCE - Departure Constant: 09:30 AM - 10:00 AM,@((df.tour_category == 'joint') & (df.tour_type == 'othmaint') & (df.start==14)),coef_maint_Departure_Constant_09_30_AM_10_00_AM +util_maint_Departure_Constant_10_00_AM_10_30_AM,MAINTENANCE - Departure Constant: 10:00 AM - 10:30 AM,@((df.tour_category == 'joint') & (df.tour_type == 'othmaint') & (df.start==15)),coef_maint_Departure_Constant_10_00_AM_10_30_AM +util_maint_Departure_Constant_10_30_AM_11_00_AM,MAINTENANCE - Departure Constant: 10:30 AM - 11:00 AM,@((df.tour_category == 'joint') & (df.tour_type == 'othmaint') & (df.start==16)),coef_maint_Departure_Constant_10_30_AM_11_00_AM +util_maint_Departure_Constant_After_11_00_AM,MAINTENANCE - Departure Constant: After 11:00 AM,@((df.tour_category == 'joint') & (df.tour_type == 'othmaint') & (df.start>16)),coef_maint_Departure_Constant_After_11_00_AM +util_maint_Departure_Constant_Shift_for_every_30_minutes_after_11_30_am_Linear,MAINTENANCE - Departure Constant: Shift for every 30 minutes after 11:30 am - Linear,"@np.where(((df.tour_category == 'joint') & (df.tour_type == 'othmaint') & (df.start>17)), ((17-df.start)*(df.start<=17) + (df.start-17)*(df.start>17)), 0)",coef_maint_Departure_Constant_Shift_for_every_30_minutes_after_11_30_am_Linear +util_maint_Departure_Constant_Shift_for_every_30_minutes_after_11_30_am_Squared,MAINTENANCE - Departure Constant: Shift for every 30 minutes after 11:30 am - Squared,"@np.where(((df.tour_category == 'joint') & (df.tour_type == 'othmaint') & (df.start>17)), (((17-df.start)*(df.start<=17) + (df.start-17)*(df.start>17)) ** 2), 0)",coef_maint_Departure_Constant_Shift_for_every_30_minutes_after_11_30_am_Squared +util_maint_Arrival_Constant_Shift_for_every_30_minutes_before_10_00_am_Linear,MAINTENANCE - Arrival Constant: Shift for every 30 minutes before 10:00 am - Linear,"@np.where(((df.tour_category == 'joint') & (df.tour_type == 'othmaint') & (df.end<15)), ((15-df.end)*(df.end<=15) + (df.end-15)*(df.end>15)), 0)",coef_maint_Arrival_Constant_Shift_for_every_30_minutes_before_10_00_am_Linear +util_maint_Arrival_Constant_Before_10_30_AM,MAINTENANCE - Arrival Constant: Before 10:30 AM,@((df.tour_category == 'joint') & (df.tour_type == 'othmaint') & (df.end<16)),coef_maint_Arrival_Constant_Before_10_30_AM +util_maint_Arrival_Constant_10_30_AM_11_00_AM,MAINTENANCE - Arrival Constant: 10:30 AM - 11:00 AM,@((df.tour_category == 'joint') & (df.tour_type == 'othmaint') & (df.end==16)),coef_maint_Arrival_Constant_10_30_AM_11_00_AM +util_maint_Arrival_Constant_11_00_AM_11_30_AM,MAINTENANCE - Arrival Constant: 11:00 AM - 11:30 AM,@((df.tour_category == 'joint') & (df.tour_type == 'othmaint') & (df.end==17)),coef_maint_Arrival_Constant_11_00_AM_11_30_AM +util_maint_Arrival_Constant_11_30_AM_01_30_PM,MAINTENANCE - Arrival Constant: 11:30 AM - 01:30 PM,@((df.tour_category == 'joint') & (df.tour_type == 'othmaint') & (df.end>=18) & (df.end<=21)),coef_maint_Arrival_Constant_11_30_AM_01_30_PM +util_maint_Arrival_Constant_01_30_PM_02_30_PM,MAINTENANCE - Arrival Constant: 01:30 PM - 02:30 PM,@((df.tour_category == 'joint') & (df.tour_type == 'othmaint') & (df.end>=22) & (df.end<=23)),coef_maint_Arrival_Constant_01_30_PM_02_30_PM +util_maint_Arrival_Constant_02_30_PM_04_00_PM,MAINTENANCE - Arrival Constant: 02:30 PM - 04:00 PM,@((df.tour_category == 'joint') & (df.tour_type == 'othmaint') & (df.end>=24) & (df.end<=26)),coef_maint_Arrival_Constant_02_30_PM_04_00_PM +util_maint_Arrival_Constant_04_00_PM_04_30_PM,MAINTENANCE - Arrival Constant: 04:00 PM - 04:30 PM,@((df.tour_category == 'joint') & (df.tour_type == 'othmaint') & (df.end==27)),coef_maint_Arrival_Constant_04_00_PM_04_30_PM +util_maint_Arrival_Constant_After_04_30_PM,MAINTENANCE - Arrival Constant: After 04:30 PM,@((df.tour_category == 'joint') & (df.tour_type == 'othmaint') & (df.end>27)),coef_maint_Arrival_Constant_After_04_30_PM +util_maint_Arrival_Constant_Shift_for_every_30_minutes_after_5_00_pm_Linear,MAINTENANCE - Arrival Constant: Shift for every 30 minutes after 5:00 pm - Linear,"@np.where(((df.tour_category == 'joint') & (df.tour_type == 'othmaint') & (df.end>28)), ((28-df.end)*(df.end<=28) + (df.end-28)*(df.end>28)), 0)",coef_maint_Arrival_Constant_Shift_for_every_30_minutes_after_5_00_pm_Linear +util_maint_Duration_Constant_0_hrs,MAINTENANCE - Duration Constant: 0 hrs,@((df.tour_category == 'joint') & (df.tour_type == 'othmaint') & (df.duration==0)),coef_maint_Duration_Constant_0_hrs +util_maint_Duration_Constant_0p5_hrs,MAINTENANCE - Duration Constant: 0.5 hrs,@((df.tour_category == 'joint') & (df.tour_type == 'othmaint') & (df.duration==1)),coef_maint_Duration_Constant_0p5_hrs +util_maint_Duration_Constant_Longer_than_0p5_hrs,MAINTENANCE - Duration Constant: Longer than 0.5 hrs,@((df.tour_category == 'joint') & (df.tour_type == 'othmaint') & (df.duration>1)),coef_maint_Duration_Constant_Longer_than_0p5_hrs +util_maint_Duration_Constant_Duration_gt_1_hrs_Linear,MAINTENANCE - Duration Constant: Duration > 1 hrs - Linear,"@np.where(((df.tour_category == 'joint') & (df.tour_type == 'othmaint') & (df.duration>2)), ((2-df.duration)*(df.duration<=2) + (df.duration-2)*(df.duration>2)), 0)",coef_maint_Duration_Constant_Duration_gt_1_hrs_Linear +util_maint_Duration_Constant_Duration_gt_1_hrs_Square_Root,MAINTENANCE - Duration Constant: Duration > 1 hrs - Square Root,"@np.where(((df.tour_category == 'joint') & (df.tour_type == 'othmaint') & (df.duration>2)), (((2-df.duration)*(df.duration<=2) + (df.duration-2)*(df.duration>2))** 0.5), 0)",coef_maint_Duration_Constant_Duration_gt_1_hrs_Square_Root +util_maint_Calibration_Constant_Duration_eq_1,MAINTENANCE - Calibration Constant - Duration = 1,@((df.tour_category == 'joint') & (df.tour_type == 'othmaint') & (df.duration==0)),coef_maint_Calibration_Constant_Duration_eq_1 +util_maint_Calibration_Constant_Duration_eq_2,MAINTENANCE - Calibration Constant - Duration = 2,@((df.tour_category == 'joint') & (df.tour_type == 'othmaint') & (df.duration==1)),coef_maint_Calibration_Constant_Duration_eq_2 +util_maint_Calibration_Constant_Duration_eq_3,MAINTENANCE - Calibration Constant - Duration = 3,@((df.tour_category == 'joint') & (df.tour_type == 'othmaint') & (df.duration==2)),coef_maint_Calibration_Constant_Duration_eq_3 +util_maint_Calibration_Constant_Duration_eq_4,MAINTENANCE - Calibration Constant - Duration = 4,@((df.tour_category == 'joint') & (df.tour_type == 'othmaint') & (df.duration==3)),coef_maint_Calibration_Constant_Duration_eq_4 +util_maint_Calibration_Constant_Duration_eq_5,MAINTENANCE - Calibration Constant - Duration = 5,@((df.tour_category == 'joint') & (df.tour_type == 'othmaint') & (df.duration==4)),coef_maint_Calibration_Constant_Duration_eq_5 +#EATOUT,#EAT-OUT,,EATOUT +util_eatout_Distance_to_destination_Duration_lt_1_hrs,EAT-OUT - Distance to destination - Duration < 1 hrs,"@np.where(((df.tour_category == 'joint') & (df.tour_type == 'eatout') & (df.duration<2)), ((2-df.duration)*(df.duration<=2) + (df.duration-2)*(df.duration>2)) * (df.origin_to_destination_distance), 0)",coef_eatout_Distance_to_destination_Duration_lt_1_hrs +util_eatout_Distance_to_destination_Duration_gt_1_hrs,EAT-OUT - Distance to destination - Duration > 1 hrs,"@np.where(((df.tour_category == 'joint') & (df.tour_type == 'eatout') & (df.duration>2)), ((2-df.duration)*(df.duration<=2) + (df.duration-2)*(df.duration>2)) *(df.origin_to_destination_distance), 0)",coef_eatout_Distance_to_destination_Duration_gt_1_hrs +util_eatout_Low_income_lt25000_Duration_lt_1_hrs,EAT-OUT - Low income (<25000) - Duration < 1 hrs,"@np.where(((df.tour_category == 'joint') & (df.tour_type == 'eatout') & (df.is_income_less25K) & (df.duration<2)), ((2-df.duration)*(df.duration<=2) + (df.duration-2)*(df.duration>2)), 0)",coef_eatout_Low_income_lt25000_Duration_lt_1_hrs +util_eatout_Medium_25k_to_60k_Duration_lt_1_hrs,EAT-OUT - Medium (25k to 60k) - Duration < 1 hrs,"@np.where(((df.tour_category == 'joint') & (df.tour_type == 'eatout') & (df.is_income_25K_to_60K) & (df.duration<2)), ((2-df.duration)*(df.duration<=2) + (df.duration-2)*(df.duration>2)), 0)",coef_eatout_Medium_25k_to_60k_Duration_lt_1_hrs +util_eatout_Zero_auto_HH_Duration_gt_1_hrs,EAT-OUT - Zero auto HH - Duration > 1 hrs,"@np.where(((df.tour_category == 'joint') & (df.tour_type == 'eatout') & (df.auto_ownership == 0) & (df.duration>2)), ((2-df.duration)*(df.duration<=2) + (df.duration-2)*(df.duration>2)), 0)",coef_eatout_Zero_auto_HH_Duration_gt_1_hrs +util_eatout_Kids_in_Joint_tour_Duration_lt_1_hrs,EAT-OUT - Kids in Joint tour - Duration < 1 hrs,"@np.where(((df.tour_category == 'joint') & (df.tour_type == 'eatout') & (df.duration<2) & ((df.composition=='children')|(df.composition=='mixed'))), ((2-df.duration)*(df.duration<=2) + (df.duration-2)*(df.duration>2)), 0)",coef_eatout_Kids_in_Joint_tour_Duration_lt_1_hrs +util_eatout_Joint_Tours_Party_Size_greater_than_2_Duration_lt_1_hrs,EAT-OUT - Joint Tours Party Size greater than 2 - Duration < 1 hrs,"@np.where(((df.tour_category == 'joint') & (df.tour_type == 'eatout') & (df.duration<2) & (df.number_of_participants > 2)), ((2-df.duration)*(df.duration<=2) + (df.duration-2)*(df.duration>2)), 0)",coef_eatout_Joint_Tours_Party_Size_greater_than_2_Duration_lt_1_hrs +util_eatout_Departure_Constant_11_00_AM_12_00_PM,EAT-OUT - Departure Constant: 11:00 AM - 12:00 PM,@((df.tour_category == 'joint') & (df.tour_type == 'eatout') & (df.start>=17) & (df.start<=18)),coef_eatout_Departure_Constant_11_00_AM_12_00_PM +util_eatout_Departure_Constant_12_00_PM_12_30_PM,EAT-OUT - Departure Constant: 12:00 PM - 12:30 PM,@(df.tour_category == 'joint') & (df.tour_type == 'eatout') & (df.start==19),coef_eatout_Departure_Constant_12_00_PM_12_30_PM +util_eatout_Departure_Constant_12_30_PM_to_01_00_PM,EAT-OUT - Departure Constant: 12:30 PM to 01:00 PM,@(df.tour_category == 'joint') & (df.tour_type == 'eatout') & (df.start==20),coef_eatout_Departure_Constant_12_30_PM_to_01_00_PM +util_eatout_Departure_Constant_Shift_for_every_30_minutes_before_05_00_pm_Linear,EAT-OUT - Departure Constant: Shift for every 30 minutes before 05:00 pm - Linear,"@np.where(((df.tour_category == 'joint') & (df.tour_type == 'eatout') & (df.start<29)), ((29-df.start)*(df.start<=29) + (df.start-29)*(df.start>29)), 0)",coef_eatout_Departure_Constant_Shift_for_every_30_minutes_before_05_00_pm_Linear +util_eatout_Departure_Constant_Before_05_30_PM,EAT-OUT - Departure Constant: Before 05:30 PM,@(df.tour_category == 'joint') & (df.tour_type == 'eatout') & (df.start<30),coef_eatout_Departure_Constant_Before_05_30_PM +util_eatout_Departure_Constant_05_30_PM_06_00_PM,EAT-OUT - Departure Constant: 05:30 PM - 06:00 PM,@(df.tour_category == 'joint') & (df.tour_type == 'eatout') & (df.start==30),coef_eatout_Departure_Constant_05_30_PM_06_00_PM +util_eatout_Departure_Constant_06_00_PM_06_30_PM,EAT-OUT - Departure Constant: 06:00 PM - 06:30 PM,@(df.tour_category == 'joint') & (df.tour_type == 'eatout') & (df.start==31),coef_eatout_Departure_Constant_06_00_PM_06_30_PM +util_eatout_Departure_Constant_06_30_PM_07_00_PM,EAT-OUT - Departure Constant: 06:30 PM - 07:00 PM,@(df.tour_category == 'joint') & (df.tour_type == 'eatout') & (df.start==32),coef_eatout_Departure_Constant_06_30_PM_07_00_PM +util_eatout_Departure_Constant_07_00_PM_07_30_PM,EAT-OUT - Departure Constant: 07:00 PM - 07:30 PM,@(df.tour_category == 'joint') & (df.tour_type == 'eatout') & (df.start==33),coef_eatout_Departure_Constant_07_00_PM_07_30_PM +util_eatout_Departure_Constant_After_07_30_PM,EAT-OUT - Departure Constant: After 07:30 PM,@(df.tour_category == 'joint') & (df.tour_type == 'eatout') & (df.start>33),coef_eatout_Departure_Constant_After_07_30_PM +util_eatout_Departure_Constant_Shift_for_every_30_minutes_after_08_00_pm_Linear,EAT-OUT - Departure Constant: Shift for every 30 minutes after 08:00 pm - Linear,"@np.where(((df.tour_category == 'joint') & (df.tour_type == 'eatout') & (df.start>34)), ((34-df.start)*(df.start<=34) + (df.start-34)*(df.start>34)), 0)",coef_eatout_Departure_Constant_Shift_for_every_30_minutes_after_08_00_pm_Linear +util_eatout_Arrival_Constant_12_30_PM_to_01_00_PM,EAT-OUT - Arrival Constant: 12:30 PM to 01:00 PM,@(df.tour_category == 'joint') & (df.tour_type == 'eatout') & (df.end==20),coef_eatout_Arrival_Constant_12_30_PM_to_01_00_PM +util_eatout_Arrival_Constant_01_00_PM_to_01_30_PM,EAT-OUT - Arrival Constant: 01:00 PM to 01:30 PM,@(df.tour_category == 'joint') & (df.tour_type == 'eatout') & (df.end==21),coef_eatout_Arrival_Constant_01_00_PM_to_01_30_PM +util_eatout_Arrival_Constant_01_30_PM_to_02_00_PM,EAT-OUT - Arrival Constant: 01:30 PM to 02:00 PM,@(df.tour_category == 'joint') & (df.tour_type == 'eatout') & (df.end==22),coef_eatout_Arrival_Constant_01_30_PM_to_02_00_PM +util_eatout_Arrival_Constant_02_00_PM_to_02_30_PM,EAT-OUT - Arrival Constant: 02:00 PM to 02:30 PM,@(df.tour_category == 'joint') & (df.tour_type == 'eatout') & (df.end==23),coef_eatout_Arrival_Constant_02_00_PM_to_02_30_PM +util_eatout_Arrival_Constant_Shift_for_every_30_minutes_before_06_30_pm_Linear,EAT-OUT - Arrival Constant: Shift for every 30 minutes before 06:30 pm - Linear,"@np.where(((df.tour_category == 'joint') & (df.tour_type == 'eatout') & (df.end<32)), ((32-df.end)*(df.end<=32) + (df.end-32)*(df.end>32)), 0)",coef_eatout_Arrival_Constant_Shift_for_every_30_minutes_before_06_30_pm_Linear +util_eatout_Arrival_Constant_Before_7_00_PM,EAT-OUT - Arrival Constant: Before 7:00 PM,@(df.tour_category == 'joint') & (df.tour_type == 'eatout') & (df.end<33),coef_eatout_Arrival_Constant_Before_7_00_PM +util_eatout_Arrival_Constant_7_00_PM_to_7_30_PM,EAT-OUT - Arrival Constant: 7:00 PM to 7:30 PM,@(df.tour_category == 'joint') & (df.tour_type== 'eatout') & (df.end==33),coef_eatout_Arrival_Constant_7_00_PM_to_7_30_PM +util_eatout_Arrival_Constant_7_30_PM_to_8_00_PM,EAT-OUT - Arrival Constant: 7:30 PM to 8:00 PM,@(df.tour_category == 'joint') & (df.tour_type == 'eatout') & (df.end==34),coef_eatout_Arrival_Constant_7_30_PM_to_8_00_PM +util_eatout_Arrival_Constant_8_00_PM_to_8_30_PM,EAT-OUT - Arrival Constant: 8:00 PM to 8:30 PM,@(df.tour_category == 'joint') & (df.tour_type == 'eatout') & (df.end==35),coef_eatout_Arrival_Constant_8_00_PM_to_8_30_PM +util_eatout_Arrival_Constant_8_30_PM_to_9_00_PM,EAT-OUT - Arrival Constant: 8:30 PM to 9:00 PM,@(df.tour_category == 'joint') & (df.tour_type == 'eatout') & (df.end==36),coef_eatout_Arrival_Constant_8_30_PM_to_9_00_PM +util_eatout_Arrival_Constant_After_09_00_PM,EAT-OUT - Arrival Constant: After 09:00 PM,@(df.tour_category == 'joint') & (df.tour_type== 'eatout') & (df.end>36),coef_eatout_Arrival_Constant_After_09_00_PM +util_eatout_Arrival_Constant_Shift_for_every_30_minutes_after_09_30_pm_Linear,EAT-OUT - Arrival Constant: Shift for every 30 minutes after 09:30 pm - Linear,"@np.where(((df.tour_type== 'eatout') & (df.end>37) & (df.tour_category == 'joint')), ((37-df.end)*(df.end<=37) + (df.end-37)*(df.end>37)), 0)",coef_eatout_Arrival_Constant_Shift_for_every_30_minutes_after_09_30_pm_Linear +util_eatout_Duration_Constant_Shift_for_every_30_minutes_more_than_3_hrs_Linear,EAT-OUT - Duration Constant: Shift for every 30 minutes more than 3 hrs - Linear,"@np.where(((df.tour_category == 'joint') & (df.tour_type == 'eatout') & (df.duration>6)), ((6-df.duration)*(df.duration<=6) + (df.duration-6)*(df.duration>6)), 0)",coef_eatout_Duration_Constant_Shift_for_every_30_minutes_more_than_3_hrs_Linear +util_eatout_Duration_Constant_0_hours,EAT-OUT - Duration Constant: 0 hours,@((df.tour_category == 'joint') & (df.tour_type == 'eatout') & (df.duration==0)),coef_eatout_Duration_Constant_0_hours +util_eatout_Duration_Constant_0p5_hous,EAT-OUT - Duration Constant: 0.5 hous,@((df.tour_category == 'joint') & (df.tour_type == 'eatout') & (df.duration==1)),coef_eatout_Duration_Constant_0p5_hous +util_eatout_Duration_Constant_1_hour,EAT-OUT - Duration Constant: 1 hour,@((df.tour_category == 'joint') & (df.tour_type== 'eatout') & (df.duration==2)),coef_eatout_Duration_Constant_1_hour +util_eatout_Duration_Constant_1p5_hours,EAT-OUT - Duration Constant: 1.5 hours,@((df.tour_category == 'joint') & (df.tour_type == 'eatout') & (df.duration==3)),coef_eatout_Duration_Constant_1p5_hours +util_eatout_Duration_Constant_2_hours_or_more,EAT-OUT - Duration Constant: 2 hours or more,@(df.tour_category == 'joint') & (df.tour_type == 'eatout') & (df.duration>4),coef_eatout_Duration_Constant_2_hours_or_more +util_eatout_Duration_Constant_Shift_for_every_30_minutes_more_than_2p5_hrs_Linear,EAT-OUT - Duration Constant: Shift for every 30 minutes more than 2.5 hrs - Linear,"@np.where(((df.tour_category == 'joint') & (df.tour_type == 'eatout') & (df.duration>5)), ((5-df.duration)*(df.duration<=5) + (df.duration-5)*(df.duration>5)), 0)",coef_eatout_Duration_Constant_Shift_for_every_30_minutes_more_than_2p5_hrs_Linear +util_eatout_Calibration_Constant_Duration_eq_1,EAT-OUT - Calibration Constant - Duration = 1,@((df.tour_category == 'joint') & (df.tour_type == 'eatout') & (df.duration==0)),coef_eatout_Calibration_Constant_Duration_eq_1 +util_eatout_Calibration_Constant_Duration_eq_2,EAT-OUT - Calibration Constant - Duration = 2,@((df.tour_category == 'joint') & (df.tour_type == 'eatout') & (df.duration==1)),coef_eatout_Calibration_Constant_Duration_eq_2 +util_eatout_Calibration_Constant_Duration_eq_3,EAT-OUT - Calibration Constant - Duration = 3,@((df.tour_category == 'joint') & (df.tour_type== 'eatout') & (df.duration==2)),coef_eatout_Calibration_Constant_Duration_eq_3 +util_eatout_Calibration_Constant_Duration_eq_4,EAT-OUT - Calibration Constant - Duration = 4,@((df.tour_category == 'joint') & (df.tour_type == 'eatout') & (df.duration==3)),coef_eatout_Calibration_Constant_Duration_eq_4 +util_eatout_Calibration_Constant_Departure_eq_1,EAT-OUT - Calibration Constant - Departure = 1,@((df.tour_category == 'joint') & (df.tour_type == 'eatout') & (df.start == 1)),coef_eatout_Calibration_Constant_Departure_eq_1 +util_eatout_Calibration_Constant_Departure_eq_2,EAT-OUT - Calibration Constant - Departure = 2,@((df.tour_category == 'joint') & (df.tour_type == 'eatout') & (df.start == 2)),coef_eatout_Calibration_Constant_Departure_eq_2 +util_eatout_Calibration_Constant_Departure_eq_3,EAT-OUT - Calibration Constant - Departure = 3,@((df.tour_category == 'joint') & (df.tour_type== 'eatout') & (df.start == 3)),coef_eatout_Calibration_Constant_Departure_eq_3 +util_eatout_Calibration_Constant_Departure_eq_17,EAT-OUT - Calibration Constant - Departure = 17,@((df.tour_category == 'joint') & (df.tour_type == 'eatout') & (df.start ==17)),coef_eatout_Calibration_Constant_Departure_eq_17 +util_eatout_Calibration_Constant_Departure_eq_18,EAT-OUT - Calibration Constant - Departure = 18,@((df.tour_category == 'joint') & (df.tour_type== 'eatout') & (df.start ==18)),coef_eatout_Calibration_Constant_Departure_eq_18 +util_eatout_Calibration_Constant_Departure_eq_19,EAT-OUT - Calibration Constant - Departure = 19,@((df.tour_category == 'joint') & (df.tour_type == 'eatout') & (df.start ==19)),coef_eatout_Calibration_Constant_Departure_eq_19 +util_eatout_Calibration_Constant_Departure_eq_20,EAT-OUT - Calibration Constant - Departure = 20,@((df.tour_category == 'joint') & (df.tour_type == 'eatout') & (df.start ==20)),coef_eatout_Calibration_Constant_Departure_eq_20 +util_eatout_Calibration_Constant_Departure_eq_21,EAT-OUT - Calibration Constant - Departure = 21,@((df.tour_category == 'joint') & (df.tour_type == 'eatout') & (df.start ==21)),coef_eatout_Calibration_Constant_Departure_eq_21 +#SOCIAL,#SOCIAL,,SOCIAL +util_social_Retiree_Nonworking_senior_only_HH_Duration_lt_1p5_hrs_Linear,SOCIAL - Retiree/ Non-working senior only HH: Duration < reference,"@np.where(((df.tour_category == 'joint') & (df.num_retired_adults == df.hhsize) & (df.tour_type == 'social') & (df.duration<4)), ((4-df.duration)*(df.duration<=4) + (df.duration-4)*(df.duration>4)), 0)",coef_social_Retiree_Nonworking_senior_only_HH_Duration_lt_1p5_hrs_Linear +util_social_Zero_auto_households_Duration_lt_1p5_hrs_Linear,SOCIAL - Zero auto households: Duration < reference,"@np.where(((df.tour_category == 'joint') & (df.tour_type == 'social') & (df.auto_ownership == 0) & (df.duration<4)), ((4-df.duration)*(df.duration<=4) + (df.duration-4)*(df.duration>4)), 0)",coef_social_Zero_auto_households_Duration_lt_1p5_hrs_Linear +util_social_Zero_auto_households_Duration_gt_1p5_hrs_Linear,SOCIAL - Zero auto households: Duration > reference,"@np.where(((df.tour_category == 'joint') & (df.tour_type == 'social') & (df.auto_ownership == 0) & (df.duration>4)), ((4-df.duration)*(df.duration<=4) + (df.duration-4)*(df.duration>4)), 0)",coef_social_Zero_auto_households_Duration_gt_1p5_hrs_Linear +util_social_Number_of_auto_more_that_number_of_adults_Duration_lt_1p5_hrs_Linear,SOCIAL - Number of auto more that number of adults: Duration < reference - Linear,"@np.where(((df.tour_category == 'joint')&(df.tour_type == 'social') & (df.auto_ownership > 0) &(df.auto_ownership > df.num_adults) & (df.duration<4)), ((4-df.duration)*(df.duration<=4) + (df.duration-4)*(df.duration>4)), 0)",coef_social_Number_of_auto_more_that_number_of_adults_Duration_lt_1p5_hrs_Linear +util_social_Number_of_auto_more_that_number_of_adults_Duration_gt_1p5_hrs_Linear,SOCIAL - Number of auto more that number of adults: Duration > reference - Linear,"@np.where(((df.tour_category == 'joint') & (df.tour_type == 'social') & (df.auto_ownership > 0) & (df.auto_ownership > df.num_adults) & (df.duration>4)), ((4-df.duration)*(df.duration<=4) + (df.duration-4)*(df.duration>4)), 0)",coef_social_Number_of_auto_more_that_number_of_adults_Duration_gt_1p5_hrs_Linear +util_social_Kids_in_Joint_Tour_Duration_lt_1p5_hrs,SOCIAL - Kids in Joint Tour: Duration < reference,"@np.where(((df.tour_category == 'joint') & (df.tour_type == 'social') & (df.duration<4) & ((df.composition=='children')|(df.composition=='mixed'))), ((4-df.duration)*(df.duration<=4) + (df.duration-4)*(df.duration>4)), 0)",coef_social_Kids_in_Joint_Tour_Duration_lt_1p5_hrs +util_social_Kids_in_Joint_Tour_Duration_gt_1p5_hr,SOCIAL - Kids in Joint Tour: Duration > reference,"@np.where(((df.tour_category == 'joint') & (df.tour_type == 'social') & (df.duration>4) & ((df.composition=='children')|(df.composition=='mixed'))), ((4-df.duration)*(df.duration<=4) + (df.duration-4)*(df.duration>4)), 0)",coef_social_Kids_in_Joint_Tour_Duration_gt_1p5_hr +util_social_Joint_Tours_Party_Size_gt_2_Duration_gt_1p5_hr,SOCIAL - Joint Tours Party Size > 2: Duration > reference,"@np.where(((df.tour_category == 'joint') & (df.tour_type == 'social') & (df.duration>4) & (df.number_of_participants > 2)), ((4-df.duration)*(df.duration<=4) + (df.duration-4)*(df.duration>4)), 0)",coef_social_Joint_Tours_Party_Size_gt_2_Duration_gt_1p5_hr +util_social_Auto_Distance_Duration_lt_1_hrs_Linear,SOCIAL - Auto Distance: Duration < reference - Linear,"@np.where(((df.tour_category == 'joint') & (df.tour_type == 'social') & (df.duration<4)), ((4-df.duration)*(df.duration<=4) + (df.duration-4)*(df.duration>4)) * (df.origin_to_destination_distance), 0) ",coef_social_Auto_Distance_Duration_lt_1_hrs_Linear +util_social_Auto_Distance_Duration_gt_1_hrs_Linear,SOCIAL - Auto Distance: Duration > reference - Linear,"@np.where(((df.tour_category == 'joint') & (df.tour_type == 'social') & (df.duration>4)), ((4-df.duration)*(df.duration<=4) + (df.duration-4)*(df.duration>4)) * (df.origin_to_destination_distance), 0)",coef_social_Auto_Distance_Duration_gt_1_hrs_Linear +util_social_Departure_Constant_Shift_for_every_30_minutes_before_08_30_am_Linear,SOCIAL - Departure Constant: Shift for every 30 minutes before 08:30 am - Linear,"@np.where(((df.tour_category == 'joint') & (df.tour_type == 'social') & (df.start<12)), ((12-df.start)*(df.start<=12) + (df.start-12)*(df.start>12)), 0)",coef_social_Departure_Constant_Shift_for_every_30_minutes_before_08_30_am_Linear +util_social_Departure_Constant_Before_09_00_AM,SOCIAL - Departure Constant: Before 09:00 AM,@(df.tour_category == 'joint') & (df.tour_type == 'social') & (df.start<13),coef_social_Departure_Constant_Before_09_00_AM +util_social_Departure_Constant_09_00_AM_to_09_30_AM,SOCIAL - Departure Constant: 09:00 AM to 09:30 AM,@(df.tour_category == 'joint') & (df.tour_type == 'social') & (df.start==13),coef_social_Departure_Constant_09_00_AM_to_09_30_AM +util_social_Departure_Constant_Shift_for_every_30_minutes_before_05_00_pm_Linear,SOCIAL - Departure Constant: Shift for every 30 minutes before 05:00 pm - Linear,"@np.where((df.tour_category == 'joint') & (df.tour_type == 'social') & (df.start<29), ((29-df.start)*(df.start<=29) + (df.start-29)*(df.start>29)), 0)",coef_social_Departure_Constant_Shift_for_every_30_minutes_before_05_00_pm_Linear +util_social_Departure_Constant_Before_05_30_PM,SOCIAL - Departure Constant: Before 05:30 PM,@((df.tour_category == 'joint') & (df.tour_type == 'social') & (df.start<30)),coef_social_Departure_Constant_Before_05_30_PM +util_social_Departure_Constant_05_30_PM_06_00_PM,SOCIAL - Departure Constant: 05:30 PM - 06:00 PM,@((df.tour_category == 'joint') & (df.tour_type == 'social') & (df.start==30)),coef_social_Departure_Constant_05_30_PM_06_00_PM +util_social_Departure_Constant_06_00_PM_06_30_PM,SOCIAL - Departure Constant: 06:00 PM - 06:30 PM,@((df.tour_category == 'joint') & (df.tour_type == 'social') & (df.start==31)),coef_social_Departure_Constant_06_00_PM_06_30_PM +util_social_Departure_Constant_06_30_PM_07_00_PM,SOCIAL - Departure Constant: 06:30 PM - 07:00 PM,@((df.tour_category == 'joint') & (df.tour_type == 'social') & (df.start==32)),coef_social_Departure_Constant_06_30_PM_07_00_PM +util_social_Departure_Constant_07_00_PM_07_30_PM,SOCIAL - Departure Constant: 07:00 PM - 07:30 PM,@((df.tour_category == 'joint') & (df.tour_type == 'social') & (df.start==33)),coef_social_Departure_Constant_07_00_PM_07_30_PM +util_social_Departure_Constant_After_07_30_PM,SOCIAL - Departure Constant: After 07:30 PM,@((df.tour_category == 'joint') & (df.tour_type == 'social') & (df.start>33)),coef_social_Departure_Constant_After_07_30_PM +util_social_Departure_Constant_Shift_for_every_30_minutes_after_08_00_pm_Linear,SOCIAL - Departure Constant: Shift for every 30 minutes after 08:00 pm - Linear,"@np.where((df.tour_category == 'joint') & (df.tour_type == 'social') & (df.start>34), ((34-df.start)*(df.start<=34) + (df.start-34)*(df.start>34)), 0)",coef_social_Departure_Constant_Shift_for_every_30_minutes_after_08_00_pm_Linear +util_social_Arrival_Constant_03_00_PM_to_03_30_PM,SOCIAL - Arrival Constant: 03:00 PM to 03:30 PM,@((df.tour_category == 'joint') & (df.tour_type == 'social') & (df.end==25)),coef_social_Arrival_Constant_03_00_PM_to_03_30_PM +util_social_Arrival_Constant_03_30_PM_to_04_00_PM,SOCIAL - Arrival Constant: 03:30 PM to 04:00 PM,@((df.tour_category == 'joint') & (df.tour_type == 'social') & (df.end==26)),coef_social_Arrival_Constant_03_30_PM_to_04_00_PM +util_social_Arrival_Constant_04_00_PM_to_04_30_PM,SOCIAL - Arrival Constant: 04:00 PM to 04:30 PM,@((df.tour_category == 'joint') & (df.tour_type == 'social') & (df.end==27)),coef_social_Arrival_Constant_04_00_PM_to_04_30_PM +util_social_Arrival_Constant_05_00_PM_to_06_00_PM,SOCIAL - Arrival Constant: 05:00 PM to 06:00 PM,@((df.tour_category == 'joint') & (df.tour_type == 'social') & (df.end>=29) & (df.end<=30)),coef_social_Arrival_Constant_05_00_PM_to_06_00_PM +util_social_Arrival_Constant_Shift_for_every_30_minutes_before_08_00_pm_Linear,SOCIAL - Arrival Constant: Shift for every 30 minutes before 08:00 pm - Linear,"@np.where(((df.tour_category == 'joint') & (df.tour_type == 'social') & (df.end<35)), ((35-df.end)*(df.end<=35) + (df.end-35)*(df.end>35)), 0)",coef_social_Arrival_Constant_Shift_for_every_30_minutes_before_08_00_pm_Linear +util_social_Arrival_Constant_Before_8_30_PM,SOCIAL - Arrival Constant: Before 8:30 PM,@((df.tour_category == 'joint') & (df.tour_type == 'social') & (df.end<36)),coef_social_Arrival_Constant_Before_8_30_PM +util_social_Arrival_Constant_8_30_PM_to_9_00_PM,SOCIAL - Arrival Constant: 8:30 PM to 9:00 PM,@((df.tour_category == 'joint') & (df.tour_type == 'social') & (df.end==36)),coef_social_Arrival_Constant_8_30_PM_to_9_00_PM +util_social_Arrival_Constant_9_00_PM_to_9_30_PM,SOCIAL - Arrival Constant: 9:00 PM to 9:30 PM,@((df.tour_category == 'joint') & (df.tour_type == 'social') & (df.end==37)),coef_social_Arrival_Constant_9_00_PM_to_9_30_PM +util_social_Arrival_Constant_9_30_PM_to10_00_PM,SOCIAL - Arrival Constant: 9:30 PM to10:00 PM,@((df.tour_category == 'joint') & (df.tour_type == 'social') & (df.end==38)),coef_social_Arrival_Constant_9_30_PM_to10_00_PM +util_social_Arrival_Constant_10_00_PM_to_10_30_PM,SOCIAL - Arrival Constant: 10:00 PM to 10:30 PM,@((df.tour_category == 'joint') & (df.tour_type == 'social') & (df.end==39)),coef_social_Arrival_Constant_10_00_PM_to_10_30_PM +util_social_Arrival_Constant_After_10_30_PM,SOCIAL - Arrival Constant: After 10:30 PM,@((df.tour_category == 'joint') & (df.tour_type == 'social') & (df.end>39)),coef_social_Arrival_Constant_After_10_30_PM +util_social_Arrival_Constant_Shift_for_every_30_minutes_after_11_00_pm_Linear,SOCIAL - Arrival Constant: Shift for every 30 minutes after 11:00 pm - Linear,"@np.where(((df.tour_category == 'joint') & (df.tour_type == 'social') & (df.end>40)), ((40-df.end)*(df.end<=40) + (df.end-40)*(df.end>40)), 0)",coef_social_Arrival_Constant_Shift_for_every_30_minutes_after_11_00_pm_Linear +util_social_Duration_Constant_Shift_for_every_30_minutes_less_than_1p5_hrs_Linear,SOCIAL - Duration Constant: Shift for every 30 minutes less than 1.5 hrs - Linear,@((df.tour_category == 'joint') & (df.tour_type == 'social') & (df.duration<3)) * ((3-df.duration)*(df.duration<=3) + (df.duration-3)*(df.duration>3)),coef_social_Duration_Constant_Shift_for_every_30_minutes_less_than_1p5_hrs_Linear +util_social_Duration_Constant_Less_than_2_hours,SOCIAL - Duration Constant: Less than 2 hours,@((df.tour_category == 'joint') & (df.tour_type == 'social') & (df.duration<4)),coef_social_Duration_Constant_Less_than_2_hours +util_social_Duration_Constant_2_hours,SOCIAL - Duration Constant: 2 hours,@((df.tour_category == 'joint') & (df.tour_type == 'social') & (df.duration==4)),coef_social_Duration_Constant_2_hours +util_social_Duration_Constant_2p5_hours,SOCIAL - Duration Constant: 2.5 hours,@((df.tour_category == 'joint') & (df.tour_type == 'social') & (df.duration==5)),coef_social_Duration_Constant_2p5_hours +util_social_Duration_Constant_3_hours_or_more,SOCIAL - Duration Constant: 3 hours or more,@((df.tour_category == 'joint') & (df.tour_type == 'social') & (df.duration>5)),coef_social_Duration_Constant_3_hours_or_more +util_social_Duration_Constant_Shift_for_every_30_minutes_more_than_3p5_hrs_Linear,SOCIAL - Duration Constant: Shift for every 30 minutes more than 3.5 hrs - Linear,@((df.tour_category == 'joint') & (df.tour_type == 'social') & (df.duration>6)) * ((6-df.duration)*(df.duration<=6) + (df.duration-6)*(df.duration>6)),coef_social_Duration_Constant_Shift_for_every_30_minutes_more_than_3p5_hrs_Linear +util_social_Calibration_Constant_Duration_eq_1,SOCIAL - Calibration Constant - Duration = 1,@((df.tour_category == 'joint') & (df.tour_type == 'social') & (df.duration ==0)),coef_social_Calibration_Constant_Duration_eq_1 +util_social_Calibration_Constant_Duration_eq_2,SOCIAL - Calibration Constant - Duration = 2,@((df.tour_category == 'joint') & (df.tour_type == 'social') & (df.duration == 1)),coef_social_Calibration_Constant_Duration_eq_2 +util_social_Calibration_Constant_Duration_eq_3,SOCIAL - Calibration Constant - Duration = 3,@((df.tour_category == 'joint') & (df.tour_type == 'social') & (df.duration ==2)),coef_social_Calibration_Constant_Duration_eq_3 +util_social_Calibration_Constant_Duration_eq_4,SOCIAL - Calibration Constant - Duration = 4,@((df.tour_category == 'joint') & (df.tour_type == 'social') & (df.duration ==3)),coef_social_Calibration_Constant_Duration_eq_4 +util_social_Calibration_Constant_Duration_eq_5,SOCIAL - Calibration Constant - Duration = 5,@((df.tour_category == 'joint') & (df.tour_type == 'social') & (df.duration ==4)),coef_social_Calibration_Constant_Duration_eq_5 +util_social_Calibration_Constant_Duration_eq_6,SOCIAL - Calibration Constant - Duration = 6,@((df.tour_category == 'joint') & (df.tour_type == 'social') & (df.duration ==5)),coef_social_Calibration_Constant_Duration_eq_6 +util_social_Calibration_Constant_Duration_eq_7,SOCIAL - Calibration Constant - Duration = 7,@((df.tour_category == 'joint') & (df.tour_type == 'social') & (df.duration ==6)),coef_social_Calibration_Constant_Duration_eq_7 +util_social_Calibration_Constant_Duration_eq_8,SOCIAL - Calibration Constant - Duration = 8,@((df.tour_category == 'joint') & (df.tour_type == 'social') & (df.duration ==7)),coef_social_Calibration_Constant_Duration_eq_8 +util_social_Calibration_Constant_Duration_eq_9,SOCIAL - Calibration Constant - Duration = 9,@((df.tour_category == 'joint') & (df.tour_type == 'social') & (df.duration ==8)),coef_social_Calibration_Constant_Duration_eq_9 +#DISCRETIONARY,#DISCRETIONARY,,DISCRETIONARY +util_disc_Retiree_Nonworking_senior_only_HH_Duration_lt_1p5_hrs_Linear,DISCRETIONARY - Retiree/ Non-working senior only HH: Duration < 1.5 hrs - Linear,"@np.where(((df.tour_category == 'joint') & (df.num_retired_adults == df.hhsize) & (df.tour_type == 'othdiscr') & (df.duration<3)), ((3-df.duration)*(df.duration<=3) + (df.duration-3)*(df.duration>3)), 0)",coef_disc_Retiree_Nonworking_senior_only_HH_Duration_lt_1p5_hrs_Linear +util_disc_Zero_auto_households_Duration_lt_1p5_hrs_Linear,DISCRETIONARY - Zero auto households: Duration < 1.5 hrs - Linear,"@np.where(((df.tour_category == 'joint') & (df.auto_ownership == 0) & (df.tour_type == 'othdiscr') & (df.duration<3)), ((3-df.duration)*(df.duration<=3) + (df.duration-3)*(df.duration>3)), 0)",coef_disc_Zero_auto_households_Duration_lt_1p5_hrs_Linear +util_disc_Zero_auto_households_Duration_gt_1p5_hrs_Linear,DISCRETIONARY - Zero auto households: Duration > 1.5 hrs - Linear,"@np.where(((df.tour_category == 'joint') & (df.auto_ownership == 0) & (df.tour_type == 'othdiscr') & (df.duration>3)), ((3-df.duration)*(df.duration<=3) + (df.duration-3)*(df.duration>3)), 0)",coef_disc_Zero_auto_households_Duration_gt_1p5_hrs_Linear +util_disc_Number_of_auto_more_that_number_of_adults_Duration_lt_1p5_hrs_Linear,DISCRETIONARY - Number of auto more that number of adults: Duration < 1.5 hrs - Linear,"@np.where(((df.tour_category == 'joint') & (df.auto_ownership > 0) & (df.tour_type == 'othdiscr') & (df.auto_ownership > df.num_adults) & (df.duration<3)), ((3-df.duration)*(df.duration<=3) + (df.duration-3)*(df.duration>3)),0)",coef_disc_Number_of_auto_more_that_number_of_adults_Duration_lt_1p5_hrs_Linear +util_disc_Number_of_auto_more_that_number_of_adults_Duration_gt_1p5_hrs_Linear,DISCRETIONARY - Number of auto more that number of adults: Duration > 1.5 hrs - Linear,"@np.where(((df.tour_category == 'joint') & (df.auto_ownership > 0) &(df.tour_type == 'othdiscr')&(df.auto_ownership > df.num_adults) & (df.duration>3)), ((3-df.duration)*(df.duration<=3) + (df.duration-3)*(df.duration>3)), 0)",coef_disc_Number_of_auto_more_that_number_of_adults_Duration_gt_1p5_hrs_Linear +util_disc_Kids_in_Joint_Tour_Duration_lt_1p5_hrs,DISCRETIONARY - Kids in Joint Tour: Duration < 1.5 hrs,"@np.where(((df.tour_category == 'joint') & (df.tour_type == 'othdiscr') & (df.duration<3) & ((df.ptype == 6) | (df.ptype == 7) | (df.ptype == 8))), ((3-df.duration)*(df.duration<=3) + (df.duration-3)*(df.duration>3)), 0)",coef_disc_Kids_in_Joint_Tour_Duration_lt_1p5_hrs +util_disc_Kids_in_Joint_Tour_Duration_gt_1p5_hr,DISCRETIONARY - Kids in Joint Tour: Duration > 1.5 hr,"@np.where(((df.tour_category == 'joint') & (df.tour_type == 'othdiscr') & (df.duration>3) & ((df.ptype == 6) | (df.ptype == 7) | (df.ptype == 8))), ((3-df.duration)*(df.duration<=3) + (df.duration-3)*(df.duration>3)), 0)",coef_disc_Kids_in_Joint_Tour_Duration_gt_1p5_hr +util_disc_Joint_Tours_Party_Size_gt_2_Duration_gt_1p5_hr,DISCRETIONARY - Joint Tours Party Size > 2: Duration > 1.5 hr,"@np.where(((df.tour_category == 'joint') & (df.tour_type == 'othdiscr') & (df.duration>3) & (df.number_of_participants > 2)), ((3-df.duration)*(df.duration<=3) + (df.duration-3)*(df.duration>3)), 0)",coef_disc_Joint_Tours_Party_Size_gt_2_Duration_gt_1p5_hr +util_disc_Auto_Distance_Duration_lt_1_hrs_Linear,DISCRETIONARY - Auto Distance: Duration < 1 hrs - Linear,"@np.where(((df.tour_category == 'joint') & (df.tour_type == 'othdiscr') & (df.duration<3)), ((3-df.duration)*(df.duration<=3) + (df.duration-3)*(df.duration>3)) * (df.origin_to_destination_distance), 0)",coef_disc_Auto_Distance_Duration_lt_1_hrs_Linear +util_disc_Auto_Distance_Duration_gt_1_hrs_Linear,DISCRETIONARY - Auto Distance: Duration > 1 hrs - Linear,"@np.where(((df.tour_category == 'joint') & (df.tour_type == 'othdiscr') & (df.duration>3)), ((3-df.duration)*(df.duration<=3) + (df.duration-3)*(df.duration>3)) * (df.origin_to_destination_distance), 0)",coef_disc_Auto_Distance_Duration_gt_1_hrs_Linear +util_disc_Departure_Constant_Shift_for_every_30_minutes_before_07_30_pm_Linear,DISCRETIONARY - Departure Constant: Shift for every 30 minutes before 07:30 pm - Linear,"@np.where(((df.tour_category == 'joint') & (df.tour_type == 'othdiscr') & (df.start<9)), ((9-df.start)*(df.start<=9) + (df.start-9)*(df.start>9)), 0)",coef_disc_Departure_Constant_Shift_for_every_30_minutes_before_07_30_pm_Linear +util_disc_Departure_Constant_Before_7_30_AM_,DISCRETIONARY - Departure Constant: Before 7:30 AM ,@((df.tour_category == 'joint') & (df.tour_type == 'othdiscr') & (df.start<10)),coef_disc_Departure_Constant_Before_7_30_AM_ +util_disc_Departure_Constant_7_30_AM_to_8_00_AM,DISCRETIONARY - Departure Constant: 7:30 AM to 8:00 AM,@((df.tour_category == 'joint') & (df.tour_type == 'othdiscr') & (df.start==10)),coef_disc_Departure_Constant_7_30_AM_to_8_00_AM +util_disc_Departure_Constant_8_00_AM_to_8_30_AM,DISCRETIONARY - Departure Constant: 8:00 AM to 8:30 AM,@((df.tour_category == 'joint') & (df.tour_type == 'othdiscr') & (df.start==11)),coef_disc_Departure_Constant_8_00_AM_to_8_30_AM +util_disc_Departure_Constant_8_30_AM_to_9_00_AM,DISCRETIONARY - Departure Constant: 8:30 AM to 9:00 AM,@((df.tour_category == 'joint') & (df.tour_type == 'othdiscr') & (df.start==12)),coef_disc_Departure_Constant_8_30_AM_to_9_00_AM +util_disc_Departure_Constant_9_00_AM_to_9_30_AM,DISCRETIONARY - Departure Constant: 9:00 AM to 9:30 AM,@((df.tour_category == 'joint') & (df.tour_type == 'othdiscr') & (df.start==13)),coef_disc_Departure_Constant_9_00_AM_to_9_30_AM +util_disc_Departure_Constant_9_30_AM_to_10_00_AM,DISCRETIONARY - Departure Constant: 9:30 AM to 10:00 AM,@((df.tour_category == 'joint') & (df.tour_type == 'othdiscr') & (df.start==14)),coef_disc_Departure_Constant_9_30_AM_to_10_00_AM +util_disc_Departure_Constant_10_00_AM_to_10_30_AM,DISCRETIONARY - Departure Constant: 10:00 AM to 10:30 AM,@((df.tour_category == 'joint') & (df.tour_type == 'othdiscr') & (df.start==15)),coef_disc_Departure_Constant_10_00_AM_to_10_30_AM +util_disc_Departure_Constant_Shift_for_every_30_minutes_before_04_30_pm_Linear,DISCRETIONARY - Departure Constant: Shift for every 30 minutes before 04:30 pm - Linear,"@np.where(((df.tour_category == 'joint') & (df.tour_type == 'othdiscr') & (df.start<28)), ((28-df.start)*(df.start<=28) + (df.start-28)*(df.start>28)), 0)",coef_disc_Departure_Constant_Shift_for_every_30_minutes_before_04_30_pm_Linear +util_disc_Departure_Constant_Before_05_00_PM,DISCRETIONARY - Departure Constant: Before 05:00 PM,@((df.tour_category == 'joint') & (df.tour_type == 'othdiscr') & (df.start<29)),coef_disc_Departure_Constant_Before_05_00_PM +util_disc_Departure_Constant_05_00_PM_05_30_PM,DISCRETIONARY - Departure Constant: 05:00 PM - 05:30 PM,@((df.tour_category == 'joint') & (df.tour_type == 'othdiscr') & (df.start==29)),coef_disc_Departure_Constant_05_00_PM_05_30_PM +util_disc_Departure_Constant_05_30_PM_06_00_PM,DISCRETIONARY - Departure Constant: 05:30 PM - 06:00 PM,@((df.tour_category == 'joint') & (df.tour_type == 'othdiscr') & (df.start==30)),coef_disc_Departure_Constant_05_30_PM_06_00_PM +util_disc_Departure_Constant_06_00_PM_06_30_PM,DISCRETIONARY - Departure Constant: 06:00 PM - 06:30 PM,@((df.tour_category == 'joint') & (df.tour_type == 'othdiscr') & (df.start==31)),coef_disc_Departure_Constant_06_00_PM_06_30_PM +util_disc_Departure_Constant_06_30_PM_07_00_PM,DISCRETIONARY - Departure Constant: 06:30 PM - 07:00 PM,@((df.tour_category == 'joint') & (df.tour_type == 'othdiscr') & (df.start==32)),coef_disc_Departure_Constant_06_30_PM_07_00_PM +util_disc_Departure_Constant_After_07_00_PM,DISCRETIONARY - Departure Constant: After 07:00 PM,@((df.tour_category == 'joint') & (df.tour_type == 'othdiscr') & (df.start>32)),coef_disc_Departure_Constant_After_07_00_PM +util_disc_Departure_Constant_Shift_for_every_30_minutes_after_07_30_pm_Linear,DISCRETIONARY - Departure Constant: Shift for every 30 minutes after 07:30 pm - Linear,"@np.where(((df.tour_category == 'joint') & (df.tour_type == 'othdiscr') & (df.start>33)), ((33-df.start)*(df.start<=33) + (df.start-33)*(df.start>33)), 0)",coef_disc_Departure_Constant_Shift_for_every_30_minutes_after_07_30_pm_Linear +util_disc_Arrival_Constant_Shift_for_every_30_minutes_before_06_00_pm_Linear,DISCRETIONARY - Arrival Constant: Shift for every 30 minutes before 06:00 pm - Linear,"@np.where(((df.tour_category == 'joint') & (df.tour_type == 'othdiscr') & (df.end<31)), ((31-df.end)*(df.end<=31) + (df.end-31)*(df.end>31)), 0)",coef_disc_Arrival_Constant_Shift_for_every_30_minutes_before_06_00_pm_Linear +util_disc_Arrival_Constant_Before_6_30_PM,DISCRETIONARY - Arrival Constant: Before 6:30 PM,@((df.tour_category == 'joint') & (df.tour_type == 'othdiscr') & (df.end<32)),coef_disc_Arrival_Constant_Before_6_30_PM +util_disc_Arrival_Constant_6_30_PM_to_7_00_PM,DISCRETIONARY - Arrival Constant: 6:30 PM to 7:00 PM,@((df.tour_category == 'joint') & (df.tour_type == 'othdiscr') & (df.end==32)),coef_disc_Arrival_Constant_6_30_PM_to_7_00_PM +util_disc_Arrival_Constant_7_00_PM_to_7_30_PM,DISCRETIONARY - Arrival Constant: 7:00 PM to 7:30 PM,@((df.tour_category == 'joint') & (df.tour_type == 'othdiscr') & (df.end==33)),coef_disc_Arrival_Constant_7_00_PM_to_7_30_PM +util_disc_Arrival_Constant_7_30_PM_to_8_00_PM,DISCRETIONARY - Arrival Constant: 7:30 PM to 8:00 PM,@((df.tour_category == 'joint') & (df.tour_type == 'othdiscr') & (df.end==34)),coef_disc_Arrival_Constant_7_30_PM_to_8_00_PM +util_disc_Arrival_Constant_8_00_PM_to_8_30_PM,DISCRETIONARY - Arrival Constant: 8:00 PM to 8:30 PM,@((df.tour_category == 'joint') & (df.tour_type == 'othdiscr') & (df.end==35)),coef_disc_Arrival_Constant_8_00_PM_to_8_30_PM +util_disc_Arrival_Constant_8_30_PM_to_9_00_PM,DISCRETIONARY - Arrival Constant: 8:30 PM to 9:00 PM,@((df.tour_category == 'joint') & (df.tour_type == 'othdiscr') & (df.end==36)),coef_disc_Arrival_Constant_8_30_PM_to_9_00_PM +util_disc_Arrival_Constant_After_9_00_PM,DISCRETIONARY - Arrival Constant: After 9:00 PM,@((df.tour_category == 'joint') & (df.tour_type == 'othdiscr') & (df.end>36)),coef_disc_Arrival_Constant_After_9_00_PM +util_disc_Arrival_Constant_Shift_for_every_30_minutes_after_09_30_pm_Linear,DISCRETIONARY - Arrival Constant: Shift for every 30 minutes after 09:30 pm - Linear,"@np.where(((df.tour_category == 'joint') & (df.tour_type == 'othdiscr') & (df.end>37)), ((37-df.end)*(df.end<=37) + (df.end-37)*(df.end>37)), 0)",coef_disc_Arrival_Constant_Shift_for_every_30_minutes_after_09_30_pm_Linear +util_disc_Duration_Constant_0_hours,DISCRETIONARY - Duration Constant: 0 hours,@(df.tour_category == 'joint') & (df.tour_type == 'othdiscr') & (df.duration==0),coef_disc_Duration_Constant_0_hours +util_disc_Duration_Constant_0p5_hous,DISCRETIONARY - Duration Constant: 0.5 hous,@(df.tour_category == 'joint') & (df.tour_type == 'othdiscr') & (df.duration==1),coef_disc_Duration_Constant_0p5_hous +util_disc_Duration_Constant_1_hour,DISCRETIONARY - Duration Constant: 1 hour,@(df.tour_category == 'joint') & (df.tour_type == 'othdiscr') & (df.duration==2),coef_disc_Duration_Constant_1_hour +util_disc_Duration_Constant_1p5_hours,DISCRETIONARY - Duration Constant: 1.5 hours,@(df.tour_category == 'joint') & (df.tour_type == 'othdiscr') & (df.duration==3),coef_disc_Duration_Constant_1p5_hours +util_disc_Duration_Constant_2_hours,DISCRETIONARY - Duration Constant: 2 hours,@(df.tour_category == 'joint') & (df.tour_type == 'othdiscr') & (df.duration==4),coef_disc_Duration_Constant_2_hours +util_disc_Duration_Constant_2p5_hours_or_more,DISCRETIONARY - Duration Constant: 2.5 hours or more,@(df.tour_category == 'joint') & (df.tour_type == 'othdiscr') & (df.duration>4),coef_disc_Duration_Constant_2p5_hours_or_more +util_disc_Duration_Constant_Shift_for_every_30_minutes_more_than_3_hrs_Linear,DISCRETIONARY - Duration Constant: Shift for every 30 minutes more than 3 hrs - Linear,"@np.where(((df.tour_category == 'joint') & (df.tour_type == 'othdiscr') & (df.duration>5)), ((5-df.duration)*(df.duration<=5) + (df.duration-5)*(df.duration>5)), 0)",coef_disc_Duration_Constant_Shift_for_every_30_minutes_more_than_3_hrs_Linear +util_disc_Calibration_Constant_Duration_eq_4,DISCRETIONARY -Calibration Constant - Duration = 4,@((df.tour_category == 'joint') & (df.tour_type == 'othdiscr') & (df.duration==3)),coef_disc_Calibration_Constant_Duration_eq_4 +util_disc_Calibration_Constant_Duration_eq_5,DISCRETIONARY -Calibration Constant - Duration = 5,@((df.tour_category == 'joint') & (df.tour_type == 'othdiscr') & (df.duration==4)),coef_disc_Calibration_Constant_Duration_eq_5 +util_disc_Calibration_Constant_Departure_eq_29,DISCRETIONARY -Calibration Constant - Departure = 29,@((df.tour_category == 'joint') & (df.tour_type == 'othdiscr') & (df.start==29)),coef_disc_Calibration_Constant_Departure_eq_29 +util_disc_Calibration_Constant_Departure_eq_30,DISCRETIONARY -Calibration Constant - Departure = 30,@((df.tour_category == 'joint') & (df.tour_type == 'othdiscr') & (df.start==30)),coef_disc_Calibration_Constant_Departure_eq_30 +util_disc_Calibration_Constant_Departure_eq_31,DISCRETIONARY -Calibration Constant - Departure = 31,@((df.tour_category == 'joint') & (df.tour_type == 'othdiscr') & (df.start==31)),coef_disc_Calibration_Constant_Departure_eq_31 +util_disc_Calibration_Constant_Departure_eq_32,DISCRETIONARY -Calibration Constant - Departure = 32,@((df.tour_category == 'joint') & (df.tour_type == 'othdiscr') & (df.start==32)),coef_disc_Calibration_Constant_Departure_eq_32 diff --git a/resident/configs/tour_scheduling_joint_coefficients.csv b/resident/configs/tour_scheduling_joint_coefficients.csv new file mode 100644 index 0000000..f1ea1d9 --- /dev/null +++ b/resident/configs/tour_scheduling_joint_coefficients.csv @@ -0,0 +1,307 @@ +coefficient_name,value,constrain +coef_escort_Mode_Choice_Logsum,1.1731730340000002,F +coef_escort_Distance_to_destination_Duration_less_than_0p5_hours_depart_and_arrive_in_the_same_period,-0.335017673,F +coef_escort_Distance_to_destination_Duration_greater_than_0p5_hours,0.005298165,F +coef_escort_Fulltime_worker_Departure_after_8_00_am_Linear,-0.037980109,F +coef_escort_Fulltime_worker_Departure_after_3_00_am_Linear,0.163254125,F +coef_escort_Fulltime_worker_Duration_lt_0p5_hrs,-0.275077482,F +coef_escort_Fulltime_worker_Duration_gt_0p5_hrs,0.051530545,F +coef_escort_University_student_Duration_lt_0p5_hrs,-0.4268027179999999,F +coef_escort_Nondriving_age_student_Duration_gt_0p5_hrs,0.240582361,F +coef_escort_Driving_age_student_Duration_lt_0p5_hrs,-0.5541461910000001,F +coef_escort_Driving_age_student_Duration_gt_0p5_hrs,0.299387708,F +coef_escort_Preschool_kid_Duration_gt_0p5_hrs,0.195482563,F +coef_escort_Medhigh_income_60k_to_120k_Duration_gt_0p5_hrs,-0.029281467,F +coef_escort_Households_with_no_kids_Dummy_1_0_Departure_before_7_30_AM,0.589083327,F +coef_escort_Households_with_no_kids_Dummy_1_0_Departure_after_8_00_AM,0.086690827,F +coef_escort_Households_with_no_kids_Dummy_1_0_Departure_before_2_30_PM,0.4775826479999999,F +coef_escort_Households_with_no_kids_Dummy_1_0_Departure_after_3_00_PM,-0.2040655019999999,F +coef_escort_Households_with_no_kids_Dummy_1_0_Arrival_before_8_00_AM,-0.360039254,F +coef_escort_Households_with_no_kids_Dummy_1_0_Arrival_after_8_30_AM,0.0916141069999999,F +coef_escort_Households_with_no_kids_Dummy_1_0_Arrival_before_3_00_PM,0.432854268,F +coef_escort_Households_with_no_kids_Dummy_1_0_Arrival_after_3_30_PM,0.131037275,F +coef_escort_PreSchool_Child_in_HH_with_Mandatory_tour_Dummy_1_0_Departure_after_8_00_AM,0.109700265,F +coef_escort_PreSchool_Child_in_HH_with_Mandatory_tour_Dummy_1_0_Departure_after_3_00_PM,-0.224568648,F +coef_escort_PreSchool_Child_in_HH_with_Mandatory_tour_Dummy_1_0_Arrival_before_8_00_AM,-0.357416434,F +coef_escort_PreSchool_Child_in_HH_with_Mandatory_tour_Dummy_1_0_Arrival_before_3_00_PM,0.629285298,F +coef_escort_Driving_age_School_Child_in_HH_with_Mandatory_tour_Dummy_1_0_Departure_after_8_00_AM,0.0390051479999999,F +coef_escort_Driving_age_School_Child_in_HH_with_Mandatory_tour_Dummy_1_0_Arrival_after_8_30_AM,-0.06556611,F +coef_escort_Driving_age_School_Child_in_HH_with_Mandatory_tour_Dummy_1_0_Arrival_after_3_30_PM,0.1176809769999999,F +coef_escort_Number_of_autos_greater_than_number_of_adults_Duration_gt_0p5_hrs,-0.057322708,F +coef_escort_Number_of_Individual_Tours_excluding_escorting_Duration_gt_0p5_hrs,-0.062899692,F +coef_escort_Number_of_joint_tours_Duration_gt_0p5_hrs,-0.048533895,F +coef_escort_Departure_Constant_Shift_for_every_30_minutes_before_06_30_am_Linear,-1.4692400019999998,F +coef_escort_Departure_Constant_Before_07_00_AM,-2.070292862,F +coef_escort_Departure_Constant_07_00_AM_07_30_AM,-0.642734296,F +coef_escort_Departure_Constant_07_30_AM_08_00_AM,0.0,T +coef_escort_Departure_Constant_08_00_AM_08_30_AM,-0.2146176669999999,F +coef_escort_Departure_Constant_08_30_AM_09_00_AM,-0.147266606,F +coef_escort_Departure_Constant_After_09_00_AM,-1.356686422,F +coef_escort_Departure_Constant_01_30_PM_02_00_PM,0.368092381,F +coef_escort_Departure_Constant_02_00_PM_02_30_PM,1.166803383,F +coef_escort_Departure_Constant_02_30_PM_03_00_PM,1.28466083,F +coef_escort_Departure_Constant_03_00_PM_03_30_PM,0.581891245,F +coef_escort_Departure_Constant_After_03_30_PM,0.834510243,F +coef_escort_Departure_Constant_Shift_for_every_30_minutes_after_9_30_am_Linear,0.175257649,F +coef_escort_Departure_Constant_Shift_for_every_30_minutes_after_4_00_pm_Linear,-0.019161202,F +coef_escort_Arrival_Constant_Shift_for_every_30_minutes_before_6_30_am_Linear,0.44978138,F +coef_escort_Arrival_Constant_Before_07_00_AM,0.549584585,F +coef_escort_Arrival_Constant_07_00_AM_07_30_AM,0.488181278,F +coef_escort_Arrival_Constant_07_30_AM_08_00_AM,0.236447651,F +coef_escort_Arrival_Constant_08_00_AM_08_30_AM,0.0,T +coef_escort_Arrival_Constant_08_30_AM_09_00_AM,-0.6837568009999999,F +coef_escort_Arrival_Constant_After_09_00_AM,-1.428888485,F +coef_escort_Arrival_Constant_02_30_PM_03_00_PM,1.311480662,F +coef_escort_Arrival_Constant_03_00_PM_03_30_PM,1.316883154,F +coef_escort_Arrival_Constant_03_30_PM_04_00_PM,1.3968383919999998,F +coef_escort_Arrival_Constant_04_00_PM_04_30_PM,1.03146139,F +coef_escort_Arrival_Constant_After_04_30_PM,0.907344583,F +coef_escort_Arrival_Constant_Shift_for_every_30_minutes_after_9_30_am_Linear,-0.148408887,F +coef_escort_Arrival_Constant_Shift_for_every_30_minutes_after_5_00_pm_Linear,-0.389082896,F +coef_escort_Duration_Constant_0_hrs,-0.173757322,F +coef_escort_Duration_Constant_0p5_hrs,0.0,T +coef_escort_Duration_Constant_1_hrs,-0.4312877429999999,F +coef_escort_Duration_Constant_1p5hrs,-0.7004739590000001,F +coef_escort_Duration_Constant_2_hrs,-1.071871358,F +coef_escort_Duration_Constant_Longer_than_2_hrs,-1.691098421,F +coef_escort_Calibration_Constant_Duration_eq_1,-0.047200214,F +coef_escort_Calibration_Constant_Duration_eq_2,0.035611332,F +coef_escort_Calibration_Constant_Departure_eq_9,0.106814756,F +coef_escort_Calibration_Constant_Departure_eq_10,0.2153868639999999,F +coef_escort_Calibration_Constant_Departure_eq_23,-0.255087318,F +coef_escort_Calibration_Constant_Departure_eq_24,-0.296870428,F +coef_shop_Joint_Shopping_tours_dummy_Departure_before_10_00_AM_Linear,0.0,F +coef_shop_Joint_Shopping_tours_dummy_Departure_after_10_30_AM_Linear,0.0,F +coef_shop_Joint_Tours_Party_Size_gt_2_Duration_lt_1p5_hrs,0.0,F +coef_shop_Joint_Tours_Party_Size_gt_2_Duration_gt_1p5_hr,0.0,F +coef_shop_Joint_Tour_with_only_adults_Duration_lt_1p5_hrs,0.0,F +coef_shop_Kids_in_Joint_Tour_Duration_lt_1p5_hrs,0.0,F +coef_shop_Kids_in_Joint_Tour_Duration_gt_1p5_hr,0.0,F +coef_shop_Low_Income_lteq25_000_Duration_gt_1p5_hr,0.0,F +coef_shop_Medium_Income_25_001_to_60_000_Duration_lt_1p5_hrs,0.0,F +coef_shop_MediumHigh_Income_60_001_to_120_00_Duration_gt_1p5_hr,0.0,F +coef_shop_Distance_Duration_lt_1p5_hrs,-0.028607893883491196,F +coef_shop_Distance_Duration_gt_1p5_hr,0.017461281006706703,F +coef_shop_Departure_Constant_Shift_for_every_30_minutes_before_08_30_am_Linear,-1.0512104309818944,F +coef_shop_Departure_Constant_Shift_for_every_30_minutes_before_08_30_am_Square_root,0.0,F +coef_shop_Departure_Constant_Before_09_00_AM,0.0,F +coef_shop_Departure_Constant_09_00_AM_09_30_AM,0.0,F +coef_shop_Departure_Constant_09_30_AM_10_00_AM,0.0,F +coef_shop_Departure_Constant_10_00_AM_10_30_AM,0.0,T +coef_shop_Departure_Constant_10_30_AM_11_00_AM,0.0,F +coef_shop_Departure_Constant_After_11_00_AM,0.0,F +coef_shop_Departure_Constant_Shift_for_every_30_minutes_after_11_30_am_Linear,0.0,F +coef_shop_Departure_Constant_Shift_for_every_30_minutes_after_11_30_am_Squared,-0.00408426422243028,F +coef_shop_Arrival_Constant_Shift_for_every_30_minutes_before_12_00_pm_Linear,-0.17368025954187133,F +coef_shop_Arrival_Constant_Before_12_30_PM,3.1418509688037584,F +coef_shop_Arrival_Constant_12_30_PM_03_00_PM,2.9684267424496387,F +coef_shop_Arrival_Constant_03_00_PM_03_30_PM,3.0135292397053517,F +coef_shop_Arrival_Constant_03_30_PM_04_00_PM,2.540812806437939,F +coef_shop_Arrival_Constant_04_00_PM_04_30_PM,2.634709629448098,T +coef_shop_Arrival_Constant_04_30_PM_05_00_PM,2.8095914129852653,F +coef_shop_Arrival_Constant_05_00_PM_05_30_PM,2.9949590441118166,F +coef_shop_Arrival_Constant_05_30_PM_07_00_PM,2.703929925055346,F +coef_shop_Arrival_Constant_07_00_PM_09_30_PM,0.0,F +coef_shop_Arrival_Constant_After_09_30_PM,1.4252235053772202,F +coef_shop_Arrival_Constant_Shift_for_every_30_minutes_after_10_00_pm_Linear,-0.7628329902801455,F +coef_shop_Duration_Constant_0_hrs,-1.4315217235346374,F +coef_shop_Duration_Constant_0p5_hrs,0.0,F +coef_shop_Duration_Constant_1_hrs,0.0,T +coef_shop_Duration_Constant_1p5hrs,0.5674112263052391,F +coef_shop_Duration_Constant_2_hrs,0.33353948408736633,F +coef_shop_Duration_Constant_Longer_than_2_hrs,0.0,F +coef_shop_Duration_Constant_Duration_gt_2p5_hrs_Linear,-0.5010185517563869,F +coef_shop_Duration_Constant_Duration_gt_2p5_hrs_Square_root,0.0,F +coef_shop_Calibration_Constant_Duration_eq_1,0.0,F +coef_shop_Calibration_Constant_Duration_eq_2,0.0,F +coef_shop_Calibration_Constant_Duration_eq_3,0.0,F +coef_maint_Joint_Maintenance_tours_dummy_Departure_before_10_00_AM_Linear,-0.7266678344083689,F +coef_maint_Joint_Maintenance_tours_dummy_Departure_after_10_30_AM_Linear,0.4158223149950908,F +coef_maint_Joint_Tours_Party_Size_gt_2_Duration_lt_1p5_hrs,0.0,F +coef_maint_Joint_Tours_Party_Size_gt_2_Duration_gt_1p5_hr,0.0,F +coef_maint_Joint_Tour_with_only_adults_Duration_lt_1p5_hrs,0.0,F +coef_maint_Kids_in_Joint_Tour_Duration_lt_1p5_hrs,0.0,F +coef_maint_Kids_in_Joint_Tour_Duration_gt_1p5_hr,0.0,F +coef_maint_Low_Income_lteq25_000_Duration_gt_1p5_hr,0.0,F +coef_maint_Medium_Income_25_001_to_60_000_Duration_lt_1p5_hrs,0.0,F +coef_maint_Medium_Income_25_001_to_60_000_Duration_gt_1p5_hr,0.0,T +coef_maint_MediumHigh_Income_60_001_to_120_00_Duration_gt_1p5_hr,0.0,F +coef_maint_Distance_Duration_lt_1p5_hrs,-0.10895512058986324,F +coef_maint_Distance_Duration_gt_1p5_hr,0.0076226279567043346,F +coef_maint_Departure_Constant_Shift_for_every_30_minutes_before_07_30_am_Linear,0.0,F +coef_maint_Departure_Constant_Shift_for_every_30_minutes_before_07_30_am_Square_Root,0.0,F +coef_maint_Departure_Constant_Before_08_00_AM,0.0,F +coef_maint_Departure_Constant_08_00_AM_08_30_AM,0.0,F +coef_maint_Departure_Constant_08_30_AM_09_00_AM,0.0,F +coef_maint_Departure_Constant_09_00_AM_09_30_AM,0.0,F +coef_maint_Departure_Constant_09_30_AM_10_00_AM,0.0,F +coef_maint_Departure_Constant_10_00_AM_10_30_AM,0.0,T +coef_maint_Departure_Constant_10_30_AM_11_00_AM,0.0,F +coef_maint_Departure_Constant_After_11_00_AM,0.0,F +coef_maint_Departure_Constant_Shift_for_every_30_minutes_after_11_30_am_Linear,0.0,F +coef_maint_Departure_Constant_Shift_for_every_30_minutes_after_11_30_am_Squared,0.0,F +coef_maint_Arrival_Constant_Shift_for_every_30_minutes_before_10_00_am_Linear,0.0,F +coef_maint_Arrival_Constant_Before_10_30_AM,4.343768880893401,F +coef_maint_Arrival_Constant_10_30_AM_11_00_AM,4.083350521602396,T +coef_maint_Arrival_Constant_11_00_AM_11_30_AM,3.8938125537705606,F +coef_maint_Arrival_Constant_11_30_AM_01_30_PM,2.2487727466277105,F +coef_maint_Arrival_Constant_01_30_PM_02_30_PM,1.2142794905263539,F +coef_maint_Arrival_Constant_02_30_PM_04_00_PM,0.0,F +coef_maint_Arrival_Constant_04_00_PM_04_30_PM,-0.6951930245707939,F +coef_maint_Arrival_Constant_After_04_30_PM,-0.8895314765132972,F +coef_maint_Arrival_Constant_Shift_for_every_30_minutes_after_5_00_pm_Linear,-0.6960130813905834,F +coef_maint_Duration_Constant_0_hrs,0.0,F +coef_maint_Duration_Constant_0p5_hrs,0.0,T +coef_maint_Duration_Constant_Longer_than_0p5_hrs,0.0,F +coef_maint_Duration_Constant_Duration_gt_1_hrs_Linear,0.15548429306392084,F +coef_maint_Duration_Constant_Duration_gt_1_hrs_Square_Root,0.0,F +coef_maint_Calibration_Constant_Duration_eq_1,0.0,F +coef_maint_Calibration_Constant_Duration_eq_2,0.0,F +coef_maint_Calibration_Constant_Duration_eq_3,0.0,F +coef_maint_Calibration_Constant_Duration_eq_4,0.0,F +coef_maint_Calibration_Constant_Duration_eq_5,0.0,F +coef_eatout_Distance_to_destination_Duration_lt_1_hrs,-0.19108787133440508,F +coef_eatout_Distance_to_destination_Duration_gt_1_hrs,0.023006152654857007,F +coef_eatout_Low_income_lt25000_Duration_lt_1_hrs,0.0,F +coef_eatout_Medium_25k_to_60k_Duration_lt_1_hrs,-1.180918837464798,F +coef_eatout_Zero_auto_HH_Duration_gt_1_hrs,0.0,F +coef_eatout_Kids_in_Joint_tour_Duration_lt_1_hrs,1.3374472139338802,F +coef_eatout_Joint_Tours_Party_Size_greater_than_2_Duration_lt_1_hrs,-1.0580259127605358,F +coef_eatout_Departure_Constant_11_00_AM_12_00_PM,0.9434750816323397,F +coef_eatout_Departure_Constant_12_00_PM_12_30_PM,0.6554959058136046,F +coef_eatout_Departure_Constant_12_30_PM_to_01_00_PM,0.0,F +coef_eatout_Departure_Constant_Shift_for_every_30_minutes_before_05_00_pm_Linear,-0.9675269394009861,F +coef_eatout_Departure_Constant_Before_05_30_PM,-1.302314187436131,F +coef_eatout_Departure_Constant_05_30_PM_06_00_PM,0.0,F +coef_eatout_Departure_Constant_06_00_PM_06_30_PM,0.0,T +coef_eatout_Departure_Constant_06_30_PM_07_00_PM,1.6531878364152586,F +coef_eatout_Departure_Constant_07_00_PM_07_30_PM,2.8956060466853746,F +coef_eatout_Departure_Constant_After_07_30_PM,2.8296134095518095,F +coef_eatout_Departure_Constant_Shift_for_every_30_minutes_after_08_00_pm_Linear,0.48366538096929135,F +coef_eatout_Arrival_Constant_12_30_PM_to_01_00_PM,0.0,F +coef_eatout_Arrival_Constant_01_00_PM_to_01_30_PM,1.0896358400995683,F +coef_eatout_Arrival_Constant_01_30_PM_to_02_00_PM,0.9881110200110167,F +coef_eatout_Arrival_Constant_02_00_PM_to_02_30_PM,0.0,F +coef_eatout_Arrival_Constant_Shift_for_every_30_minutes_before_06_30_pm_Linear,0.8009605390373155,F +coef_eatout_Arrival_Constant_Before_7_00_PM,4.585410018832126,F +coef_eatout_Arrival_Constant_7_00_PM_to_7_30_PM,3.4838111763141164,F +coef_eatout_Arrival_Constant_7_30_PM_to_8_00_PM,2.9063054928957155,F +coef_eatout_Arrival_Constant_8_00_PM_to_8_30_PM,0.0,T +coef_eatout_Arrival_Constant_8_30_PM_to_9_00_PM,0.7383205534380342,F +coef_eatout_Arrival_Constant_After_09_00_PM,0.0,F +coef_eatout_Arrival_Constant_Shift_for_every_30_minutes_after_09_30_pm_Linear,-1.322397671171091,F +coef_eatout_Duration_Constant_Shift_for_every_30_minutes_more_than_3_hrs_Linear,0.0,F +coef_eatout_Duration_Constant_0_hours,-2.938143755725757,F +coef_eatout_Duration_Constant_0p5_hous,-0.8272449464493281,F +coef_eatout_Duration_Constant_1_hour,0.0,T +coef_eatout_Duration_Constant_1p5_hours,0.5471544500112203,F +coef_eatout_Duration_Constant_2_hours_or_more,1.6524845358696236,F +coef_eatout_Duration_Constant_Shift_for_every_30_minutes_more_than_2p5_hrs_Linear,0.0,F +coef_eatout_Calibration_Constant_Duration_eq_1,0.0,F +coef_eatout_Calibration_Constant_Duration_eq_2,0.0,F +coef_eatout_Calibration_Constant_Duration_eq_3,0.0,F +coef_eatout_Calibration_Constant_Duration_eq_4,0.0,F +coef_eatout_Calibration_Constant_Departure_eq_1,0.0,F +coef_eatout_Calibration_Constant_Departure_eq_2,0.0,F +coef_eatout_Calibration_Constant_Departure_eq_3,0.0,F +coef_eatout_Calibration_Constant_Departure_eq_17,0.0,F +coef_eatout_Calibration_Constant_Departure_eq_18,0.0,F +coef_eatout_Calibration_Constant_Departure_eq_19,0.0,F +coef_eatout_Calibration_Constant_Departure_eq_20,0.0,F +coef_eatout_Calibration_Constant_Departure_eq_21,0.0,F +coef_social_Retiree_Nonworking_senior_only_HH_Duration_lt_1p5_hrs_Linear,0.0,F +coef_social_Zero_auto_households_Duration_lt_1p5_hrs_Linear,0.0,F +coef_social_Zero_auto_households_Duration_gt_1p5_hrs_Linear,0.0,F +coef_social_Number_of_auto_more_that_number_of_adults_Duration_lt_1p5_hrs_Linear,0.0,F +coef_social_Number_of_auto_more_that_number_of_adults_Duration_gt_1p5_hrs_Linear,0.0,F +coef_social_Kids_in_Joint_Tour_Duration_lt_1p5_hrs,0.0,F +coef_social_Kids_in_Joint_Tour_Duration_gt_1p5_hr,0.0,F +coef_social_Joint_Tours_Party_Size_gt_2_Duration_gt_1p5_hr,0.0,F +coef_social_Auto_Distance_Duration_lt_1_hrs_Linear,-0.1028458537293977,F +coef_social_Auto_Distance_Duration_gt_1_hrs_Linear,0.007031551489618816,F +coef_social_Departure_Constant_Shift_for_every_30_minutes_before_08_30_am_Linear,-1.2628280327123185,F +coef_social_Departure_Constant_Before_09_00_AM,0.0,F +coef_social_Departure_Constant_09_00_AM_to_09_30_AM,0.0,F +coef_social_Departure_Constant_Shift_for_every_30_minutes_before_05_00_pm_Linear,-0.8559017615401381,F +coef_social_Departure_Constant_Before_05_30_PM,-2.6714616893962466,F +coef_social_Departure_Constant_05_30_PM_06_00_PM,-0.9852611855634258,F +coef_social_Departure_Constant_06_00_PM_06_30_PM,0.0,T +coef_social_Departure_Constant_06_30_PM_07_00_PM,0.0,F +coef_social_Departure_Constant_07_00_PM_07_30_PM,1.3469930919944328,F +coef_social_Departure_Constant_After_07_30_PM,1.9688176180344494,F +coef_social_Departure_Constant_Shift_for_every_30_minutes_after_08_00_pm_Linear,0.0,F +coef_social_Arrival_Constant_03_00_PM_to_03_30_PM,0.0,F +coef_social_Arrival_Constant_03_30_PM_to_04_00_PM,0.0,F +coef_social_Arrival_Constant_04_00_PM_to_04_30_PM,0.0,F +coef_social_Arrival_Constant_05_00_PM_to_06_00_PM,0.0,F +coef_social_Arrival_Constant_Shift_for_every_30_minutes_before_08_00_pm_Linear,0.850025482539915,F +coef_social_Arrival_Constant_Before_8_30_PM,3.8598870393892537,F +coef_social_Arrival_Constant_8_30_PM_to_9_00_PM,2.8509434322380205,F +coef_social_Arrival_Constant_9_00_PM_to_9_30_PM,2.4329664354881664,F +coef_social_Arrival_Constant_9_30_PM_to10_00_PM,0.0,T +coef_social_Arrival_Constant_10_00_PM_to_10_30_PM,0.0,F +coef_social_Arrival_Constant_After_10_30_PM,-1.5468997489955005,F +coef_social_Arrival_Constant_Shift_for_every_30_minutes_after_11_00_pm_Linear,-1.2916125250149844,F +coef_social_Duration_Constant_Shift_for_every_30_minutes_less_than_1p5_hrs_Linear,-1.0969745826065251,F +coef_social_Duration_Constant_Less_than_2_hours,0.0,F +coef_social_Duration_Constant_2_hours,0.0,F +coef_social_Duration_Constant_2p5_hours,1.2748916919490108,T +coef_social_Duration_Constant_3_hours_or_more,1.9047173259221608,F +coef_social_Duration_Constant_Shift_for_every_30_minutes_more_than_3p5_hrs_Linear,0.5841656016243181,F +coef_social_Calibration_Constant_Duration_eq_1,0.0,F +coef_social_Calibration_Constant_Duration_eq_2,0.0,F +coef_social_Calibration_Constant_Duration_eq_3,0.0,F +coef_social_Calibration_Constant_Duration_eq_4,0.0,F +coef_social_Calibration_Constant_Duration_eq_5,0.0,F +coef_social_Calibration_Constant_Duration_eq_6,0.0,F +coef_social_Calibration_Constant_Duration_eq_7,0.0,F +coef_social_Calibration_Constant_Duration_eq_8,0.0,F +coef_social_Calibration_Constant_Duration_eq_9,0.0,F +coef_disc_Retiree_Nonworking_senior_only_HH_Duration_lt_1p5_hrs_Linear,0.0,F +coef_disc_Zero_auto_households_Duration_lt_1p5_hrs_Linear,0.0,F +coef_disc_Zero_auto_households_Duration_gt_1p5_hrs_Linear,0.24546994148668883,F +coef_disc_Number_of_auto_more_that_number_of_adults_Duration_lt_1p5_hrs_Linear,0.0,F +coef_disc_Number_of_auto_more_that_number_of_adults_Duration_gt_1p5_hrs_Linear,0.0,F +coef_disc_Kids_in_Joint_Tour_Duration_lt_1p5_hrs,0.0,F +coef_disc_Kids_in_Joint_Tour_Duration_gt_1p5_hr,0.0,F +coef_disc_Joint_Tours_Party_Size_gt_2_Duration_gt_1p5_hr,0.0,F +coef_disc_Auto_Distance_Duration_lt_1_hrs_Linear,-0.320622923534169,F +coef_disc_Auto_Distance_Duration_gt_1_hrs_Linear,0.00675561463982907,F +coef_disc_Departure_Constant_Shift_for_every_30_minutes_before_07_30_pm_Linear,-0.5186347200161862,F +coef_disc_Departure_Constant_Before_7_30_AM_,0.0,F +coef_disc_Departure_Constant_7_30_AM_to_8_00_AM,0.0,F +coef_disc_Departure_Constant_8_00_AM_to_8_30_AM,0.635421144057047,F +coef_disc_Departure_Constant_8_30_AM_to_9_00_AM,1.71269871512322,F +coef_disc_Departure_Constant_9_00_AM_to_9_30_AM,1.4799612716000743,F +coef_disc_Departure_Constant_9_30_AM_to_10_00_AM,1.3795958669264374,F +coef_disc_Departure_Constant_10_00_AM_to_10_30_AM,1.3995816792658615,F +coef_disc_Departure_Constant_Shift_for_every_30_minutes_before_04_30_pm_Linear,-2.6360279383280787,F +coef_disc_Departure_Constant_Before_05_00_PM,-8.336424427296372,F +coef_disc_Departure_Constant_05_00_PM_05_30_PM,-5.57379327428183,F +coef_disc_Departure_Constant_05_30_PM_06_00_PM,-2.6690034899691253,F +coef_disc_Departure_Constant_06_00_PM_06_30_PM,0.0,F +coef_disc_Departure_Constant_06_30_PM_07_00_PM,2.3719797586368685,T +coef_disc_Departure_Constant_After_07_00_PM,4.1766375477943045,F +coef_disc_Departure_Constant_Shift_for_every_30_minutes_after_07_30_pm_Linear,1.9886912851879812,F +coef_disc_Arrival_Constant_Shift_for_every_30_minutes_before_06_00_pm_Linear,2.5709925185002573,F +coef_disc_Arrival_Constant_Before_6_30_PM,12.082668378061866,F +coef_disc_Arrival_Constant_6_30_PM_to_7_00_PM,9.784761393655252,F +coef_disc_Arrival_Constant_7_00_PM_to_7_30_PM,7.3488908585128465,F +coef_disc_Arrival_Constant_7_30_PM_to_8_00_PM,5.142548978840155,F +coef_disc_Arrival_Constant_8_00_PM_to_8_30_PM,2.624682599127591,T +coef_disc_Arrival_Constant_8_30_PM_to_9_00_PM,0.0,F +coef_disc_Arrival_Constant_After_9_00_PM,-2.912055108760538,F +coef_disc_Arrival_Constant_Shift_for_every_30_minutes_after_09_30_pm_Linear,-3.0596989905953733,F +coef_disc_Duration_Constant_0_hours,-8.83825066827061,F +coef_disc_Duration_Constant_0p5_hous,-4.72064177864728,F +coef_disc_Duration_Constant_1_hour,-1.7354740845419863,F +coef_disc_Duration_Constant_1p5_hours,0.0,F +coef_disc_Duration_Constant_2_hours,2.4362046406329068,T +coef_disc_Duration_Constant_2p5_hours_or_more,4.76036400219139,F +coef_disc_Duration_Constant_Shift_for_every_30_minutes_more_than_3_hrs_Linear,2.272588544620332,F +coef_disc_Calibration_Constant_Duration_eq_4,0.0,F +coef_disc_Calibration_Constant_Duration_eq_5,0.0,F +coef_disc_Calibration_Constant_Departure_eq_29,0.0,F +coef_disc_Calibration_Constant_Departure_eq_30,0.0,F +coef_disc_Calibration_Constant_Departure_eq_31,0.0,F +coef_disc_Calibration_Constant_Departure_eq_32,0.0,F diff --git a/resident/configs/tour_scheduling_nonmandatory_eatout.csv b/resident/configs/tour_scheduling_nonmandatory_eatout.csv new file mode 100644 index 0000000..4803d20 --- /dev/null +++ b/resident/configs/tour_scheduling_nonmandatory_eatout.csv @@ -0,0 +1,65 @@ +Label,Description,Expression,Coefficient +#EAT-OUT,#EAT-OUT,,#EAT-OUT +util_mode_choice_logsum,Mode choice logsum,mode_choice_logsum,coef_mode_choice_logsum +util_eatout_distance_to_destination_duration_less_than_ref,EAT-OUT - Distance to destination - Duration < Reference,"@np.where((df.duration<2), ((2-df.duration)*(df.duration<=2) + (df.duration-2)*(df.duration>2)) * (df.origin_to_destination_distance), 0)",coef_eatout_distance_to_destination_duration_less_than_ref +util_eatout_distance_to_destination_duration_greater_than_ref,EAT-OUT - Distance to destination - Duration > Reference,"@np.where((df.duration>2), ((2-df.duration)*(df.duration<=2) + (df.duration-2)*(df.duration>2)) * (df.origin_to_destination_distance), 0)",coef_eatout_distance_to_destination_duration_greater_than_ref +util_eatout_low_income_duration_less_than_ref,EAT-OUT - Low income (<25000) - Duration < Reference,"@np.where(((df.is_income_less25K) & (df.duration<2)), ((2-df.duration)*(df.duration<=2) + (df.duration-2)*(df.duration>2)), 0)",coef_eatout_low_income_duration_less_than_ref +util_eatout_medium_income_duration_less_than_ref,EAT-OUT - Medium (25k to 60k) - Duration < Reference,"@np.where(((df.is_income_25K_to_60K) & (df.duration<2)), ((2-df.duration)*(df.duration<=2) + (df.duration-2)*(df.duration>2)), 0)",coef_eatout_medium_income_duration_less_than_ref +util_eatout_zeroauto_HH_duration_greater_than_ref,EAT-OUT - Zero auto HH - Duration > Reference,"@np.where(((df.auto_ownership == 0) & (df.duration>2)), ((2-df.duration)*(df.duration<=2) + (df.duration-2)*(df.duration>2)), 0)",coef_eatout_zeroauto_HH_duration_greater_than_ref +util_eatout_university_student_departure_after_7_pm_linear,EAT-OUT - University student - Departure after 7:00 pm - Linear,"@np.where(((df.start>31) & (df.ptype == 3)), ((31-df.start)*(df.start<=31) + (df.start-31)*(df.start>31)), 0)",coef_eatout_university_student_departure_after_7_pm_linear +util_eatout_female_duration_less_than_ref,EAT-OUT - Female - Duration < Reference,"@np.where(((df.duration<2) & (df.female)), ((2-df.duration)*(df.duration<=2) + (df.duration-2)*(df.duration>2)), 0)",coef_eatout_female_duration_less_than_ref +util_eatout_female_duration_greater_than_ref,EAT-OUT - Female - Duration > Reference,"@np.where(((df.duration>2) & (df.female)), ((2-df.duration)*(df.duration<=2) + (df.duration-2)*(df.duration>2)), 0)",coef_eatout_female_duration_greater_than_ref +util_eatout_time_pressure_departure_before_6_30_pm,EAT-OUT - Time Pressure - Departure before 6:30 pm,"@np.where(((df.start<31)), ((31-df.start)*(df.start<=31) + (df.start-31)*(df.start>31)) * (np.log10 (30 *(tt.max_time_block_available(df.person_id)/(1.0 + df.tour_count - df.tour_num)))), 0)",coef_eatout_time_pressure_departure_before_6_30_pm +util_eatout_time_pressure_duration_less_than_ref,EAT-OUT - Time Pressure - Duration < Reference,"@np.where(((df.duration<2)), ((2-df.duration)*(df.duration<=2) + (df.duration-2)*(df.duration>2)) * (np.log10 (30 * (tt.max_time_block_available(df.person_id)/(1.0 + df.tour_count - df.tour_num)))), 0)",coef_eatout_time_pressure_duration_less_than_ref +util_eatout_departure_constant_7_30_am_to_9_am,EAT-OUT - Departure Constant: 07:30 AM - 09:00 AM,@((df.start>=10) & (df.start<=12)),coef_eatout_departure_constant_7_30_am_to_9_am +util_eatout_departure_constant_10_30_am_to_11_am,EAT-OUT - Departure Constant: 10:30 AM - 11:00 AM,@((df.start==16)),coef_eatout_departure_constant_10_30_am_to_11_am +util_eatout_departure_constant_11_am_to_11_30_am,EAT-OUT - Departure Constant: 11:00 AM - 11:30 AM,@((df.start==17)),coef_eatout_departure_constant_11_am_to_11_30_am +util_eatout_departure_constant_11_30_am_to_12_pm,EAT-OUT - Departure Constant: 11:30 AM - 12:00 PM,@((df.start==18)),coef_eatout_departure_constant_11_30_am_to_12_pm +util_eatout_departure_constant_12_pm_to_12_30_pm,EAT-OUT - Departure Constant: 12:00 PM - 12:30 PM,@((df.start==19)),coef_eatout_departure_constant_12_pm_to_12_30_pm +util_eatout_departure_constant_12_30_pm_to_1_pm,EAT-OUT - Departure Constant: 12:30 PM - 01:00 PM,@((df.start==20)),coef_eatout_departure_constant_12_30_pm_to_1_pm +util_eatout_departure_constant_1_pm_to_1_30_pm,EAT-OUT - Departure Constant: 01:00 PM - 01:30 PM,@((df.start==21)),coef_eatout_departure_constant_1_pm_to_1_30_pm +util_eatout_departure_constant_shift_for_every_30_minutes_before_5_30_pm_linear,EAT-OUT - Departure Constant: Shift for every 30 minutes before 05:30 pm - Linear,"@np.where(((df.start<30)), ((30-df.start)*(df.start<=30) + (df.start-30)*(df.start>30)), 0)",coef_eatout_departure_constant_shift_for_every_30_minutes_before_5_30_pm_linear +util_eatout_departure_constant_before_6_pm,EAT-OUT - Departure Constant: Before 06:00 PM,@((df.start<31)),coef_eatout_departure_constant_before_6_pm +util_eatout_departure_constant_6_pm_to_6_30_pm,EAT-OUT - Departure Constant: 06:00 PM - 06:30 PM,@((df.start==31)),coef_eatout_departure_constant_6_pm_to_6_30_pm +util_eatout_departure_constant_6_30_pm_to_7_pm,EAT-OUT - Departure Constant: 06:30 PM - 07:00 PM,@((df.start==32)),coef_eatout_departure_constant_6_30_pm_to_7_pm +util_eatout_departure_constant_7_pm_to_7_30_pm,EAT-OUT - Departure Constant: 07:00 PM - 07:30 PM,@((df.start==33)),coef_eatout_departure_constant_7_pm_to_7_30_pm +util_eatout_departure_constant_after_7_30_pm,EAT-OUT - Departure Constant: After 07:30 PM,@((df.start>33)),coef_eatout_departure_constant_after_7_30_pm +util_eatout_departure_constant_shift_for_every_30_minutes_after_8_pm_linear,EAT-OUT - Departure Constant: Shift for every 30 minutes after 08:00 pm - Linear,"@np.where(((df.start>34)), ((34-df.start)*(df.start<=34) + (df.start-34)*(df.start>34)), 0)",coef_eatout_departure_constant_shift_for_every_30_minutes_after_8_pm_linear +util_eatout_arrival_constant_9_30_am_to_11_am,EAT-OUT - Arrival Constant: 9:30 AM to 11:00 AM,@((df.end>=14) & (df.end<=16)),coef_eatout_arrival_constant_9_30_am_to_11_am +util_eatout_arrival_constant_12_30_pm_to_1_pm,EAT-OUT - Arrival Constant: 12:30 PM to 01:00 PM,@((df.end==20)),coef_eatout_arrival_constant_12_30_pm_to_1_pm +util_eatout_arrival_constant_1_pm_to_1_30_pm,EAT-OUT - Arrival Constant: 01:00 PM to 01:30 PM,@((df.end==21)),coef_eatout_arrival_constant_1_pm_to_1_30_pm +util_eatout_arrival_constant_1_30_pm_to_2_pm,EAT-OUT - Arrival Constant: 01:30 PM to 02:00 PM,@((df.end==22)),coef_eatout_arrival_constant_1_30_pm_to_2_pm +util_eatout_arrival_constant_2_pm_to_2_30_pm,EAT-OUT - Arrival Constant: 02:00 PM to 02:30 PM,@((df.end==23)),coef_eatout_arrival_constant_2_pm_to_2_30_pm +util_eatout_arrival_constant_2_30_pm_to_3_pm,EAT-OUT - Arrival Constant: 02:30 PM to 03:00 PM,@((df.end==24)),coef_eatout_arrival_constant_2_30_pm_to_3_pm +util_eatout_arrival_constant_shift_for_every_30_minutes_before_6_pm_linear,EAT-OUT - Arrival Constant: Shift for every 30 minutes before 06:00 pm - Linear,@((df.end<31)),coef_eatout_arrival_constant_shift_for_every_30_minutes_before_6_pm_linear +util_eatout_arrival_constant_before_6_30_pm,EAT-OUT - Arrival Constant: Before 6:30 PM,"@np.where(((df.end<32)), ((32-df.end)*(df.end<=32) + (df.end-32)*(df.end>32)), 0)",coef_eatout_arrival_constant_before_6_30_pm +util_eatout_arrival_constant_6_30_pm_to_7_pm,EAT-OUT - Arrival Constant: 6:30 PM to 7:00 PM,@((df.end==32)),coef_eatout_arrival_constant_6_30_pm_to_7_pm +util_eatout_arrival_constant_7_pm_to_7_30_pm,EAT-OUT - Arrival Constant: 7:00 PM to 7:30 PM,@((df.end==33)),coef_eatout_arrival_constant_7_pm_to_7_30_pm +util_eatout_arrival_constant_7_30_pm_to_8_pm,EAT-OUT - Arrival Constant: 7:30 PM to 8:00 PM,@((df.end==34)),coef_eatout_arrival_constant_7_30_pm_to_8_pm +util_eatout_arrival_constant_8_pm_to_8_30_pm,EAT-OUT - Arrival Constant: 8:00 PM to 8:30 PM,@((df.end==35)),coef_eatout_arrival_constant_8_pm_to_8_30_pm +util_eatout_arrival_constant_8_30_pm_to_9_pm,EAT-OUT - Arrival Constant: 8:30 PM to 9:00 PM,@((df.end==36)),coef_eatout_arrival_constant_8_30_pm_to_9_pm +util_eatout_arrival_constant_after_9_pm,EAT-OUT - Arrival Constant: After 9:00 PM,@((df.end>36)),coef_eatout_arrival_constant_after_9_pm +util_eatout_arrival_constant_shift_for_every_30_minutes_after_9_30_pm_linear,EAT-OUT - Arrival Constant: Shift for every 30 minutes after 09:30 pm - Linear,"@np.where(((df.end>37)), ((37-df.end)*(df.end<=37) + (df.end-37)*(df.end>37)), 0)",coef_eatout_arrival_constant_shift_for_every_30_minutes_after_9_30_pm_linear +util_eatout_duration_constant_0_hour,EAT-OUT - Duration Constant: 0 hour,@((df.duration==0)),coef_eatout_duration_constant_0_hour +util_eatout_duration_constant_30_minutes,EAT-OUT - Duration Constant: 0.5 hour,@((df.duration==1)),coef_eatout_duration_constant_30_minutes +util_eatout_duration_constant_1_hour,EAT-OUT - Duration Constant: 1 hour,@((df.duration==2)),coef_eatout_duration_constant_1_hour +util_eatout_duration_constant_1_hour_30_minutes,EAT-OUT - Duration Constant: 1.5 hours,@((df.duration==3)),coef_eatout_duration_constant_1_hour_30_minutes +util_eatout_duration_constant_2_hours,EAT-OUT - Duration Constant: 2 hours,@((df.duration==4)),coef_eatout_duration_constant_2_hours +util_eatout_duration_constant_2_hour_30_minutes_or_more,EAT-OUT - Duration Constant: 2.5 hours or more,@((df.duration>4)),coef_eatout_duration_constant_2_hour_30_minutes_or_more +util_eatout_duration_constant_shift_for_every_30_minutes_more_than_3_hrs_linear,EAT-OUT - Duration Constant: Shift for every 30 minutes more than 3 hrs - Linear,"@np.where((df.duration>5), ((5-df.duration)*(df.duration<=5) + (df.duration-5)*(df.duration>5)), 0)",coef_eatout_duration_constant_shift_for_every_30_minutes_more_than_3_hrs_linear +util_eatout_calibration_constant_duration_1,EAT-OUT - Calibration Constant - Duration = 1,@((df.duration==0)),coef_eatout_calibration_constant_duration_1 +util_eatout_calibration_constant_duration_2,EAT-OUT - Calibration Constant - Duration = 2,@((df.duration==1)),coef_eatout_calibration_constant_duration_2 +util_eatout_calibration_constant_duration_3,EAT-OUT - Calibration Constant - Duration = 3,@((df.duration==2)),coef_eatout_calibration_constant_duration_3 +util_eatout_calibration_constant_duration_4,EAT-OUT - Calibration Constant - Duration = 4,@((df.duration==3)),coef_eatout_calibration_constant_duration_4 +util_eatout_calibration_constant_departure_1,EAT-OUT - Calibration Constant - Departure = 1,@((df.start == 1)),coef_eatout_calibration_constant_departure_1 +util_eatout_calibration_constant_departure_2,EAT-OUT - Calibration Constant - Departure = 2,@((df.start == 2)),coef_eatout_calibration_constant_departure_2 +util_eatout_calibration_constant_departure_3,EAT-OUT - Calibration Constant - Departure = 3,@((df.start == 3)),coef_eatout_calibration_constant_departure_3 +util_eatout_calibration_constant_departure_17,EAT-OUT - Calibration Constant - Departure = 17,@((df.start ==17)),coef_eatout_calibration_constant_departure_17 +util_eatout_calibration_constant_departure_18,EAT-OUT - Calibration Constant - Departure = 18,@((df.start ==18)),coef_eatout_calibration_constant_departure_18 +util_eatout_calibration_constant_departure_19,EAT-OUT - Calibration Constant - Departure = 19,@((df.start ==19)),coef_eatout_calibration_constant_departure_19 +util_eatout_calibration_constant_departure_20,EAT-OUT - Calibration Constant - Departure = 20,@((df.start ==20)),coef_eatout_calibration_constant_departure_20 +util_eatout_calibration_constant_departure_21,EAT-OUT - Calibration Constant - Departure = 21,@((df.start ==21)),coef_eatout_calibration_constant_departure_21 +# Including terms for school escorting,,, +util_outbound_school_escort_tour_start,Outbound school escort tours must match the start time of the escort tour,is_outbound_school_escort_tour & (start != school_escort_tour_start),coef_unavailable +util_outbound_school_escort_tour_next_start,Outbound school escort tours must end before next escort tour start,is_outbound_school_escort_tour & (end > school_escort_tour_next_start) & (school_escort_tour_next_start > 0),coef_unavailable +util_inbound_school_escort_tour_end,Inbound school escort tours must match the end time of the escort tour,is_inbound_school_escort_tour & (end != school_escort_tour_end),coef_unavailable \ No newline at end of file diff --git a/resident/configs/tour_scheduling_nonmandatory_eatout_coefficients.csv b/resident/configs/tour_scheduling_nonmandatory_eatout_coefficients.csv new file mode 100644 index 0000000..e8232c0 --- /dev/null +++ b/resident/configs/tour_scheduling_nonmandatory_eatout_coefficients.csv @@ -0,0 +1,61 @@ +coefficient_name,value,constrain +coef_eatout_distance_to_destination_duration_less_than_ref,-0.22213088025234518,F +coef_eatout_distance_to_destination_duration_greater_than_ref,0.006882886760254519,F +coef_eatout_low_income_duration_less_than_ref,0.0,F +coef_eatout_medium_income_duration_less_than_ref,0.0,F +coef_eatout_zeroauto_HH_duration_greater_than_ref,0.0,F +coef_eatout_university_student_departure_after_7_pm_linear,0.0,F +coef_eatout_female_duration_less_than_ref,0.0,F +coef_eatout_female_duration_greater_than_ref,0.07587262787357908,F +coef_eatout_time_pressure_departure_before_6_30_pm,0.12152468184322475,F +coef_eatout_time_pressure_duration_less_than_ref,-1.1004669059917198,F +coef_eatout_departure_constant_7_30_am_to_9_am,0.5692134031282525,F +coef_eatout_departure_constant_10_30_am_to_11_am,0.8995439384100027,F +coef_eatout_departure_constant_11_am_to_11_30_am,1.0520049600035701,F +coef_eatout_departure_constant_11_30_am_to_12_pm,1.0552687311323523,F +coef_eatout_departure_constant_12_pm_to_12_30_pm,0.0,F +coef_eatout_departure_constant_12_30_pm_to_1_pm,0.5514341640403775,F +coef_eatout_departure_constant_1_pm_to_1_30_pm,0.0,F +coef_eatout_departure_constant_shift_for_every_30_minutes_before_5_30_pm_linear,-0.7733197656863885,F +coef_eatout_departure_constant_before_6_pm,1.2101753740729,F +coef_eatout_departure_constant_6_pm_to_6_30_pm,0.0,F +coef_eatout_departure_constant_6_30_pm_to_7_pm,2.01511052873696,T +coef_eatout_departure_constant_7_pm_to_7_30_pm,2.154439497883138,F +coef_eatout_departure_constant_after_7_30_pm,2.3557017509401206,F +coef_eatout_departure_constant_shift_for_every_30_minutes_after_8_pm_linear,0.0,F +coef_eatout_arrival_constant_9_30_am_to_11_am,0.5477594779688595,F +coef_eatout_arrival_constant_12_30_pm_to_1_pm,0.0,F +coef_eatout_arrival_constant_1_pm_to_1_30_pm,0.0,F +coef_eatout_arrival_constant_1_30_pm_to_2_pm,0.6150279185861535,F +coef_eatout_arrival_constant_2_pm_to_2_30_pm,0.0,F +coef_eatout_arrival_constant_2_30_pm_to_3_pm,0.0,F +coef_eatout_arrival_constant_shift_for_every_30_minutes_before_6_pm_linear,1.1113521693562283,F +coef_eatout_arrival_constant_before_6_30_pm,0.31077849460990015,F +coef_eatout_arrival_constant_6_30_pm_to_7_pm,1.411751007495926,F +coef_eatout_arrival_constant_7_pm_to_7_30_pm,0.580944990788283,F +coef_eatout_arrival_constant_7_30_pm_to_8_pm,0.8068518512811886,F +coef_eatout_arrival_constant_8_pm_to_8_30_pm,0.0,T +coef_eatout_arrival_constant_8_30_pm_to_9_pm,0.0,F +coef_eatout_arrival_constant_after_9_pm,-0.45642114111135573,F +coef_eatout_arrival_constant_shift_for_every_30_minutes_after_9_30_pm_linear,-0.5535494562837153,F +coef_eatout_duration_constant_0_hour,7.7136294531232235,F +coef_eatout_duration_constant_30_minutes,5.243585849897661,F +coef_eatout_duration_constant_1_hour,0.0,F +coef_eatout_duration_constant_1_hour_30_minutes,1.2046725893713652,T +coef_eatout_duration_constant_2_hours,1.3298595485891556,F +coef_eatout_duration_constant_2_hour_30_minutes_or_more,1.4613341980210828,F +coef_eatout_duration_constant_shift_for_every_30_minutes_more_than_3_hrs_linear,0.0,F +coef_eatout_calibration_constant_duration_1,0.0,F +coef_eatout_calibration_constant_duration_2,0.0,F +coef_eatout_calibration_constant_duration_3,0.0,F +coef_eatout_calibration_constant_duration_4,0.0,F +coef_eatout_calibration_constant_departure_1,0.0,F +coef_eatout_calibration_constant_departure_2,0.0,F +coef_eatout_calibration_constant_departure_3,0.0,F +coef_eatout_calibration_constant_departure_17,0.0,F +coef_eatout_calibration_constant_departure_18,0.0,F +coef_eatout_calibration_constant_departure_19,0.0,F +coef_eatout_calibration_constant_departure_20,0.0,F +coef_eatout_calibration_constant_departure_21,0.0,F +coef_mode_choice_logsum,0.2,F +coef_unavailable,-999.0,F \ No newline at end of file diff --git a/resident/configs/tour_scheduling_nonmandatory_escort.csv b/resident/configs/tour_scheduling_nonmandatory_escort.csv new file mode 100644 index 0000000..c89079f --- /dev/null +++ b/resident/configs/tour_scheduling_nonmandatory_escort.csv @@ -0,0 +1,80 @@ +Label,Description,Expression,Coefficient +# ESCORT,,, +util_escort_mode_choice_logsum,ESCORT - Mode Choice Logsum,"@np.where(df.tour_type == 'escort', df.mode_choice_logsum, 0) ",coef_escort_mode_choice_logsum +"#Note: In CTRAMP expressions, duration alternative is from 1 to 48 but in ActivitySim, it is from 0 to 47 since the duration alternative ID was calculated as (end - start). Therefore, duration in ActivitySim expression = CTRAMP duration expresssion - 1 ",,,# +util_escort_distance_to_destination_duration_less_than_30_minutes,ESCORT - Distance to destination - Duration less than 0.5 hour (depart and arrive in the same period),"@np.where(((df.duration<1)), ((df.origin_to_destination_distance) * ((1-df.duration)*(df.duration<=1) + (df.duration-1)*(df.duration>1))), 0)",coef_escort_distance_to_destination_duration_less_than_30_minutes +util_escort_distance_to_destination_duration_greater_than_30_minutes,ESCORT - Distance to destination - Duration greater than 0.5 hour,"@np.where(((df.duration>1)), ((df.origin_to_destination_distance) * ((1-df.duration)*(df.duration<=1) + (df.duration-1)*(df.duration>1))), 0)",coef_escort_distance_to_destination_duration_greater_than_30_minutes +util_escort_fulltime_worker_departure_after_8_am_linear,ESCORT - Full-time worker - Departure after Reference - Linear,"@np.where(((df.ptype == 1) & (df.start>9)), ((9-df.start)*(df.start<=9) + (df.start-9)*(df.start>9)),0)",coef_escort_fulltime_worker_departure_after_8_am_linear +"#Note: In CTRAMP expression file, the description below says departure is after 3 am but from the expression it seems that it would be 3 pm instead of 3 am",,, +util_escort_fulltime_worker_departure_after_3_pm_linear,ESCORT - Full-time worker - Departure after 3:00 pm - Linear,"@np.where(((df.ptype == 1) & (df.start>24)), ((24-df.start)*(df.start<=24) + (df.start-24)*(df.start>24)), 0)",coef_escort_fulltime_worker_departure_after_3_pm_linear +util_escort_fulltime_worker_duration_less_than_30_minutes,ESCORT - Full-time worker - Duration < 0.5 hr,"@np.where(((df.ptype == 1) & (df.duration<1)), ((1-df.duration)*(df.duration<=1) + (df.duration-1)*(df.duration>1)), 0)",coef_escort_fulltime_worker_duration_less_than_30_minutes +util_escort_fulltime_worker_duration_greater_than_30_minutes,ESCORT - Full-time worker - Duration > 0.5 hr,"@np.where(((df.ptype == 1) & (df.duration>1)), ((1-df.duration)*(df.duration<=1) + (df.duration-1)*(df.duration>1)), 0)",coef_escort_fulltime_worker_duration_greater_than_30_minutes +util_escort_university_student_duration_less_than_30_minutes,ESCORT - University student - Duration < 0.5 hr,"@np.where(((df.ptype == 3) & (df.duration<1)), ((1-df.duration)*(df.duration<=1) + (df.duration-1)*(df.duration>1)), 0)",coef_escort_university_student_duration_less_than_30_minutes +util_escort_non_driving_age_student_duration_greater_than_30_minutes,ESCORT - Non-driving age student - Duration > 0.5 hr,"@np.where((((df.ptype == 7)|(df.ptype == 8)) & (df.duration>1)), ((1-df.duration)*(df.duration<=1) + (df.duration-1)*(df.duration>1)), 0)",coef_escort_non_driving_age_student_duration_greater_than_30_minutes +util_escort_driving_age_student_duration_less_than_30_minutes,ESCORT - Driving age student - Duration < 0.5 hr,"@np.where(((df.ptype == 6) & (df.duration<1)), ((1-df.duration)*(df.duration<=1) + (df.duration-1)*(df.duration>1)), 0)",coef_escort_driving_age_student_duration_less_than_30_minutes +util_escort_driving_age_student_duration_greater_than_30_minutes,ESCORT - Driving age student - Duration > 0.5 hr,"@np.where(((df.ptype == 6) & (df.duration>1)), ((1-df.duration)*(df.duration<=1) + (df.duration-1)*(df.duration>1)), 0)",coef_escort_driving_age_student_duration_greater_than_30_minutes +"#Note: In CTRAMP expression file, description says the expression below is for duration > 0.5 hr but the expression says duration < 0.5 hr",,, +util_escort_pre_school_kid_duration_greater_than_30_minutes,ESCORT - Pre-school kid - Duration > 0.5 hr,"@np.where(((df.ptype == 8) & (df.duration<1)), ((1-df.duration)*(df.duration<=1) + (df.duration-1)*(df.duration>1)), 0)",coef_escort_pre_school_kid_duration_greater_than_30_minutes +util_escort_med_high_income_duration_greater_than_30_minutes,ESCORT - Med-high income (60k to 120k) - Duration > 0.5 hr,"@np.where(((df.is_income_60K_to_120K) & (df.duration>1)), ((1-df.duration)*(df.duration<=1) + (df.duration-1)*(df.duration>1)), 0)",coef_escort_med_high_income_duration_greater_than_30_minutes +util_escort_households_with_no_kids_departure_before_7_30_am,"ESCORT - Households with no kids (Dummy- 1,0) - Departure before 7:30 AM","@np.where(((df.num_children == 0) & (df.start<10)), ((10-df.start)*(df.start<=10) + (df.start-10)*(df.start>10)), 0)",coef_escort_households_with_no_kids_departure_before_7_30_am +util_escort_households_with_no_kids_departure_after_8_00_am,"ESCORT - Households with no kids (Dummy- 1,0) - Departure after 8:00 AM","@np.where(((df.num_children == 0) & (df.start>10)), ((10-df.start)*(df.start<=10) + (df.start-10)*(df.start>10)), 0)",coef_escort_households_with_no_kids_departure_after_8_00_am +util_escort_households_with_no_kids_departure_before_2_30_pm,"ESCORT - Households with no kids (Dummy- 1,0) - Departure before 2:30 PM","@np.where(((df.num_children == 0) & (df.start<24)), ((24-df.start)*(df.start<=24) + (df.start-24)*(df.start>24)), 0)",coef_escort_households_with_no_kids_departure_before_2_30_pm +util_escort_households_with_no_kids_departure_after_3_00_pm,"ESCORT - Households with no kids (Dummy- 1,0) - Departure after 3:00 PM","@np.where(((df.num_children == 0) & (df.start>24)), ((24-df.start)*(df.start<=24) + (df.start-24)*(df.start>24)), 0)",coef_escort_households_with_no_kids_departure_after_3_00_pm +util_escort_households_with_no_kids_arrival_before_8_am,"ESCORT - Households with no kids (Dummy- 1,0) - Arrival before 8:00 AM","@np.where(((df.num_children == 0) & (df.end<11)), ((11-df.end)*(df.end<=11) + (df.end-11)*(df.end>11)), 0)",coef_escort_households_with_no_kids_arrival_before_8_am +util_escort_households_with_no_kids_arrival_after_8_30_am,"ESCORT - Households with no kids (Dummy- 1,0) - Arrival after 8:30 AM","@np.where(((df.num_children == 0) & (df.end>11)), ((11-df.end)*(df.end<=11) + (df.end-11)*(df.end>11)), 0)",coef_escort_households_with_no_kids_arrival_after_8_30_am +util_escort_households_with_no_kids_arrival_before_3_pm,"ESCORT - Households with no kids (Dummy- 1,0) - Arrival before 3:00 PM","@np.where(((df.num_children == 0) & (df.end<25)), ((25-df.end)*(df.end<=25) + (df.end-25)*(df.end>25)), 0)",coef_escort_households_with_no_kids_arrival_before_3_pm +util_escort_households_with_no_kids_arrival_after_3_30_pm,"ESCORT - Households with no kids (Dummy- 1,0) - Arrival after 3:30 PM","@np.where(((df.num_children == 0) & (df.end>25)), ((25-df.end)*(df.end<=25) + (df.end-25)*(df.end>25)), 0)",coef_escort_households_with_no_kids_arrival_after_3_30_pm +util_escort_pre_school_child_in_hh_with_mandatory_tour_departure_after_8_am,"ESCORT - Pre-School Child in HH with Mandatory tour (Dummy- 1,0) - Departure after 8:00 AM","@np.where(((df.has_pre_school_child_with_mandatory > 0) & (df.start>10)), ((10-df.start)*(df.start<=10) + (df.start-10)*(df.start>10)), 0)",coef_escort_pre_school_child_in_hh_with_mandatory_tour_departure_after_8_am +util_escort_pre_school_child_in_hh_with_mandatory_tour_departure_after_3_pm,"ESCORT - Pre-School Child in HH with Mandatory tour (Dummy- 1,0) - Departure after 3:00 PM","@np.where(((df.has_pre_school_child_with_mandatory > 0) & (df.start>24)), ((24-df.start)*(df.start<=24) + (df.start-24)*(df.start>24)), 0)",coef_escort_pre_school_child_in_hh_with_mandatory_tour_departure_after_3_pm +util_escort_pre_school_child_in_hh_with_mandatory_tour_arrival_before_8_am,"ESCORT -Pre-School Child in HH with Mandatory tour (Dummy- 1,0) - Arrival before 8:00 AM","@np.where(((df.has_pre_school_child_with_mandatory > 0) & (df.end<11)), ((11-df.end)*(df.end<=11) + (df.end-11)*(df.end>11)), 0)",coef_escort_pre_school_child_in_hh_with_mandatory_tour_arrival_before_8_am +util_escort_pre_school_child_in_hh_with_mandatory_tour_arrival_before_3_pm,"ESCORT - Pre-School Child in HH with Mandatory tour (Dummy- 1,0) - Arrival before 3:00 PM","@np.where(((df.has_pre_school_child_with_mandatory > 0) & (df.end<25)), ((25-df.end)*(df.end<=25) + (df.end-25)*(df.end>25)), 0)",coef_escort_pre_school_child_in_hh_with_mandatory_tour_arrival_before_3_pm +util_escort_driving_age_school_child_in_hh_with_mandatory_tour_departure_after_8_am,"ESCORT - Driving age School Child in HH with Mandatory tour (Dummy- 1,0) - Departure after 8:00 AM","@np.where(((df.has_driving_age_child_with_mandatory > 0) & (df.start>10)), ((10-df.start)*(df.start<=10) + (df.start-10)*(df.start>10)), 0)",coef_escort_driving_age_school_child_in_hh_with_mandatory_tour_departure_after_8_am +util_escort_driving_age_school_child_in_hh_with_mandatory_tour_arrival_after_8_30_am,"ESCORT - Driving age School Child in HH with Mandatory tour (Dummy- 1,0) - Arrival after 8:30 AM","@np.where(((df.has_driving_age_child_with_mandatory > 0) & (df.end>11)), ((11-df.end)*(df.end<=11) + (df.end-11)*(df.end>11)), 0)",coef_escort_driving_age_school_child_in_hh_with_mandatory_tour_arrival_after_8_30_am +util_escort_driving_age_school_child_in_hh_with_mandatory_tour_arrival_after_3_30_pm,"ESCORT - Driving age School Child in HH with Mandatory tour (Dummy- 1,0) - Arrival after 3:30 PM","@np.where(((df.has_driving_age_child_with_mandatory > 0) & (df.end>25)), ((25-df.end)*(df.end<=25) + (df.end-25)*(df.end>25)), 0)",coef_escort_driving_age_school_child_in_hh_with_mandatory_tour_arrival_after_3_30_pm +util_escort_number_of_autos_greater_than_number_of_adults_duration_greater_than_30_minutes,ESCORT - Number of autos greater than number of adults - Duration > 0.5 hr,"@np.where(((df.auto_ownership > 0) & (df.auto_ownership > df.num_adults) & (df.duration>1)), ((1-df.duration)*(df.duration<=1) + (df.duration-1)*(df.duration>1)), 0)",coef_escort_number_of_autos_greater_than_number_of_adults_duration_greater_than_30_minutes +util_escort_number_of_individual_tours_duration_greater_than_30_minutes,ESCORT -Number of Individual Tours (excluding escorting) - Duration > 0.5 hr,"@np.where(((df.num_non_escort_tours > 0) & (df.duration>1)), ((1-df.duration)*(df.duration<=1) + (df.duration-1)*(df.duration>1)) * (df.num_non_escort_tours), 0)",coef_escort_number_of_individual_tours_duration_greater_than_30_minutes +util_escort_number_of_joint_tours_duration_greater_than_30_minutes,ESCORT - Number of joint tours - Duration > 0.5 hr,"@np.where(((df.duration>1)), ((1-df.duration)*(df.duration<=1) + (df.duration-1)*(df.duration>1)) *(df.num_joint_tours), 0)",coef_escort_number_of_joint_tours_duration_greater_than_30_minutes +util_escort_departure_constant_shift_for_every_30_minutes_before_6_30_am_linear,ESCORT - Departure Constant: Shift for every 30 minutes before 06:30 am - Linear,"@np.where(((df.start<8)), ((8-df.start)*(df.start<=8) + (df.start-8)*(df.start>8)), 0)",coef_escort_departure_constant_shift_for_every_30_minutes_before_6_30_am_linear +util_escort_departure_constant_before_7_am,ESCORT - Departure Constant: Before 07:00 AM,@((df.start<9)),coef_escort_departure_constant_before_7_am +util_escort_departure_constant_7_am_to_7_30_am,ESCORT - Departure Constant: 07:00 AM - 07:30 AM,@((df.start==9)),coef_escort_departure_constant_7_am_to_7_30_am +util_escort_departure_constant_7_30_am_to_8_am,ESCORT - Departure Constant: 07:30 AM - 08:00 AM,@((df.start==10)),coef_escort_departure_constant_7_30_am_to_8_am +util_escort_departure_constant_8_am_to_8_30_am,ESCORT - Departure Constant: 08:00 AM - 08:30 AM,@((df.start==11)),coef_escort_departure_constant_8_am_to_8_30_am +util_escort_departure_constant_8_30_am_to_9_am,ESCORT - Departure Constant: 08:30 AM - 09:00 AM,@((df.start==12)),coef_escort_departure_constant_8_30_am_to_9_am +util_escort_departure_constant_after_9_am,ESCORT - Departure Constant: After 09:00 AM,@((df.start>12)),coef_escort_departure_constant_after_9_am +util_escort_departure_constant_1_30_pm_to_2_pm,ESCORT - Departure Constant: 01:30 PM - 02:00 PM,@((df.start==22)),coef_escort_departure_constant_1_30_pm_to_2_pm +util_escort_departure_constant_2_pm_to_2_30_pm,ESCORT - Departure Constant: 02:00 PM - 02:30 PM,@((df.start==23)),coef_escort_departure_constant_2_pm_to_2_30_pm +util_escort_departure_constant_2_30_pm_to_3_pm,ESCORT - Departure Constant: 02:30 PM - 03:00 PM,@((df.start==24)),coef_escort_departure_constant_2_30_pm_to_3_pm +util_escort_departure_constant_3_pm_to_3_30_pm,ESCORT - Departure Constant: 03:00 PM - 03:30 PM,@((df.start==25)),coef_escort_departure_constant_3_pm_to_3_30_pm +util_escort_departure_constant_after_3_30_pm,ESCORT - Departure Constant: After 03:30 PM,@((df.start>25)),coef_escort_departure_constant_after_3_30_pm +util_escort_departure_constant_shift_for_every_30_minutes_after_9_30_am_linear,ESCORT - Departure Constant: Shift for every 30 minutes after 9:30 am - Linear,"@np.where(((df.start>13)), ((13-df.start)*(df.start<=13) + (df.start-13)*(df.start>13)), 0)",coef_escort_departure_constant_shift_for_every_30_minutes_after_9_30_am_linear +util_escort_departure_constant_shift_for_every_30_minutes_after_4_pm_linear,ESCORT - Departure Constant: Shift for every 30 minutes after 4:00 pm - Linear,"@np.where(((df.start>26)), ((26-df.start)*(df.start<=26) + (df.start-26)*(df.start>26)), 0)",coef_escort_departure_constant_shift_for_every_30_minutes_after_4_pm_linear +util_escort_arrival_constant_shift_for_every_30_minutes_before_6_30_am_linear,ESCORT - Arrival Constant: Shift for every 30 minutes before 6:30 am - Linear,"@np.where(((df.end<8)), ((8-df.end)*(df.end<=8) + (df.end-8)*(df.end>8)), 0)",coef_escort_arrival_constant_shift_for_every_30_minutes_before_6_30_am_linear +util_escort_arrival_constant_before_7_am,ESCORT - Arrival Constant: Before 07:00 AM,@((df.end<9)),coef_escort_arrival_constant_before_7_am +util_escort_arrival_constant_7_am_to_7_30_am,ESCORT - Arrival Constant: 07:00 AM - 07:30 AM,@((df.end==9)),coef_escort_arrival_constant_7_am_to_7_30_am +util_escort_arrival_constant_7_30_am_to_8_am,ESCORT - Arrival Constant: 07:30 AM - 08:00 AM,@((df.end==10)),coef_escort_arrival_constant_7_30_am_to_8_am +util_escort_arrival_constant_8_am_to_8_30_am,ESCORT - Arrival Constant: 08:00 AM - 08:30 AM,@((df.end==11)),coef_escort_arrival_constant_8_am_to_8_30_am +util_escort_arrival_constant_8_30_am_to_9_am,ESCORT - Arrival Constant: 08:30 AM - 09:00 AM,@((df.end==12)),coef_escort_arrival_constant_8_30_am_to_9_am +util_escort_arrival_constant_after_9_am,ESCORT - Arrival Constant: After 09:00 AM,@((df.end>12)),coef_escort_arrival_constant_after_9_am +util_escort_arrival_constant_2_30_pm_to_3_pm,ESCORT - Arrival Constant: 02:30 PM - 03:00 PM,@((df.end==24)),coef_escort_arrival_constant_2_30_pm_to_3_pm +util_escort_arrival_constant_3_pm_to_3_30_pm,ESCORT - Arrival Constant: 03:00 PM - 03:30 PM,@((df.end==25)),coef_escort_arrival_constant_3_pm_to_3_30_pm +util_escort_arrival_constant_3_30_pm_to_4_pm,ESCORT - Arrival Constant: 03:30 PM - 04:00 PM,@((df.end==26)),coef_escort_arrival_constant_3_30_pm_to_4_pm +util_escort_arrival_constant_4_pm_to_4_30_pm,ESCORT - Arrival Constant: 04:00 PM - 04:30 PM,@((df.end==27)),coef_escort_arrival_constant_4_pm_to_4_30_pm +util_escort_arrival_constant_after_4_30_pm,ESCORT - Arrival Constant: After 04:30 PM,@((df.end>27)),coef_escort_arrival_constant_after_4_30_pm +util_escort_arrival_constant_shift_for_every_30_minutes_after_9_30_am_linear,ESCORT - Arrival Constant: Shift for every 30 minutes after 9:30 am - Linear,"@np.where(((df.end>13)), ((13-df.end)*(df.end<=13) + (df.end-13)*(df.end>13)), 0)",coef_escort_arrival_constant_shift_for_every_30_minutes_after_9_30_am_linear +util_escort_arrival_constant_shift_for_every_30_minutes_after_5_pm_linear,ESCORT - Arrival Constant: Shift for every 30 minutes after 5:00 pm - Linear,"@np.where(((df.end>28)), ((28-df.end)*(df.end<=28) + (df.end-28)*(df.end>28)), 0)",coef_escort_arrival_constant_shift_for_every_30_minutes_after_5_pm_linear +util_escort_duration_constant_0_hour,ESCORT - Duration Constant: 0 hr,@((df.duration==0)),coef_escort_duration_constant_0_hour +util_escort_duration_constant_30_minutes,ESCORT - Duration Constant: 0.5 hr,@((df.duration==1)),coef_escort_duration_constant_30_minutes +util_escort_duration_constant_1_hour,ESCORT - Duration Constant: 1 hr,@((df.duration==2)),coef_escort_duration_constant_1_hour +util_escort_duration_constant_1_hour_30_minutes,ESCORT - Duration Constant: 1.5hrs,@((df.duration==3)),coef_escort_duration_constant_1_hour_30_minutes +util_escort_duration_constant_2_hours,ESCORT - Duration Constant: 2 hrs,@((df.duration==4)),coef_escort_duration_constant_2_hours +util_escort_duration_constant_longer_than_2_hours,ESCORT - Duration Constant: Longer than 2 hrs,@((df.duration>4)),coef_escort_duration_constant_longer_than_2_hours +util_escort_calibration_constant_duration_1,ESCORT - Calibration Constant - Duration = 1,@((df.duration==0)),coef_escort_calibration_constant_duration_1 +util_escort_calibration_constant_duration_2,ESCORT - Calibration Constant - Duration = 2,@((df.duration==1)),coef_escort_calibration_constant_duration_2 +util_escort_calibration_constant_departure_9,ESCORT - Calibration Constant - Departure = 9,@((df.start==9)),coef_escort_calibration_constant_departure_9 +util_escort_calibration_constant_departure_10,ESCORT - Calibration Constant - Departure = 10,@((df.start==10)),coef_escort_calibration_constant_departure_10 +util_escort_calibration_constant_departure_23,ESCORT - Calibration Constant - Departure = 23,@((df.start==23)),coef_escort_calibration_constant_departure_23 +util_escort_calibration_constant_departure_24,ESCORT - Calibration Constant - Departure = 24,@((df.start==24)),coef_escort_calibration_constant_departure_24 +# Including terms for school escorting,,, +util_outbound_school_escort_tour_start,Outbound school escort tours must match the start time of the escort tour,is_outbound_school_escort_tour & (start != school_escort_tour_start),coef_unlikely +util_outbound_school_escort_tour_next_start,Outbound school escort tours must end before next escort tour start,is_outbound_school_escort_tour & (end > school_escort_tour_next_start) & (school_escort_tour_next_start > 0),coef_unlikely +util_inbound_school_escort_tour_end,Inbound school escort tours must match the end time of the escort tour,is_inbound_school_escort_tour & (end != school_escort_tour_end),coef_unlikely \ No newline at end of file diff --git a/resident/configs/tour_scheduling_nonmandatory_escort_coefficients.csv b/resident/configs/tour_scheduling_nonmandatory_escort_coefficients.csv new file mode 100644 index 0000000..d901984 --- /dev/null +++ b/resident/configs/tour_scheduling_nonmandatory_escort_coefficients.csv @@ -0,0 +1,74 @@ +coefficient_name,value,constrain +coef_escort_mode_choice_logsum,0.2,F +coef_escort_distance_to_destination_duration_less_than_30_minutes,-0.4733878369607008,F +coef_escort_distance_to_destination_duration_greater_than_30_minutes,0.008568718107435931,F +coef_escort_fulltime_worker_departure_after_8_am_linear,0.0,F +coef_escort_fulltime_worker_departure_after_3_pm_linear,0.10265282779895883,F +coef_escort_fulltime_worker_duration_less_than_30_minutes,0.48717921412555765,F +coef_escort_fulltime_worker_duration_greater_than_30_minutes,0.06360020624220894,F +coef_escort_university_student_duration_less_than_30_minutes,0.0,F +coef_escort_non_driving_age_student_duration_greater_than_30_minutes,0.13619138148103171,F +coef_escort_driving_age_student_duration_less_than_30_minutes,0.0,F +coef_escort_driving_age_student_duration_greater_than_30_minutes,0.2898061598287889,F +coef_escort_pre_school_kid_duration_greater_than_30_minutes,0.0,F +coef_escort_med_high_income_duration_greater_than_30_minutes,0.0,F +coef_escort_households_with_no_kids_departure_before_7_30_am,0.0,F +coef_escort_households_with_no_kids_departure_after_8_00_am,0.0,F +coef_escort_households_with_no_kids_departure_before_2_30_pm,0.0,F +coef_escort_households_with_no_kids_departure_after_3_00_pm,0.0,F +coef_escort_households_with_no_kids_arrival_before_8_am,0.0,F +coef_escort_households_with_no_kids_arrival_after_8_30_am,0.0,F +coef_escort_households_with_no_kids_arrival_before_3_pm,0.0,F +coef_escort_households_with_no_kids_arrival_after_3_30_pm,0.0,F +coef_escort_pre_school_child_in_hh_with_mandatory_tour_departure_after_8_am,0.2193957525105046,F +coef_escort_pre_school_child_in_hh_with_mandatory_tour_departure_after_3_pm,-0.33725705669741124,F +coef_escort_pre_school_child_in_hh_with_mandatory_tour_arrival_before_8_am,-1.843839469217804,F +coef_escort_pre_school_child_in_hh_with_mandatory_tour_arrival_before_3_pm,0.2844617723897163,F +coef_escort_driving_age_school_child_in_hh_with_mandatory_tour_departure_after_8_am,0.0,F +coef_escort_driving_age_school_child_in_hh_with_mandatory_tour_arrival_after_8_30_am,0.0,F +coef_escort_driving_age_school_child_in_hh_with_mandatory_tour_arrival_after_3_30_pm,0.0,F +coef_escort_number_of_autos_greater_than_number_of_adults_duration_greater_than_30_minutes,0.0,F +coef_escort_number_of_individual_tours_duration_greater_than_30_minutes,-0.0620079827984566,F +coef_escort_number_of_joint_tours_duration_greater_than_30_minutes,0.0,F +coef_escort_departure_constant_shift_for_every_30_minutes_before_6_30_am_linear,-1.236018962947682,F +coef_escort_departure_constant_before_7_am,0.7319683480497287,F +coef_escort_departure_constant_7_am_to_7_30_am,0.0,F +coef_escort_departure_constant_7_30_am_to_8_am,4.122740011813239,T +coef_escort_departure_constant_8_am_to_8_30_am,4.835810362294344,F +coef_escort_departure_constant_8_30_am_to_9_am,5.122388952675412,F +coef_escort_departure_constant_after_9_am,4.283724261944397,F +coef_escort_departure_constant_1_30_pm_to_2_pm,0.0,F +coef_escort_departure_constant_2_pm_to_2_30_pm,0.7940143785524454,F +coef_escort_departure_constant_2_30_pm_to_3_pm,1.0810066025980387,F +coef_escort_departure_constant_3_pm_to_3_30_pm,1.326721246058783,F +coef_escort_departure_constant_after_3_30_pm,1.1856245248532122,F +coef_escort_departure_constant_shift_for_every_30_minutes_after_9_30_am_linear,0.19604399657492097,F +coef_escort_departure_constant_shift_for_every_30_minutes_after_4_pm_linear,0.0,F +coef_escort_arrival_constant_shift_for_every_30_minutes_before_6_30_am_linear,0.0,F +coef_escort_arrival_constant_before_7_am,4.009035240130145,F +coef_escort_arrival_constant_7_am_to_7_30_am,4.18385718813814,F +coef_escort_arrival_constant_7_30_am_to_8_am,2.9375790166530416,F +coef_escort_arrival_constant_8_am_to_8_30_am,0.0,T +coef_escort_arrival_constant_8_30_am_to_9_am,0.0,F +coef_escort_arrival_constant_after_9_am,-0.8516924916440327,F +coef_escort_arrival_constant_2_30_pm_to_3_pm,0.8821519927765968,F +coef_escort_arrival_constant_3_pm_to_3_30_pm,1.3755379509692238,F +coef_escort_arrival_constant_3_30_pm_to_4_pm,1.5074800730702913,F +coef_escort_arrival_constant_4_pm_to_4_30_pm,1.6943356568396335,F +coef_escort_arrival_constant_after_4_30_pm,1.2683991826495213,F +coef_escort_arrival_constant_shift_for_every_30_minutes_after_9_30_am_linear,-0.2792532861601614,F +coef_escort_arrival_constant_shift_for_every_30_minutes_after_5_pm_linear,-0.17999830126346059,F +coef_escort_duration_constant_0_hour,-0.2939194972058852,F +coef_escort_duration_constant_30_minutes,0.0,T +coef_escort_duration_constant_1_hour,0.0,F +coef_escort_duration_constant_1_hour_30_minutes,-0.717021512011088,F +coef_escort_duration_constant_2_hours,-1.1701511666645092,F +coef_escort_duration_constant_longer_than_2_hours,-1.1702599996232288,F +coef_escort_calibration_constant_duration_1,0.0,F +coef_escort_calibration_constant_duration_2,0.0,F +coef_escort_calibration_constant_departure_9,0.0,F +coef_escort_calibration_constant_departure_10,0.0,F +coef_escort_calibration_constant_departure_23,0.0,F +coef_escort_calibration_constant_departure_24,0.0,F +coef_unavailable,-999.0,T +coef_unlikely,-50.0,T \ No newline at end of file diff --git a/resident/configs/tour_scheduling_nonmandatory_othdiscr.csv b/resident/configs/tour_scheduling_nonmandatory_othdiscr.csv new file mode 100644 index 0000000..bc20b39 --- /dev/null +++ b/resident/configs/tour_scheduling_nonmandatory_othdiscr.csv @@ -0,0 +1,59 @@ +Label,Description,Expression,Coefficient +#DISCRETIONARY,#DISCRETIONARY,,#DISCRETIONARY +util_mode_choice_logsum,Mode choice logsum,mode_choice_logsum,coef_mode_choice_logsum +util_discretionary_person_less_than_18_years_old_duration_less_than_reference_linear,DISCRETIONARY - Person< 18 years old: Duration < Reference - Linear,"@np.where(((df.duration<4) & (df.age<18)), ((4-df.duration)*(df.duration<=4) + (df.duration-4)*(df.duration>4)), 0)",coef_discretionary_person_less_than_18_years_old_duration_less_than_reference_linear +util_discretionary_person_less_than_18_years_old_duration_greater_than_reference_linear,DISCRETIONARY - Person< 18 years old: Duration > Reference - Linear,"@np.where(((df.duration>4) & (df.age<18)), ((4-df.duration)*(df.duration<=4) + (df.duration-4)*(df.duration>4)), 0)",coef_discretionary_person_less_than_18_years_old_duration_greater_than_reference_linear +util_discretionary_non_working_senior_retiree_duration_less_than_reference_linear,DISCRETIONARY - Non-working senior/ retiree: Duration < Reference - Linear,"@np.where(((df.duration<4) & (df.ptype == 5)), ((4-df.duration)*(df.duration<=4) + (df.duration-4)*(df.duration>4)), 0)",coef_discretionary_non_working_senior_retiree_duration_less_than_reference_linear +util_discretionary_retiree_non_working_senior_only_HH_duration_reference_linear,DISCRETIONARY - Retiree/ Non-working senior only HH: Duration < Reference - Linear,"@np.where(((df.retired_adults_only_hh) & (df.duration<4)), ((4-df.duration)*(df.duration<=4) + (df.duration-4)*(df.duration>4)), 0)",coef_discretionary_retiree_non_working_senior_only_HH_duration_reference_linear +util_discretionary_zero_auto_households_duration_less_than_reference_linear,DISCRETIONARY - Zero auto households: Duration < Reference - Linear,"@np.where(((df.auto_ownership == 0) & (df.duration<4)), ((4-df.duration)*(df.duration<=4) + (df.duration-4)*(df.duration>4)), 0)",coef_discretionary_zero_auto_households_duration_less_than_reference_linear +util_discretionary_zero_auto_households_duration_greater_than_reference_linear,DISCRETIONARY - Zero auto households: Duration > Reference - Linear,"@np.where(((df.auto_ownership == 0) & (df.duration>4)), ((4-df.duration)*(df.duration<=4) + (df.duration-4)*(df.duration>4)), 0)",coef_discretionary_zero_auto_households_duration_greater_than_reference_linear +util_discretionary_number_of_auto_more_than_number_of_adults_duration_less_than_reference_linear,DISCRETIONARY - Number of auto more that number of adults: Duration < Reference - Linear,"@np.where(((df.auto_ownership > 0) & (df.auto_ownership > df.num_adults) & (df.duration<4)), ((4-df.duration)*(df.duration<=4) + (df.duration-4)*(df.duration>4)),0)",coef_discretionary_number_of_auto_more_than_number_of_adults_duration_less_than_reference_linear +util_discretionary_number_of_auto_more_than_number_of_adults_duration_greater_than_reference_linear,DISCRETIONARY - Number of auto more that number of adults: Duration > Reference - Linear,"@np.where(((df.auto_ownership > 0) & (df.auto_ownership > df.num_adults) & (df.duration>4)), ((4-df.duration)*(df.duration<=4) + (df.duration-4)*(df.duration>4)), 0)",coef_discretionary_number_of_auto_more_than_number_of_adults_duration_greater_than_reference_linear +util_discretionary_auto_distance_duration_less_than_reference_linear,DISCRETIONARY - Auto Distance: Duration < Reference,"@np.where(((df.duration<4)), ((4-df.duration)*(df.duration<=4) + (df.duration-4)*(df.duration>4)) * (df.origin_to_destination_distance), 0)",coef_discretionary_auto_distance_duration_less_than_reference_linear +util_discretionary_auto_distance_duration_greater_than_reference_linear,DISCRETIONARY - Auto Distance: Duration > Reference,"@np.where(((df.duration>4)), ((4-df.duration)*(df.duration<=4) + (df.duration-4)*(df.duration>4)) * (df.origin_to_destination_distance), 0)",coef_discretionary_auto_distance_duration_greater_than_reference_linear +util_discretionary_time_pressure_duration_less_reference,DISCRETIONARY - Time Pressure - Duration < Reference,"@np.where(((df.duration<4)), ((4-df.duration)*(df.duration<=4) + (df.duration-4)*(df.duration>4)) * (np.log10 (30 * (tt.max_time_block_available(df.person_id)/(1.0 + df.tour_count - df.tour_num)))), 0)",coef_discretionary_time_pressure_duration_less_reference +util_discretionary_time_pressure_duration_greater_reference,DISCRETIONARY - Time Pressure - Duration > Reference,"@np.where(((df.duration>4)), ((4-df.duration)*(df.duration<=4) + (df.duration-4)*(df.duration>4)) * (np.log10 (30 * (tt.max_time_block_available(df.person_id)/(1.0 + df.tour_count - df.tour_num)))), 0)",coef_discretionary_time_pressure_duration_greater_reference +util_discretionary_number_of_additional_individual_social_and_dicretionary_tours_duration_less_reference,DISCRETIONARY - Number of additional individual social and dicretionary tours - Duration < Reference,"@np.where((df.duration<4), ((4-df.duration)*(df.duration<=4) + (df.duration-4)*(df.duration>4))*(df.num_add_soc_discr_tours),0)",coef_discretionary_number_of_additional_individual_social_and_dicretionary_tours_duration_less_reference +util_discretionary_departure_constant_shift_for_every_30_minutes_before_7_30_pm_linear,DISCRETIONARY - Departure Constant: Shift for every 30 minutes before 07:30 pm - Linear,"@np.where(((df.start<9)), ((9-df.start)*(df.start<=9) + (df.start-9)*(df.start>9)), 0)",coef_discretionary_departure_constant_shift_for_every_30_minutes_before_7_30_pm_linear +util_discretionary_departure_constant_before_7_30_am,DISCRETIONARY - Departure Constant: Before 7:30 AM ,@((df.start<10)),coef_discretionary_departure_constant_before_7_30_am +util_discretionary_departure_constant_7_30_am_to_8_am,DISCRETIONARY - Departure Constant: 7:30 AM to 8:00 AM,@((df.start==10)),coef_discretionary_departure_constant_7_30_am_to_8_am +util_discretionary_departure_constant_8_am_to_8_30_am,DISCRETIONARY - Departure Constant: 8:00 AM to 8:30 AM,@((df.start==11)),coef_discretionary_departure_constant_8_am_to_8_30_am +util_discretionary_departure_constant_8_30_am_to_9_am,DISCRETIONARY - Departure Constant: 8:30 AM to 9:00 AM,@((df.start==12)),coef_discretionary_departure_constant_8_30_am_to_9_am +util_discretionary_departure_constant_9_am_to_9_30_am,DISCRETIONARY - Departure Constant: 9:00 AM to 9:30 AM,@((df.start==13)),coef_discretionary_departure_constant_9_am_to_9_30_am +util_discretionary_departure_constant_9_30_am_to_10_am,DISCRETIONARY - Departure Constant: 9:30 AM to 10:00 AM,@((df.start==14)),coef_discretionary_departure_constant_9_30_am_to_10_am +util_discretionary_departure_constant_10_am_to_10_30_am,DISCRETIONARY - Departure Constant: 10:00 AM to 10:30 AM,@((df.start==15)),coef_discretionary_departure_constant_10_am_to_10_30_am +util_discretionary_departure_constant_shift_for_every_30_minutes_before_4_30_pm_linear,DISCRETIONARY - Departure Constant: Shift for every 30 minutes before 04:30 pm - Linear,"@np.where(((df.start<28)), ((28-df.start)*(df.start<=28) + (df.start-28)*(df.start>28)), 0)",coef_discretionary_departure_constant_shift_for_every_30_minutes_before_4_30_pm_linear +util_discretionary_departure_constant_before_5_pm,DISCRETIONARY - Departure Constant: Before 05:00 PM,@((df.start<29)),coef_discretionary_departure_constant_before_5_pm +util_discretionary_departure_constant_5_pm_to_5_30_pm,DISCRETIONARY - Departure Constant: 05:00 PM - 05:30 PM,@((df.start==29)),coef_discretionary_departure_constant_5_pm_to_5_30_pm +util_discretionary_departure_constant_5_30_pm_to_6_pm,DISCRETIONARY - Departure Constant: 05:30 PM - 06:00 PM,@((df.start==30)),coef_discretionary_departure_constant_5_30_pm_to_6_pm +util_discretionary_departure_constant_6_pm_to_6_30_pm,DISCRETIONARY - Departure Constant: 06:00 PM - 06:30 PM,@((df.start==31)),coef_discretionary_departure_constant_6_pm_to_6_30_pm +util_discretionary_departure_constant_6_30_pm_to_7_pm,DISCRETIONARY - Departure Constant: 06:30 PM - 07:00 PM,@((df.start==32)),coef_discretionary_departure_constant_6_30_pm_to_7_pm +util_discretionary_departure_constant_after_7_pm,DISCRETIONARY - Departure Constant: After 07:00 PM,@((df.start>32)),coef_discretionary_departure_constant_after_7_pm +util_discretionary_departure_constant_shift_for_every_30_minutes_after_7_30_pm_linear,DISCRETIONARY - Departure Constant: Shift for every 30 minutes after 07:30 pm - Linear,"@np.where(((df.start>33)), ((33-df.start)*(df.start<=33) + (df.start-33)*(df.start>33)), 0)",coef_discretionary_departure_constant_shift_for_every_30_minutes_after_7_30_pm_linear +util_discretionary_arrival_constant_shift_for_every_30_minutes_before_6_pm_linear,DISCRETIONARY - Arrival Constant: Shift for every 30 minutes before 06:00 pm - Linear,"@np.where(((df.end<31)), ((31-df.end)*(df.end<=31) + (df.end-31)*(df.end>31)), 0)",coef_discretionary_arrival_constant_shift_for_every_30_minutes_before_6_pm_linear +util_discretionary_arrival_constant_before_6_30_pm,DISCRETIONARY - Arrival Constant: Before 6:30 PM,@((df.end<32)),coef_discretionary_arrival_constant_before_6_30_pm +util_discretionary_arrival_constant_6_30_pm_to_7_pm,DISCRETIONARY - Arrival Constant: 6:30 PM to 7:00 PM,@((df.end==32)),coef_discretionary_arrival_constant_6_30_pm_to_7_pm +util_discretionary_arrival_constant_7_pm_to_7_30_pm,DISCRETIONARY - Arrival Constant: 7:00 PM to 7:30 PM,@((df.end==33)),coef_discretionary_arrival_constant_7_pm_to_7_30_pm +util_discretionary_arrival_constant_7_30_pm_to_8_pm,DISCRETIONARY - Arrival Constant: 7:30 PM to 8:00 PM,@((df.end==34)),coef_discretionary_arrival_constant_7_30_pm_to_8_pm +util_discretionary_arrival_constant_8_pm_to_8_30_pm,DISCRETIONARY - Arrival Constant: 8:00 PM to 8:30 PM,@((df.end==35)),coef_discretionary_arrival_constant_8_pm_to_8_30_pm +util_discretionary_arrival_constant_8_30_pm_to_9_pm,DISCRETIONARY - Arrival Constant: 8:30 PM to 9:00 PM,@((df.end==36)),coef_discretionary_arrival_constant_8_30_pm_to_9_pm +util_discretionary_arrival_constant_after_9_pm,DISCRETIONARY - Arrival Constant: After 9:00 PM,@((df.end>36)),coef_discretionary_arrival_constant_after_9_pm +util_discretionary_arrival_constant_shift_for_every_30_minutes_after_9_30_pm_linear,DISCRETIONARY - Arrival Constant: Shift for every 30 minutes after 09:30 pm - Linear,"@np.where(((df.end>37)), ((37-df.end)*(df.end<=37) + (df.end-37)*(df.end>37)), 0)",coef_discretionary_arrival_constant_shift_for_every_30_minutes_after_9_30_pm_linear +util_discretionary_duration_constant_0_hour,DISCRETIONARY - Duration Constant: 0 hour,@((df.duration==0)),coef_discretionary_duration_constant_0_hour +util_discretionary_duration_constant_30_minutes,DISCRETIONARY -Duration Constant: 0.5 hour,@((df.duration==1)),coef_discretionary_duration_constant_30_minutes +util_discretionary_duration_constant_1_hour,DISCRETIONARY -Duration Constant: 1 hour,@((df.duration==2)),coef_discretionary_duration_constant_1_hour +util_discretionary_duration_constant_1_hr_30_minutes,DISCRETIONARY -Duration Constant: 1.5 hours,@((df.duration==3)),coef_discretionary_duration_constant_1_hr_30_minutes +util_discretionary_duration_constant_2_hours,DISCRETIONARY -Duration Constant: 2 hours,@((df.duration==4)),coef_discretionary_duration_constant_2_hours +util_discretionary_duration_constant_2_hr_30_minutes,DISCRETIONARY -Duration Constant: 2.5 hours,@((df.duration==5)),coef_discretionary_duration_constant_2_hr_30_minutes +util_discretionary_duration_constant_3_hours_or_more,DISCRETIONARY -Duration Constant: 3 hours or more,@((df.duration>5)),coef_discretionary_duration_constant_3_hours_or_more +util_discretionary_duration_constant_shift_for_every_30_minutes_more_than_3_hrs_linear,DISCRETIONARY -Duration Constant: Shift for every 30 minutes more than 3 hrs - Linear,"@np.where(((df.duration>6)), ((6-df.duration)*(df.duration<=6) + (df.duration-6)*(df.duration>6)), 0)",coef_discretionary_duration_constant_shift_for_every_30_minutes_more_than_3_hrs_linear +util_discretionary_calibration_constant_duration_4,DISCRETIONARY -Calibration Constant - Duration = 4,@((df.duration==3)),coef_discretionary_calibration_constant_duration_4 +util_discretionary_calibration_constant_duration_5,DISCRETIONARY -Calibration Constant - Duration = 5,@((df.duration==4)),coef_discretionary_calibration_constant_duration_5 +util_discretionary_calibration_constant_departure_29,DISCRETIONARY -Calibration Constant - Departure = 29,@((df.start==29)),coef_discretionary_calibration_constant_departure_29 +util_discretionary_calibration_constant_departure_30,DISCRETIONARY -Calibration Constant - Departure = 30,@((df.start==30)),coef_discretionary_calibration_constant_departure_30 +util_discretionary_calibration_constant_departure_31,DISCRETIONARY -Calibration Constant - Departure = 31,@((df.start==31)),coef_discretionary_calibration_constant_departure_31 +util_discretionary_calibration_constant_departure_32,DISCRETIONARY -Calibration Constant - Departure = 32,@((df.start==32)),coef_discretionary_calibration_constant_departure_32 +# Including terms for school escorting,,, +util_outbound_school_escort_tour_start,Outbound school escort tours must match the start time of the escort tour,is_outbound_school_escort_tour & (start != school_escort_tour_start),coef_unavailable +util_outbound_school_escort_tour_next_start,Outbound school escort tours must end before next escort tour start,is_outbound_school_escort_tour & (end > school_escort_tour_next_start) & (school_escort_tour_next_start > 0),coef_unavailable +util_inbound_school_escort_tour_end,Inbound school escort tours must match the end time of the escort tour,is_inbound_school_escort_tour & (end != school_escort_tour_end),coef_unavailable \ No newline at end of file diff --git a/resident/configs/tour_scheduling_nonmandatory_othdiscr_coefficients.csv b/resident/configs/tour_scheduling_nonmandatory_othdiscr_coefficients.csv new file mode 100644 index 0000000..87e3924 --- /dev/null +++ b/resident/configs/tour_scheduling_nonmandatory_othdiscr_coefficients.csv @@ -0,0 +1,55 @@ +coefficient_name,value,constrain +coef_discretionary_person_less_than_18_years_old_duration_less_than_reference_linear,0.098366285148487,F +coef_discretionary_person_less_than_18_years_old_duration_greater_than_reference_linear,0.0,F +coef_discretionary_non_working_senior_retiree_duration_less_than_reference_linear,-0.1705916260525488,F +coef_discretionary_retiree_non_working_senior_only_HH_duration_reference_linear,0.0,F +coef_discretionary_zero_auto_households_duration_less_than_reference_linear,0.0,F +coef_discretionary_zero_auto_households_duration_greater_than_reference_linear,0.05222497126898865,F +coef_discretionary_number_of_auto_more_than_number_of_adults_duration_less_than_reference_linear,0.0,F +coef_discretionary_number_of_auto_more_than_number_of_adults_duration_greater_than_reference_linear,0.0,F +coef_discretionary_auto_distance_duration_less_than_reference_linear,-0.03606825659562718,F +coef_discretionary_auto_distance_duration_greater_than_reference_linear,0.004596600975929468,F +coef_discretionary_time_pressure_duration_less_reference,0.6728960123740134,F +coef_discretionary_time_pressure_duration_greater_reference,0.25402774161065456,F +coef_discretionary_number_of_additional_individual_social_and_dicretionary_tours_duration_less_reference,0.08104208628580123,F +coef_discretionary_departure_constant_shift_for_every_30_minutes_before_7_30_pm_linear,-0.6104747153010481,F +coef_discretionary_departure_constant_before_7_30_am,-0.3390521412898814,F +coef_discretionary_departure_constant_7_30_am_to_8_am,0.0,F +coef_discretionary_departure_constant_8_am_to_8_30_am,0.0,F +coef_discretionary_departure_constant_8_30_am_to_9_am,0.0,F +coef_discretionary_departure_constant_9_am_to_9_30_am,0.33705457332633537,F +coef_discretionary_departure_constant_9_30_am_to_10_am,0.14050652594559154,F +coef_discretionary_departure_constant_10_am_to_10_30_am,0.0,F +coef_discretionary_departure_constant_shift_for_every_30_minutes_before_4_30_pm_linear,2.055051892448251,F +coef_discretionary_departure_constant_before_5_pm,10.078028457555423,F +coef_discretionary_departure_constant_5_pm_to_5_30_pm,8.809104255087856,F +coef_discretionary_departure_constant_5_30_pm_to_6_pm,7.066441289657018,F +coef_discretionary_departure_constant_6_pm_to_6_30_pm,4.909660731901318,F +coef_discretionary_departure_constant_6_30_pm_to_7_pm,2.825818687356497,T +coef_discretionary_departure_constant_after_7_pm,0.0,F +coef_discretionary_departure_constant_shift_for_every_30_minutes_after_7_30_pm_linear,-2.407069142986441,F +coef_discretionary_arrival_constant_shift_for_every_30_minutes_before_6_pm_linear,-1.9913325130310646,F +coef_discretionary_arrival_constant_before_6_30_pm,-8.318569502929208,F +coef_discretionary_arrival_constant_6_30_pm_to_7_pm,-6.434917972534465,F +coef_discretionary_arrival_constant_7_pm_to_7_30_pm,-4.138799434182262,F +coef_discretionary_arrival_constant_7_30_pm_to_8_pm,-2.1058225272583,F +coef_discretionary_arrival_constant_8_pm_to_8_30_pm,0.0,T +coef_discretionary_arrival_constant_8_30_pm_to_9_pm,2.1493158879532435,F +coef_discretionary_arrival_constant_after_9_pm,3.8558666359863616,F +coef_discretionary_arrival_constant_shift_for_every_30_minutes_after_9_30_pm_linear,1.6858826820414816,F +coef_discretionary_duration_constant_0_hour,0.0,F +coef_discretionary_duration_constant_30_minutes,0.0,F +coef_discretionary_duration_constant_1_hour,0.0,F +coef_discretionary_duration_constant_1_hr_30_minutes,0.0,F +coef_discretionary_duration_constant_2_hours,0.0,F +coef_discretionary_duration_constant_2_hr_30_minutes,-2.9894794551717427,T +coef_discretionary_duration_constant_3_hours_or_more,-6.039866672397804,F +coef_discretionary_duration_constant_shift_for_every_30_minutes_more_than_3_hrs_linear,-2.9211224892215153,F +coef_discretionary_calibration_constant_duration_4,0.0,F +coef_discretionary_calibration_constant_duration_5,0.0,F +coef_discretionary_calibration_constant_departure_29,0.0,F +coef_discretionary_calibration_constant_departure_30,0.0,F +coef_discretionary_calibration_constant_departure_31,0.0,F +coef_discretionary_calibration_constant_departure_32,0.0,F +coef_mode_choice_logsum,0.06338187336512106,F +coef_unavailable,-999.0,F \ No newline at end of file diff --git a/resident/configs/tour_scheduling_nonmandatory_othmaint.csv b/resident/configs/tour_scheduling_nonmandatory_othmaint.csv new file mode 100644 index 0000000..d9bda3c --- /dev/null +++ b/resident/configs/tour_scheduling_nonmandatory_othmaint.csv @@ -0,0 +1,59 @@ +Label,Description,Expression,Coefficient +#MAINTENANCE,#MAINTENANCE,,#MAINTENANCE +util_mode_choice_logsum,Mode choice logsum,mode_choice_logsum,coef_mode_choice_logsum +util_maintenance_driving_age_student_duration_greater_than_reference,MAINTENANCE - Driving age student: Duration > Reference,"@np.where(((df.duration>2) & (df.ptype == 6)), ((2-df.duration)*(df.duration<=2) + (df.duration-2)*(df.duration>2)), 0)",coef_maintenance_driving_age_student_duration_greater_than_reference +util_maintenance_full_time_worker_duration_greater_than_reference,MAINTENANCE - Full-time worker: Duration > Reference,"@np.where(((df.duration>2) & (df.ptype == 1)), ((2-df.duration)*(df.duration<=2) + (df.duration-2)*(df.duration>2)), 0)",coef_maintenance_full_time_worker_duration_greater_than_reference +util_maintenance_non_driving_student_duration_greater_than_reference,MAINTENANCE - Non-driving Student: Duration > Reference,"@np.where (((df.duration>2) & ((df.ptype == 7)|(df.ptype == 8))), ((2-df.duration)*(df.duration<=2) + (df.duration-2)*(df.duration>2)), 0)",coef_maintenance_non_driving_student_duration_greater_than_reference +util_maintenance_pre_school_child_duration_less_than_reference,MAINTENANCE - Pre-school Child: Duration < Reference,"@np.where(((df.duration<2) & (df.ptype == 8)), ((2-df.duration)*(df.duration<=2) + (df.duration-2)*(df.duration>2)), 0)",coef_maintenance_pre_school_child_duration_less_than_reference +util_maintenance_part_time_worker_duration_less_than_reference,MAINTENANCE - Part Time Worker: Duration < Reference,"@np.where(((df.duration<2) & (df.ptype == 2)), ((2-df.duration)*(df.duration<=2) + (df.duration-2)*(df.duration>2)), 0)",coef_maintenance_part_time_worker_duration_less_than_reference +util_maintenance_part_time_worker_duration_greater_than_reference,MAINTENANCE - Part Time Worker: Duration > Reference,"@np.where(((df.duration>2) & (df.ptype == 2)), ((2-df.duration)*(df.duration<=2) + (df.duration-2)*(df.duration>2)), 0)",coef_maintenance_part_time_worker_duration_greater_than_reference +util_maintenance_retired_duration_less_than_reference,MAINTENANCE - Retired: Duration < Reference,"@np.where(((df.duration<2) & (df.ptype == 5)), ((2-df.duration)*(df.duration<=2) + (df.duration-2)*(df.duration>2)), 0)",coef_maintenance_retired_duration_less_than_reference +util_maintenance_retired_duration_greater_than_reference,MAINTENANCE - Retired: Duration > 1.5 hr,"@np.where(((df.duration>2) & (df.ptype == 5)), ((2-df.duration)*(df.duration<=2) + (df.duration-2)*(df.duration>2)), 0)",coef_maintenance_retired_duration_greater_than_reference +util_maintenance_university_student_duration_greater_than_reference,MAINTENANCE - University Student: Duration > Reference,"@np.where(((df.duration>2) & (df.ptype == 3)), ((2-df.duration)*(df.duration<=2) + (df.duration-2)*(df.duration>2)), 0)",coef_maintenance_university_student_duration_greater_than_reference +util_maintenance_female_duration_less_than_reference,MAINTENANCE - Female: Duration < Reference,"@np.where(((df.duration<2) & (df.female)), ((2-df.duration)*(df.duration<=2) + (df.duration-2)*(df.duration>2)), 0)",coef_maintenance_female_duration_less_than_reference +util_maintenance_female_duration_greater_than_reference,MAINTENANCE - Female: Duration > Reference,"@np.where(((df.duration>2) & (df.female)), ((2-df.duration)*(df.duration<=2) + (df.duration-2)*(df.duration>2)), 0)",coef_maintenance_female_duration_greater_than_reference +util_maintenance_low_income_duration_greater_than_reference,"MAINTENANCE - Low Income (<=$25,000): Duration > Reference","@np.where(((df.is_income_less25K) & (df.duration>2)), ((2-df.duration)*(df.duration<=2) + (df.duration-2)*(df.duration>2)), 0)",coef_maintenance_low_income_duration_greater_than_reference +util_maintenance_medium_income_duration_less_than_reference,"MAINTENANCE - Medium Income ($25,001 to $60,000): Duration < Reference","@np.where(((df.is_income_25K_to_60K) & (df.duration<2)), ((2-df.duration)*(df.duration<=2) + (df.duration-2)*(df.duration>2)), 0)",coef_maintenance_medium_income_duration_less_than_reference +util_maintenance_medium_income_duration_greater_than_reference,"MAINTENANCE - Medium Income ($25,001 to $60,000): Duration > Reference","@np.where(((df.is_income_25K_to_60K) & (df.duration>2)), ((2-df.duration)*(df.duration<=2) + (df.duration-2)*(df.duration>2)), 0)",coef_maintenance_medium_income_duration_greater_than_reference +util_maintenance_medium_high_income_duration_greater_than_reference,"MAINTENANCE - Medium-High Income ($60,001 to $120,00): Duration > Reference","@np.where(((df.is_income_60K_to_120K) & (df.duration>2)), ((2-df.duration)*(df.duration<=2) + (df.duration-2)*(df.duration>2)), 0)",coef_maintenance_medium_high_income_duration_greater_than_reference +util_maintenance_distance_duration_less_than_reference,MAINTENANCE - Distance: Duration < Reference,"@np.where(((df.duration<2)), ((2-df.duration)*(df.duration<=2) + (df.duration-2)*(df.duration>2)) *(df.origin_to_destination_distance), 0)",coef_maintenance_distance_duration_less_than_reference +util_maintenance_distance_duration_greater_than_reference,MAINTENANCE - Distance: Duration > Reference,"@np.where(((df.duration>2)), ((2-df.duration)*(df.duration<=2) + (df.duration-2)*(df.duration>2)) * (df.origin_to_destination_distance), 0)",coef_maintenance_distance_duration_greater_than_reference +util_maintenance_time_pressure_duration_greater_than_reference,Time Pressure - Duration > Reference,"@np.where(((df.duration>2)), ((2-df.duration)*(df.duration<=2) + (df.duration-2)*(df.duration>2)) * (np.log10 (30 * (tt.max_time_block_available(df.person_id)/(1.0 + df.tour_count - df.tour_num)))), 0)",coef_maintenance_time_pressure_duration_greater_than_reference +util_maintenance_number_of_additional_individual_shop_and_maint_tours_duration_less_than_reference,MAINTENANCE - Number of additional individual shop and maint. tours - Duration < Reference,"@np.where((df.duration<2), ((2-df.duration)*(df.duration<=2) + (df.duration-2)*(df.duration>2)) * (df.num_add_shop_maint_tours), 0)",coef_maintenance_number_of_additional_individual_shop_and_maint_tours_duration_less_than_reference +util_maintenance_number_of_additional_individual_shop_and_maint_tours_duration_greater_than_reference,MAINTENANCE - Number of additional individual shop and maint. tours - Duration > Reference,"@np.where((df.duration>2), ((2-df.duration)*(df.duration<=2) + (df.duration-2)*(df.duration>2)) * (df.num_add_shop_maint_tours), 0)",coef_maintenance_number_of_additional_individual_shop_and_maint_tours_duration_greater_than_reference +util_maintenance_departure_constant_shift_for_every_30_minutes_before_7_30_am_linear,MAINTENANCE - Departure Constant: Shift for every 30 minutes before 07:30 am - Linear,"@np.where((df.start<10), ((10-df.start)*(df.start<=10) + (df.start-10)*(df.start>10)), 0)",coef_maintenance_departure_constant_shift_for_every_30_minutes_before_7_30_am_linear +util_maintenance_departure_constant_shift_for_every_30_minutes_before_7_30_am_square_root,MAINTENANCE - Departure Constant: Shift for every 30 minutes before 07:30 am - Square Root,"@np.where((df.start<10), (((10-df.start)*(df.start<=10) + (df.start-10)*(df.start>10))** 0.5), 0)",coef_maintenance_departure_constant_shift_for_every_30_minutes_before_7_30_am_square_root +util_maintenance_departure_constant_before_8_am,MAINTENANCE - Departure Constant: Before 08:00 AM,@((df.start<11)),coef_maintenance_departure_constant_before_8_am +util_maintenance_departure_constant_8_am_to_8_30_am,MAINTENANCE - Departure Constant: 08:00 AM - 08:30 AM,@((df.start==11)),coef_maintenance_departure_constant_8_am_to_8_30_am +util_maintenance_departure_constant_8_30_am_to_9_00_am,MAINTENANCE - Departure Constant: 08:30 AM - 09:00 AM,@((df.start==12)),coef_maintenance_departure_constant_8_30_am_to_9_00_am +util_maintenance_departure_constant_9_am_to_9_30_am,MAINTENANCE - Departure Constant: 09:00 AM - 09:30 AM,@((df.start==13)),coef_maintenance_departure_constant_9_am_to_9_30_am +util_maintenance_departure_constant_9_30_am_to_10_am,MAINTENANCE - Departure Constant: 09:30 AM - 10:00 AM,@((df.start==14)),coef_maintenance_departure_constant_9_30_am_to_10_am +util_maintenance_departure_constant_10_am_to_10_30_am,MAINTENANCE - Departure Constant: 10:00 AM - 10:30 AM,@((df.start==15)),coef_maintenance_departure_constant_10_am_to_10_30_am +util_maintenance_departure_constant_10_30_am_to_11_am,MAINTENANCE - Departure Constant: 10:30 AM - 11:00 AM,@((df.start==16)),coef_maintenance_departure_constant_10_30_am_to_11_am +util_maintenance_departure_constant_after_11_am,MAINTENANCE - Departure Constant: After 11:00 AM,@((df.start>16)),coef_maintenance_departure_constant_after_11_am +util_maintenance_departure_constant_shift_for_every_30_minutes_after_11_30_am_linear,MAINTENANCE - Departure Constant: Shift for every 30 minutes after 11:30 am - Linear,"@np.where((df.start>17), ((17-df.start)*(df.start<=17) + (df.start-17)*(df.start>17)), 0)",coef_maintenance_departure_constant_shift_for_every_30_minutes_after_11_30_am_linear +util_maintenance_departure_constant_shift_for_every_30_minutes_after_11_30_am_squared,MAINTENANCE - Departure Constant: Shift for every 30 minutes after 11:30 am - Squared,"@np.where((df.start>17), (((17-df.start)*(df.start<=17) + (df.start-17)*(df.start>17)) ** 2), 0)",coef_maintenance_departure_constant_shift_for_every_30_minutes_after_11_30_am_squared +util_maintenance_arrival_constant_shift_for_every_30_minutes_before_10_am_linear,MAINTENANCE - Arrival Constant: Shift for every 30 minutes before 10:00 am - Linear,"@np.where(((df.end<15)), ((15-df.end)*(df.end<=15) + (df.end-15)*(df.end>15)), 0)",coef_maintenance_arrival_constant_shift_for_every_30_minutes_before_10_am_linear +util_maintenance_arrival_constant_before_10_30_am,MAINTENANCE - Arrival Constant: Before 10:30 AM,@((df.end<16)),coef_maintenance_arrival_constant_before_10_30_am +util_maintenance_arrival_constant_10_30_am_to_11_am,MAINTENANCE - Arrival Constant: 10:30 AM - 11:00 AM,@((df.end==16)),coef_maintenance_arrival_constant_10_30_am_to_11_am +util_maintenance_arrival_constant_11_am_to_11_30_am,MAINTENANCE - Arrival Constant: 11:00 AM - 11:30 AM,@((df.end==17)),coef_maintenance_arrival_constant_11_am_to_11_30_am +util_maintenance_arrival_constant_11_30_am_to_1_30_pm,MAINTENANCE - Arrival Constant: 11:30 AM - 01:30 PM,@((df.end>=18) & (df.end<=21)),coef_maintenance_arrival_constant_11_30_am_to_1_30_pm +util_maintenance_arrival_constant_1_30_pm_to_2_30_pm,MAINTENANCE - Arrival Constant: 01:30 PM - 02:30 PM,@((df.end>=22) & (df.end<=23)),coef_maintenance_arrival_constant_1_30_pm_to_2_30_pm +util_maintenance_arrival_constant_2_30_pm_to_4_pm,MAINTENANCE - Arrival Constant: 02:30 PM - 04:00 PM,@((df.end>=24) & (df.end<=26)),coef_maintenance_arrival_constant_2_30_pm_to_4_pm +util_maintenance_arrival_constant_4_pm_to_4_30_pm,MAINTENANCE - Arrival Constant: 04:00 PM - 04:30 PM,@((df.end==27)),coef_maintenance_arrival_constant_4_pm_to_4_30_pm +util_maintenance_arrival_constant_after_4_30_pm,MAINTENANCE - Arrival Constant: After 04:30 PM,@((df.end>27)),coef_maintenance_arrival_constant_after_4_30_pm +util_maintenance_arrival_constant_shift_for_every_30_minutes_after_5_pm_linear,MAINTENANCE - Arrival Constant: Shift for every 30 minutes after 5:00 pm - Linear,"@np.where(((df.end>28)), ((28-df.end)*(df.end<=28) + (df.end-28)*(df.end>28)), 0)",coef_maintenance_arrival_constant_shift_for_every_30_minutes_after_5_pm_linear +util_maintenance_duration_constant_0_hr,MAINTENANCE - Duration Constant: 0 hr,@((df.duration==0)),coef_maintenance_duration_constant_0_hr +util_maintenance_duration_constant_30_minutes,MAINTENANCE - Duration Constant: 0.5 hr,@((df.duration==1)),coef_maintenance_duration_constant_30_minutes +util_maintenance_duration_constant_longer_than_30_minutes,MAINTENANCE - Duration Constant: Longer than 0.5 hr,@((df.duration>1)),coef_maintenance_duration_constant_longer_than_30_minutes +util_maintenance_duration_constant_duration_greater_than_1_hr_linear,MAINTENANCE - Duration Constant: Duration > 1 hr - Linear,"@np.where(((df.duration>2)), ((2-df.duration)*(df.duration<=2) + (df.duration-2)*(df.duration>2)), 0)",coef_maintenance_duration_constant_duration_greater_than_1_hr_linear +util_maintenance_duration_constant_duration_greater_than_1_hr_square_root,MAINTENANCE - Duration Constant: Duration > 1 hr - Square Root,"@np.where(((df.duration>2)), (((2-df.duration)*(df.duration<=2) + (df.duration-2)*(df.duration>2))** 0.5), 0)",coef_maintenance_duration_constant_duration_greater_than_1_hr_square_root +util_maintenance_calibration_constant_duration_1,MAINTENANCE - Calibration Constant - Duration = 1,@((df.duration==0)),coef_maintenance_calibration_constant_duration_1 +util_maintenance_calibration_constant_duration_2,MAINTENANCE - Calibration Constant - Duration = 2,@((df.duration==1)),coef_maintenance_calibration_constant_duration_2 +util_maintenance_calibration_constant_duration_3,MAINTENANCE - Calibration Constant - Duration = 3,@((df.duration==2)),coef_maintenance_calibration_constant_duration_3 +util_maintenance_calibration_constant_duration_4,MAINTENANCE - Calibration Constant - Duration = 4,@((df.duration==3)),coef_maintenance_calibration_constant_duration_4 +util_maintenance_calibration_constant_duration_5,MAINTENANCE - Calibration Constant - Duration = 5,@((df.duration==4)),coef_maintenance_calibration_constant_duration_5 +# Including terms for school escorting,,, +util_outbound_school_escort_tour_start,Outbound school escort tours must match the start time of the escort tour,is_outbound_school_escort_tour & (start != school_escort_tour_start),coef_unavailable +util_outbound_school_escort_tour_next_start,Outbound school escort tours must end before next escort tour start,is_outbound_school_escort_tour & (end > school_escort_tour_next_start) & (school_escort_tour_next_start > 0),coef_unavailable +util_inbound_school_escort_tour_end,Inbound school escort tours must match the end time of the escort tour,is_inbound_school_escort_tour & (end != school_escort_tour_end),coef_unavailable \ No newline at end of file diff --git a/resident/configs/tour_scheduling_nonmandatory_othmaint_coefficients.csv b/resident/configs/tour_scheduling_nonmandatory_othmaint_coefficients.csv new file mode 100644 index 0000000..e5d214b --- /dev/null +++ b/resident/configs/tour_scheduling_nonmandatory_othmaint_coefficients.csv @@ -0,0 +1,55 @@ +coefficient_name,value,constrain +coef_maintenance_driving_age_student_duration_greater_than_reference,0.0,F +coef_maintenance_full_time_worker_duration_greater_than_reference,0.13520704870454775,F +coef_maintenance_non_driving_student_duration_greater_than_reference,0.0,F +coef_maintenance_pre_school_child_duration_less_than_reference,-2.157370739161894,F +coef_maintenance_part_time_worker_duration_less_than_reference,0.0,F +coef_maintenance_part_time_worker_duration_greater_than_reference,0.0,F +coef_maintenance_retired_duration_less_than_reference,0.0,F +coef_maintenance_retired_duration_greater_than_reference,-0.03534027470461993,F +coef_maintenance_university_student_duration_greater_than_reference,0.0,F +coef_maintenance_female_duration_less_than_reference,-0.33642495066387595,F +coef_maintenance_female_duration_greater_than_reference,0.0,F +coef_maintenance_low_income_duration_greater_than_reference,0.040103190219137164,F +coef_maintenance_medium_income_duration_less_than_reference,0.0,F +coef_maintenance_medium_income_duration_greater_than_reference,0.0,T +coef_maintenance_medium_high_income_duration_greater_than_reference,0.0,F +coef_maintenance_distance_duration_less_than_reference,-0.28516479340386963,F +coef_maintenance_distance_duration_greater_than_reference,0.004293750630955166,F +coef_maintenance_time_pressure_duration_greater_than_reference,0.2152799372032978,F +coef_maintenance_number_of_additional_individual_shop_and_maint_tours_duration_less_than_reference,0.0,F +coef_maintenance_number_of_additional_individual_shop_and_maint_tours_duration_greater_than_reference,-0.03864547690327943,F +coef_maintenance_departure_constant_shift_for_every_30_minutes_before_7_30_am_linear,-0.8990260296725098,F +coef_maintenance_departure_constant_shift_for_every_30_minutes_before_7_30_am_square_root,0.9516096316489431,F +coef_maintenance_departure_constant_before_8_am,-0.5598527748241708,F +coef_maintenance_departure_constant_8_am_to_8_30_am,-0.14921181662286023,F +coef_maintenance_departure_constant_8_30_am_to_9_00_am,0.0,F +coef_maintenance_departure_constant_9_am_to_9_30_am,0.0,F +coef_maintenance_departure_constant_9_30_am_to_10_am,0.0,F +coef_maintenance_departure_constant_10_am_to_10_30_am,0.0,T +coef_maintenance_departure_constant_10_30_am_to_11_am,-0.17187147382272855,F +coef_maintenance_departure_constant_after_11_am,-0.3600477839542276,F +coef_maintenance_departure_constant_shift_for_every_30_minutes_after_11_30_am_linear,0.02639222500693473,F +coef_maintenance_departure_constant_shift_for_every_30_minutes_after_11_30_am_squared,-0.0017054145463435359,F +coef_maintenance_arrival_constant_shift_for_every_30_minutes_before_10_am_linear,-0.23175571400118172,F +coef_maintenance_arrival_constant_before_10_30_am,0.0,F +coef_maintenance_arrival_constant_10_30_am_to_11_am,0.0,T +coef_maintenance_arrival_constant_11_am_to_11_30_am,0.0,F +coef_maintenance_arrival_constant_11_30_am_to_1_30_pm,0.0,F +coef_maintenance_arrival_constant_1_30_pm_to_2_30_pm,0.0,F +coef_maintenance_arrival_constant_2_30_pm_to_4_pm,0.0,F +coef_maintenance_arrival_constant_4_pm_to_4_30_pm,0.0,F +coef_maintenance_arrival_constant_after_4_30_pm,0.0,F +coef_maintenance_arrival_constant_shift_for_every_30_minutes_after_5_pm_linear,-0.31053576324304255,F +coef_maintenance_duration_constant_0_hr,5.99100088789488,F +coef_maintenance_duration_constant_30_minutes,6.268122806796934,T +coef_maintenance_duration_constant_longer_than_30_minutes,4.8989465058844175,F +coef_maintenance_duration_constant_duration_greater_than_1_hr_linear,-0.7339031911682701,F +coef_maintenance_duration_constant_duration_greater_than_1_hr_square_root,-0.13942646219715638,F +coef_maintenance_calibration_constant_duration_1,0.0,F +coef_maintenance_calibration_constant_duration_2,0.0,F +coef_maintenance_calibration_constant_duration_3,0.0,F +coef_maintenance_calibration_constant_duration_4,0.25854243359403917,F +coef_maintenance_calibration_constant_duration_5,0.22211610318873198,F +coef_mode_choice_logsum,0.23911188143948614,F +coef_unavailable,-999.0,F \ No newline at end of file diff --git a/resident/configs/tour_scheduling_nonmandatory_shopping.csv b/resident/configs/tour_scheduling_nonmandatory_shopping.csv new file mode 100644 index 0000000..fa7fbf1 --- /dev/null +++ b/resident/configs/tour_scheduling_nonmandatory_shopping.csv @@ -0,0 +1,59 @@ +Label,Description,Expression,Coefficient +#SHOPPING,,,#SHOPPING +util_mode_choice_logsum,Mode choice logsum,mode_choice_logsum,coef_mode_choice_logsum +util_shoppping_driving_age_student_duration_greater_than_reference,SHOPPING - Driving age student: Duration > Reference,"@np.where(((df.duration>2) & (df.ptype == 6)), ((2-df.duration)*(df.duration<=2) + (df.duration-2)*(df.duration>2)), 0)",coef_shoppping_driving_age_student_duration_greater_than_reference +util_shoppping_full_time_worker_duration_greater_than_reference,SHOPPING - Full-time worker: Duration > Reference,"@np.where(((df.duration>2) & (df.ptype == 1)), ((2-df.duration)*(df.duration<=2) + (df.duration-2)*(df.duration>2)), 0)",coef_shoppping_full_time_worker_duration_greater_than_reference +util_shoppping_non_driving_student_duration_greater_than_reference,SHOPPING - Non-driving Student: Duration > Reference,"@np.where(((df.duration>2) & ((df.ptype == 7)|(df.ptype == 8))), ((2-df.duration)*(df.duration<=2) + (df.duration-2)*(df.duration>2)), 0)",coef_shoppping_non_driving_student_duration_greater_than_reference +util_shoppping_pre_school_child_duration_less_than_reference,SHOPPING - Pre-school Child: Duration < Reference,"@np.where(((df.duration<2) & (df.ptype == 8)), ((2-df.duration)*(df.duration<=2) + (df.duration-2)*(df.duration>2)), 0)",coef_shoppping_pre_school_child_duration_less_than_reference +util_shoppping_part_time_worker_duration_less_than_reference,SHOPPING - Part Time Worker: Duration < Reference,"@np.where(((df.duration<2) & (df.ptype == 2)), ((2-df.duration)*(df.duration<=2) + (df.duration-2)*(df.duration>2)), 0)",coef_shoppping_part_time_worker_duration_less_than_reference +util_shopping_part_time_worker_duration_greater_than_reference,SHOPPING - Part Time Worker: Duration > Reference,"@np.where(((df.duration>2) & (df.ptype == 2)), ((2-df.duration)*(df.duration<=2) + (df.duration-2)*(df.duration>2)), 0)",coef_shopping_part_time_worker_duration_greater_than_reference +util_shopping_retired_duration_less_than_reference,SHOPPING - Retired: Duration < Reference,"@np.where(((df.duration<2) & (df.ptype == 5)), ((2-df.duration)*(df.duration<=2) + (df.duration-2)*(df.duration>2)), 0)",coef_shopping_retired_duration_less_than_reference +util_shopping_retired_duration_greater_than_reference,SHOPPING - Retired: Duration > Reference,"@np.where(((df.duration>2) & (df.ptype == 5)), ((2-df.duration)*(df.duration<=2) + (df.duration-2)*(df.duration>2)), 0)",coef_shopping_retired_duration_greater_than_reference +util_shopping_university_student_duration_greater_than_reference,SHOPPING - University Student: Duration > Reference,"@np.where(((df.duration>2) & (df.ptype == 3)), ((2-df.duration)*(df.duration<=2) + (df.duration-2)*(df.duration>2)), 0)",coef_shopping_university_student_duration_greater_than_reference +util_shopping_female_duration_less_than_reference,SHOPPING - Female: Duration < Reference,"@np.where(((df.duration<2) & (df.female)), ((2-df.duration)*(df.duration<=2) + (df.duration-2)*(df.duration>2)), 0)",coef_shopping_female_duration_less_than_reference +util_shopping_female_duration_greater_than_reference,SHOPPING - Female: Duration > Reference,"@np.where(((df.duration>2) & (df.female)), ((2-df.duration)*(df.duration<=2) + (df.duration-2)*(df.duration>2)), 0)",coef_shopping_female_duration_greater_than_reference +util_shopping_low_income_duration_greater_than_reference,"SHOPPING - Low Income (<=$25,000): Duration > Reference","@np.where(((df.is_income_less25K) & (df.duration>2)), ((2-df.duration)*(df.duration<=2) + (df.duration-2)*(df.duration>2)), 0)",coef_shopping_low_income_duration_greater_than_reference +util_shopping_medium_income_duration_less_than_reference,"SHOPPING - Medium Income ($25,001 to $60,000): Duration < Reference","@np.where(((df.is_income_25K_to_60K) & (df.duration<2)), ((2-df.duration)*(df.duration<=2) + (df.duration-2)*(df.duration>2)), 0)",coef_shopping_medium_income_duration_less_than_reference +util_shopping_medium_high_income_duration_greater_than_reference,"SHOPPING - Medium-High Income ($60,001 to $120,00): Duration > Reference","@np.where(((df.is_income_60K_to_120K) & (df.duration>2)), ((2-df.duration)*(df.duration<=2) + (df.duration-2)*(df.duration>2)), 0)",coef_shopping_medium_high_income_duration_greater_than_reference +util_shopping_distance_duration_less_than_reference,SHOPPING - Distance: Duration < Reference,"@np.where(((df.duration<2)), ((2-df.duration)*(df.duration<=2) + (df.duration-2)*(df.duration>2)) * (df.origin_to_destination_distance), 0)",coef_shopping_distance_duration_less_than_reference +util_shopping_distance_duration_greater_than_reference,SHOPPING - Distance: Duration > Reference,"@np.where(((df.duration>2)), ((2-df.duration)*(df.duration<=2) + (df.duration-2)*(df.duration>2)) * (df.origin_to_destination_distance), 0)",coef_shopping_distance_duration_greater_than_reference +util_shopping_time_pressure_duration_greater_than_reference,SHOPPING - Time Pressure - Duration > Reference,"@np.where(((df.duration>2)), np.minimum(df.duration-2,26) *(np.log10 (30 *(tt.max_time_block_available(df.person_id)/(1.0 + df.tour_count - df.tour_num)))), 0)",coef_shopping_time_pressure_duration_greater_than_reference +util_shopping_number_of_additional_individual_shop_and_maint_tours_duration_less_than_reference,SHOPPING - Number of additional individual shop and maint. tours - Duration < Reference,"@np.where(((df.duration<2)), ((2-df.duration)*(df.duration<=2) + (df.duration-2)*(df.duration>2)) *(df.num_add_shop_maint_tours), 0)",coef_shopping_number_of_additional_individual_shop_and_maint_tours_duration_less_than_reference +util_shopping_number_of_additional_individual_shop_and_maint_tours_duration_greater_than_reference,SHOPPING - Number of additional individual shop and maint. tours - Duration > Reference,"@np.where(((df.tour_type == 'shopping') &(df.duration>2)), ((2-df.duration)*(df.duration<=2) + (df.duration-2)*(df.duration>2)) *(df.num_add_shop_maint_tours), 0)",coef_shopping_number_of_additional_individual_shop_and_maint_tours_duration_greater_than_reference +util_shopping_departure_constant_shift_for_every_30_minutes_before_8_30_am_linear,SHOPPING - Departure Constant: Shift for every 30 minutes before 08:30 am - Linear,"@np.where(((df.start<12)), ((12-df.start)*(df.start<=12) + (df.start-12)*(df.start>12)), 0)",coef_shopping_departure_constant_shift_for_every_30_minutes_before_8_30_am_linear +util_shopping_departure_constant_shift_for_every_30_minutes_before_8_30_am_square_root,SHOPPING - Departure Constant: Shift for every 30 minutes before 08:30 am - Square root,"@np.where(((df.start<12)), ((12-df.start)*(df.start<=12) + (df.start-12)*(df.start>12))**0.5, 0)",coef_shopping_departure_constant_shift_for_every_30_minutes_before_8_30_am_square_root +util_shopping_departure_constant_before_9_am,SHOPPING - Departure Constant: Before 09:00 AM,@((df.start<13)),coef_shopping_departure_constant_before_9_am +util_shopping_departure_constant_9_am_to_9_30_am,SHOPPING - Departure Constant: 09:00 AM - 09:30 AM,@((df.start==13)),coef_shopping_departure_constant_9_am_to_9_30_am +util_shopping_departure_constant_9_30_am_to_10_am,SHOPPING - Departure Constant: 09:30 AM - 10:00 AM,@((df.start==14)),coef_shopping_departure_constant_9_30_am_to_10_am +util_shopping_departure_constant_10_am_to_10_30_am,SHOPPING - Departure Constant: 10:00 AM - 10:30 AM,@((df.start==15)),coef_shopping_departure_constant_10_am_to_10_30_am +util_shopping_departure_constant_10_30_am_to_11_00_am,SHOPPING - Departure Constant: 10:30 AM - 11:00 AM,@((df.start==16)),coef_shopping_departure_constant_10_30_am_to_11_00_am +util_shopping_departure_constant_after_11_am,SHOPPING - Departure Constant: After 11:00 AM,@((df.start>16)),coef_shopping_departure_constant_after_11_am +util_shopping_departure_constant_shift_for_every_30_minutes_after_11_30_am_linear,SHOPPING - Departure Constant: Shift for every 30 minutes after 11:30 am - Linear,"@np.where(((df.start>17)), ((17-df.start)*(df.start<=17) + (df.start-17)*(df.start>17)), 0)",coef_shopping_departure_constant_shift_for_every_30_minutes_after_11_30_am_linear +util_shopping_departure_constant_shift_for_every_30_minutes_after_11_30_am_squared,SHOPPING - Departure Constant: Shift for every 30 minutes after 11:30 am - Squared,"@np.where(((df.start>17)), (((17-df.start)*(df.start<=17) + (df.start-17)*(df.start>17)) ** 2), 0)",coef_shopping_departure_constant_shift_for_every_30_minutes_after_11_30_am_squared +util_shopping_arrival_constant_shift_for_every_30_minutes_before_12_pm_linear,SHOPPING - Arrival Constant: Shift for every 30 minutes before 12:00 pm - Linear,"@np.where(((df.end<19)), ((19-df.end)*(df.end<=19) + (df.end-19)*(df.end>19)), 0)",coef_shopping_arrival_constant_shift_for_every_30_minutes_before_12_pm_linear +util_shopping_arrival_constant_before_12_30_pm,SHOPPING - Arrival Constant: Before 12:30 PM,@((df.end<20)),coef_shopping_arrival_constant_before_12_30_pm +util_shopping_arrival_constant_12_30_pm_to_3_pm,SHOPPING - Arrival Constant: 12:30 PM - 03:00 PM,@(( df.end>=20) & (df.end<=24)),coef_shopping_arrival_constant_12_30_pm_to_3_pm +util_shopping_arrival_constant_3_pm_to_3_30_pm,SHOPPING - Arrival Constant: 03:00 PM - 03:30 PM,@((df.end==25)),coef_shopping_arrival_constant_3_pm_to_3_30_pm +util_shopping_arrival_constant_3_30_pm_to_4_pm,SHOPPING - Arrival Constant: 03:30 PM - 04:00 PM,@((df.end==26)),coef_shopping_arrival_constant_3_30_pm_to_4_pm +util_shopping_arrival_constant_4_pm_to_4_30_pm,SHOPPING - Arrival Constant: 04:00 PM - 04:30 PM,@((df.end==27)),coef_shopping_arrival_constant_4_pm_to_4_30_pm +util_shopping_arrival_constant_4_30_pm_to_5_pm,SHOPPING - Arrival Constant: 04:30 PM - 05:00 PM,@((df.end==28)),coef_shopping_arrival_constant_4_30_pm_to_5_pm +util_shopping_arrival_constant_5_pm_to_5_30_pm,SHOPPING - Arrival Constant: 05:00 PM - 05:30 PM,@((df.end==29)),coef_shopping_arrival_constant_5_pm_to_5_30_pm +util_shopping_arrival_constant_5_30_pm_to_7_pm,SHOPPING - Arrival Constant: 05:30 PM - 07:00 PM,@((df.end>=30) & (df.end<=32)),coef_shopping_arrival_constant_5_30_pm_to_7_pm +util_shopping_arrival_constant_7_pm_to_9_30_pm,SHOPPING - Arrival Constant: 07:00 PM - 09:30 PM,@((df.end>=33) & (df.end<=37)),coef_shopping_arrival_constant_7_pm_to_9_30_pm +util_shopping_arrival_constant_after_9_30_pm,SHOPPING - Arrival Constant: After 09:30 PM,@((df.end>37)),coef_shopping_arrival_constant_after_9_30_pm +util_shopping_arrival_constant_shift_for_every_30_minutes_after_10_pm_linear,SHOPPING - Arrival Constant: Shift for every 30 minutes after 10:00 pm - Linear,"@np.where(((df.end>38)), ((38-df.end)*(df.end<=38) + (df.end-38)*(df.end>38)), 0)",coef_shopping_arrival_constant_shift_for_every_30_minutes_after_10_pm_linear +util_shopping_duration_constant_0_hr,SHOPPING - Duration Constant: 0 hr,@((df.duration==0)),coef_shopping_duration_constant_0_hr +util_shopping_duration_constant_30_minutes,SHOPPING - Duration Constant: 0.5 hr,@((df.duration==1)),coef_shopping_duration_constant_30_minutes +util_shopping_duration_constant_1_hr,SHOPPING - Duration Constant: 1 hr,@((df.duration==2)),coef_shopping_duration_constant_1_hr +util_shopping_duration_constant_1_hour_30_minutes,SHOPPING - Duration Constant: 1.5hrs,@(df.duration==3),coef_shopping_duration_constant_1_hour_30_minutes +util_shopping_duration_constant_2_hrs,SHOPPING - Duration Constant: 2 hrs,@((df.duration==4)),coef_shopping_duration_constant_2_hrs +util_shopping_duration_constant_longer_than_2_hrs,SHOPPING - Duration Constant: Longer than 2 hrs,@((df.duration>4)),coef_shopping_duration_constant_longer_than_2_hrs +util_shopping_duration_constant_duration_greater_than_2_hr_30_minutes_linear,SHOPPING - Duration Constant: Duration > 2.5 hrs - Linear,"@np.where(((df.duration>5)), ((5-df.duration)*(df.duration<=5) + (df.duration-5)*(df.duration>5)), 0)",coef_shopping_duration_constant_duration_greater_than_2_hr_30_minutes_linear +util_shopping_duration_constant_duration_greater_than_2_hr_30_minutes_square_root,SHOPPING - Duration Constant: Duration > 2.5 hrs - Square root,"@np.where(((df.duration>5)), (((5-df.duration)*(df.duration<=5) + (df.duration-5)*(df.duration>5)) ** 0.5), 0)",coef_shopping_duration_constant_duration_greater_than_2_hr_30_minutes_square_root +util_shopping_calibration_constant_duration_1,SHOPPING - Calibration Constant - Duration = 1,@((df.duration==0)),coef_shopping_calibration_constant_duration_1 +util_shopping_calibration_constant_duration_2,SHOPPING - Calibration Constant - Duration = 2,@((df.duration==1)),coef_shopping_calibration_constant_duration_2 +util_shopping_calibration_constant_duration_3,SHOPPING - Calibration Constant - Duration = 3,@((df.duration==2)),coef_shopping_calibration_constant_duration_3 +# Including terms for school escorting,,, +util_outbound_school_escort_tour_start,Outbound school escort tours must match the start time of the escort tour,is_outbound_school_escort_tour & (start != school_escort_tour_start),coef_unavailable +util_outbound_school_escort_tour_next_start,Outbound school escort tours must end before next escort tour start,is_outbound_school_escort_tour & (end > school_escort_tour_next_start) & (school_escort_tour_next_start > 0),coef_unavailable +util_inbound_school_escort_tour_end,Inbound school escort tours must match the end time of the escort tour,is_inbound_school_escort_tour & (end != school_escort_tour_end),coef_unavailable \ No newline at end of file diff --git a/resident/configs/tour_scheduling_nonmandatory_shopping_coefficients.csv b/resident/configs/tour_scheduling_nonmandatory_shopping_coefficients.csv new file mode 100644 index 0000000..d566754 --- /dev/null +++ b/resident/configs/tour_scheduling_nonmandatory_shopping_coefficients.csv @@ -0,0 +1,55 @@ +coefficient_name,value,constrain +coef_shoppping_driving_age_student_duration_greater_than_reference,0.0,F +coef_shoppping_full_time_worker_duration_greater_than_reference,0.051734822464608594,F +coef_shoppping_non_driving_student_duration_greater_than_reference,0.0,F +coef_shoppping_pre_school_child_duration_less_than_reference,0.0,F +coef_shoppping_part_time_worker_duration_less_than_reference,0.0,F +coef_shopping_part_time_worker_duration_greater_than_reference,0.0,F +coef_shopping_retired_duration_less_than_reference,-0.2659967828872843,F +coef_shopping_retired_duration_greater_than_reference,0.0,F +coef_shopping_university_student_duration_greater_than_reference,0.0,F +coef_shopping_female_duration_less_than_reference,-0.7103244823264783,F +coef_shopping_female_duration_greater_than_reference,0.061234392582043815,F +coef_shopping_low_income_duration_greater_than_reference,0.07853541383214849,F +coef_shopping_medium_income_duration_less_than_reference,0.0,F +coef_shopping_medium_high_income_duration_greater_than_reference,0.0,F +coef_shopping_distance_duration_less_than_reference,-0.26768192696442833,F +coef_shopping_distance_duration_greater_than_reference,0.011027213392240262,F +coef_shopping_time_pressure_duration_greater_than_reference,0.18521165094993614,F +coef_shopping_number_of_additional_individual_shop_and_maint_tours_duration_less_than_reference,0.0,F +coef_shopping_number_of_additional_individual_shop_and_maint_tours_duration_greater_than_reference,-0.07338200940622887,F +coef_shopping_departure_constant_shift_for_every_30_minutes_before_8_30_am_linear,-1.273440003246542,F +coef_shopping_departure_constant_shift_for_every_30_minutes_before_8_30_am_square_root,0.5863247462472069,F +coef_shopping_departure_constant_before_9_am,-0.8347867320157243,F +coef_shopping_departure_constant_9_am_to_9_30_am,0.0,F +coef_shopping_departure_constant_9_30_am_to_10_am,0.4960610257653812,F +coef_shopping_departure_constant_10_am_to_10_30_am,0.0,T +coef_shopping_departure_constant_10_30_am_to_11_00_am,1.4667804167469976,F +coef_shopping_departure_constant_after_11_am,2.1721583581726733,F +coef_shopping_departure_constant_shift_for_every_30_minutes_after_11_30_am_linear,0.43973246404978805,F +coef_shopping_departure_constant_shift_for_every_30_minutes_after_11_30_am_squared,-0.002212882762524496,F +coef_shopping_arrival_constant_shift_for_every_30_minutes_before_12_pm_linear,0.4117758420908247,F +coef_shopping_arrival_constant_before_12_30_pm,2.184513901207351,F +coef_shopping_arrival_constant_12_30_pm_to_3_pm,0.0,F +coef_shopping_arrival_constant_3_pm_to_3_30_pm,-0.9695940929413058,F +coef_shopping_arrival_constant_3_30_pm_to_4_pm,-1.3735993941519913,F +coef_shopping_arrival_constant_4_pm_to_4_30_pm,-1.8739500715809843,T +coef_shopping_arrival_constant_4_30_pm_to_5_pm,-2.5156987192481095,F +coef_shopping_arrival_constant_5_pm_to_5_30_pm,-2.953009790810334,F +coef_shopping_arrival_constant_5_30_pm_to_7_pm,-4.394261858507323,F +coef_shopping_arrival_constant_7_pm_to_9_30_pm,-6.569745153088775,F +coef_shopping_arrival_constant_after_9_30_pm,-9.11110014040306,F +coef_shopping_arrival_constant_shift_for_every_30_minutes_after_10_pm_linear,-0.8231833778531431,F +coef_shopping_duration_constant_0_hr,0.0,F +coef_shopping_duration_constant_30_minutes,1.1937104784952166,F +coef_shopping_duration_constant_1_hr,0.0,T +coef_shopping_duration_constant_1_hour_30_minutes,0.0,F +coef_shopping_duration_constant_2_hrs,-0.4668647914524515,F +coef_shopping_duration_constant_longer_than_2_hrs,-1.107254910068048,F +coef_shopping_duration_constant_duration_greater_than_2_hr_30_minutes_linear,-0.42751861595763324,F +coef_shopping_duration_constant_duration_greater_than_2_hr_30_minutes_square_root,0.0,F +coef_shopping_calibration_constant_duration_1,0.0,F +coef_shopping_calibration_constant_duration_2,0.0,F +coef_shopping_calibration_constant_duration_3,0.0,F +coef_mode_choice_logsum,0.2,F +coef_unavailable,-999.0,F \ No newline at end of file diff --git a/resident/configs/tour_scheduling_nonmandatory_social.csv b/resident/configs/tour_scheduling_nonmandatory_social.csv new file mode 100644 index 0000000..ceb875d --- /dev/null +++ b/resident/configs/tour_scheduling_nonmandatory_social.csv @@ -0,0 +1,59 @@ +Label,Description,Expression,Coefficient +#SOCIAL,#SOCIAL,,#SOCIAL +util_mode_choice_logsum,Mode choice logsum,mode_choice_logsum,coef_mode_choice_logsum +util_social_person_less_than_18_years_old_duration_less_than_reference_linear,SOCIAL - Person< 18 years old: Duration < Reference - Linear,"@np.where(((df.duration<6) & (df.age<18)), ((6-df.duration)*(df.duration<=6) + (df.duration-6)*(df.duration>6)), 0)",coef_social_person_less_than_18_years_old_duration_less_than_reference_linear +util_social_person_less_than_18_years_old_duration_greater_than_reference_linear,SOCIAL - Person< 18 years old: Duration > Reference - Linear,"@np.where(((df.duration>6) & (df.age<18)), ((6-df.duration)*(df.duration<=6) + (df.duration-6)*(df.duration>6)), 0)",coef_social_person_less_than_18_years_old_duration_greater_than_reference_linear +util_social_non_working_senior_or_retiree_duration_less_than_reference_linear,SOCIAL - Non-working senior/ retiree: Duration < Reference - Linear,"@np.where(((df.duration<6) & (df.ptype == 5)), ((6-df.duration)*(df.duration<=6) + (df.duration-6)*(df.duration>6)), 0)",coef_social_non_working_senior_or_retiree_duration_less_than_reference_linear +util_social_retiree_or_non_working_senior_only_HH_duration_less_than_reference_linear,SOCIAL - Retiree/ Non-working senior only HH: Duration < Reference - Linear,"@np.where(((df.retired_adults_only_hh) & (df.duration<6)), ((6-df.duration)*(df.duration<=6) + (df.duration-6)*(df.duration>6)), 0)",coef_social_retiree_or_non_working_senior_only_HH_duration_less_than_reference_linear +util_social_zero_auto_households_duration_less_than_reference_linear,SOCIAL - Zero auto households: Duration < Reference - Linear,"@np.where(((df.auto_ownership == 0) & (df.duration<6)), ((6-df.duration)*(df.duration<=6) + (df.duration-6)*(df.duration>6)), 0)",coef_social_zero_auto_households_duration_less_than_reference_linear +util_social_zero_auto_households_duration_greater_than_reference_linear,SOCIAL - Zero auto households: Duration > Reference - Linear,"@np.where(((df.auto_ownership == 0) & (df.duration>6)), ((6-df.duration)*(df.duration<=6) + (df.duration-6)*(df.duration>6)), 0)",coef_social_zero_auto_households_duration_greater_than_reference_linear +util_social_number_of_auto_more_than_number_of_adults_duration_less_than_reference_linear,SOCIAL - Number of auto more than number of adults: Duration < Reference - Linear,"@np.where(((df.auto_ownership > 0) &(df.auto_ownership > df.num_adults) & (df.duration<6)), ((6-df.duration)*(df.duration<=6) + (df.duration-6)*(df.duration>6)), 0)",coef_social_number_of_auto_more_than_number_of_adults_duration_less_than_reference_linear +util_social_number_of_auto_more_than_number_of_adults_duration_greater_than_reference_linear,SOCIAL - Number of auto more than number of adults: Duration > Reference - Linear,"@np.where(((df.auto_ownership > 0) & (df.auto_ownership > df.num_adults) & (df.duration>6)), ((6-df.duration)*(df.duration<=6) + (df.duration-6)*(df.duration>6)), 0)",coef_social_number_of_auto_more_than_number_of_adults_duration_greater_than_reference_linear +"# In CTRAMP, although the description below says duration is less than 1 hr, expression is for less than 1.5 hr",,, +util_social_auto_distance_duration_less_than_reference_linear,SOCIAL - Auto Distance: Duration < Reference - Linear,"@np.where(((df.duration<6)), ((6-df.duration)*(df.duration<=6) + (df.duration-6)*(df.duration>6)) * (df.origin_to_destination_distance), 0)",coef_social_auto_distance_duration_less_than_reference_linear +util_social_auto_distance_duration_greater_than_reference_linear,SOCIAL - Auto Distance: Duration > Reference - Linear,"@np.where(((df.duration>6)), ((6-df.duration)*(df.duration<=6) + (df.duration-6)*(df.duration>6)) * (df.origin_to_destination_distance), 0)",coef_social_auto_distance_duration_greater_than_reference_linear +util_social_time_pressure_duration_less_than_reference,SOCIAL - Time Pressure - Duration < Reference,"@np.where(((df.duration<6)), ((6-df.duration)*(df.duration<=6) + (df.duration-6)*(df.duration>6))* (np.log10 (30 * (tt.max_time_block_available(df.person_id)/(1.0 + df.tour_count - df.tour_num)))), 0)",coef_social_time_pressure_duration_less_than_reference +util_social_time_pressure_duration_greater_than_reference,SOCIAL - Time Pressure - Duration > Reference,"@np.where(((df.duration>6)), ((6-df.duration)*(df.duration<=6) + (df.duration-6)*(df.duration>6)) * (np.log10 (30 * (tt.max_time_block_available(df.person_id)/(1.0 + df.tour_count - df.tour_num)))), 0)",coef_social_time_pressure_duration_greater_than_reference +util_social_number_of_additional_individual_social_and_dicretionary_tours_duration_less_than_reference,SOCIAL - Number of additional individual social and dicretionary tours - Duration < 1 hr,"@np.where(((df.duration<6)), ((6-df.duration)*(df.duration<=6) + (df.duration-6)*(df.duration>6)) * (df.num_add_soc_discr_tours), 0)",coef_social_number_of_additional_individual_social_and_dicretionary_tours_duration_less_than_reference +util_social_departure_constant_shift_for_every_30_minutes_before_8_30_am_linear,SOCIAL - Departure Constant: Shift for every 30 minutes before 08:30 am - Linear,@((df.start<12)) * ((12-df.start)*(df.start<=12) + (df.start-12)*(df.start>12)),coef_social_departure_constant_shift_for_every_30_minutes_before_8_30_am_linear +util_social_departure_constant_before_9_am,SOCIAL - Departure Constant: Before 09:00 AM,@(df.start<13),coef_social_departure_constant_before_9_am +util_social_departure_constant_9_am_to_9_30_am,SOCIAL - Departure Constant: 09:00 AM to 09:30 AM,@(df.start==13),coef_social_departure_constant_9_am_to_9_30_am +util_social_departure_constant_shift_for_every_30_minutes_before_5_pm_linear,SOCIAL - Departure Constant: Shift for every 30 minutes before 05:00 pm - Linear,"@np.where((df.start<29), ((29-df.start)*(df.start<=29) + (df.start-29)*(df.start>29)), 0)",coef_social_departure_constant_shift_for_every_30_minutes_before_5_pm_linear +util_social_departure_constant_before_5_30_pm,SOCIAL - Departure Constant: Before 05:30 PM,@((df.start<30)),coef_social_departure_constant_before_5_30_pm +util_social_departure_constant_5_30_pm_to_6_pm,SOCIAL - Departure Constant: 05:30 PM - 06:00 PM,@((df.start==30)),coef_social_departure_constant_5_30_pm_to_6_pm +util_social_departure_constant_6_pm_to_6_30_pm,SOCIAL - Departure Constant: 06:00 PM - 06:30 PM,@((df.start==31)),coef_social_departure_constant_6_pm_to_6_30_pm +util_social_departure_constant_6_30_pm_to_7_pm,SOCIAL - Departure Constant: 06:30 PM - 07:00 PM,@((df.start==32)),coef_social_departure_constant_6_30_pm_to_7_pm +util_social_departure_constant_7_pm_to_7_30_pm,SOCIAL - Departure Constant: 07:00 PM - 07:30 PM,@((df.start==33)),coef_social_departure_constant_7_pm_to_7_30_pm +util_social_departure_constant_after_7_30_pm,SOCIAL - Departure Constant: After 07:30 PM,@((df.start>33)),coef_social_departure_constant_after_7_30_pm +util_social_departure_constant_shift_for_every_30_minutes_after_8_pm_linear,SOCIAL - Departure Constant: Shift for every 30 minutes after 08:00 pm - Linear,"@np.where((df.start>34), ((34-df.start)*(df.start<=34) + (df.start-34)*(df.start>34)), 0)",coef_social_departure_constant_shift_for_every_30_minutes_after_8_pm_linear +util_social_arrival_constant_3_pm_to_3_30_pm,SOCIAL - Arrival Constant: 03:00 PM to 03:30 PM,@((df.end==25)),coef_social_arrival_constant_3_pm_to_3_30_pm +util_social_arrival_constant_3_30_pm_to_4_pm,SOCIAL - Arrival Constant: 03:30 PM to 04:00 PM,@((df.end==26)),coef_social_arrival_constant_3_30_pm_to_4_pm +util_social_arrival_constant_4_pm_to_4_30_pm,SOCIAL - Arrival Constant: 04:00 PM to 04:30 PM,@((df.end==27)),coef_social_arrival_constant_4_pm_to_4_30_pm +util_social_arrival_constant_5_pm_to_6_pm,SOCIAL - Arrival Constant: 05:00 PM to 06:00 PM,@((df.end>=29) & (df.end<=30)),coef_social_arrival_constant_5_pm_to_6_pm +util_social_arrival_constant_shift_for_every_30_minutes_before_8_pm_linear,SOCIAL - Arrival Constant: Shift for every 30 minutes before 08:00 pm - Linear,"@np.where(((df.end<35)), ((35-df.end)*(df.end<=35) + (df.end-35)*(df.end>35)), 0)",coef_social_arrival_constant_shift_for_every_30_minutes_before_8_pm_linear +util_social_arrival_constant_before_8_30_pm,SOCIAL - Arrival Constant: Before 8:30 PM,@((df.end<36)),coef_social_arrival_constant_before_8_30_pm +util_social_arrival_constant_8_30_pm_to_9_pm,SOCIAL - Arrival Constant: 8:30 PM to 9:00 PM,@((df.end==36)),coef_social_arrival_constant_8_30_pm_to_9_pm +util_social_arrival_constant_9_pm_to_9_30_pm,SOCIAL - Arrival Constant: 9:00 PM to 9:30 PM,@((df.end==37)),coef_social_arrival_constant_9_pm_to_9_30_pm +util_social_arrival_constant_9_30_pm_to_10_pm,SOCIAL - Arrival Constant: 9:30 PM to10:00 PM,@((df.end==38)),coef_social_arrival_constant_9_30_pm_to_10_pm +util_social_arrival_constant_10_pm_to_10_30_pm,SOCIAL - Arrival Constant: 10:00 PM to 10:30 PM,@((df.end==39)),coef_social_arrival_constant_10_pm_to_10_30_pm +util_social_arrival_constant_after_10_30_pm,SOCIAL - Arrival Constant: After 10:30 PM,@((df.end>39)),coef_social_arrival_constant_after_10_30_pm +util_social_arrival_constant_shift_for_every_30_minutes_after_11_pm_linear,SOCIAL - Arrival Constant: Shift for every 30 minutes after 11:00 pm - Linear,"@np.where(((df.end>40)), ((40-df.end)*(df.end<=40) + (df.end-40)*(df.end>40)), 0)",coef_social_arrival_constant_shift_for_every_30_minutes_after_11_pm_linear +util_social_duration_constant_shift_for_every_30_minutes_less_than_2_hr_30_minutes_linear,SOCIAL - Duration Constant: Shift for every 30 minutes less than 2.5 hrs - Linear,"@np.where(((df.duration<5)), ((5-df.duration)*(df.duration<=5) + (df.duration-5)*(df.duration>5)), 0)",coef_social_duration_constant_shift_for_every_30_minutes_less_than_2_hr_30_minutes_linear +util_social_duration_constant_less_than_3_hours,SOCIAL - Duration Constant: Less than 3 hrs,@((df.duration<6)),coef_social_duration_constant_less_than_3_hours +util_social_duration_constant_3_hours,SOCIAL - Duration Constant: 3 hours,@((df.duration==6)),coef_social_duration_constant_3_hours +util_social_duration_constant_3_hrs_30_minutes,SOCIAL - Duration Constant: 3.5 hours,@((df.duration==7)),coef_social_duration_constant_3_hrs_30_minutes +util_social_duration_constant_4_hours_or_more,SOCIAL - Duration Constant: 4 hours or more,@((df.duration>7)),coef_social_duration_constant_4_hours_or_more +util_social_duration_constant_shift_for_every_30_minutes_more_than_4_hr_30_minutes_linear,SOCIAL - Duration Constant: Shift for every 30 minutes more than 4.5 hrs - Linear,"@np.where(((df.duration>8)), ((8-df.duration)*(df.duration<=8) + (df.duration-8)*(df.duration>8)), 0)",coef_social_duration_constant_shift_for_every_30_minutes_more_than_4_hr_30_minutes_linear +util_social_calibration_constant_duration_1,SOCIAL - Calibration Constant - Duration = 1,@((df.duration ==0)),coef_social_calibration_constant_duration_1 +util_social_calibration_constant_duration_2,SOCIAL - Calibration Constant - Duration = 2,@((df.duration == 1)),coef_social_calibration_constant_duration_2 +util_social_calibration_constant_duration_3,SOCIAL - Calibration Constant - Duration = 3,@((df.duration ==2)),coef_social_calibration_constant_duration_3 +util_social_calibration_constant_duration_4,SOCIAL - Calibration Constant - Duration = 4,@((df.duration ==3)),coef_social_calibration_constant_duration_4 +util_social_calibration_constant_duration_5,SOCIAL - Calibration Constant - Duration = 5,@((df.duration ==4)),coef_social_calibration_constant_duration_5 +util_social_calibration_constant_duration_6,SOCIAL - Calibration Constant - Duration = 6,@((df.duration ==5)),coef_social_calibration_constant_duration_6 +util_social_calibration_constant_duration_7,SOCIAL - Calibration Constant - Duration = 7,@((df.duration ==6)),coef_social_calibration_constant_duration_7 +util_social_calibration_constant_duration_8,SOCIAL - Calibration Constant - Duration = 8,@((df.duration ==7)),coef_social_calibration_constant_duration_8 +util_social_calibration_constant_duration_9,SOCIAL - Calibration Constant - Duration = 9,@((df.duration ==8)),coef_social_calibration_constant_duration_9 +# Including terms for school escorting,,, +util_outbound_school_escort_tour_start,Outbound school escort tours must match the start time of the escort tour,is_outbound_school_escort_tour & (start != school_escort_tour_start),coef_unavailable +util_outbound_school_escort_tour_next_start,Outbound school escort tours must end before next escort tour start,is_outbound_school_escort_tour & (end > school_escort_tour_next_start) & (school_escort_tour_next_start > 0),coef_unavailable +util_inbound_school_escort_tour_end,Inbound school escort tours must match the end time of the escort tour,is_inbound_school_escort_tour & (end != school_escort_tour_end),coef_unavailable \ No newline at end of file diff --git a/resident/configs/tour_scheduling_nonmandatory_social_coefficients.csv b/resident/configs/tour_scheduling_nonmandatory_social_coefficients.csv new file mode 100644 index 0000000..1cc972d --- /dev/null +++ b/resident/configs/tour_scheduling_nonmandatory_social_coefficients.csv @@ -0,0 +1,54 @@ +coefficient_name,value,constrain +coef_social_person_less_than_18_years_old_duration_less_than_reference_linear,0.0,F +coef_social_person_less_than_18_years_old_duration_greater_than_reference_linear,0.1848544664093724,F +coef_social_non_working_senior_or_retiree_duration_less_than_reference_linear,0.0,F +coef_social_retiree_or_non_working_senior_only_HH_duration_less_than_reference_linear,0.0,F +coef_social_zero_auto_households_duration_less_than_reference_linear,0.0,F +coef_social_zero_auto_households_duration_greater_than_reference_linear,0.0,F +coef_social_number_of_auto_more_than_number_of_adults_duration_less_than_reference_linear,0.0,F +coef_social_number_of_auto_more_than_number_of_adults_duration_greater_than_reference_linear,0.0,F +coef_social_auto_distance_duration_less_than_reference_linear,-0.037191211367614524,F +coef_social_auto_distance_duration_greater_than_reference_linear,0.004103999782121744,F +coef_social_time_pressure_duration_less_than_reference,0.0,F +coef_social_time_pressure_duration_greater_than_reference,0.2472747423164598,F +coef_social_number_of_additional_individual_social_and_dicretionary_tours_duration_less_than_reference,0.0,F +coef_social_departure_constant_shift_for_every_30_minutes_before_8_30_am_linear,-0.35038347561648353,F +coef_social_departure_constant_before_9_am,0.0,F +coef_social_departure_constant_9_am_to_9_30_am,0.48320177182957835,F +coef_social_departure_constant_shift_for_every_30_minutes_before_5_pm_linear,0.10579475979139634,F +coef_social_departure_constant_before_5_30_pm,2.9248450547864326,F +coef_social_departure_constant_5_30_pm_to_6_pm,3.396566813557204,F +coef_social_departure_constant_6_pm_to_6_30_pm,0.0,T +coef_social_departure_constant_6_30_pm_to_7_pm,3.2332806668467815,F +coef_social_departure_constant_7_pm_to_7_30_pm,2.9564723583835772,F +coef_social_departure_constant_after_7_30_pm,2.5033367748702537,F +coef_social_departure_constant_shift_for_every_30_minutes_after_8_pm_linear,-0.31575816708433413,F +coef_social_arrival_constant_3_pm_to_3_30_pm,0.6128608441643291,F +coef_social_arrival_constant_3_30_pm_to_4_pm,0.0,F +coef_social_arrival_constant_4_pm_to_4_30_pm,0.0,F +coef_social_arrival_constant_5_pm_to_6_pm,0.0,F +coef_social_arrival_constant_shift_for_every_30_minutes_before_8_pm_linear,-0.11543939695197131,F +coef_social_arrival_constant_before_8_30_pm,0.0,F +coef_social_arrival_constant_8_30_pm_to_9_pm,0.3725347628206525,F +coef_social_arrival_constant_9_pm_to_9_30_pm,0.34127159737778273,F +coef_social_arrival_constant_9_30_pm_to_10_pm,0.0,T +coef_social_arrival_constant_10_pm_to_10_30_pm,0.349324126243515,F +coef_social_arrival_constant_after_10_30_pm,0.0,F +coef_social_arrival_constant_shift_for_every_30_minutes_after_11_pm_linear,-0.22982597094534407,F +coef_social_duration_constant_shift_for_every_30_minutes_less_than_2_hr_30_minutes_linear,0.0,F +coef_social_duration_constant_less_than_3_hours,0.0,F +coef_social_duration_constant_3_hours,0.0,T +coef_social_duration_constant_3_hrs_30_minutes,-0.9879910839979239,F +coef_social_duration_constant_4_hours_or_more,-1.9671125098564861,F +coef_social_duration_constant_shift_for_every_30_minutes_more_than_4_hr_30_minutes_linear,-1.081195937845935,F +coef_social_calibration_constant_duration_1,0.0,F +coef_social_calibration_constant_duration_2,0.0,F +coef_social_calibration_constant_duration_3,0.0,F +coef_social_calibration_constant_duration_4,0.0,F +coef_social_calibration_constant_duration_5,0.0,F +coef_social_calibration_constant_duration_6,0.0,F +coef_social_calibration_constant_duration_7,0.0,F +coef_social_calibration_constant_duration_8,0.0,F +coef_social_calibration_constant_duration_9,0.0,F +coef_mode_choice_logsum,0.014255126759576003,F +coef_unavailable,-999.0,F \ No newline at end of file diff --git a/resident/configs/tour_scheduling_school.csv b/resident/configs/tour_scheduling_school.csv new file mode 100644 index 0000000..6470156 --- /dev/null +++ b/resident/configs/tour_scheduling_school.csv @@ -0,0 +1,62 @@ +Label,Description,Expression,Coefficient +# writing out varibles we may need for estimation,,, +round_trip_auto_time_to_work,round_trip_auto_time_to_work,@df.roundtrip_auto_time_to_work,zero_coef +age,age,@df.age,zero_coef +util_Mode_Choice_Logsum,SCHOOL - Mode Choice Logsum,mode_choice_logsum,coef_Mode_Choice_Logsum +util_Low_income_lt25000_Departure_before_730_am__Linear,SCHOOL - Low income (<25000) - Departure before 7:30 am - Linear,"@((df.is_income_less25K) & (df.start < df.departureRefBin_school)) *df.departureLinearShift1",coef_Low_income_lt25000_Departure_before_730_am__Linear +util_Low_income_lt25000_Departure_after_800_am_Linear,SCHOOL - Low income (<25000) - Departure after 8:00 am - Linear,"@((df.is_income_less25K) & (df.start > df.departureRefBin_school)) *df.departureLinearShift1",coef_Low_income_lt25000_Departure_after_800_am_Linear +util_Low_income_lt25000_Duration_lt_8hrs,SCHOOL - Low income (<25000) - Duration < 8hrs,"@((df.is_income_less25K) & (df.start < df.durationRefBin_school)) * df.durationShift_school",coef_Low_income_lt25000_Duration_lt_8hrs +util_Low_income_lt25000_Duration_gt_8hrs,SCHOOL - Low income (<25000) - Duration > 8hrs,"@((df.is_income_less25K) & (df.start > df.durationRefBin_school)) * df.durationShift_school",coef_Low_income_lt25000_Duration_gt_8hrs +util_Med_income_25k_to_60k_Departure_before_730_am__Linear,SCHOOL - Med income (25k to 60k) - Departure before 7:30 am - Linear,"@((df.is_income_25K_to_60K) & (df.start < df.departureRefBin_school)) *df.departureLinearShift1",coef_Med_income_25k_to_60k_Departure_before_730_am__Linear +util_Age_0_to_5_yrs_Departure_Before_730_am,SCHOOL - Age 0 to 5 yrs - Departure Before 7:30 am,"@(((df.age>=0) & (df.age<=5)) & (df.start < df.departureRefBin_school)) *df.departureLinearShift1",coef_Age_0_to_5_yrs_Departure_Before_730_am +util_Age_13_to_15_yrs_Departure_Before_730_am,SCHOOL - Age 13 to 15 yrs - Departure Before 7:30 am,"@(((df.age>=13) & (df.age<=15)) & (df.start < df.departureRefBin_school)) *df.departureLinearShift1",coef_Age_13_to_15_yrs_Departure_Before_730_am +util_Age_13_to_15_yrs_Departure_After_800_am,SCHOOL - Age 13 to 15 yrs - Departure After 8:00 am,"@(((df.age>=13) & (df.age<=15)) & (df.start > df.departureRefBin_school)) *df.departureLinearShift1",coef_Age_13_to_15_yrs_Departure_After_800_am +util_Age_16_to_17_yrs_Departure_After_800_am,SCHOOL - Age 16 to 17 yrs - Departure After 8:00 am,"@(((df.age>=16) & (df.age<=17)) & (df.start > df.departureRefBin_school)) *df.departureLinearShift1",coef_Age_16_to_17_yrs_Departure_After_800_am +util_Age_0_to_5_yrs_Duration_lt_8hrs,SCHOOL - Age 0 to 5 yrs - Duration < 8hrs,"@(((df.age>0) & (df.age<=5)) & (df.start < df.durationRefBin_school)) * df.durationShift_school",coef_Age_0_to_5_yrs_Duration_lt_8hrs +util_Age_0_to_5_yrs_Duration_gt_8hrs,SCHOOL - Age 0 to 5 yrs - Duration > 8hrs,"@(((df.age>0) & (df.age<=5)) & (df.start > df.durationRefBin_school)) * df.durationShift_school",coef_Age_0_to_5_yrs_Duration_gt_8hrs +util_Age_13_to_15_yrs_Duration_lt_8hrs,SCHOOL - Age 13 to 15 yrs - Duration < 8hrs,"@(((df.age>=13) & (df.age<=15)) & (df.start < df.durationRefBin_school)) * df.durationShift_school",coef_Age_13_to_15_yrs_Duration_lt_8hrs +util_Age_13_to_15_yrs_Duration_gt_8hrs,SCHOOL - Age 13 to 15 yrs - Duration > 8hrs,"@(((df.age>=13) & (df.age<=15)) & (df.start > df.durationRefBin_school)) * df.durationShift_school",coef_Age_13_to_15_yrs_Duration_gt_8hrs +util_Age_16_to_17_yrs_Duration_gt_8hrs,SCHOOL - Age 16 to 17 yrs - Duration > 8hrs,"@(((df.age>=16) & (df.age<=17)) & (df.start > df.durationRefBin_school)) * df.durationShift_school",coef_Age_16_to_17_yrs_Duration_gt_8hrs +util_Time_SOV_freeflow_to_destination_Departure_before_730_am__Linear,SCHOOL - Time (SOV freeflow) to destination - Departure before 7:30 am - Linear,"@(df.start < df.departureRefBin_school) *df.departureLinearShift1 * (df.roundtrip_auto_time_to_school)",coef_Time_SOV_freeflow_to_destination_Departure_before_730_am__Linear +util_Time_SOV_freeflow_to_destination_Departure_after_800_am_Linear,SCHOOL - Time (SOV freeflow) to destination - Departure after 8:00 am - Linear,"@(df.start > df.departureRefBin_school) *df.departureLinearShift1 * (df.roundtrip_auto_time_to_school)",coef_Time_SOV_freeflow_to_destination_Departure_after_800_am_Linear +util_Time_SOV_freeflow_to_destination_arrival_before_ref,SCHOOL - Time (SOV freeflow) to destination - Arrival < Reference Bin,"@(df.end < df.arrivalRefBin_school) * df.arrivalLinearShift1_school * (df.roundtrip_auto_time_to_school)",coef_Time_SOV_freeflow_to_destination_arrival_before_ref +util_Time_SOV_freeflow_to_destination_arrival_after_ref,SCHOOL - Time (SOV freeflow) to destination - Duration > 8hrs,"@(df.end > df.arrivalRefBin_school) * df.arrivalLinearShift1_school * (df.roundtrip_auto_time_to_school)",coef_Time_SOV_freeflow_to_destination_arrival_after_ref +util_All_adults_in_the_household_are_fulltime_workers_Departure_before_730_am__Linear,SCHOOL - All adults in the household are fulltime workers - Departure before 7:30 am - Linear,"@((df.is_all_adults_full_time_workers) & (df.start < df.departureRefBin_school)) *df.departureLinearShift1",coef_All_adults_in_the_household_are_fulltime_workers_Departure_before_730_am__Linear +util_All_adults_in_the_household_are_fulltime_workers_Departure_after_800_am_Linear,SCHOOL - All adults in the household are fulltime workers - Departure after 8:00 am - Linear,"@((df.is_all_adults_full_time_workers) & (df.start > df.departureRefBin_school)) *df.departureLinearShift1",coef_All_adults_in_the_household_are_fulltime_workers_Departure_after_800_am_Linear +util_All_adults_in_the_household_are_fulltime_workers_arrival_before_ref,SCHOOL - All adults in the household are fulltime workers - Duration < 8hrs,"@((df.is_all_adults_full_time_workers) & (df.end < df.arrivalRefBin_school)) * df.arrivalLinearShift1_school",coef_All_adults_in_the_household_are_fulltime_workers_arrival_before_ref +util_All_adults_in_the_household_are_fulltime_workers_arrival_after_ref,SCHOOL - All adults in the household are fulltime workers - Duration > 8hrs,"@((df.is_all_adults_full_time_workers) & (df.end > df.arrivalRefBin_school)) * df.arrivalLinearShift1_school",coef_All_adults_in_the_household_are_fulltime_workers_arrival_after_ref +util_Subsequent_tour_is_work_tour_Duration_lt_8_hours,SCHOOL - Subsequent tour is work tour: Duration < 8 hours,"@(((df.tour_count>1) & (df.tour_num == 1) & (df.tour_type.shift(-1) == 'work') & (df.start < df.durationRefBin_school))) * df.durationShift_school",coef_Subsequent_tour_is_work_tour_Duration_lt_8_hours +util_Subsequent_tour_is_work_tour_Duration_gt_8_hours,SCHOOL - Subsequent tour is work tour: Duration > 8 hours,"@(((df.tour_count>1) & (df.tour_num == 1) & (df.tour_type.shift(-1) == 'work') & (df.start > df.durationRefBin_school))) * df.durationShift_school",coef_Subsequent_tour_is_work_tour_Duration_gt_8_hours +util_Subsequent_tour_is_school_tour_Departure_after_800_am,SCHOOL - Subsequent tour is school tour: Departure after 8:00 am,"@(((df.tour_count>1) & (df.tour_num == 1) & (df.tour_type.shift(-1) == 'school') & (df.start > df.departureRefBin_school))) *df.departureLinearShift1",coef_Subsequent_tour_is_school_tour_Departure_after_800_am +util_Subsequent_tour_is_school_tour_Duration_lt_8_hours,SCHOOL - Subsequent tour is school tour: Duration < 8 hours,"@(((df.tour_count>1) & (df.tour_num == 1) & (df.tour_type.shift(-1) == 'school') & (df.start < df.durationRefBin_school))) * df.durationShift_school",coef_Subsequent_tour_is_school_tour_Duration_lt_8_hours +util_Subsequent_tour_is_school_tour_Duration_gt_8_hours,SCHOOL - Subsequent tour is school tour: Duration > 8 hours,"@(((df.tour_count>1) & (df.tour_num == 1) & (df.tour_type.shift(-1) == 'school') & (df.start > df.durationRefBin_school))) * df.durationShift_school",coef_Subsequent_tour_is_school_tour_Duration_gt_8_hours +util_Second_tour_of_two_mandatory_tours_Duration_lt_4_hours,SCHOOL - Second tour of two mandatory tours: Duration < 4 hours,"@(((df.tour_count>1) & (df.tour_num > 1)) & (df.duration<7)) * (((7-df.duration)*(df.duration<=7)) + ((df.duration-7)*(df.duration>7)))",coef_Second_tour_of_two_mandatory_tours_Duration_lt_4_hours +util_Second_tour_of_two_mandatory_tours_Duration_gt_4_hours,SCHOOL - Second tour of two mandatory tours: Duration > 4 hours,"@(((df.tour_count>1) & (df.tour_num > 1)) & (df.duration>7)) * (((7-df.duration)*(df.duration<=7)) + ((df.duration-7)*(df.duration>7)))",coef_Second_tour_of_two_mandatory_tours_Duration_gt_4_hours +util_Departure_Constant_Before_0600_AM,SCHOOL - Departure Constant: Before 06:00 AM,@(df.start<7),coef_Departure_Constant_Before_0600_AM +util_Departure_Constant_0600_AM_to_0630_AM_7,SCHOOL - Departure Constant: 06:00 AM to 06:30 AM (7),@(df.start==7),coef_Departure_Constant_0600_AM_to_0630_AM_7 +util_Departure_Constant_0630_AM_to_0700_AM_8,SCHOOL - Departure Constant: 06:30 AM to 07:00 AM (8),@(df.start==8),coef_Departure_Constant_0630_AM_to_0700_AM_8 +util_Departure_Constant_0700_AM_to_0730_AM_9,SCHOOL - Departure Constant: 07:00 AM to 07:30 AM (9),@(df.start==9),coef_Departure_Constant_0700_AM_to_0730_AM_9 +util_Departure_Constant_0730_AM_to_0800_AM_10,SCHOOL - Departure Constant: 07:30 AM to 08:00 AM (10),@(df.start==10),coef_Departure_Constant_0730_AM_to_0800_AM_10 +util_Departure_Constant_After_0800_AM,SCHOOL - Departure Constant: After 08:00 AM,@(df.start > df.departureRefBin_school),coef_Departure_Constant_After_0800_AM +util_Departure_Constant_Shift_for_every_30_minutes_after_830_am_Linear,SCHOOL - Departure Constant: Shift for every 30 minutes after 8:30 am - Linear,"@((df.start>11)) * (((11-df.start)*(df.start<11)) + ((df.start-11)*(df.start>11)))",coef_Departure_Constant_Shift_for_every_30_minutes_after_830_am_Linear +util_Departure_Constant_Shift_for_every_30_minutes_after_830_am_Square_Root,SCHOOL - Departure Constant: Shift for every 30 minutes after 8:30 am - Square Root,"@((df.start>11) * (np.maximum(df.start - 11, 0) ** 0.5))",coef_Departure_Constant_Shift_for_every_30_minutes_after_830_am_Square_Root +util_Arrival_Constant_Before_0230_PM,SCHOOL - Arrival Constant: Before 02:30 PM,@(df.end<24),coef_Arrival_Constant_Before_0230_PM +util_Arrival_Constant_0230_PM_0300_PM_24_,SCHOOL - Arrival Constant: 02:30 PM - 03:00 PM (24) ,@(df.end==24),coef_Arrival_Constant_0230_PM_0300_PM_24_ +util_Arrival_Constant_0300_PM_0330_PM_25_,SCHOOL - Arrival Constant: 03:00 PM - 03:30 PM (25) ,@(df.end==25),coef_Arrival_Constant_0300_PM_0330_PM_25_ +util_Arrival_Constant_0330_PM_0400_PM_26_,SCHOOL - Arrival Constant: 03:30 PM - 04:00 PM (26) ,@(df.end==26),coef_Arrival_Constant_0330_PM_0400_PM_26_ +util_Arrival_Constant_0400_PM_0430_PM_27_,SCHOOL - Arrival Constant: 04:00 PM - 04:30 PM (27) ,@(df.end==27),coef_Arrival_Constant_0400_PM_0430_PM_27_ +util_Arrival_Constant_0430_PM_0500_PM_28_,SCHOOL - Arrival Constant: 04:30 PM - 05:00 PM (28) ,@(df.end==28),coef_Arrival_Constant_0430_PM_0500_PM_28_ +util_Arrival_Constant_0500_PM_0530_PM_29,SCHOOL - Arrival Constant: 05:00 PM - 05:30 PM (29),@(df.end==29),coef_Arrival_Constant_0500_PM_0530_PM_29 +util_Arrival_Constant_0530_PM_0600_PM_30_,SCHOOL - Arrival Constant: 05:30 PM - 06:00 PM (30) ,@(df.end==30),coef_Arrival_Constant_0530_PM_0600_PM_30_ +util_Arrival_Constant_After_0600_PM,SCHOOL - Arrival Constant: After 06:00 PM,@(df.end>30),coef_Arrival_Constant_After_0600_PM +util_Arrival_Constant_Shift_for_every_30_minutes_after_630_pm_Linear,SCHOOL - Arrival Constant: Shift for every 30 minutes after 6:30 pm - Linear,"@(df.end>31) * (((31-df.end)*(df.end<31)) + ((df.end-31)*(df.end>31)))",coef_Arrival_Constant_Shift_for_every_30_minutes_after_630_pm_Linear +util_Duration_Constant_Shift_for_every_30_minutes_less_than_6p5_hrs_Linear,SCHOOL - Duration Constant: Shift for every 30 minutes less than 6.5 hrs - Linear,"@((df.duration<13)) * (((13-df.duration)*(df.duration<13)) + ((df.duration-13)*(df.duration>13)))",coef_Duration_Constant_Shift_for_every_30_minutes_less_than_6p5_hrs_Linear +util_Duration_Constant_Shorter_than_7_hrs,SCHOOL - Duration Constant: Shorter than 7 hrs,@(df.duration<14),coef_Duration_Constant_Shorter_than_7_hrs +util_Duration_Constant_7_hours,SCHOOL - Duration Constant: 7 hours,@(df.duration==14),coef_Duration_Constant_7_hours +util_Duration_Constant_7p5_hours,SCHOOL - Duration Constant: 7.5 hours,@(df.duration==15),coef_Duration_Constant_7p5_hours +util_Duration_Constant_8_hours,SCHOOL - Duration Constant: 8 hours,@(df.duration==16),coef_Duration_Constant_8_hours +util_Duration_Constant_8p5_hours,SCHOOL - Duration Constant: 8.5 hours,@(df.duration==17),coef_Duration_Constant_8p5_hours +util_Duration_Constant_9_hours,SCHOOL - Duration Constant: 9 hours,@(df.duration==18),coef_Duration_Constant_9_hours +util_Duration_Constant_Longer_than_9_hrs,SCHOOL - Duration Constant: Longer than 9 hrs,@(df.duration>18),coef_Duration_Constant_Longer_than_9_hrs +util_Duration_Constant_Shift_for_every_30_minutes_more_than_9p5_hrs_Linear,SCHOOL - Duration Constant: Shift for every 30 minutes more than 9.5 hrs - Linear,"@(df.duration>19) * (((19-df.duration)*(df.duration<19)) + ((df.duration-19)*(df.duration>19)))",coef_Duration_Constant_Shift_for_every_30_minutes_more_than_9p5_hrs_Linear +util_Duration_Constant_Shift_for_every_30_minutes_more_than_9p5_hrs_Squared,SCHOOL - Duration Constant: Shift for every 30 minutes more than 9.5 hrs - Squared,"@(df.duration>19) * ((((19-df.duration)*(df.duration<19)) + ((df.duration-19)*(df.duration>19))) ** 2)",coef_Duration_Constant_Shift_for_every_30_minutes_more_than_9p5_hrs_Squared diff --git a/resident/configs/tour_scheduling_school_coeffs.csv b/resident/configs/tour_scheduling_school_coeffs.csv new file mode 100644 index 0000000..1c11c6a --- /dev/null +++ b/resident/configs/tour_scheduling_school_coeffs.csv @@ -0,0 +1,60 @@ +coefficient_name,value,constrain +coef_Mode_Choice_Logsum,0.0,F +coef_Low_income_lt25000_Departure_before_730_am__Linear,0.0,F +coef_Low_income_lt25000_Departure_after_800_am_Linear,0.0,F +coef_Low_income_lt25000_Duration_lt_8hrs,0.0,F +coef_Low_income_lt25000_Duration_gt_8hrs,0.0,F +coef_Med_income_25k_to_60k_Departure_before_730_am__Linear,0.0,F +coef_Age_0_to_5_yrs_Departure_Before_730_am,-0.8148835593502417,F +coef_Age_13_to_15_yrs_Departure_Before_730_am,0.27726279249507474,F +coef_Age_13_to_15_yrs_Departure_After_800_am,-0.7597642883716563,F +coef_Age_16_to_17_yrs_Departure_After_800_am,-0.8824101711801812,F +coef_Age_0_to_5_yrs_Duration_lt_8hrs,0.1741028592807906,F +coef_Age_0_to_5_yrs_Duration_gt_8hrs,0.241662039787307,F +coef_Age_13_to_15_yrs_Duration_lt_8hrs,0.0,F +coef_Age_13_to_15_yrs_Duration_gt_8hrs,0.5963387646724607,F +coef_Age_16_to_17_yrs_Duration_gt_8hrs,0.6956001833839539,F +coef_Time_SOV_freeflow_to_destination_Departure_before_730_am__Linear,0.00454314976439104,F +coef_Time_SOV_freeflow_to_destination_Departure_after_800_am_Linear,-0.008509123281468139,F +coef_Time_SOV_freeflow_to_destination_arrival_before_ref,-0.0026124484527274773,F +coef_Time_SOV_freeflow_to_destination_arrival_after_ref,0.00213627544235268,F +coef_All_adults_in_the_household_are_fulltime_workers_Departure_before_730_am__Linear,0.0,F +coef_All_adults_in_the_household_are_fulltime_workers_Departure_after_800_am_Linear,-0.14721190410201404,F +coef_All_adults_in_the_household_are_fulltime_workers_arrival_before_ref,-0.16604467456086497,F +coef_All_adults_in_the_household_are_fulltime_workers_arrival_after_ref,0.0,F +coef_Subsequent_tour_is_work_tour_Duration_lt_8_hours,0.0,F +coef_Subsequent_tour_is_work_tour_Duration_gt_8_hours,0.0,F +coef_Subsequent_tour_is_school_tour_Departure_after_800_am,0.0,F +coef_Subsequent_tour_is_school_tour_Duration_lt_8_hours,0.0,F +coef_Subsequent_tour_is_school_tour_Duration_gt_8_hours,0.0,F +coef_Second_tour_of_two_mandatory_tours_Duration_lt_4_hours,0.0,F +coef_Second_tour_of_two_mandatory_tours_Duration_gt_4_hours,0.0,F +coef_Departure_Constant_Before_0600_AM,-8.764796175382282,F +coef_Departure_Constant_0600_AM_to_0630_AM_7,-2.707793497506736,F +coef_Departure_Constant_0630_AM_to_0700_AM_8,-0.654253451781764,F +coef_Departure_Constant_0700_AM_to_0730_AM_9,0.0,F +coef_Departure_Constant_0730_AM_to_0800_AM_10,-0.48281221099875055,T +coef_Departure_Constant_After_0800_AM,1.0336602346049464,F +coef_Departure_Constant_Shift_for_every_30_minutes_after_830_am_Linear,0.0,F +coef_Departure_Constant_Shift_for_every_30_minutes_after_830_am_Square_Root,-0.4982940561474212,F +coef_Arrival_Constant_Before_0230_PM,0.0,F +coef_Arrival_Constant_0230_PM_0300_PM_24_,0.0,F +coef_Arrival_Constant_0300_PM_0330_PM_25_,0.22741669683682353,F +coef_Arrival_Constant_0330_PM_0400_PM_26_,0.0,F +coef_Arrival_Constant_0400_PM_0430_PM_27_,0.0,T +coef_Arrival_Constant_0430_PM_0500_PM_28_,-1.0249817399820995,F +coef_Arrival_Constant_0500_PM_0530_PM_29,-1.4403670450638697,F +coef_Arrival_Constant_0530_PM_0600_PM_30_,-1.8460834370368264,F +coef_Arrival_Constant_After_0600_PM,-2.3316983953964314,F +coef_Arrival_Constant_Shift_for_every_30_minutes_after_630_pm_Linear,-0.6853763563949641,F +coef_Duration_Constant_Shift_for_every_30_minutes_less_than_6p5_hrs_Linear,-0.19770146146375506,F +coef_Duration_Constant_Shorter_than_7_hrs,-3.0269316327052733,F +coef_Duration_Constant_7_hours,-1.8715765041190864,F +coef_Duration_Constant_7p5_hours,-0.3821662280789037,F +coef_Duration_Constant_8_hours,0.0,T +coef_Duration_Constant_8p5_hours,-0.2743767294042022,F +coef_Duration_Constant_9_hours,-0.8177669270743325,F +coef_Duration_Constant_Longer_than_9_hrs,-0.5422856490451977,F +coef_Duration_Constant_Shift_for_every_30_minutes_more_than_9p5_hrs_Linear,0.21538163084141398,F +coef_Duration_Constant_Shift_for_every_30_minutes_more_than_9p5_hrs_Squared,0.0,F +zero_coef,0.0,T diff --git a/resident/configs/tour_scheduling_university.csv b/resident/configs/tour_scheduling_university.csv new file mode 100644 index 0000000..9617383 --- /dev/null +++ b/resident/configs/tour_scheduling_university.csv @@ -0,0 +1,42 @@ +Label,Description,Expression,Coefficient +util_Mode_Choice_Logsum,UNIVERSITY - Mode Choice Logsum,mode_choice_logsum,coef_Mode_Choice_Logsum +util_Low_income_lt25000_Departure_before_800_am_Linear,UNIVERSITY - Low income (<25000) - Departure before 8:00 am - Linear,"@((df.is_income_less25K) & (df.start<11))*((np.minimum(11-df.start,8)*(df.start<=11)) + (np.minimum(df.start-11,22)*(df.start>11)))",coef_Low_income_lt25000_Departure_before_800_am_Linear +util_Low_income_lt25000_Duration_lt_4hrs,UNIVERSITY - Low income (<25000) - Duration < 4hrs,"@((df.is_income_less25K) & (df.duration<8))*((np.minimum(8-df.duration,6)*(df.duration<=8)) + (np.minimum(df.duration-8,28)*(df.duration>8)))",coef_Low_income_lt25000_Duration_lt_4hrs +util_Medium_high_income_60k_to_120k_Departure_after_830_am_Linear,UNIVERSITY - Medium high income (60k to 120k) - Departure after 8:30 am - Linear,"@((df.is_income_60K_to_120K) & (df.start>11))*((np.minimum(11-df.start,8)*(df.start<=11)) + (np.minimum(df.start-11,22)*(df.start>11)))",coef_Medium_high_income_60k_to_120k_Departure_after_830_am_Linear +util_Medium_high_income_60k_to_120k_Duration_gt_4hrs,UNIVERSITY - Medium high income (60k to 120k) - Duration > 4hrs,"@((df.is_income_60K_to_120K) & (df.duration>8))*((np.minimum(8-df.duration,6)*(df.duration<=8)) + (np.minimum(df.duration-8,28)*(df.duration>8)))",coef_Medium_high_income_60k_to_120k_Duration_gt_4hrs +util_High_income_120k_plus_Departure_after_830_am_Linear,UNIVERSITY - High income (120k+) - Departure after 8:30 am - Linear,"@((df.is_income_greater120K) & (df.start>11))*((np.minimum(11-df.start,8)*(df.start<=11)) + (np.minimum(df.start-11,22)*(df.start>11)))",coef_High_income_120k_plus_Departure_after_830_am_Linear +util_Age_41_plus_Departure_after_830_am_Linear,UNIVERSITY - Age 41+ - Departure after 8:30 am - Linear,"@((df.age >= 41) & (df.start>11))*((np.minimum(11-df.start,8)*(df.start<=11)) + (np.minimum(df.start-11,22)*(df.start>11)))",coef_Age_41_plus_Departure_after_830_am_Linear +util_Age_41_plus_Durationlt_4_hrs_Linear,UNIVERSITY - Age 41+ - Duration< 4 hrs -Linear,"@((df.age >= 41) & (df.duration<8))*((np.minimum(8-df.duration,6)*(df.duration<=8)) + (np.minimum(df.duration-8,28)*(df.duration>8)))",coef_Age_41_plus_Durationlt_4_hrs_Linear +util_Distance_to_destination_Departure_before_800_am_Linear,UNIVERSITY - Distance to destination - Departure before 8:00 am - Linear,"@((df.start<11))*((np.minimum(11-df.start,8)*(df.start<=11)) + (np.minimum(df.start-11,22)*(df.start>11)))*(df.distance_to_school)",coef_Distance_to_destination_Departure_before_800_am_Linear +util_Distance_to_destination_Departure_after_830_am_Linear,UNIVERSITY - Distance to destination - Departure after 8:30 am - Linear,"@((df.start>11))*((np.minimum(11-df.start,8)*(df.start<=11)) + (np.minimum(df.start-11,22)*(df.start>11)))*(df.distance_to_school)",coef_Distance_to_destination_Departure_after_830_am_Linear +util_Distance_to_destination_Durationlt_4_hrs_Linear,UNIVERSITY - Distance to destination - Duration< 4 hrs -Linear,"@((df.duration<8))*((np.minimum(8-df.duration,6)*(df.duration<=8)) + (np.minimum(df.duration-8,28)*(df.duration>8)))*(df.distance_to_school)",coef_Distance_to_destination_Durationlt_4_hrs_Linear +util_Distance_to_destination_Durationgt_4_hrs_Linear,UNIVERSITY - Distance to destination - Duration> 4 hrs- Linear,"@((df.duration>8))*((np.minimum(8-df.duration,6)*(df.duration<=8)) + (np.minimum(df.duration-8,28)*(df.duration>8)))*(df.distance_to_school)",coef_Distance_to_destination_Durationgt_4_hrs_Linear +util_Distance_to_destination_Durationlt_4_hrs_Square_Root,UNIVERSITY - Distance to destination - Duration< 4 hrs - Square Root,"@((df.duration<8))*(abs(((np.minimum(8-df.duration,6)*(df.duration<=8)) + (np.minimum(df.duration-8,28)*(df.duration>8)))) ** 0.5)*(df.distance_to_school)",coef_Distance_to_destination_Durationlt_4_hrs_Square_Root +util_Subsequent_tour_is_work_tour_Departure_after_830_am,UNIVERSITY - Subsequent tour is work tour: Departure after 8:30 am,"@(((df.tour_count>1) & (df.tour_num == 1) & (df.tour_type.shift(-1) == 'work') & (df.start>11)))*((np.minimum(11-df.start,8)*(df.start<=11)) + (np.minimum(df.start-11,22)*(df.start>11)))",coef_Subsequent_tour_is_work_tour_Departure_after_830_am +util_Subsequent_tour_is_work_tour_Duration_lt_4_hours,UNIVERSITY - Subsequent tour is work tour: Duration < 4 hours,"@(((df.tour_count>1) & (df.tour_num == 1) & (df.tour_type.shift(-1) == 'work') & (df.duration<8)))*((np.minimum(8-df.duration,6)*(df.duration<=8)) + (np.minimum(df.duration-8,28)*(df.duration>8)))",coef_Subsequent_tour_is_work_tour_Duration_lt_4_hours +util_Subsequent_tour_is_work_tour_Duration_gt_4_hours,UNIVERSITY - Subsequent tour is work tour: Duration > 4 hours,"@(((df.tour_count>1) & (df.tour_num == 1) & (df.tour_type.shift(-1) == 'work') & (df.duration>8)))*((np.minimum(8-df.duration,6)*(df.duration<=8)) + (np.minimum(df.duration-8,28)*(df.duration>8)))",coef_Subsequent_tour_is_work_tour_Duration_gt_4_hours +util_Subsequent_tour_is_school_tour_Departure_after_830_am,UNIVERSITY - Subsequent tour is school tour: Departure after 8:30 am,"@(((df.tour_count>1) & (df.tour_num == 1) & (df.tour_type.shift(-1) == 'school') & (df.start>11)))*((np.minimum(11-df.start,8)*(df.start<=11)) + (np.minimum(df.start-11,22)*(df.start>11)))",coef_Subsequent_tour_is_school_tour_Departure_after_830_am +util_Subsequent_tour_is_school_tour_Duration_lt_4_hours,UNIVERSITY - Subsequent tour is school tour: Duration < 4 hours,"@(((df.tour_count>1) & (df.tour_num == 1) & (df.tour_type.shift(-1) == 'school') & (df.duration<8)))*((np.minimum(8-df.duration,6)*(df.duration<=8)) + (np.minimum(df.duration-8,28)*(df.duration>8)))",coef_Subsequent_tour_is_school_tour_Duration_lt_4_hours +util_Subsequent_tour_is_school_tour_Duration_gt_4_hours,UNIVERSITY - Subsequent tour is school tour: Duration > 4 hours,"@(((df.tour_count>1) & (df.tour_num == 1) & (df.tour_type.shift(-1) == 'school') & (df.duration>8)))*((np.minimum(8-df.duration,6)*(df.duration<=8)) + (np.minimum(df.duration-8,28)*(df.duration>8)))",coef_Subsequent_tour_is_school_tour_Duration_gt_4_hours +util_Second_tour_of_two_mandatory_tours_Duration_lt_4_hours,UNIVERSITY - Second tour of two mandatory tours: Duration < 4 hours,"@(((df.tour_count>1) & (df.tour_num > 1)) & (df.duration<8))*((np.minimum(8-df.duration,6)*(df.duration<=8)) + (np.minimum(df.duration-8,28)*(df.duration>8)))",coef_Second_tour_of_two_mandatory_tours_Duration_lt_4_hours +util_Second_tour_of_two_mandatory_tours_Duration_gt_4_hours,UNIVERSITY - Second tour of two mandatory tours: Duration > 4 hours,"@(((df.tour_count>1) & (df.tour_num > 1)) & (df.duration>8))*((np.minimum(8-df.duration,6)*(df.duration<=8)) + (np.minimum(df.duration-8,28)*(df.duration>8)))",coef_Second_tour_of_two_mandatory_tours_Duration_gt_4_hours +util_Departure_Constant_Shift_for_every_30_minutes_before_0700_am_Linear,UNIVERSITY - Departure Constant: Shift for every 30 minutes before 07:00 am - Linear,"@((df.start<9))*((np.minimum(9-df.start,6)*(df.start<9)) + (np.minimum(df.start-13,20)*(df.start>13)))",coef_Departure_Constant_Shift_for_every_30_minutes_before_0700_am_Linear +util_Departure_Constant_Before_0730_AM,UNIVERSITY - Departure Constant: Before 07:30 AM,@(df.start<10),coef_Departure_Constant_Before_0730_AM +util_Departure_Constant_0730_AM_0800_AM,UNIVERSITY - Departure Constant: 07:30 AM - 08:00 AM,@(df.start==10),coef_Departure_Constant_0730_AM_0800_AM +util_Departure_Constant_0800_AM_0830_AM,UNIVERSITY - Departure Constant: 08:00 AM - 08:30 AM,@(df.start==11),coef_Departure_Constant_0800_AM_0830_AM +util_Departure_Constant_0830_AM_0900_AM,UNIVERSITY - Departure Constant: 08:30 AM - 09:00 AM,@(df.start==12),coef_Departure_Constant_0830_AM_0900_AM +util_Departure_Constant_After_0900_AM,UNIVERSITY - Departure Constant: After 09:00 AM,@(df.start>12),coef_Departure_Constant_After_0900_AM +util_Departure_Constant_Shift_for_every_30_minutes_after_0930_am_Square_Root,UNIVERSITY - Departure Constant: Shift for every 30 minutes after 09:30 am - Square Root,"@((df.start>13))*(((np.minimum(9-df.start,6)*(df.start<9)) + (np.minimum(df.start-13,20)*(df.start>13))) ** 0.5)",coef_Departure_Constant_Shift_for_every_30_minutes_after_0930_am_Square_Root +util_Arrival_Constant_Shift_for_every_30_minutes_before_0230_pm_Linear,UNIVERSITY - Arrival Constant: Shift for every 30 minutes before 02:30 pm - Linear,"@((df.end<24)) * ((np.minimum(24-df.end,12) * (df.end<24)) + (np.minimum(df.end-28,19) * (df.end>28)))",coef_Arrival_Constant_Shift_for_every_30_minutes_before_0230_pm_Linear +util_Arrival_Constant_Before_0300_PM,UNIVERSITY - Arrival Constant: Before 03:00 PM,@((df.end<25)),coef_Arrival_Constant_Before_0300_PM +util_Arrival_Constant_0300_PM_0330_PM,UNIVERSITY - Arrival Constant: 03:00 PM - 03:30 PM,@(df.end==25),coef_Arrival_Constant_0300_PM_0330_PM +util_Arrival_Constant_0330_PM_0400_PM,UNIVERSITY - Arrival Constant: 03:30 PM - 04:00 PM,@(df.end==26),coef_Arrival_Constant_0330_PM_0400_PM +util_Arrival_Constant_0400_PM_0430_PM,UNIVERSITY - Arrival Constant: 04:00 PM - 04:30 PM,@(df.end==27),coef_Arrival_Constant_0400_PM_0430_PM +util_Arrival_Constant_After_0430_PM,UNIVERSITY - Arrival Constant: After 04:30 PM,@(df.end>27),coef_Arrival_Constant_After_0430_PM +util_Arrival_Constant_Shift_for_every_30_minutes_after_0500_pm_Linear,UNIVERSITY - Arrival Constant: Shift for every 30 minutes after 05:00 pm - Linear,"@((df.end>28))*((np.minimum(24-df.end,12)*(df.end<24)) + (np.minimum(df.end-28,19)*(df.end>28)))",coef_Arrival_Constant_Shift_for_every_30_minutes_after_0500_pm_Linear +util_Arrival_Constant_Shift_for_every_30_minutes_after_0500_pm_Square_Root,UNIVERSITY - Arrival Constant: Shift for every 30 minutes after 05:00 pm - Square Root,"@((df.end>28)) *(((np.minimum(24-df.end,12)*(df.end<24)) + (np.minimum(df.end-28,19)*(df.end>28))) ** 0.5)",coef_Arrival_Constant_Shift_for_every_30_minutes_after_0500_pm_Square_Root +util_Duration_Constant_Shift_for_every_30_minutes_less_than_4p5_hrs_Square_Root,UNIVERSITY - Duration Constant: Shift for every 30 minutes less than 4.5 hrs - Square Root,"@((df.duration<9))*((np.minimum(9-df.duration,7)*(df.duration<9)) + (np.minimum(df.duration-11,25)*(df.duration>11)))",coef_Duration_Constant_Shift_for_every_30_minutes_less_than_4p5_hrs_Square_Root +util_Duration_Constant_4p5_hours_or_less,UNIVERSITY - Duration Constant: 4.5 hours or less,@(df.duration<10),coef_Duration_Constant_4p5_hours_or_less +util_Duration_Constant_5_hours,UNIVERSITY - Duration Constant: 5 hours,@(df.duration==10),coef_Duration_Constant_5_hours +util_Duration_Constant_5p5_hours_or_more,UNIVERSITY - Duration Constant: 5.5 hours or more,@(df.duration>10),coef_Duration_Constant_5p5_hours_or_more +util_Duration_Constant_Shift_for_every_30_minutes_more_than_5p5_hrs_Linear,UNIVERSITY - Duration Constant: Shift for every 30 minutes more than 5.5 hrs - Linear,"@((df.duration>11))*(((np.minimum(9-df.duration,7)*(df.duration<9)) + (np.minimum(df.duration-11,25)*(df.duration>11))) ** 0.5)",coef_Duration_Constant_Shift_for_every_30_minutes_more_than_5p5_hrs_Linear diff --git a/resident/configs/tour_scheduling_university_coeffs.csv b/resident/configs/tour_scheduling_university_coeffs.csv new file mode 100644 index 0000000..1472dfb --- /dev/null +++ b/resident/configs/tour_scheduling_university_coeffs.csv @@ -0,0 +1,42 @@ +coefficient_name,value,constrain +coef_Mode_Choice_Logsum,0.384091138,F +coef_Low_income_lt25000_Departure_before_800_am_Linear,0.246389489,F +coef_Low_income_lt25000_Duration_lt_4hrs,-0.262288853,F +coef_Medium_high_income_60k_to_120k_Departure_after_830_am_Linear,-0.039079271,F +coef_Medium_high_income_60k_to_120k_Duration_gt_4hrs,-0.041536976,F +coef_High_income_120k_plus_Departure_after_830_am_Linear,-0.039306518,F +coef_Age_41_plus_Departure_after_830_am_Linear,0.055344625,F +coef_Age_41_plus_Durationlt_4_hrs_Linear,-0.152498075,F +coef_Distance_to_destination_Departure_before_800_am_Linear,0.006869786,F +coef_Distance_to_destination_Departure_after_830_am_Linear,0.003686402,F +coef_Distance_to_destination_Durationlt_4_hrs_Linear,-0.04027172,F +coef_Distance_to_destination_Durationgt_4_hrs_Linear,0.003803244,F +coef_Distance_to_destination_Durationlt_4_hrs_Square_Root,0.041070113,F +coef_Subsequent_tour_is_work_tour_Departure_after_830_am,-0.29166292,F +coef_Subsequent_tour_is_work_tour_Duration_lt_4_hours,-0.482292817,F +coef_Subsequent_tour_is_work_tour_Duration_gt_4_hours,-0.364624965,F +coef_Subsequent_tour_is_school_tour_Departure_after_830_am,-0.286206955,F +coef_Subsequent_tour_is_school_tour_Duration_lt_4_hours,0.30341795,F +coef_Subsequent_tour_is_school_tour_Duration_gt_4_hours,-0.247436221,F +coef_Second_tour_of_two_mandatory_tours_Duration_lt_4_hours,-0.211059285,F +coef_Second_tour_of_two_mandatory_tours_Duration_gt_4_hours,-0.35316727,F +coef_Departure_Constant_Shift_for_every_30_minutes_before_0700_am_Linear,-0.947594485,F +coef_Departure_Constant_Before_0730_AM,-0.296228472,F +coef_Departure_Constant_0730_AM_0800_AM,-0.650538708,F +coef_Departure_Constant_0800_AM_0830_AM,0,T +coef_Departure_Constant_0830_AM_0900_AM,-0.525569176,F +coef_Departure_Constant_After_0900_AM,-0.536008149,F +coef_Departure_Constant_Shift_for_every_30_minutes_after_0930_am_Square_Root,-0.500045988,F +coef_Arrival_Constant_Shift_for_every_30_minutes_before_0230_pm_Linear,-0.209375282,F +coef_Arrival_Constant_Before_0300_PM,-0.962572172,F +coef_Arrival_Constant_0300_PM_0330_PM,-0.627901132,F +coef_Arrival_Constant_0330_PM_0400_PM,0,T +coef_Arrival_Constant_0400_PM_0430_PM,-0.190818088,F +coef_Arrival_Constant_After_0430_PM,-0.66545038,F +coef_Arrival_Constant_Shift_for_every_30_minutes_after_0500_pm_Linear,-0.209562151,F +coef_Arrival_Constant_Shift_for_every_30_minutes_after_0500_pm_Square_Root,0.503497689,F +coef_Duration_Constant_Shift_for_every_30_minutes_less_than_4p5_hrs_Square_Root,0.225706446,F +coef_Duration_Constant_4p5_hours_or_less,0.03106769,F +coef_Duration_Constant_5_hours,0,T +coef_Duration_Constant_5p5_hours_or_more,0.343447232,F +coef_Duration_Constant_Shift_for_every_30_minutes_more_than_5p5_hrs_Linear,-0.115312573,F diff --git a/resident/configs/tour_scheduling_work.csv b/resident/configs/tour_scheduling_work.csv new file mode 100644 index 0000000..ce63a8c --- /dev/null +++ b/resident/configs/tour_scheduling_work.csv @@ -0,0 +1,103 @@ +Label,Description,Expression,Coefficient +# writing out varibles we may need for estimation,,, +round_trip_auto_time_to_work,round_trip_auto_time_to_work,@df.roundtrip_auto_time_to_work,zero_coef +age,age,@df.age,zero_coef +util_Mode_Choice_Logsum,Mode Choice Logsum,mode_choice_logsum,coef_Mode_Choice_Logsum +util_Female_Departure_before_7_am,Female - Departure before 7:00 am - Linear,@((df.female) & (df.start < df.departureRefBin)) * df.departureLinearShift1,coef_Female_Departure_before_7_am +util_Female_Arrival_after_6_pm,Female - Arrival after 6:00 pm - Linear,@((df.female) & (df.end > df.arrivalRefBin)) * df.arrivalLinearShift1,coef_Female_Arrival_after_6_pm +util_Female_with_preschool_child_Departure_before_7_am,Female with preschool child - Departure before 7:00 am - Linear,@((df.female) & (df.is_pre_drive_child_in_HH) & (df.start < df.departureRefBin)) * df.departureLinearShift1,coef_Female_with_preschool_child_Departure_before_7_am +util_Female_with_preschool_child_Departure_after_7_am,Female with preschool child - Departure after 7:30 am - Linear,@((df.female) & (df.is_pre_drive_child_in_HH) & (df.start > df.departureRefBin)) * df.departureLinearShift1,coef_Female_with_preschool_child_Departure_after_7_am +util_Female_with_preschool_child_Arrival_after_6_pm,Female with preschool child - Arrival after 6:00 pm - Linear,@((df.female) & (df.is_pre_drive_child_in_HH) & (df.end > df.arrivalRefBin)) * df.arrivalLinearShift1,coef_Female_with_preschool_child_Arrival_after_6_pm +util_Low_income_lt_25000_Departure_before_7_am,Low income (<25000) - Departure before 7:00 am - Linear,@((df.is_income_less25K) & (df.start < df.departureRefBin)) * df.departureLinearShift1,coef_Low_income_lt_25000_Departure_before_7_am +util_Low_income_lt_25000_Departure_after_7_am,Low income (<25000) - Departure after 7:30 am - Linear,@((df.is_income_less25K) & (df.start > df.departureRefBin)) * df.departureLinearShift1,coef_Low_income_lt_25000_Departure_after_7_am +util_Low_income_lt_25000_Arrival_after_6_pm,Low income (<25000) - Arrival after 6:00 pm - Linear,@((df.is_income_less25K) & (df.start>30)) * df.arrivalLinearShift1,coef_Low_income_lt_25000_Arrival_after_6_pm +util_Med_income_25k_to_60k_Departure_before_7_am,Med income (25k to 60k) - Departure before 7:00 am - Linear,@((df.is_income_25K_to_60K) & (df.start < df.departureRefBin)) * df.departureLinearShift1,coef_Med_income_25k_to_60k_Departure_before_7_am +util_Med_income_25k_to_60k_Arrival_after_6_pm,Med income (25k to 60k) - Arrival after 6:00 pm - Linear,@((df.is_income_25K_to_60K) & (df.end > df.arrivalRefBin)) * df.arrivalLinearShift1,coef_Med_income_25k_to_60k_Arrival_after_6_pm +util_Medhigh_income_60k_to_120k_Departure_before_7_am,Med-high income (60k to 120k) - Departure before 7:00 am - Linear,@((df.is_income_60K_to_120K) & (df.start < df.departureRefBin)) * df.departureLinearShift1,coef_Medhigh_income_60k_to_120k_Departure_before_7_am +util_Age_16_to_18_yrs_Departure_Before_7_am,Age 16 to 18 yrs - Departure Before 7:00 am,@(((df.age>=16) & (df.age<=18)) & (df.start < df.departureRefBin)) * df.departureLinearShift1,coef_Age_16_to_18_yrs_Departure_Before_7_am +util_Age_16_to_18_yrs_Departure_After_7_am,Age 16 to 18 yrs - Departure After 7:30 am,@(((df.age>=16) & (df.age<=18)) & (df.start > df.departureRefBin)) * df.departureLinearShift1,coef_Age_16_to_18_yrs_Departure_After_7_am +util_Age_19_to_24_yrs_Departure_After_7_am,Age 19 to 24 yrs - Departure After 7:30 am,@(((df.age>=19) & (df.age<=24)) & (df.start > df.departureRefBin)) * df.departureLinearShift1,coef_Age_19_to_24_yrs_Departure_After_7_am +util_Age_25_to_40_yrs_Departure_Before_7_am,Age 25 to 40 yrs - Departure Before 7:00 am,@(((df.age>=25) & (df.age<=40)) & (df.start < df.departureRefBin)) * df.departureLinearShift1,coef_Age_25_to_40_yrs_Departure_Before_7_am +util_Age_65_plus_yrs_Departure_After_7_am,Age 65+ yrs - Departure After 7:30 am,@((df.age>=65) & (df.start > df.departureRefBin)) * df.departureLinearShift1,coef_Age_65_plus_yrs_Departure_After_7_am +util_Age_19_to_24_yrs_Arrival_after_6_pm,Age 19 to 24 yrs - Arrival after 6:00 pm ,@(((df.age>=19) & (df.age<=24)) & (df.end > df.arrivalRefBin)) * df.arrivalLinearShift1,coef_Age_19_to_24_yrs_Arrival_after_6_pm +util_Age_25_to_40_yrs_Arrival_before_5_pm,Age 25 to 40 yrs - Arrival before 5:30 pm ,@(((df.age>=25) & (df.age<=40)) & (df.end < df.arrivalRefBin)) * df.arrivalLinearShift1,coef_Age_25_to_40_yrs_Arrival_before_5_pm +util_Age_56_to_64_yrs_Arrival_after_6_pm,Age 56 to 64 yrs - Arrival after 6:00 pm ,@(((df.age>=56) & (df.age<65)) & (df.end > df.arrivalRefBin)) * df.arrivalLinearShift1,coef_Age_56_to_64_yrs_Arrival_after_6_pm +util_Age_65_plus_yrs_Arrival_before_5_pm,Age 65+ yrs - Arrival before 5:30 pm ,@((df.age>=65) & (df.end < df.arrivalRefBin)) * df.arrivalLinearShift1,coef_Age_65_plus_yrs_Arrival_before_5_pm +util_Age_65_plus_yrs_Arrival_after_6_pm,Age 65+ yrs - Arrival after 6:00 pm ,@((df.age>=65) & (df.end > df.arrivalRefBin)) * df.arrivalLinearShift1,coef_Age_65_plus_yrs_Arrival_after_6_pm +util_Zero_auto_HH_Departure_before_7_am,Zero auto HH - Departure before 7:00 am - Linear,@((df.auto_ownership == 0) & (df.start < df.departureRefBin)) * df.departureLinearShift1,coef_Zero_auto_HH_Departure_before_7_am +util_Zero_auto_HH_Arrival_after_6_pm,Zero auto HH - Arrival after 6:00 pm - Linear,@((df.auto_ownership == 0) & (df.end > df.arrivalRefBin)) * df.arrivalLinearShift1,coef_Zero_auto_HH_Arrival_after_6_pm +util_Parttime_worker_Departure_before_7_am,Part-time worker - Departure before 7:00 am - Linear,@((df.ptype==2) & (df.start < df.departureRefBin)) * df.departureLinearShift1,coef_Parttime_worker_Departure_before_7_am +util_Parttime_worker_Departure_after_7_am,Part-time worker - Departure after 7:30 am - Linear,@((df.ptype==2) & (df.start > df.departureRefBin)) * df.departureLinearShift1,coef_Parttime_worker_Departure_after_7_am +util_Parttime_worker_Arrival_before_5_pm,Part-time worker - Arrival before 5:30 pm - Linear,@((df.ptype==2) & (df.end < df.arrivalRefBin)) * df.arrivalLinearShift1,coef_Parttime_worker_Arrival_before_5_pm +util_Parttime_worker_Arrival_after_6_pm,Part-time worker - Arrival after 6:00 pm - Linear,@((df.ptype==2) & (df.end > df.arrivalRefBin)) * df.arrivalLinearShift1,coef_Parttime_worker_Arrival_after_6_pm +util_University_student_Departure_after_7_am,University student - Departure after 7:30 am - Linear,@((df.ptype==3) & (df.start > df.departureRefBin)) * df.departureLinearShift1,coef_University_student_Departure_after_7_am +util_University_student_Arrival_before_5_pm,University student - Arrival before 5:30 pm - Linear,@((df.ptype==3) & (df.end < df.arrivalRefBin)) * df.arrivalLinearShift1,coef_University_student_Arrival_before_5_pm +util_University_student_Arrival_after_6_pm,University student - Arrival after 6:00 pm - Linear,@((df.ptype==3) & (df.end > df.arrivalRefBin)) * df.arrivalLinearShift1,coef_University_student_Arrival_after_6_pm +#util_Blue_collar_Departure_before_7_am,#Blue collar - Departure before 7:00 am - Linear,@((df.work_segment==5) & (df.start < df.departureRefBin)) * df.departureLinearShift1,coef_Blue_collar_Departure_before_7_am +#util_Blue_collar_Departure_after_7_am,#Blue collar - Departure after 7:30 am - Linear,@((df.work_segment==5)& (df.start > df.departureRefBin)) * df.departureLinearShift1,coef_Blue_collar_Departure_after_7_am +#util_Blue_collar_Arrival_before_5_pm,#Blue collar - Arrival before 5:30 pm - Linear,@((df.work_segment==5)& (df.end < df.arrivalRefBin)) * df.arrivalLinearShift1,coef_Blue_collar_Arrival_before_5_pm +#util_Service_Departure_before_7_am,#Service - Departure before 7:00 am - Linear,@((df.work_segment==2) & (df.start < df.departureRefBin)) * df.departureLinearShift1,coef_Service_Departure_before_7_am +#util_Service_Departure_after_7_am,#Service - Departure after 7:30 am - Linear,@((df.work_segment==2) & (df.start > df.departureRefBin)) * df.departureLinearShift1,coef_Service_Departure_after_7_am +#util_Service_Arrival_before_5_pm,#Service - Arrival before 5:30 pm - Linear,@((df.work_segment==2) & (df.end < df.arrivalRefBin)) * df.arrivalLinearShift1,coef_Service_Arrival_before_5_pm +#util_Health_Departure_before_7_am,#Health - Departure before 7:00 am - Linear,@((df.work_segment==3) & (df.start < df.departureRefBin)) * df.departureLinearShift1,coef_Health_Departure_before_7_am +#util_Health_Arrival_after_6_pm,#Health - Arrival after 6:00 pm - Linear,@((df.work_segment==3) & (df.end > df.arrivalRefBin)) * df.arrivalLinearShift1,coef_Health_Arrival_after_6_pm +#util_Retail_and_food_Departure_after_7_am,#Retail and food - Departure after 7:30 am - Linear,@((df.work_segment==4) & (df.start > df.departureRefBin)) * df.departureLinearShift1,coef_Retail_and_food_Departure_after_7_am +#util_Retail_and_food_Arrival_before_5_pm,#Retail and food - Arrival before 5:30 pm - Linear,@((df.work_segment==4) & (df.end < df.arrivalRefBin)) * df.arrivalLinearShift1,coef_Retail_and_food_Arrival_before_5_pm +#util_Retail_and_food_Arrival_after_6_pm,#Retail and food - Arrival after 6:00 pm - Linear,@((df.work_segment==4) & (df.end > df.arrivalRefBin)) * df.arrivalLinearShift1,coef_Retail_and_food_Arrival_after_6_pm +util_Time_SOV_freeflowto_destination_Departure_before_7_am,Time (SOV freeflow) to destination - Departure before 7:00 am - Linear,@(df.start < df.departureRefBin) * df.departureLinearShift1* (df.roundtrip_auto_time_to_work),coef_Time_SOV_freeflowto_destination_Departure_before_7_am +util_Time_SOV_freeflowto_destination_Departure_after_7_am,Time (SOV freeflow) to destination - Departure after 7:30 am - Linear,@(df.start > df.departureRefBin) * df.departureLinearShift1 * (df.roundtrip_auto_time_to_work),coef_Time_SOV_freeflowto_destination_Departure_after_7_am +util_Time_SOV_freeflowto_destination_Arrival_before_5_pm,Time (SOV freeflow) to destination - Arrival before 5:30 pm - Linear,@(df.end < df.arrivalRefBin) * df.arrivalLinearShift1 * (df.roundtrip_auto_time_to_work),coef_Time_SOV_freeflowto_destination_Arrival_before_5_pm +util_Time_SOV_freeflowto_destination_Arrival_after_6_pm,Time (SOV freeflow) to destination - Arrival after 6:00 pm - Linear,@(df.end > df.arrivalRefBin) * df.arrivalLinearShift1 * (df.roundtrip_auto_time_to_work),coef_Time_SOV_freeflowto_destination_Arrival_after_6_pm +util_Presence_of_NonWorking_Adult_in_the_HH_Departure_before_7_am,Presence of Non-Working Adult in the HH - Departure before 7:00 am - Linear,@((df.is_non_worker_in_HH) & (df.start < df.departureRefBin)) * df.departureLinearShift1,coef_Presence_of_NonWorking_Adult_in_the_HH_Departure_before_7_am +util_Presence_of_NonWorking_Adult_in_the_HH_Arrival_before_5_pm,Presence of Non-Working Adult in the HH - Arrival before 5:30 pm - Linear,@((df.is_non_worker_in_HH) & (df.end < df.arrivalRefBin)) * df.arrivalLinearShift1,coef_Presence_of_NonWorking_Adult_in_the_HH_Arrival_before_5_pm +util_Presence_of_PreDriving_Age_Children_in_the_HH_Departure_before_7_am,Presence of Pre-Driving Age Children in the HH - Departure before 7:30 am - Linear,@((df.is_pre_drive_child_in_HH) & (df.start < df.departureRefBin)) * df.departureLinearShift1,coef_Presence_of_PreDriving_Age_Children_in_the_HH_Departure_before_7_am +util_Presence_of_PreDriving_Age_Children_in_the_HH_Departure_after_8_am,Presence of Pre-Driving Age Children in the HH - Departure after 8 am - Linear,@((df.is_pre_drive_child_in_HH) & (df.start > df.departureRefBin)) * df.departureLinearShift1,coef_Presence_of_PreDriving_Age_Children_in_the_HH_Departure_after_8_am +util_Presence_of_PreDriving_Age_Children_in_the_HH_Arrival_before_5_pm,Presence of Pre-Driving Age Children in the HH - Arrival before 5:30 pm - Linear,@((df.is_pre_drive_child_in_HH) & (df.end < df.arrivalRefBin)) * df.arrivalLinearShift1,coef_Presence_of_PreDriving_Age_Children_in_the_HH_Arrival_before_5_pm +util_Presence_of_PreDriving_Age_Children_in_the_HH_Arrival_after_6_pm,Presence of Pre-Driving Age Children in the HH - Arrival after 6:00 pm - Linear,@((df.is_pre_drive_child_in_HH)& (df.end > df.arrivalRefBin)) * df.arrivalLinearShift1,coef_Presence_of_PreDriving_Age_Children_in_the_HH_Arrival_after_6_pm +util_First_of_2_plus_mandatory_tour_Departure_before_7_am,First of 2+ mandatory tour - Departure before 7:00 am,@(((df.tour_count>1) & (df.tour_num == 1)) & (df.start < df.departureRefBin)) * df.departureLinearShift1,coef_First_of_2_plus_mandatory_tour_Departure_before_7_am +util_First_of_2_plus_mandatory_tour_Departure_after_7_am,First of 2+ mandatory tour - Departure after 7:30 am,@(((df.tour_count>1) & (df.tour_num == 1)) & (df.start > df.departureRefBin)) * df.departureLinearShift1,coef_First_of_2_plus_mandatory_tour_Departure_after_7_am +util_First_of_2_plus_mandatory_tour_Duration_lt_dur_ref,First of 2+ mandatory tour - Duration < 9.5 hours,@(((df.tour_count>1) & (df.tour_num == 1)) & (df.end < df.durationRefBin)) * df.durationShift,coef_First_of_2_plus_mandatory_tour_Duration_lt_dur_ref +util_First_of_2_plus_mandatory_tour_Duration_gt_dur_ref,First of 2+ mandatory tour - Duration > 9.5 hours,@(((df.tour_count>1) & (df.tour_num == 1)) & (df.end > df.durationRefBin)) * df.durationShift,coef_First_of_2_plus_mandatory_tour_Duration_gt_dur_ref +util_2nd_or_later_of_2_plus_mandatory_tour_Departure_before_1_pm,2nd or later of 2+ mandatory tour - Departure before 1:30 pm,@(((df.tour_count>1) & (df.tour_num > 1)) & (df.start<22)) * (((22-df.start)*(df.start<22)) + ((df.start-22)*(df.start>22))),coef_2nd_or_later_of_2_plus_mandatory_tour_Departure_before_1_pm +util_2nd_or_later_of_2_plus_mandatory_tour_Departure_after_2_pm,2nd or later of 2+ mandatory tour - Departure after 2:00 pm,@(((df.tour_count>1) & (df.tour_num > 1)) & (df.start>22)) * (((22-df.start)*(df.start<22)) + ((df.start-22)*(df.start>22))),coef_2nd_or_later_of_2_plus_mandatory_tour_Departure_after_2_pm +util_2nd_or_later_of_2_plus_mandatory_tour_Duration_lt_dur_ref,2nd or later of 2+ mandatory tour - Duration < 9.5 hours,@(((df.tour_count>1) & (df.tour_num > 1)) & (df.end < df.durationRefBin)) * df.durationShift,coef_2nd_or_later_of_2_plus_mandatory_tour_Duration_lt_dur_ref +util_2nd_or_later_of_2_plus_mandatory_tour_Duration_gt_dur_ref,2nd or later of 2+ mandatory tour - Duration > 9.5 hours,@(((df.tour_count>1) & (df.tour_num > 1)) & (df.end > df.durationRefBin)) * df.durationShift,coef_2nd_or_later_of_2_plus_mandatory_tour_Duration_gt_dur_ref +#,#Departure Constants,,coef_Departure_Constants +util_Departure_Constant_Shift_for_every_30_minutes_before_6_am,Departure Constant: Shift for every 30 minutes before 6:00 am - Linear,"@((df.start<7) * (7-df.start))",coef_Departure_Constant_Shift_for_every_30_minutes_before_6_am +util_Departure_Constant_Before_06_AM,Departure Constant: Before 06:00 AM,start<7,coef_Departure_Constant_Before_06_AM +util_Departure_Constant_06_AM_06_AM_7,Departure Constant: 06:00 AM - 06:30 AM (7) ,start==7,coef_Departure_Constant_06_AM_06_AM_7 +util_Departure_Constant_06_AM_07_AM_8,Departure Constant: 06:30 AM - 07:00 AM (8) ,start==8,coef_Departure_Constant_06_AM_07_AM_8 +util_Departure_Constant_07_AM_07_AM_9,Departure Constant: 07:00 AM - 07:30 AM (9) ,start==9,coef_Departure_Constant_07_AM_07_AM_9 +util_Departure_Constant_07_AM_08_AM_10,Departure Constant: 07:30 AM - 08:00 AM (10) ,start==10,coef_Departure_Constant_07_AM_08_AM_10 +util_Departure_Constant_08_AM_08_AM_11,Departure Constant: 08:00 AM - 08:30 AM (11) ,start==11,coef_Departure_Constant_08_AM_08_AM_11 +util_Departure_Constant_08_AM_09_AM_12,Departure Constant: 08:30 AM - 09:00 AM (12) ,start==12,coef_Departure_Constant_08_AM_09_AM_12 +util_Departure_Constant_After_09_AM,Departure Constant: After 09:00 AM,start>12,coef_Departure_Constant_After_09_AM +util_Departure_Constant_Shift_for_every_30_minutes_after_9_am_Square_Root,Departure Constant: Shift for every 30 minutes after 9:30 am - Square Root,"@((df.start>13) * (np.maximum(df.start-13,0) ** 0.5))",coef_Departure_Constant_Shift_for_every_30_minutes_after_9_am_Square_Root +#,#Arrival Constants,,coef_Arrival_Constants +util_Arrival_Constant_Shift_for_every_30_minutes_before_3_pm,Arrival Constant: Shift for every 30 minutes before 3:00 pm - Linear,"@((df.end<25) * (25-df.end))",coef_Arrival_Constant_Shift_for_every_30_minutes_before_3_pm +util_Arrival_Constant_Before_03_PM,Arrival Constant: Before 03:30 PM,end<26,coef_Arrival_Constant_Before_03_PM +util_Arrival_Constant_03_PM_04_PM_26,Arrival Constant: 03:30 PM - 04:00 PM (26) ,end==26,coef_Arrival_Constant_03_PM_04_PM_26 +util_Arrival_Constant_04_PM_04_PM_27,Arrival Constant: 04:00 PM - 04:30 PM (27) ,end==27,coef_Arrival_Constant_04_PM_04_PM_27 +util_Arrival_Constant_04_PM_05_PM_28,Arrival Constant: 04:30 PM - 05:00 PM (28) ,end==28,coef_Arrival_Constant_04_PM_05_PM_28 +util_Arrival_Constant_05_PM_05_PM_29,Arrival Constant: 05:00 PM - 05:30 PM (29),end==29,coef_Arrival_Constant_05_PM_05_PM_29 +util_Arrival_Constant_05_PM_06_PM_30,Arrival Constant: 05:30 PM - 06:00 PM (30) ,end==30,coef_Arrival_Constant_05_PM_06_PM_30 +util_Arrival_Constant_06_PM_06_PM_31,Arrival Constant: 06:00 PM - 06:30 PM (31) ,end==31,coef_Arrival_Constant_06_PM_06_PM_31 +util_Arrival_Constant_06_PM_7_PM_32,Arrival Constant: 06:30 PM - 7:00 PM (32) ,end==32,coef_Arrival_Constant_06_PM_7_PM_32 +util_Arrival_Constant_7_PM_7_PM_33,Arrival Constant: 7:00 PM - 7:30 PM (33) ,end==33,coef_Arrival_Constant_7_PM_7_PM_33 +util_Arrival_Constant_7_PM_8_PM_34,Arrival Constant: 7:30 PM - 8:00 PM (34) ,end==34,coef_Arrival_Constant_7_PM_8_PM_34 +util_Arrival_Constant_After_08_PM,Arrival Constant: After 08:00 PM,end>34,coef_Arrival_Constant_After_08_PM +util_Arrival_Constant_Shift_for_every_30_minutes_after_830_pm_Square_root,Arrival Constant: Shift for every 30 minutes after 830 pm - Square root,"@((df.end>35) * (np.maximum(df.end-35,0) ** 0.5))",coef_Arrival_Constant_Shift_for_every_30_minutes_after_830_pm_Square_root +#,#Duration Constants,,coef_Duration_Constants +util_Duration_Constant_Shift_for_every_30_minutes_less_than_8p5_hrs,Duration Constant: Shift for every 30 minutes less than 8.5 hrs - Linear,"@((df.duration<17) * (17-df.duration))",coef_Duration_Constant_Shift_for_every_30_minutes_less_than_8p5_hrs +util_Duration_Constant_Shorter_than_8p5_hrs,Duration Constant: Shorter than 8.5 hrs,duration<17,coef_Duration_Constant_Shorter_than_8p5_hrs +util_Duration_Constant_8p5_hours,Duration Constant: 8.5 hours,duration==17,coef_Duration_Constant_8p5_hours +util_Duration_Constant_9_hours,Duration Constant: 9 hours,duration==18,coef_Duration_Constant_9_hours +util_Duration_Constant_9p5_hours,Duration Constant: 9.5 hours,duration==19,coef_Duration_Constant_9p5_hours +util_Duration_Constant_10_hours,Duration Constant: 10 hours,duration==20,coef_Duration_Constant_10_hours +util_Duration_Constant_10p5_hours,Duration Constant: 10.5 hours,duration==21,coef_Duration_Constant_10p5_hours +util_Duration_Constant_11_hours,Duration Constant: 11 hours,duration==22,coef_Duration_Constant_11_hours +util_Duration_Constant_11p5_hours,Duration Constant: 11.5 hours,duration==23,coef_Duration_Constant_11p5_hours +util_Duration_Constant_12_hours,Duration Constant: 12 hours,duration==24,coef_Duration_Constant_12_hours +util_Duration_Constant_Longer_than_12_hrs,Duration Constant: Longer than 12 hrs,duration>24,coef_Duration_Constant_Longer_than_12_hrs +util_Duration_Constant_Shift_for_every_30_minutes_more_than_12_hrs,Duration Constant: Shift for every 30 minutes more than 12 hrs - Linear,"@((df.duration>24) * (df.duration-24))",coef_Duration_Constant_Shift_for_every_30_minutes_more_than_12_hrs +util_Calibration_constant_Duration_0,Calibration constant: Duration = 0,duration == 0,coef_Calibration_constant_Duration_0 diff --git a/resident/configs/tour_scheduling_work_coeffs.csv b/resident/configs/tour_scheduling_work_coeffs.csv new file mode 100644 index 0000000..e8d9607 --- /dev/null +++ b/resident/configs/tour_scheduling_work_coeffs.csv @@ -0,0 +1,98 @@ +coefficient_name,value,constrain +coef_Mode_Choice_Logsum,0.2,T +coef_Female_Departure_before_7_am,-0.21983073617010354,F +coef_Female_Arrival_after_6_pm,-0.028931718675078898,F +coef_Female_with_preschool_child_Departure_before_7_am,0.0,F +coef_Female_with_preschool_child_Departure_after_7_am,0.0,F +coef_Female_with_preschool_child_Arrival_after_6_pm,0.0,F +coef_Low_income_lt_25000_Departure_before_7_am,0.23142273676542863,F +coef_Low_income_lt_25000_Departure_after_7_am,0.06394624853227485,F +coef_Low_income_lt_25000_Arrival_after_6_pm,-0.2101481937242358,F +coef_Med_income_25k_to_60k_Departure_before_7_am,0.20582925253502693,F +coef_Med_income_25k_to_60k_Arrival_after_6_pm,0.030759330025131475,F +coef_Medhigh_income_60k_to_120k_Departure_before_7_am,0.12339331900370523,F +coef_Age_16_to_18_yrs_Departure_Before_7_am,0.0,F +coef_Age_16_to_18_yrs_Departure_After_7_am,0.1569218088558017,F +coef_Age_19_to_24_yrs_Departure_After_7_am,0.03280621940285348,F +coef_Age_25_to_40_yrs_Departure_Before_7_am,-0.14071003044101923,F +coef_Age_65_plus_yrs_Departure_After_7_am,0.0,F +coef_Age_19_to_24_yrs_Arrival_after_6_pm,0.03801692388513126,F +coef_Age_25_to_40_yrs_Arrival_before_5_pm,-0.028091109013424525,F +coef_Age_56_to_64_yrs_Arrival_after_6_pm,0.0,F +coef_Age_65_plus_yrs_Arrival_before_5_pm,0.0,F +coef_Age_65_plus_yrs_Arrival_after_6_pm,0.0,F +coef_Zero_auto_HH_Departure_before_7_am,0.0,F +coef_Zero_auto_HH_Arrival_after_6_pm,0.054809482440729494,F +coef_Parttime_worker_Departure_before_7_am,-0.465300506980724,F +coef_Parttime_worker_Departure_after_7_am,0.24035601017298427,F +coef_Parttime_worker_Arrival_before_5_pm,0.27263218681666973,F +coef_Parttime_worker_Arrival_after_6_pm,-0.2533496036708649,F +coef_University_student_Departure_after_7_am,0.10708190818422741,F +coef_University_student_Arrival_before_5_pm,0.0,F +coef_University_student_Arrival_after_6_pm,-0.0784130766004373,F +coef_Blue_collar_Departure_before_7_am,0.327242475,F +coef_Blue_collar_Departure_after_7_am,0.047214248,F +coef_Blue_collar_Arrival_before_5_pm,0.04197056,F +coef_Service_Departure_before_7_am,0.117783508,F +coef_Service_Departure_after_7_am,0.081611629,F +coef_Service_Arrival_before_5_pm,0.0,T +coef_Health_Departure_before_7_am,0.135275931,F +coef_Health_Arrival_after_6_pm,0.062010123,F +coef_Retail_and_food_Departure_after_7_am,0.076302969,F +coef_Retail_and_food_Arrival_before_5_pm,0.052905387,F +coef_Retail_and_food_Arrival_after_6_pm,0.0270691939999999,F +coef_Time_SOV_freeflowto_destination_Departure_before_7_am,0.006152588569993294,F +coef_Time_SOV_freeflowto_destination_Departure_after_7_am,-0.002638509520279399,F +coef_Time_SOV_freeflowto_destination_Arrival_before_5_pm,-0.003924739241949497,F +coef_Time_SOV_freeflowto_destination_Arrival_after_6_pm,0.0016423390591779922,F +coef_Presence_of_NonWorking_Adult_in_the_HH_Departure_before_7_am,0.0,F +coef_Presence_of_NonWorking_Adult_in_the_HH_Arrival_before_5_pm,0.0,F +coef_Presence_of_PreDriving_Age_Children_in_the_HH_Departure_before_7_am,-0.08289214126546349,F +coef_Presence_of_PreDriving_Age_Children_in_the_HH_Departure_after_8_am,0.0,F +coef_Presence_of_PreDriving_Age_Children_in_the_HH_Arrival_before_5_pm,0.0,F +coef_Presence_of_PreDriving_Age_Children_in_the_HH_Arrival_after_6_pm,-0.04292555821779037,F +coef_First_of_2_plus_mandatory_tour_Departure_before_7_am,-0.15631713590985458,F +coef_First_of_2_plus_mandatory_tour_Departure_after_7_am,-0.037321682863975456,F +coef_First_of_2_plus_mandatory_tour_Duration_lt_dur_ref,0.3054062365147087,F +coef_First_of_2_plus_mandatory_tour_Duration_gt_dur_ref,0.15794972916369113,F +coef_2nd_or_later_of_2_plus_mandatory_tour_Departure_before_1_pm,0.0,F +coef_2nd_or_later_of_2_plus_mandatory_tour_Departure_after_2_pm,-0.13397035652802908,F +coef_2nd_or_later_of_2_plus_mandatory_tour_Duration_lt_dur_ref,0.45835848769473103,F +coef_2nd_or_later_of_2_plus_mandatory_tour_Duration_gt_dur_ref,0.5272527491008135,F +coef_Departure_Constant_Shift_for_every_30_minutes_before_6_am,-0.7231020452773588,F +coef_Departure_Constant_Before_06_AM,-0.1915960040171813,F +coef_Departure_Constant_06_AM_06_AM_7,-0.5420940244335356,F +coef_Departure_Constant_06_AM_07_AM_8,-0.0814829941363625,F +coef_Departure_Constant_07_AM_07_AM_9,0.0,T +coef_Departure_Constant_07_AM_08_AM_10,-0.12487604386728021,F +coef_Departure_Constant_08_AM_08_AM_11,-0.4486786777966309,F +coef_Departure_Constant_08_AM_09_AM_12,-0.9838612097965408,F +coef_Departure_Constant_After_09_AM,-1.5618817419634867,F +coef_Departure_Constant_Shift_for_every_30_minutes_after_9_am_Square_Root,-0.5783447729979645,F +coef_Arrival_Constant_Shift_for_every_30_minutes_before_3_pm,-0.20009504631293196,F +coef_Arrival_Constant_Before_03_PM,-0.8629231486548913,F +coef_Arrival_Constant_03_PM_04_PM_26,-0.644880302920333,F +coef_Arrival_Constant_04_PM_04_PM_27,-0.3047263117628279,F +coef_Arrival_Constant_04_PM_05_PM_28,-0.1886361914686483,F +coef_Arrival_Constant_05_PM_05_PM_29,0.0,F +coef_Arrival_Constant_05_PM_06_PM_30,0.2500583541484266,T +coef_Arrival_Constant_06_PM_06_PM_31,0.23990107930944812,F +coef_Arrival_Constant_06_PM_7_PM_32,0.15526238473246975,F +coef_Arrival_Constant_7_PM_7_PM_33,0.0,F +coef_Arrival_Constant_7_PM_8_PM_34,-0.18208024865786762,F +coef_Arrival_Constant_After_08_PM,0.0,F +coef_Arrival_Constant_Shift_for_every_30_minutes_after_830_pm_Square_root,0.0,F +coef_Duration_Constant_Shift_for_every_30_minutes_less_than_8p5_hrs,-0.10663128725700759,F +coef_Duration_Constant_Shorter_than_8p5_hrs,0.0,F +coef_Duration_Constant_8p5_hours,-0.2403003279562287,F +coef_Duration_Constant_9_hours,0.0,F +coef_Duration_Constant_9p5_hours,0.0,F +coef_Duration_Constant_10_hours,0.0,F +coef_Duration_Constant_10p5_hours,-0.3088419298533547,T +coef_Duration_Constant_11_hours,-0.6583531336885191,F +coef_Duration_Constant_11p5_hours,-0.8734017033609268,F +coef_Duration_Constant_12_hours,-1.3551244638084843,F +coef_Duration_Constant_Longer_than_12_hrs,-1.0959910217157498,F +coef_Duration_Constant_Shift_for_every_30_minutes_more_than_12_hrs,-0.4918560543397093,F +coef_Calibration_constant_Duration_0,-1.2069106964427703,F +zero_coef,0.0,T diff --git a/resident/configs/transit_pass_ownership.csv b/resident/configs/transit_pass_ownership.csv new file mode 100644 index 0000000..6d7a25c --- /dev/null +++ b/resident/configs/transit_pass_ownership.csv @@ -0,0 +1,27 @@ +Label,Description,Expression,no_pass,pass +util_pass_asc,Constant,1,0,asc_pass +util_age_19_to_34,Age Group - 19 yrs to 34 yrs,@(df.age >= 19) & (df.age < 35),0,coef_age_19_to_34_pass +util_age_35_to_44,Age Group - 35 yrs to 44 yrs,@(df.age >= 35) & (df.age < 45),0,coef_age_35_to_44_pass +util_age_55_to_64,Age Group - 55 yrs to 64 yrs,@(df.age >= 55) & (df.age < 65),0,coef_age_55_to_64_pass +util_age_65_to_79,Age Group - 65 yrs to 79 yrs,@(df.age >= 65) & (df.age < 80),0,coef_age_65_to_79_pass +util_hhinc_less_15,"Income less than 15,000",@df.income<15000,0,coef_hhinc_less_15 +util_hhinc_15_25,"income between 15,000- 24,999","@df.income.between(15000,24999)",0,coef_hhinc_15_25 +util_hhinc_50_100,"income between 50,000 and 99,999","@df.income.between(50000,99999)",0,coef_hhinc_50_100 +util_hhinc_100_200,"income between 100,000 and 199,999","@df.income.between(100000,199999)",0,coef_hhinc_100_200 +util_hhinc_200_plus,"income greater than 250,000",@df.income>=200000,0,coef_hhinc_200_plus +util_persons_0_4_in_hhld,Persons age 0-4 in hhld,@df.num_young_children,0,coef_persons_0_4_in_hhld_pass +util_persons_5_15_in_hhld,Persons age 5-15 in hhld,@df.num_children_5_to_15,0,coef_persons_5_15_in_hhld_pass +util_zero_autos,zero auto household ownership,@df.auto_ownership==0,0,coef_zero_autos_pass +util_auto_deficient,auto deficient household,@((df.auto_ownership < df.num_workers) & (df.auto_ownership > 0)),0,coef_auto_deficient_pass +util_subs,Subsidy offered,@df.transit_pass_subsidy,0,coef_subs_pass +util_inc_adj_transit_pass_cost,Income adjusted transit pass cost,"@df.transit_pass_cost_per_trip / (np.maximum(df.income, 1000) ** df.income_exponent) / 60",0,coef_tripmc_cost_coef +util_parking_cost_at_work,Parking cost at work,@df.exp_daily_work,0,coef_parking_cost_at_work_pass +#util_total_acc_transit,Total accessibility (0.66*PK + 0.34*OP) by transit,@(0.66*df.trPkTotal+0.34*df.trOpTotal),0,coef_total_transit_acc_pass +util_is_univ,University student,@df.pstudent == PSTUDENT_UNIVERSITY,0,coef_is_univ +util_ft_ASC,ptype_ft calibration constant,@df.ptype == 1,0,coef_ft_asc +util_pt_ASC,ptype_pt calibration constant,@df.ptype == 2,0,coef_pt_asc +util_un_ASC,ptype_un calibration constant,@df.ptype == 3,0,coef_un_asc +util_na_ASC,ptype_na calibration constant,@df.ptype == 4,0,coef_na_asc +util_da_ASC,ptype_da calibration constant,@df.ptype == 5,0,coef_da_asc +util_nd_ASC,ptype_nd calibration constant,@df.ptype == 6,0,coef_nd_asc +util_ps_ASC,ptype_ps calibration constant,@df.ptype == 7,0,coef_ps_asc diff --git a/resident/configs/transit_pass_ownership.yaml b/resident/configs/transit_pass_ownership.yaml new file mode 100644 index 0000000..9dc3644 --- /dev/null +++ b/resident/configs/transit_pass_ownership.yaml @@ -0,0 +1,13 @@ + +SPEC: transit_pass_ownership.csv +COEFFICIENTS: transit_pass_ownership_coeffs.csv + +#LOGIT_TYPE: NL +LOGIT_TYPE: MNL + +preprocessor: + SPEC: transit_pass_ownership_preprocessor + DF: choosers + TABLES: + - land_use + - persons \ No newline at end of file diff --git a/resident/configs/transit_pass_ownership_coeffs.csv b/resident/configs/transit_pass_ownership_coeffs.csv new file mode 100644 index 0000000..1b29485 --- /dev/null +++ b/resident/configs/transit_pass_ownership_coeffs.csv @@ -0,0 +1,27 @@ +coefficient_name,value,constrain +asc_pass,-4.1,F +coef_age_19_to_34_pass,0.52,F +coef_age_35_to_44_pass,0.346,F +coef_age_55_to_64_pass,0.384,F +coef_age_65_to_79_pass,0.315,F +coef_auto_deficient_pass,1.21,F +coef_hhinc_100_200,1.17,F +coef_hhinc_15_25,0.336,F +coef_hhinc_200_plus,1.17,F +coef_hhinc_50_100,0.233,F +coef_hhinc_less_15,0.492,F +coef_is_univ,1.22,F +coef_parking_cost_at_work_pass,0.0484,F +coef_persons_0_4_in_hhld_pass,-0.551,F +coef_persons_5_15_in_hhld_pass,-0.578,F +coef_subs_pass,1.49,F +coef_total_transit_acc_pass,0.0692,F +coef_tripmc_cost_coef,-1.25,T +coef_zero_autos_pass,3.32,F +coef_ft_asc,1.310,F +coef_pt_asc,1.364,F +coef_un_asc,0.654,F +coef_na_asc,1.650,F +coef_da_asc,1.000,F +coef_nd_asc,-2.0,F +coef_ps_asc,-2.0,F diff --git a/resident/configs/transit_pass_ownership_preprocessor.csv b/resident/configs/transit_pass_ownership_preprocessor.csv new file mode 100644 index 0000000..b570bd3 --- /dev/null +++ b/resident/configs/transit_pass_ownership_preprocessor.csv @@ -0,0 +1,6 @@ +Description,Target,Expression +# transit pass cost converted to cost per trip and multiplied by trip mode choice cost coefficient in model,, +# assumes 22 working days per month and 2 trips per work day. reduced fare for children and seniors half price.,, +,income_exponent,0.6 +,transit_pass_cost_per_trip,monthly_transit_pass_cost / (22 * 2) +,transit_pass_cost_per_trip,"np.where((df.age >= 65) | (df.age <= 18), transit_pass_cost_per_trip / 2, transit_pass_cost_per_trip)" \ No newline at end of file diff --git a/resident/configs/transit_pass_subsidy.csv b/resident/configs/transit_pass_subsidy.csv new file mode 100644 index 0000000..2082f6e --- /dev/null +++ b/resident/configs/transit_pass_subsidy.csv @@ -0,0 +1,23 @@ +Label,Description,Expression,no_subsidy,subsidy +util_pt,Part-time worker,@df.pemploy==PEMPLOY_PART,0,coef_pt +util_un,University/College,@df.ptype==PTYPE_UNIVERSITY,0,coef_un +util_tr_hh_emp,Household transit accessibility to Employment,@df.trPkTotal,0,coef_tr_hh_emp +util_tr_emp_hh,Employment transit access to HHs,@df.trn_wk_access_hh,0,coef_tr_emp_hh +util_pkcost,Peak parking cost (dollars),@df.exp_daily_work,0,coef_pkcost +util_hhinc_less_15,"Income less than 15,000",@df.income<15000,0,coef_hhinc_less_15 +util_hhinc_100_200,"income between 100,000 and 200,000","@df.income.between(100000,199999)",0,coef_hhinc_100_200 +util_ind_bus_srv,Industry type is business services,@df.naics_code==54,0,coef_ind_bus_srv +util_ind_construct,Industry type is construction,@df.naics_code==23,0,coef_ind_construct +util_ind_edu,Industry type is education,@df.naics_code==61,0,coef_ind_edu +util_ind_food_srv,Industry type is food services,@df.naics_code==722,0,coef_ind_food_srv +util_ind_gov,Industry type is government,@df.naics_code==92,0,coef_ind_gov +util_ind_health,Industry type is healthcare,@df.naics_code==62,0,coef_ind_health +util_ind_mgmt_srv,Industry type is management services,@df.naics_code==55,0,coef_ind_mgmt_srv +util_ind_mil,Industry type is military,@df.naics_code==9000,0,coef_ind_mil +util_ind_retail,Industry type is retail,@df.naics_code==721,0,coef_ind_retail +util_is_student,Student status,@df.is_student,0,coef_is_student +utils_sub_asc,Constant,1,0,coef_sub_asc +util_availability,Availability of transit pass,transit_subsidy_available==False,0,coef_unavailable +util_ft_ASC,ptype_ft calibration constant,@df.ptype == 1,0,coef_ft_asc +util_pt_ASC,ptype_pt calibration constant,@df.ptype == 2,0,coef_pt_asc +util_un_ASC,ptype_un calibration constant,@df.ptype == 3,0,coef_un_asc diff --git a/resident/configs/transit_pass_subsidy.yaml b/resident/configs/transit_pass_subsidy.yaml new file mode 100644 index 0000000..a717359 --- /dev/null +++ b/resident/configs/transit_pass_subsidy.yaml @@ -0,0 +1,14 @@ +SPEC: transit_pass_subsidy.csv +COEFFICIENTS: transit_pass_subsidy_coefficients.csv + +#LOGIT_TYPE: NL +LOGIT_TYPE: MNL + +preprocessor: + SPEC: transit_pass_subsidy_preprocessor + DF: persons + TABLES: + - accessibility + - land_use + +CHOOSER_FILTER_COLUMN_NAME: transit_subsidy_available diff --git a/resident/configs/transit_pass_subsidy_coefficients.csv b/resident/configs/transit_pass_subsidy_coefficients.csv new file mode 100644 index 0000000..a7b3539 --- /dev/null +++ b/resident/configs/transit_pass_subsidy_coefficients.csv @@ -0,0 +1,23 @@ +coefficient_name,value,constrain +coef_hhinc_100_200,-0.34,F +coef_hhinc_less_15,-0.866,F +coef_ind_bus_srv,1.07,F +coef_ind_construct,0.904,F +coef_ind_edu,0.635,F +coef_ind_food_srv,-2.11,F +coef_ind_gov,1.46,F +coef_ind_health,0.895,F +coef_ind_mgmt_srv,0.8,F +coef_ind_mil,1.72,F +coef_ind_retail,-1.59,F +coef_is_student,-0.87,F +coef_pkcost,0.063,F +coef_pt,-0.685,F +coef_sub_asc,-4.19,F +coef_tr_emp_hh,0.0431,F +coef_tr_hh_emp,0.0434,F +coef_un,0.508,F +coef_unavailable,-999.0,F +coef_ft_asc,-0.240,F +coef_pt_asc,-0.250,F +coef_un_asc,0.30,F diff --git a/resident/configs/transit_pass_subsidy_preprocessor.csv b/resident/configs/transit_pass_subsidy_preprocessor.csv new file mode 100644 index 0000000..6f213cb --- /dev/null +++ b/resident/configs/transit_pass_subsidy_preprocessor.csv @@ -0,0 +1,5 @@ +Description,Target,Expression +,_transit_ivt_home_to_work_walk_peak,"skim_dict.lookup(persons.home_zone_id, persons.workplace_zone_id, ('WTW_TIV', 'AM'))" +,_transit_ivt_home_to_work_drive_peak,"skim_dict.lookup(persons.home_zone_id, persons.workplace_zone_id, ('KTW_TIV', 'AM'))" +,transit_subsidy_available,((_transit_ivt_home_to_work_walk_peak > 0) | (_transit_ivt_home_to_work_drive_peak > 0)) & (persons.workplace_zone_id > 0) +,trn_wk_access_hh,"np.where(persons.workplace_zone_id > 0, reindex(accessibility.trPkHH, persons.workplace_zone_id), 0)" \ No newline at end of file diff --git a/resident/configs/trip_destination.csv b/resident/configs/trip_destination.csv new file mode 100644 index 0000000..db222c6 --- /dev/null +++ b/resident/configs/trip_destination.csv @@ -0,0 +1,51 @@ +Label,Description,Expression,work,univ,school,escort,shopping,othmaint,eatout,social,othdiscr,atwork +local_dist_od,,"_od_DIST@od_skims[('SOV_M_DIST', 'MD')]",1,1,1,1,1,1,1,1,1,1 +local_dist_dp,,"_dp_DIST@dp_skims[('SOV_M_DIST', 'MD')]",1,1,1,1,1,1,1,1,1,1 +loca_dist_op,,"_op_DIST@op_skims[('SOV_M_DIST', 'MD')]",1,1,1,1,1,1,1,1,1,1 +local_dist_nd,,"_nd_DIST@nd_skims[('SOV_M_DIST', 'MD')]",1,1,1,1,1,1,1,1,1,1 +distance_deviation,distance deviation,_dist_dev@(_od_DIST +_dp_DIST - _op_DIST),1,1,1,1,1,1,1,1,1,1 +dist_deviation_logged,logged deviation distance,"_dist_dev_logged@np.where((_dist_dev > 0), np.log(_dist_dev),0)",1,1,1,1,1,1,1,1,1,1 +#,,,,,,,,,,,, +util_sizeterm,size term,"@np.log1p(size_terms.get(df.dest_taz, df.purpose))",coef_one,coef_one,coef_one,coef_one,coef_one,coef_one,coef_one,coef_one,coef_one,coef_one +util_Sampleofalternativescorrectionfactor,Sample of alternatives correction factor,"@np.minimum(np.log(df.pick_count/df.prob), 60)",coef_one,coef_one,coef_one,coef_one,coef_one,coef_one,coef_one,coef_one,coef_one,coef_one +util_Modechoicelogsum,Mode choice logsum,od_logsum + dp_logsum,coef_mode_choice_logsum_mandatory,coef_mode_choice_logsum_mandatory,coef_mode_choice_logsum_mandatory,coef_mode_choice_logsum_maint,coef_mode_choice_logsum_maint,coef_mode_choice_logsum_maint,coef_mode_choice_logsum_discr,coef_mode_choice_logsum_discr,coef_mode_choice_logsum_discr,coef_mode_choice_logsum_discr +#,,,,,,,,,,,, +util_DistanceDeviationLinearRelative,Distance Deviation Linear - Relative,"@np.minimum((_dist_dev)/_op_DIST, 100)",,,,coef_DistanceDeviationLinearRelative_maint,coef_DistanceDeviationLinearRelative_maint,coef_DistanceDeviationLinearRelative_maint,coef_DistanceDeviationLinearRelative_discr,coef_DistanceDeviationLinearRelative_discr,coef_DistanceDeviationLinearRelative_discr,coef_DistanceDeviationLinearRelative_discr +util_DistanceDeviationLinearAbsolute,Distance Deviation Linear - Absolute,@(_dist_dev),coef_DistanceDeviationLinearAbsolute_mandatory,coef_DistanceDeviationLinearAbsolute_mandatory,coef_DistanceDeviationLinearAbsolute_mandatory,coef_DistanceDeviationLinearAbsolute_maint,coef_DistanceDeviationLinearAbsolute_maint,coef_DistanceDeviationLinearAbsolute_maint,coef_DistanceDeviationLinearAbsolute_discr,coef_DistanceDeviationLinearAbsolute_discr,coef_DistanceDeviationLinearAbsolute_discr,coef_DistanceDeviationLinearAbsolute_discr +util_LogofDistanceRelativeDeviation,Log of Distance Relative Deviation,"@np.where((_dist_dev > 0), np.log(np.minimum((_dist_dev)/_op_DIST, 100)), 0)",,,,coef_LogofDistanceRelativeDeviation_maint,coef_LogofDistanceRelativeDeviation_maint,coef_LogofDistanceRelativeDeviation_maint,coef_LogofDistanceRelativeDeviation_discr,coef_LogofDistanceRelativeDeviation_discr,coef_LogofDistanceRelativeDeviation_discr,coef_LogofDistanceRelativeDeviation_discr +util_LogofDistanceAbsoluteDeviation,Log of Distance Absolute Deviation,"@(_dist_dev_logged)",coef_LogofDistanceAbsoluteDeviation_mandatory,coef_LogofDistanceAbsoluteDeviation_mandatory,coef_LogofDistanceAbsoluteDeviation_mandatory,coef_LogofDistanceAbsoluteDeviation_maint,coef_LogofDistanceAbsoluteDeviation_maint,coef_LogofDistanceAbsoluteDeviation_maint,coef_LogofDistanceAbsoluteDeviation_discr,coef_LogofDistanceAbsoluteDeviation_discr,coef_LogofDistanceAbsoluteDeviation_discr,coef_LogofDistanceAbsoluteDeviation_discr +util_DistanceDeviationsquaredRelative,Distance Deviation squared - Relative,"@np.power(np.minimum((_dist_dev)/_op_DIST, 100),2)",,,,coef_DistanceDeviationsquaredRelative_maint,coef_DistanceDeviationsquaredRelative_maint,coef_DistanceDeviationsquaredRelative_maint,coef_DistanceDeviationsquaredRelative_discr,coef_DistanceDeviationsquaredRelative_discr,coef_DistanceDeviationsquaredRelative_discr,coef_DistanceDeviationsquaredRelative_discr +util_DistanceDeviationsquaredAbsolute,Distance Deviation squared - Absolute,"@np.power(_dist_dev,2)",coef_DistanceDeviationsquaredAbsolute_mandatory,coef_DistanceDeviationsquaredAbsolute_mandatory,coef_DistanceDeviationsquaredAbsolute_mandatory,coef_DistanceDeviationsquaredAbsolute_maint,coef_DistanceDeviationsquaredAbsolute_maint,coef_DistanceDeviationsquaredAbsolute_maint,coef_DistanceDeviationsquaredAbsolute_discr,coef_DistanceDeviationsquaredAbsolute_discr,coef_DistanceDeviationsquaredAbsolute_discr,coef_DistanceDeviationsquaredAbsolute_discr +util_DistanceDeviation2ndstopofhalftour,Distance Deviation - 2nd stop of half tour,@(df.trip_num==2) * (_dist_dev),coef_DistanceDeviation2ndstopofhalftour_mandatory,coef_DistanceDeviation2ndstopofhalftour_mandatory,coef_DistanceDeviation2ndstopofhalftour_mandatory,coef_DistanceDeviation2ndstopofhalftour_maint,coef_DistanceDeviation2ndstopofhalftour_maint,coef_DistanceDeviation2ndstopofhalftour_maint,coef_DistanceDeviation2ndstopofhalftour_discr,coef_DistanceDeviation2ndstopofhalftour_discr,coef_DistanceDeviation2ndstopofhalftour_discr,coef_DistanceDeviation2ndstopofhalftour_discr +util_DistanceDeviation3rdmorestoponhalftour,Distance Deviation - 3rd+ stop on half tour,@(df.trip_num>2) * (_dist_dev),coef_DistanceDeviation3rdmorestoponhalftour_mandatory,coef_DistanceDeviation3rdmorestoponhalftour_mandatory,coef_DistanceDeviation3rdmorestoponhalftour_mandatory,coef_DistanceDeviation3rdmorestoponhalftour_maint,coef_DistanceDeviation3rdmorestoponhalftour_maint,coef_DistanceDeviation3rdmorestoponhalftour_maint,coef_DistanceDeviation3rdmorestoponhalftour_discr,coef_DistanceDeviation3rdmorestoponhalftour_discr,coef_DistanceDeviation3rdmorestoponhalftour_discr,coef_DistanceDeviation3rdmorestoponhalftour_discr +util_DistanceDeviationNumberofStopsonthehalftour,Distance Deviation - Number of Stops on the half-tour,"@np.minimum(_dist_dev,7.0)*df.trip_count",coef_DistanceDeviationNumberofStopsonthehalftour_mandatory,coef_DistanceDeviationNumberofStopsonthehalftour_mandatory,coef_DistanceDeviationNumberofStopsonthehalftour_mandatory,coef_DistanceDeviationNumberofStopsonthehalftour_maint,coef_DistanceDeviationNumberofStopsonthehalftour_maint,coef_DistanceDeviationNumberofStopsonthehalftour_maint,coef_DistanceDeviationNumberofStopsonthehalftour_discr,coef_DistanceDeviationNumberofStopsonthehalftour_discr,coef_DistanceDeviationNumberofStopsonthehalftour_discr,coef_DistanceDeviationNumberofStopsonthehalftour_discr +util_DistanceDeviationwalkbiketour,Distance Deviation - walk/bike tour,nonmotorTour * (_dist_dev),coef_DistanceDeviationwalkbiketour_mandatory,coef_DistanceDeviationwalkbiketour_mandatory,coef_DistanceDeviationwalkbiketour_mandatory,coef_DistanceDeviationwalkbiketour_maint,coef_DistanceDeviationwalkbiketour_maint,coef_DistanceDeviationwalkbiketour_maint,coef_DistanceDeviationwalkbiketour_discr,coef_DistanceDeviationwalkbiketour_discr,coef_DistanceDeviationwalkbiketour_discr,coef_DistanceDeviationwalkbiketour_discr +util_DistanceDeviationworkstoppurpose,Distance Deviation - work stop purpose,workStop *_dist_dev,coef_DistanceDeviationworkstoppurpose_mandatory,coef_DistanceDeviationworkstoppurpose_mandatory,coef_DistanceDeviationworkstoppurpose_mandatory,coef_DistanceDeviationworkstoppurpose_maint,coef_DistanceDeviationworkstoppurpose_maint,coef_DistanceDeviationworkstoppurpose_maint,coef_DistanceDeviationworkstoppurpose_discr,coef_DistanceDeviationworkstoppurpose_discr,coef_DistanceDeviationworkstoppurpose_discr,coef_DistanceDeviationworkstoppurpose_discr +util_DistanceDeviationuniversitystoppurpose,Distance Deviation - university stop purpose,"@df.univStop * np.minimum(_dist_dev,5.0)",coef_DistanceDeviationuniversitystoppurpose_mandatory,coef_DistanceDeviationuniversitystoppurpose_mandatory,coef_DistanceDeviationuniversitystoppurpose_mandatory,coef_DistanceDeviationuniversitystoppurpose_maint,coef_DistanceDeviationuniversitystoppurpose_maint,coef_DistanceDeviationuniversitystoppurpose_maint,coef_DistanceDeviationuniversitystoppurpose_discr,coef_DistanceDeviationuniversitystoppurpose_discr,coef_DistanceDeviationuniversitystoppurpose_discr,coef_DistanceDeviationuniversitystoppurpose_discr +util_DistanceDeviationmaintenancestoppurpose,Distance Deviation - maintenance stop purpose,othmainStop * _dist_dev,coef_DistanceDeviationmaintenancestoppurpose_mandatory,coef_DistanceDeviationmaintenancestoppurpose_mandatory,coef_DistanceDeviationmaintenancestoppurpose_mandatory,coef_DistanceDeviationmaintenancestoppurpose_maint,coef_DistanceDeviationmaintenancestoppurpose_maint,coef_DistanceDeviationmaintenancestoppurpose_maint,coef_DistanceDeviationmaintenancestoppurpose_discr,coef_DistanceDeviationmaintenancestoppurpose_discr,coef_DistanceDeviationmaintenancestoppurpose_discr,coef_DistanceDeviationmaintenancestoppurpose_discr +util_DistanceDeviationdiscretionarystoppurpose,Distance Deviation - discretionary stop purpose,"@df.discStop * np.minimum(_dist_dev,8.0)",coef_DistanceDeviationdiscretionarystoppurpose_mandatory,coef_DistanceDeviationdiscretionarystoppurpose_mandatory,coef_DistanceDeviationdiscretionarystoppurpose_mandatory,coef_DistanceDeviationdiscretionarystoppurpose_maint,coef_DistanceDeviationdiscretionarystoppurpose_maint,coef_DistanceDeviationdiscretionarystoppurpose_maint,coef_DistanceDeviationdiscretionarystoppurpose_discr,coef_DistanceDeviationdiscretionarystoppurpose_discr,coef_DistanceDeviationdiscretionarystoppurpose_discr,coef_DistanceDeviationdiscretionarystoppurpose_discr +util_DistanceDeviationshoppingstoppurpose,Distance Deviation - shopping stop purpose,shopStop *_dist_dev,,,,coef_DistanceDeviationshoppingstoppurpose_maint,coef_DistanceDeviationshoppingstoppurpose_maint,coef_DistanceDeviationshoppingstoppurpose_maint,coef_DistanceDeviationshoppingstoppurpose_discr,coef_DistanceDeviationshoppingstoppurpose_discr,coef_DistanceDeviationshoppingstoppurpose_discr,coef_DistanceDeviationshoppingstoppurpose_discr +util_DistanceDeviationeatoutstoppurpose,Distance Deviation - eatout stop purpose,eatStop *_dist_dev,,,,coef_DistanceDeviationeatoutstoppurpose_maint,coef_DistanceDeviationeatoutstoppurpose_maint,coef_DistanceDeviationeatoutstoppurpose_maint,coef_DistanceDeviationeatoutstoppurpose_discr,coef_DistanceDeviationeatoutstoppurpose_discr,coef_DistanceDeviationeatoutstoppurpose_discr,coef_DistanceDeviationeatoutstoppurpose_discr +util_DistanceDeviationsocialstoppurpose,Distance Deviation - social stop purpose,socStop * _dist_dev,,,,coef_DistanceDeviationsocialstoppurpose_maint,coef_DistanceDeviationsocialstoppurpose_maint,coef_DistanceDeviationsocialstoppurpose_maint,coef_DistanceDeviationsocialstoppurpose_discr,coef_DistanceDeviationsocialstoppurpose_discr,coef_DistanceDeviationsocialstoppurpose_discr,coef_DistanceDeviationsocialstoppurpose_discr +util_DistanceDeviationworkstoppurpose_log,Distance Deviation - work stop purpose,workStop * _dist_dev_logged,coef_DistanceDeviationworkstoppurpose,coef_DistanceDeviationworkstoppurpose,coef_DistanceDeviationworkstoppurpose,,,,,,, +util_DistanceDeviationshoppingstoppurpose_log,Distance Deviation - shopping stop purpose,shopStop * _dist_dev_logged,coef_DistanceDeviationshoppingstoppurpose_mandatory,coef_DistanceDeviationshoppingstoppurpose_mandatory,coef_DistanceDeviationshoppingstoppurpose_mandatory,,,,,,, +util_DistanceDeviationsocialvisitingstoppurpose_log,Distance Deviation - socialvisiting stop purpose,socStop * _dist_dev_logged,coef_DistanceDeviationsocialvisitingstoppurpose_mandatory,coef_DistanceDeviationsocialvisitingstoppurpose_mandatory,coef_DistanceDeviationsocialvisitingstoppurpose_mandatory,,,,,,, +util_DistanceDeviationSchool,Distance Deviation - School Tour purpose,schoolTour * _dist_dev,coef_DistanceDeviationSchool_mandatory,coef_DistanceDeviationSchool_mandatory,coef_DistanceDeviationSchool_mandatory,,,,,,, +util_DistanceDeviationUniversity,Distance Deviation - University Tour purpose,univTour * _dist_dev,coef_DistanceDeviationUniversity_mandatory,coef_DistanceDeviationUniversity_mandatory,coef_DistanceDeviationUniversity_mandatory,,,,,,, +util_DistanceDeviationShopping,Distance Deviation - Shopping Tour purpose,shopTour * _dist_dev,,,,coef_DistanceDeviationothmaint_maint,coef_DistanceDeviationothmaint_maint,coef_DistanceDeviationothmaint_maint,,,, +util_DistanceDeviationOthMaint,Distance Deviation - Other maintenanceTour purpose,othmainTour * _dist_dev,,,,coef_DistanceDeviationshopping_maint,coef_DistanceDeviationshopping_maint,coef_DistanceDeviationshopping_maint,,,, +util_DistanceDeviationDiscr,Distance Deviation - DiscTour purpose,discTour * _dist_dev,,,,,,,coef_DistanceDeviationdiscr_maint,coef_DistanceDeviationdiscr_maint,coef_DistanceDeviationdiscr_maint,coef_DistanceDeviationdiscr_maint +util_DistanceDeviationAtwork,Distance Deviation - AtworkTour purpose,atworkTour * _dist_dev,,,,,,,coef_DistanceDeviationatwork_maint,coef_DistanceDeviationatwork_maint,coef_DistanceDeviationatwork_maint,coef_DistanceDeviationatwork_maint +util_DistanceDeviationIncomeLessthan60k,"Distance Deviation - Income Less than $60,000","@(df.income_trips<60000) * np.minimum(_dist_dev,13.0)",coef_DistanceDeviationIncomeLessthan60k_mandatory,coef_DistanceDeviationIncomeLessthan60k_mandatory,coef_DistanceDeviationIncomeLessthan60k_mandatory,coef_DistanceDeviationIncomeLessthan60k_maint,coef_DistanceDeviationIncomeLessthan60k_maint,coef_DistanceDeviationIncomeLessthan60k_maint,coef_DistanceDeviationIncomeLessthan60k_discr,coef_DistanceDeviationIncomeLessthan60k_discr,coef_DistanceDeviationIncomeLessthan60k_discr,coef_DistanceDeviationIncomeLessthan60k_discr +util_DistanceDeviationFemale,Distance Deviation - Female,(female & (is_joint==0)) * (_dist_dev),coef_DistanceDeviationFemale_mandatory,coef_DistanceDeviationFemale_mandatory,coef_DistanceDeviationFemale_mandatory,coef_DistanceDeviationFemale_maint,coef_DistanceDeviationFemale_maint,coef_DistanceDeviationFemale_maint,coef_DistanceDeviationFemale_discr,coef_DistanceDeviationFemale_discr,coef_DistanceDeviationFemale_discr,coef_DistanceDeviationFemale_discr +util_DistanceDeviationAgebetween35and54years,Distance Deviation - Age between 35 and 54 years,"@(df.age_trips.between(35,54)*df.is_joint==0) * (_dist_dev)",coef_DistanceDeviationAgebetween35and54years_mandatory,coef_DistanceDeviationAgebetween35and54years_mandatory,coef_DistanceDeviationAgebetween35and54years_mandatory,coef_DistanceDeviationAgebetween35and54years_maint,coef_DistanceDeviationAgebetween35and54years_maint,coef_DistanceDeviationAgebetween35and54years_maint,coef_DistanceDeviationAgebetween35and54years_discr,coef_DistanceDeviationAgebetween35and54years_discr,coef_DistanceDeviationAgebetween35and54years_discr,coef_DistanceDeviationAgebetween35and54years_discr +util_DistanceDeviationAgeover54years,Distance Deviation - Age over 54 years,@(df.age_trips>54) * (df.is_joint==0) * (_dist_dev),coef_DistanceDeviationAgeover54years_mandatory,coef_DistanceDeviationAgeover54years_mandatory,coef_DistanceDeviationAgeover54years_mandatory,coef_DistanceDeviationAgeover54years_maint,coef_DistanceDeviationAgeover54years_maint,coef_DistanceDeviationAgeover54years_maint,coef_DistanceDeviationAgeover54years_discr,coef_DistanceDeviationAgeover54years_discr,coef_DistanceDeviationAgeover54years_discr,coef_DistanceDeviationAgeover54years_discr +util_DistanceRatio,Distance Ratio,(_nd_DIST)/ (_nd_DIST + _dp_DIST),coef_DistanceRatio_mandatory,coef_DistanceRatio_mandatory,coef_DistanceRatio_mandatory,coef_DistanceRatio_maint,coef_DistanceRatio_maint,coef_DistanceRatio_maint,coef_DistanceRatio_discr,coef_DistanceRatio_discr,coef_DistanceRatio_discr,coef_DistanceRatio_discr +util_DistanceRatioFirstOutboundStop,Distance Ratio - First Outbound Stop,@(df.trip_num==1)*(df.outbound) * ((_nd_DIST)/ (_nd_DIST + _dp_DIST)),coef_DistanceRatioFirstOutboundStop_mandatory,coef_DistanceRatioFirstOutboundStop_mandatory,coef_DistanceRatioFirstOutboundStop_mandatory,coef_DistanceRatioFirstOutboundStop_maint,coef_DistanceRatioFirstOutboundStop_maint,coef_DistanceRatioFirstOutboundStop_maint,coef_DistanceRatioFirstOutboundStop_discr,coef_DistanceRatioFirstOutboundStop_discr,coef_DistanceRatioFirstOutboundStop_discr,coef_DistanceRatioFirstOutboundStop_discr +util_DistanceRatioFirstInboundStop,Distance Ratio - First Inbound Stop,@(df.trip_num==1)*(df.outbound==0) * ((_nd_DIST)/ (_nd_DIST + _dp_DIST)),coef_DistanceRatioFirstInboundStop_mandatory,coef_DistanceRatioFirstInboundStop_mandatory,coef_DistanceRatioFirstInboundStop_mandatory,coef_DistanceRatioFirstInboundStop_maint,coef_DistanceRatioFirstInboundStop_maint,coef_DistanceRatioFirstInboundStop_maint,coef_DistanceRatioFirstInboundStop_discr,coef_DistanceRatioFirstInboundStop_discr,coef_DistanceRatioFirstInboundStop_discr,coef_DistanceRatioFirstInboundStop_discr +util_DistanceRatioMandatoryOutboundTour,Distance Ratio - Mandatory Outbound Tour,(outbound)* mandTour * ((_nd_DIST)/ (_nd_DIST + _dp_DIST)),coef_DistanceRatioMandatoryOutboundTour_mandatory,coef_DistanceRatioMandatoryOutboundTour_mandatory,coef_DistanceRatioMandatoryOutboundTour_mandatory,,,,,,, +util_DistanceRatioMandatoryInboundTour,Distance Ratio - Mandatory Inbound Tour,(outbound==0) * mandTour * ((_nd_DIST)/ (_nd_DIST + _dp_DIST)),coef_DistanceRatioMandatoryInboundTour_mandatory,coef_DistanceRatioMandatoryInboundTour_mandatory,coef_DistanceRatioMandatoryInboundTour_mandatory,,,,,,, +util_DistanceDeviationSquareWork,Distance Deviation Square - Work Tour purpose,"@df.workTour * np.power(_dist_dev,2)",coef_DistanceDeviationSquareWork_mandatory,coef_DistanceDeviationSquareWork_mandatory,coef_DistanceDeviationSquareWork_mandatory,,,,,,, +util_DistanceDeviationSocial,Distance Deviation SocialTour purpose,socTour * _dist_dev,,,,,,,coef_DistanceDeviationDiscr,coef_DistanceDeviationDiscr,coef_DistanceDeviationDiscr,coef_DistanceDeviationDiscr +util_DistanceDeviationDiscr,Distance Deviation DiscrTour purpose,discTour * _dist_dev,,,,,,,coef_DistanceDeviationDiscr,coef_DistanceDeviationDiscr,coef_DistanceDeviationDiscr,coef_DistanceDeviationDiscr +util_DistanceDeviationAtwork_calibration,Distance Deviation - AtworkTour purpose,atworkTour * _dist_dev,,,,coef_DistanceDeviationatwork_calib,coef_DistanceDeviationatwork_calib,coef_DistanceDeviationatwork_calib,coef_DistanceDeviationatwork_calib,coef_DistanceDeviationatwork_calib,coef_DistanceDeviationatwork_calib,coef_DistanceDeviationatwork_calib diff --git a/resident/configs/trip_destination.yaml b/resident/configs/trip_destination.yaml new file mode 100644 index 0000000..ee6c9fc --- /dev/null +++ b/resident/configs/trip_destination.yaml @@ -0,0 +1,46 @@ +SAMPLE_SPEC: trip_destination_sample.csv +SPEC: trip_destination.csv +COEFFICIENTS: trip_destination_coefficients.csv + +SAMPLE_SIZE: 30 + +DESTINATION_SAMPLE_SPEC: trip_destination_sample.csv +DESTINATION_SPEC: trip_destination.csv + +LOGSUM_SETTINGS: trip_mode_choice.yaml + +# optional (comment out if not desired) +DEST_CHOICE_LOGSUM_COLUMN_NAME: destination_logsum + +# comment out DEST_CHOICE_LOGSUM_COLUMN_NAME if saved alt logsum table +DEST_CHOICE_SAMPLE_TABLE_NAME: trip_destination_sample + +# model-specific logsum-related settings +TRIP_ORIGIN: origin +ALT_DEST_COL_NAME: dest_taz +PRIMARY_ORIGIN: tour_leg_origin +PRIMARY_DEST: tour_leg_dest # must be created in preprocessor + +# tour_mode is already in trips table, so we don't need it from tours +# (it is assigned in trip_destination_annotate_trips_preprocessor ) +REDUNDANT_TOURS_MERGED_CHOOSER_COLUMNS: + - tour_mode + + + +preprocessor: + SPEC: trip_destination_annotate_trips_preprocessor + DF: trips + TABLES: + - tours + - persons + - households + - land_use + + +# drop failed trips and cleanup failed trip leg_mates for consistency +# (i.e. adjust trip_count, trip_num, first for missing failed trips) +CLEANUP: False + +# this setting is used by testing code to force failed trip_destination +# fail_some_trips_for_testing: False diff --git a/resident/configs/trip_destination_annotate_trips_preprocessor.csv b/resident/configs/trip_destination_annotate_trips_preprocessor.csv new file mode 100644 index 0000000..bad7e84 --- /dev/null +++ b/resident/configs/trip_destination_annotate_trips_preprocessor.csv @@ -0,0 +1,42 @@ +Description,Target,Expression +#,, +,tour_mode,"reindex(tours.tour_mode, df.tour_id)" +,is_joint,"reindex(tours.tour_category, df.tour_id) == 'joint'" +,tour_leg_origin,"np.where(df.outbound,reindex(tours.origin, df.tour_id), reindex(tours.destination, df.tour_id))" +,tour_leg_dest,"np.where(df.outbound,reindex(tours.destination, df.tour_id), reindex(tours.origin, df.tour_id))" +#,, +,_tod,"np.where(df.outbound,reindex_i(tours.start, df.tour_id),reindex_i(tours.end, df.tour_id))" +,trip_period,network_los.skim_time_period_label(_tod) +#,, +adding _trips to avoid conflict with the variables in the tours_merged,income_trips,"reindex(households.income, df.household_id)" +adding _trips to avoid conflict with the variables in the tours_merged,age_trips,"reindex(persons.age, df.person_id)" +,_ebike_owner_trips,"reindex(households.ebike_owner, df.household_id)" +,ebike_owner_trips,"np.where(_ebike_owner_trips,1,0)" +,female,"reindex(persons.female, df.person_id)" +#,age_55p,"reindex(persons.age_55_p, df.person_id)" +#,age_35_54,"reindex(persons.age_35_to_54, df.person_id)" +,workTour,"np.where(df.primary_purpose == 'work', 1, 0)" +,schoolTour,"np.where(df.primary_purpose == 'school', 1, 0)" +,univTour,"np.where(df.primary_purpose == 'univ', 1, 0)" +,shopTour,"np.where(df.primary_purpose == 'shopping', 1, 0)" +,othmainTour,"np.where(df.primary_purpose == 'othmaint', 1, 0)" +,discTour,"np.where(df.primary_purpose == 'discr', 1, 0)" +,socTour,"np.where(df.primary_purpose == 'social', 1, 0)" +,atworkTour,"np.where(df.primary_purpose == 'atwork', 1, 0)" +,mandTour,"np.where(df.primary_purpose.isin(['work', 'school', 'univ']), 1, 0)" +,workStop,"np.where(df.purpose == 'work', 1, 0)" +,univStop,"np.where(df.purpose == 'univ', 1, 0)" +,othmainStop,"np.where(df.purpose == 'othmaint', 1, 0)" +,discStop,"np.where(df.purpose == 'othdiscr', 1, 0)" +,shopStop,"np.where(df.purpose == 'shopping', 1, 0)" +,eatStop,"np.where(df.purpose == 'eatout', 1, 0)" +,socStop,"np.where(df.purpose == 'social', 1, 0)" +,nonmotorTour,"np.where(tour_mode.isin(['WALK','BIKE']), 1, 0)" +,walkTour,"np.where(tour_mode == 'WALK', 1, 0)" +,bikeTour,"np.where(tour_mode == 'BIKE', 1, 0)" +,microTour,"np.where((tour_mode == 'ESCOOTER') | (tour_mode == 'EBIKE'), 1, 0)" +,ebikeTour,"np.where((tour_mode == 'EBIKE'), 1, 0)" +Micromobility access Time,o_MicroAccessTime,"reindex(land_use.ESCOOACCTIME,df.origin)" +,max_walk_distance,max_walk_distance +,max_bike_distance,max_bike_distance +,microAccessThreshold,microAccessThreshold \ No newline at end of file diff --git a/resident/configs/trip_destination_coefficients.csv b/resident/configs/trip_destination_coefficients.csv new file mode 100644 index 0000000..39e23a7 --- /dev/null +++ b/resident/configs/trip_destination_coefficients.csv @@ -0,0 +1,89 @@ +coefficient_name,value,constrain +coef_UNAVAILABLE,-999,T +coef_one,1,T +#,, +coef_mode_choice_logsum_mandatory,1.31417,F +coef_DistanceDeviationLinearAbsolute_mandatory,0,F +coef_LogofDistanceAbsoluteDeviation_mandatory,-0.83965,F +coef_DistanceDeviationsquaredAbsolute_mandatory,0,F +coef_DistanceDeviation2ndstopofhalftour_mandatory,-0.06176,F +coef_DistanceDeviation3rdmorestoponhalftour_mandatory,-0.07764,F +coef_DistanceDeviationNumberofStopsonthehalftour_mandatory,0,F +coef_DistanceDeviationwalkbiketour_mandatory,0,F +coef_DistanceDeviationworkstoppurpose_mandatory,-0.10657,F +coef_DistanceDeviationuniversitystoppurpose_mandatory,0.08433,F +coef_DistanceDeviationmaintenancestoppurpose_mandatory,0.02921,F +coef_DistanceDeviationdiscretionarystoppurpose_mandatory,0.07266,F +coef_DistanceDeviationworkstoppurpose,1.02985,F +coef_DistanceDeviationshoppingstoppurpose_mandatory,-0.19942,F +coef_DistanceDeviationsocialvisitingstoppurpose_mandatory,0.65223,F +coef_DistanceDeviationSchool_mandatory,0.11216,F +coef_DistanceDeviationUniversity_mandatory,0.02587,F +coef_DistanceDeviationIncomeLessthan60k_mandatory,0.0227,F +coef_DistanceDeviationFemale_mandatory,-0.07066,F +coef_DistanceDeviationAgebetween35and54years_mandatory,-0.08953,F +coef_DistanceDeviationAgeover54years_mandatory,-0.0987,F +coef_DistanceRatio_mandatory,-1.99261,F +coef_DistanceRatioFirstOutboundStop_mandatory,-0.6487,F +coef_DistanceRatioFirstInboundStop_mandatory,1.49723,F +coef_DistanceRatioMandatoryOutboundTour_mandatory,0,F +coef_DistanceRatioMandatoryInboundTour_mandatory,0.91734,F +coef_DistanceDeviationSquareWork_mandatory,0,F +#,, +coef_mode_choice_logsum_maint,1.00663,F +coef_DistanceDeviationLinearRelative_maint,0,F +coef_DistanceDeviationLinearAbsolute_maint,-0.0615,F +coef_LogofDistanceRelativeDeviation_maint,0,F +coef_LogofDistanceAbsoluteDeviation_maint,-0.94056,F +coef_DistanceDeviationsquaredRelative_maint,0,F +coef_DistanceDeviationsquaredAbsolute_maint,-0.00022,F +coef_DistanceDeviation2ndstopofhalftour_maint,-0.03146,F +coef_DistanceDeviation3rdmorestoponhalftour_maint,-0.0631,F +coef_DistanceDeviationNumberofStopsonthehalftour_maint,0.02045,F +coef_DistanceDeviationwalkbiketour_maint,-1.00489,F +coef_DistanceDeviationworkstoppurpose_maint,0.06367,F +coef_DistanceDeviationuniversitystoppurpose_maint,0.09372,F +coef_DistanceDeviationmaintenancestoppurpose_maint,0.02109,F +coef_DistanceDeviationdiscretionarystoppurpose_maint,0.05744,F +coef_DistanceDeviationshoppingstoppurpose_maint,-0.02269,F +coef_DistanceDeviationeatoutstoppurpose_maint,0.02249,F +coef_DistanceDeviationsocialstoppurpose_maint,0.06855,F +coef_DistanceDeviationothmaint_maint,0.02685,F +coef_DistanceDeviationshopping_maint,0.03415,F +coef_DistanceDeviationIncomeLessthan60k_maint,0.03634,F +coef_DistanceDeviationFemale_maint,-0.02723,F +coef_DistanceDeviationAgebetween35and54years_maint,-0.01022,F +coef_DistanceDeviationAgeover54years_maint,-0.01353,F +coef_DistanceRatio_maint,-0.4485,F +coef_DistanceRatioFirstOutboundStop_maint,-1.51595,F +coef_DistanceRatioFirstInboundStop_maint,1.80508,F +#,, +coef_mode_choice_logsum_discr,1.00663, +coef_DistanceDeviationLinearRelative_discr,0, +coef_DistanceDeviationLinearAbsolute_discr,-0.0615, +coef_LogofDistanceRelativeDeviation_discr,0, +coef_LogofDistanceAbsoluteDeviation_discr,-0.94056, +coef_DistanceDeviationsquaredRelative_discr,0, +coef_DistanceDeviationsquaredAbsolute_discr,-0.00022, +coef_DistanceDeviation2ndstopofhalftour_discr,-0.03146, +coef_DistanceDeviation3rdmorestoponhalftour_discr,-0.0631, +coef_DistanceDeviationNumberofStopsonthehalftour_discr,0.02045, +coef_DistanceDeviationwalkbiketour_discr,-1.00489, +coef_DistanceDeviationworkstoppurpose_discr,0.06367, +coef_DistanceDeviationuniversitystoppurpose_discr,0.09372, +coef_DistanceDeviationmaintenancestoppurpose_discr,0.02109, +coef_DistanceDeviationdiscretionarystoppurpose_discr,0.05744, +coef_DistanceDeviationshoppingstoppurpose_discr,-0.02269, +coef_DistanceDeviationeatoutstoppurpose_discr,0.02249, +coef_DistanceDeviationsocialstoppurpose_discr,0.06855, +coef_DistanceDeviationdiscr_maint,-0.045, +coef_DistanceDeviationatwork_maint,0.08779, +coef_DistanceDeviationIncomeLessthan60k_discr,0.03634, +coef_DistanceDeviationFemale_discr,-0.02723, +coef_DistanceDeviationAgebetween35and54years_discr,-0.01022, +coef_DistanceDeviationAgeover54years_discr,-0.01353, +coef_DistanceRatio_discr,-0.4485, +coef_DistanceRatioFirstOutboundStop_discr,-1.51595, +coef_DistanceRatioFirstInboundStop_discr,1.80508, +coef_DistanceDeviationDiscr,0.02, +coef_DistanceDeviationatwork_calib,-3.400, \ No newline at end of file diff --git a/resident/configs/trip_destination_sample.csv b/resident/configs/trip_destination_sample.csv new file mode 100644 index 0000000..853a754 --- /dev/null +++ b/resident/configs/trip_destination_sample.csv @@ -0,0 +1,19 @@ +Description,Expression,work,univ,school,escort,shopping,eatout,othmaint,social,othdiscr,atwork +,"_od_DIST@od_skims[('SOV_M_DIST', 'MD')]",1,1,1,1,1,1,1,1,1,1 +,"_dp_DIST@dp_skims[('SOV_M_DIST', 'MD')]",1,1,1,1,1,1,1,1,1,1 +,"_op_DIST@op_skims[('SOV_M_DIST', 'MD')]",1,1,1,1,1,1,1,1,1,1 +# next line gets max MAZ micromobility access time in destination TAZ +,"_d_microAccTime@land_use.sort_values(by='ESCOOACCTIME',ascending=False).drop_duplicates('TAZ',keep='first').set_index('TAZ')['ESCOOACCTIME'].reindex(df.dest_taz)",1,1,1,1,1,1,1,1,1,1 +#,,,,,,,,,,, +size term,"@np.log1p(size_terms.get(df.dest_taz, df.purpose))",1,1,1,1,1,1,1,1,1,1 +no attractions,"@size_terms.get(df.dest_taz, df.purpose) == 0",-999,-999,-999,-999,-999,-999,-999,-999,-999,-999 +#,,,,,,,,,,, +,"@df.walkTour * (np.where(_od_DIST > df.max_walk_distance,1,0) + np.where(_dp_DIST > df.max_walk_distance,1,0))",-10,-10,-10,-10,-10,-10,-10,-10,-10,-10 +,@df.walkTour * (_od_DIST + _dp_DIST),-0.5,-0.5,-0.5,-0.5,-0.5,-0.5,-0.5,-0.5,-0.5,-0.5 +#,,,,,,,,,,, +,"@df.bikeTour * (np.where(_od_DIST > df.max_bike_distance,1,0) + np.where(_dp_DIST > df.max_bike_distance,1,0))",-10,-10,-10,-10,-10,-10,-10,-10,-10,-10 +,@df.bikeTour * (_od_DIST + _dp_DIST - _op_DIST),-0.05,-0.05,-0.05,-0.05,-0.05,-0.05,-0.05,-0.05,-0.05,-0.05 +,@df.bikeTour * ((_od_DIST < max_bike_distance) & (_dp_DIST < max_bike_distance)),-999,-999,-999,-999,-999,-999,-999,-999,-999,-999 +#,,,,,,,,,,, +,@(df.nonmotorTour==0) * (_od_DIST + _dp_DIST - _op_DIST),-0.05,-0.05,-0.05,-0.05,-0.05,-0.05,-0.05,-0.05,-0.05,-0.05 +,"@(df.microTour * (np.where(_d_microAccTime > df.microAccessThreshold,1,0) + np.where(df.o_MicroAccessTime > df.microAccessThreshold,1,0)) * np.where(df.ebike_owner_trips * df.ebikeTour,0,1))",-10,-10,-10,-10,-10,-10,-10,-10,-10,-10 diff --git a/resident/configs/trip_mode_choice.csv b/resident/configs/trip_mode_choice.csv new file mode 100644 index 0000000..ee28216 --- /dev/null +++ b/resident/configs/trip_mode_choice.csv @@ -0,0 +1,238 @@ +Label,Description,Expression,DRIVEALONE,SHARED2,SHARED3,WALK,BIKE,WALK_TRANSIT,PNR_TRANSIT,KNR_TRANSIT,BIKE_TRANSIT,TAXI,TNC_SINGLE,TNC_SHARED,SCH_BUS,EBIKE,ESCOOTER +#,Drive alone,,,,,,,,,,,,,,,, +util_DRIVEALONEFREE_Unavailable,Drive alone - Unavailable,sov_available == False,-999,,,,,,,,,,,,,, +util_Drive alone - In-vehicle time,Drive alone - In-vehicle time,(da_time_skims) * autoIVTFactor * time_factor,coef_ivt,,,,,,,,,,,,,, +util_Drive alone - cost,Drive alone - cost,"@(df.auto_op_cost * df.autoCPMFactor * df.da_dist_skims + df.da_cost_skims)/(np.maximum(df.income,1000)**df.income_exponent)",coef_income,,,,,,,,,,,,,, +util_Drive alone - Parking cost ,Drive alone - Parking cost ,"@(df.parkingCost * df.autoParkingCostFactor)/(np.maximum(df.income,1000)**df.income_exponent)",coef_income,,,,,,,,,,,,,, +util_Drive alone - Terminal Time,Drive alone - Terminal Time,(oTermTime + dTermTime) * autoTermTimeFactor * time_factor,coef_acctime,,,,,,,,,,,,,, +#,Shared ride2,,,,,,,,,,,,,,,, +util_sr2_available,sr2 available,sr2_available == False,,-999,,,,,,,,,,,,, +util_sr2_tourbike_disadvantage,sr2 tourbike disadvantage,tourBike,,-30,,,,,,,,,,,,, +util_Shared ride 2 - In -vehicle time,Shared ride 2 - In -vehicle time,(s2_time_skims) * autoIVTFactor * time_factor,,coef_ivt,,,,,,,,,,,,, +util_Shared ride 2 - cost,Shared ride 2 - cost,"@((df.auto_op_cost * df.autoCPMFactor * df.s2_dist_skims) + df.s2_cost_skims)/(np.maximum(df.income,1000)**df.income_exponent)",,coef_income,,,,,,,,,,,,, +util_Shared ride 2 - Parking cost ,Shared ride 2 - Parking cost ,"@(df.parkingCost * df.autoParkingCostFactor * np.where(df.is_joint,1,df.costFactorS2))/(np.maximum(df.income,1000)**df.income_exponent)",,coef_income,,,,,,,,,,,,, +util_Shared ride 2 Terminal Time - acc,Shared ride 2 - Terminal Time ,(oTermTime + dTermTime) * autoTermTimeFactor * time_factor,,coef_acctime,,,,,,,,,,,,, +util_Shared ride 2 _Two_person_household,Shared ride 2 - Two person household,@(df.hhsize == 2),,coef_size2_sr2,,,,,,,,,,,,, +util_Shared ride 2 _Three_person_household,Shared ride 2 - Three person household,@(df.hhsize == 3),,coef_size3_sr2,,,,,,,,,,,,, +util_Shared ride 2 _Four+_person_household,Shared ride 2 - Four plus person household,@(df.hhsize > 3),,coef_size4p_sr2,,,,,,,,,,,,, +util_Shared ride 2 - Female,Shared ride 2 - female,@(df.female == 1),,coef_female_sr2,,,,,,,,,,,,, +#,Shared ride 3,,,,,,,,,,,,,,,, +util_sr3_available,sr3 available,sr3_available == False,,,-999,,,,,,,,,,,, +util_sr3_tourbike_disadvantage,sr3 tourbike disadvantage,tourBike,,,-30,,,,,,,,,,,, +util_Shared ride 3 - In -vehicle time,Shared ride 3 - In -vehicle time,(s3_time_skims) * autoIVTFactor * time_factor,,,coef_ivt,,,,,,,,,,,, +util_Shared ride 3 - Cost ,Shared ride 3 - cost,"@(df.auto_op_cost * df.s3_dist_skims * df.autoCPMFactor + df.s3_cost_skims)/(np.maximum(df.income,1000)**df.income_exponent)",,,coef_income,,,,,,,,,,,, +util_Shared ride 3 - Parking cost ,Shared ride 3 - Parking cost ,"@(df.parkingCost * df.autoParkingCostFactor * np.where(df.is_joint,1,df.costFactorS3))/(np.maximum(df.income,1000)**df.income_exponent)",,,coef_income,,,,,,,,,,,, +util_Shared ride 3 - Terminal Time,Shared ride 3 - Terminal Time,(oTermTime + dTermTime) * autoTermTimeFactor * time_factor,,,coef_acctime,,,,,,,,,,,, +util_Shared ride 3 - Two_person_household,Shared ride 3 - Two person household,@(df.hhsize == 2),,,coef_size2_sr3p,,,,,,,,,,,, +util_Shared ride 3 - Three_person_household,Shared ride 3 - Three person household,@(df.hhsize == 3),,,coef_size3_sr3p,,,,,,,,,,,, +util_Shared ride 3 - Four+_person_household,Shared ride 3 - Four plus person household,@(df.hhsize > 3),,,coef_size4p_sr3p,,,,,,,,,,,, +util_Shared ride 3 - Female,Shared ride 3 - female,@(df.female == 1),,,coef_female_sr3p,,,,,,,,,,,, +#,Walk,,,,,,,,,,,,,,,, +#,school escorting can force longer walk trips so not turning off completely if walk tour,,,,,,,,,,,,,,,, +util_Walk - Unavailable,Walk - Unavailable,(walkAvailable == 0) & (tourWalk == 0),,,,-999,,,,,,,,,,, +util_Walk - Unavailable - walkTour,Walk - Unavailable - Walk Tour,(walkAvailable == 0) & (tourWalk == 1),,,,-20,,,,,,,,,,, +util_Walk - Female,Walk - Female,@df.female,,,,coef_female_nmot,,,,,,,,,,, +util_Walk - time,Walk - time - clipped for really long walk trips to avoid zero probs,@(df.walk_time_skims * df.time_factor).clip(upper=1000),,,,coef_walkTime,,,,,,,,,,, +util_Walk - Origin Mix,Walk - Origin Mix,oMGRAMix,,,,coef_oMix_nmot,,,,,,,,,,, +util_Walk - Origin Intersection Density,Walk - Origin Intersection Density,oMGRATotInt,,,,coef_oIntDen_nmot,,,,,,,,,,, +util_Walk - Destination Employment Density,Walk - Destination Employment Density,dMGRAEmpDen,,,,coef_dEmpDen_nmot,,,,,,,,,,, +util_Walk - Age 1-5 (school only),Walk - Age 1-6,"@df.age.between(1,5)",,,,coef_age1to5_nmot,,,,,,,,,,, +util_Walk - Age 6-12 (school only),Walk - Age 6-13,"@df.age.between(6,12)",,,,coef_age6to12_nmot,,,,,,,,,,, +util_Walk - Age 13-15 (school only),Walk - Age 13-16,"@df.age.between(13,15)",,,,coef_age13to15_nmot,,,,,,,,,,, +#,Bike,,,,,,,,,,,,,,,, +util_BIKE_Mode unavailable,Bike - Mode unavailable unless on bike tour,@(df.tourBike == 0)|(df.bike_time <= 0),,,,,-999,,,,,,,,,, +util_BIKE_Female - bike,Bike - Female,@df.female,,,,,coef_female_nmot,,,,,,,,,, +util_BIKE_Bike - logsum,Bike - logsum clipped to allow for long bike trips on school escort tours,@df.bikeLS.clip(lower=-100),,,,,coef_bikeLogsum,,,,,,,,,, +#,WalktoTransit,,,,,,,,,,,,,,,, +util_WalkTransit_Unavailable,WalkTransit_Available,walk_local_available == False,,,,,,-999,,,,,,,,, +util_WALKLOC__In_vehicle_time,WALK_LOC - In-vehicle time,@(odt_skims['WTW_TIV']) * df.time_factor,,,,,,coef_ivt,,,,,,,,, +util_WALK_LOC_iwait_time,WALK_LOC - wait time,@(odt_skims['WTW_FWT']) * df.time_factor,,,,,,coef_wait,,,,,,,,, +util_WALK_LOC_transfer_wait_time,WALK_LOC - transfer wait time,@(odt_skims['WTW_XWT'])* df.time_factor,,,,,,coef_xwait,,,,,,,,, +util_WALK_LOC_Walk_access_time,WALK_LOC - Walk access time,"@np.where(df.nev_local_access_available_in & ~df.outbound, df.nev_local_access_time_in, np.where(df.microtransit_local_access_available_in & ~df.outbound, df.microtransit_local_access_time_in, df.origin_local_time))* df.time_factor",,,,,,coef_acctime,,,,,,,,, +util_WALK_LOC_wait_access_time,WALK_LOC - Access mt/nev wait time,"@np.where(df.nev_local_access_available_in & ~df.outbound, nevWaitTime, np.where(df.microtransit_local_access_available_in & ~df.outbound, microtransitWaitTime, 0))* df.time_factor",,,,,,coef_wait,,,,,,,,, +util_WALK_LOC_Walk_egress_time,WALK_LOC - Walk egress time,"@np.where(df.nev_local_egress_available_out & df.outbound, df.nev_local_egress_time_out, np.where(df.microtransit_local_egress_available_out & df.outbound, df.microtransit_local_egress_time_out, df.dest_local_time))* df.time_factor",,,,,,coef_acctime,,,,,,,,, +util_WALK_LOC_wait_egress_time,WALK_LOC - Egress mt/nev wait time,"@np.where(df.nev_local_egress_available_out & df.outbound, nevWaitTime, np.where(df.microtransit_local_egress_available_out & df.outbound, microtransitWaitTime, 0))* df.time_factor",,,,,,coef_wait,,,,,,,,, +util_WALK_LOC_transfer_walk_time,WALK_LOC - transfer walk time,@(odt_skims['WTW_AUX'])* df.time_factor,,,,,,coef_xwalk,,,,,,,,, +util_WALK_LOC_transfers_penalty,WALK_LOC - number of transfers,"@(-23+23*np.exp(0.414*np.clip(odt_skims['WTW_XFR'] + df.outbound*df.mtnev_egr_xfer_out + ~df.outbound*df.mtnev_acc_xfer_in, a_min=None,a_max=4))) * df.time_factor",,,,,,coef_xfer,,,,,,,,, +# FIXME need transit fares ,,,,,,,,,,,,,,,,, +# util_WTW_FARE,WALK_LOC - Fare,"@df.transitSubsidyPassDiscount*(odt_skims['WTW_FARE'])*100*df.number_of_participants/(np.maximum(df.income,1000)**df.income_exponent)",,,,,,coef_income,,,,,,,,, +util_WALK_LOC - Female,WALK_LOC - Female,@(df.female),,,,,,coef_female_tran,,,,,,,,, +util_WALK_LOC - Origin Mix,WALK_LOC - Origin Mix,oMGRAMix,,,,,,coef_oMix_wTran,,,,,,,,, +util_WALK_LOC - Origin Intersection Density,WALK_LOC - Origin Intersection Density,oMGRATotInt,,,,,,coef_oIntDen_wTran,,,,,,,,, +util_WALK_LOC - Destination Employment Density,WALK_LOC - Destination Employment Density,dMGRAEmpDen,,,,,,coef_dEmpDen_wTran,,,,,,,,, +#,PNR_LOC,,,,,,,,,,,,,,,, +# FIXME need PNR utilties for new PNR treatment if not restricting stops on PNR tours,,,,,,,,,,,,,,,,, +util_is_PNR_transit,Only allowed to take PNR transit on PNR tours,tourPNR,-999,-999,-999,-999,-999,-999,,-999,-999,-999,-999,-999,-999,-999, +# util_PNR_LOC_Unavailable,PNR_LOC - Unavailable,(pnr_local_available == False)|(PNR_available==0),,,,,,,-999,,,,,,,, +# util_PNR_LOC_In_vehicle_time,PNR_LOC - In-vehicle time,@(odt_skims['PNROUT_LOC_TIV'])* df.time_factor * df.outbound,,,,,,,coef_ivt,,,,,,,, +# util_PNR_LOC_iwait_time,PNR_LOC - First iwait time,@(odt_skims['PNROUT_LOC_FWT'])* df.time_factor * df.outbound,,,,,,,coef_wait,,,,,,,, +# util_PNR_LOC_transfer_wait_time,PNR_LOC - transfer wait time,@(odt_skims['PNROUT_LOC_XWT'])* df.time_factor * df.outbound,,,,,,,coef_xwait,,,,,,,, +# util_PNR_LOC_number_of_transfers,PNR_LOC - number of transfers,"@(-23+23*np.exp(0.414*np.clip(odt_skims['PNROUT_LOC_XFR'] + df.mtnev_egr_xfer_out, a_min=None,a_max=4))) * df.time_factor * df.outbound",,,,,,,coef_xferdrive,,,,,,,, +# util_PNR_LOC_PNR_time,PNR_LOC - PNR time,@odt_skims['PNROUT_LOC_ACC']* df.time_factor * df.outbound,,,,,,,coef_acctime,,,,,,,, +# util_PNR_LOC_PNR_cost,PNR_LOC - PNR cost,"@(df.auto_op_cost * (odt_skims['PNROUT_LOC_ACC']/60) *driveSpeed)* df.outbound/(np.maximum(df.income,1000)**df.income_exponent)",,,,,,,coef_income,,,,,,,, +# util_PNRIN_LOC_PNR_cost,PNR_LOC - PNR cost,"@(df.auto_op_cost * (odt_skims['PNRIN_LOC_EGR']/60) *driveSpeed) * ~df.outbound/(np.maximum(df.income,1000)**df.income_exponent)",,,,,,,coef_income,,,,,,,, +# util_PNR_LOC_Walk_egress_time_(at_attraction_end),PNR_LOC - Walk egress time (at attraction end),"@np.where(df.nev_local_egress_available_out, df.nev_local_egress_time_out, np.where(df.microtransit_local_egress_available_out, df.microtransit_local_egress_time_out, df.dest_local_time))* df.time_factor * df.outbound",,,,,,,coef_acctime,,,,,,,, +# util_PNR_LOC_wait_egress_time_(at_attraction_end),PNR_LOC - Egress mt/nev wait time (at attraction end),"@np.where(df.nev_local_egress_available_out, nevWaitTime, np.where(df.microtransit_local_egress_available_out, microtransitWaitTime, 0)) * df.time_factor * df.outbound",,,,,,,coef_wait,,,,,,,, +# util_PNR_LOC_Walk_other_time,PNR_LOC - Walk other time,@odt_skims['PNROUT_LOC_AUX']* df.time_factor * df.outbound,,,,,,,coef_xwalk,,,,,,,, +# util_PNR_LOC_Fare_and_operating_cost,PNR_LOC - Fare ,"@df.transitSubsidyPassDiscount*(odt_skims['PNROUT_LOC_FARE'])*100*df.number_of_participants/(np.maximum(df.income,1000)**df.income_exponent) * df.outbound",,,,,,,coef_income,,,,,,,, +# util_PNRIN_LOC_In_vehicle_time,PNRIN_LOC - In-vehicle time,@(odt_skims['PNRIN_LOC_TIV'])* df.time_factor * ~df.outbound,,,,,,,coef_ivt,,,,,,,, +# util_PNRIN_LOC_iwait_time,PNRIN_LOC - First iwait time,@(odt_skims['PNRIN_LOC_FWT']) * df.time_factor* ~df.outbound,,,,,,,coef_wait,,,,,,,, +# util_PNRIN_LOC_transfer_wait_time,PNRIN_LOC - transfer wait time,@(odt_skims['PNRIN_LOC_XWT'])* ~df.outbound,,,,,,,coef_xwait,,,,,,,, +# util_PNRIN_LOC_number_of_transfers,PNRIN_LOC - number of transfers,"@(-23+23*np.exp(0.414*np.clip(odt_skims['PNRIN_LOC_XFR'] + df.mtnev_acc_xfer_in, a_min=None,a_max=4))) * df.time_factor * ~df.outbound",,,,,,,coef_xferdrive,,,,,,,, +# util_PNRIN_LOC_PNRIN_time,PNRIN_LOC - PNR time,@odt_skims['PNRIN_LOC_EGR'] * df.time_factor * ~df.outbound,,,,,,,coef_acctime,,,,,,,, +# util_PNRIN_LOC_Walk_access_time,PNRIN_LOC - Walk access time,"@np.where(df.nev_local_access_available_in, df.nev_local_access_time_in, np.where(df.microtransit_local_access_available_in, df.microtransit_local_access_time_in, df.origin_local_time)) * df.time_factor * ~df.outbound",,,,,,,coef_acctime,,,,,,,, +# util_PNRIN_LOC_wait_access_time,PNRIN_LOC - Egress mt/nev wait time,"@np.where(df.nev_local_access_available_in, nevWaitTime, np.where(df.microtransit_local_access_available_in, microtransitWaitTime, 0)) * df.time_factor * ~df.outbound",,,,,,,coef_wait,,,,,,,, +# util_PNRIN_LOC_Walk_other_time,PNRIN_LOC - Walk other time,@odt_skims['PNRIN_LOC_AUX']* df.time_factor * ~df.outbound,,,,,,,coef_xwalk,,,,,,,, +# util_PNRIN_LOC_Fare_and_operating_cost,PNRIN_LOC - Fare ,"@df.transitSubsidyPassDiscount*(odt_skims['PNRIN_LOC_FARE'])*100*df.number_of_participants/(np.maximum(df.income,1000)**df.income_exponent) * ~df.outbound",,,,,,,coef_income,,,,,,,, +# util_PNRIN_LOC_Destination_zone_densityIndex,PNRIN_LOC - Destination zone densityIndex,@density_index_multiplier * df.dest_density_index,,,,,,,coef_ivt,,,,,,,, +# util_PNR_LOC - Female,PNR_LOC - Female,@(df.female),,,,,,,coef_female_tran,,,,,,,, +# util_PNR_LOC - Destination Employment Density,PNR_LOC - Destination Employment Density,dMGRAEmpDen,,,,,,,coef_dEmpDen_dTran,,,,,,,, +#,,,,,,,,,,,,,,,,, +util_KNR_LOC_Unavailable,KNR_LOC - Unavailable,(knr_local_available == False)|(KNR_available==0),,,,,,,,-999,,,,,,, +util_KNR_LOC_In_vehicle_time,KNR_LOC - In-vehicle time,@(odt_skims['KTW_TIV']) * df.time_factor * df.outbound,,,,,,,,coef_ivt,,,,,,, +util_KNR_LOC_iwait_time,KNR_LOC - First iwait time,@(odt_skims['KTW_FWT']) * df.time_factor * df.outbound,,,,,,,,coef_wait,,,,,,, +util_KNR_LOC_transfer_wait_time,KNR_LOC - transfer wait time,@(odt_skims['KTW_XWT']) * df.time_factor * df.outbound,,,,,,,,coef_xwait,,,,,,, +util_KNR_LOC_number_of_transfers,KNR_LOC - number of transfers,"@(-23+23*np.exp(0.414*np.clip(odt_skims['KTW_XFR'] + df.mtnev_egr_xfer_out, a_min=None,a_max=4))) * df.time_factor * df.outbound",,,,,,,,coef_xferdrive,,,,,,, +util_KNR_LOC_KNR_time,KNR_LOC - KNR time,@(odt_skims['KTW_ACC']) * df.time_factor * df.outbound,,,,,,,,coef_acctime,,,,,,, +util_KNR_LOC_Walk_egress_time_(at_attraction_end),KNR_LOC - Walk egress time (at attraction end),"@np.where(df.nev_local_egress_available_out, df.nev_local_egress_time_out, np.where(df.microtransit_local_egress_available_out, df.microtransit_local_egress_time_out, df.dest_local_time)) * df.time_factor * df.outbound",,,,,,,,coef_acctime,,,,,,, +util_KNR_LOC_wait_egress_time_(at_attraction_end),KNR_LOC - Egress mt/nev wait time (at attraction end),"@np.where(df.nev_local_egress_available_out, nevWaitTime, np.where(df.microtransit_local_egress_available_out, microtransitWaitTime, 0)) * df.time_factor * df.outbound",,,,,,,,coef_wait,,,,,,, +util_KNR_LOC_Walk_other_time,KNR_LOC - Walk other time,@(odt_skims['KTW_AUX']) * df.time_factor * df.outbound,,,,,,,,coef_xwalk,,,,,,, +# FIXME need transit fares,,,,,,,,,,,,,,,,, +# util_KNR_LOC_Fare,KNR_LOC - Fare,"@df.transitSubsidyPassDiscount*(odt_skims['KTW_FARE'])*100*df.number_of_participants/(np.maximum(df.income,1000)**df.income_exponent) * df.outbound",,,,,,,,coef_income,,,,,,, +util_KNR_LOC_KNR_cost,KNR_LOC - KNR cost,"@(df.auto_op_cost * df.autoCPMFactor * (odt_skims['KTW_ACC']/60) *driveSpeed )*df.outbound/(np.maximum(df.income,1000)**df.income_exponent)",,,,,,,,coef_income,,,,,,, +util_KNRIN_LOC_KNR_cost,KNR_LOC - KNR cost,"@(df.auto_op_cost * df.autoCPMFactor * (odt_skims['WTK_EGR']/60) *driveSpeed)*~df.outbound/(np.maximum(df.income,1000)**df.income_exponent)",,,,,,,,coef_income,,,,,,, +util_KNRIN_LOC_In_vehicle_time,KNRIN_LOC - In-vehicle time,@(odt_skims['WTK_TIV']) * df.time_factor * ~df.outbound,,,,,,,,coef_ivt,,,,,,, +util_KNRIN_LOC_iwait_time,KNRIN_LOC - First iwait time,@(odt_skims['WTK_FWT']) * df.time_factor * ~df.outbound,,,,,,,,coef_wait,,,,,,, +util_KNRIN_LOC_transfer_wait_time,KNRIN_LOC - transfer wait time,@(odt_skims['WTK_XWT']) * df.time_factor * ~df.outbound,,,,,,,,coef_xwait,,,,,,, +util_KNRIN_LOC_number_of_transfers,KNRIN_LOC - number of transfers,"@(-23+23*np.exp(0.414*np.clip(odt_skims['WTK_XFR'] + df.mtnev_acc_xfer_in, a_min=None,a_max=4))) * df.time_factor * ~df.outbound",,,,,,,,coef_xferdrive,,,,,,, +util_KNRIN_LOC_KNRIN_time,KNRIN_LOC - KNR time,@odt_skims['WTK_EGR'] * df.time_factor * ~df.outbound,,,,,,,,coef_acctime,,,,,,, +util_KNRIN_LOC_Walk_access_time,KNRIN_LOC - Walk access time,"@np.where(df.nev_local_access_available_in, df.nev_local_access_time_in, np.where(df.microtransit_local_access_available_in, df.microtransit_local_access_time_in, df.origin_local_time)) * df.time_factor * ~df.outbound",,,,,,,,coef_acctime,,,,,,, +util_KNRIN_LOC_wait_access_time,KNRIN_LOC - Egress mt/nev wait time,"@np.where(df.nev_local_access_available_in, nevWaitTime, np.where(df.microtransit_local_access_available_in, microtransitWaitTime, 0)) * df.time_factor * ~df.outbound",,,,,,,,coef_wait,,,,,,, +util_KNRIN_LOC_Walk_other_time,KNRIN_LOC - Walk other time,@(odt_skims['WTK_AUX']) * df.time_factor * ~df.outbound,,,,,,,,coef_xwalk,,,,,,, +# FIXME need transit fares,,,,,,,,,,,,,,,,, +#util_KNRIN_LOC_Fare_and_operating_cost,KNRIN_LOC - Fare ,"@df.transitSubsidyPassDiscount*(odt_skims['WTK_FARE'])*100*df.number_of_participants/(np.maximum(df.income,1000)**df.income_exponent) * ~df.outbound",,,,,,,,coef_income,,,,,,, +util_KNR_LOC - Female,KNR_LOC - Female,@(df.female),,,,,,,,coef_female_tran,,,,,,, +util_KNR_LOC - Destination Employment Density,KNR_LOC - Destination Employment Density,dMGRAEmpDen,,,,,,,,coef_dEmpDen_dTran,,,,,,, +#,BiketoTransit,,,,,,,,,,,,,,,, +util_WalkTransit_Unavailable,WalkTransit_Available,bike_local_available == False | (BNR_available==0),,,,,,,,,-999,,,,,, +util_BIKE_TRANSIT__In_vehicle_time,BIKE_TRANSIT - In-vehicle time,@(odt_skims['WTW_TIV']) * df.time_factor,,,,,,,,,coef_ivt,,,,,, +util_BIKE_TRANSIT_iwait_time,BIKE_TRANSIT - wait time,@(odt_skims['WTW_FWT']) * df.time_factor,,,,,,,,,coef_wait,,,,,, +util_BIKE_TRANSIT_transfer_wait_time,BIKE_TRANSIT - transfer wait time,@(odt_skims['WTW_XWT'])* df.time_factor,,,,,,,,,coef_xwait,,,,,, +util_BIKE_TRANSIT_Walk_access_time,BIKE_TRANSIT - Walk access time,"@np.where(df.nev_local_access_available_in & ~df.outbound, df.nev_local_access_time_in, np.where(df.microtransit_local_access_available_in & ~df.outbound, df.microtransit_local_access_time_in, df.origin_local_time))* df.time_factor",,,,,,,,,coef_acctime,,,,,, +util_BIKE_TRANSIT_wait_access_time,BIKE_TRANSIT - Access mt/nev wait time,"@np.where(df.nev_local_access_available_in & ~df.outbound, nevWaitTime, np.where(df.microtransit_local_access_available_in & ~df.outbound, microtransitWaitTime, 0))* df.time_factor",,,,,,,,,coef_wait,,,,,, +util_BIKE_TRANSIT_Walk_egress_time,BIKE_TRANSIT - Walk egress time,"@np.where(df.nev_local_egress_available_out & df.outbound, df.nev_local_egress_time_out, np.where(df.microtransit_local_egress_available_out & df.outbound, df.microtransit_local_egress_time_out, df.dest_local_time))* df.time_factor",,,,,,,,,coef_acctime,,,,,, +util_BIKE_TRANSIT_wait_egress_time,BIKE_TRANSIT - Egress mt/nev wait time,"@np.where(df.nev_local_egress_available_out & df.outbound, nevWaitTime, np.where(df.microtransit_local_egress_available_out & df.outbound, microtransitWaitTime, 0))* df.time_factor",,,,,,,,,coef_wait,,,,,, +util_BIKE_TRANSIT_transfer_walk_time,BIKE_TRANSIT - transfer walk time,@(odt_skims['WTW_AUX'])* df.time_factor,,,,,,,,,coef_xwalk,,,,,, +util_BIKE_TRANSIT_transfers_penalty,BIKE_TRANSIT - number of transfers,"@(-23+23*np.exp(0.414*np.clip(odt_skims['WTW_XFR'] + df.outbound*df.mtnev_egr_xfer_out + ~df.outbound*df.mtnev_acc_xfer_in, a_min=None,a_max=4))) * df.time_factor",,,,,,,,,coef_xfer,,,,,, +# FIXME need transit fares,,,,,,,,,,,,,,,,, +#util_BIKE_TRANSIT_Fare,BIKE_TRANSIT - Fare,"@df.transitSubsidyPassDiscount*(odt_skims['WTW_FARE'])*100*df.number_of_participants/(np.maximum(df.income,1000)**df.income_exponent)",,,,,,,,,coef_income,,,,,, +util_BIKE_TRANSIT - Female,BIKE_TRANSIT - Female,@(df.female),,,,,,,,,coef_female_tran,,,,,, +util_BIKE_TRANSIT - Origin Mix,BIKE_TRANSIT - Origin Mix,oMGRAMix,,,,,,,,,coef_oMix_wTran,,,,,, +util_BIKE_TRANSIT - Origin Intersection Density,BIKE_TRANSIT - Origin Intersection Density,oMGRATotInt,,,,,,,,,coef_oIntDen_wTran,,,,,, +util_BIKE_TRANSIT - Destination Employment Density,BIKE_TRANSIT - Destination Employment Density,dMGRAEmpDen,,,,,,,,,coef_dEmpDen_wTran,,,,,, +#,Taxi,,,,,,,,,,,,,,,, +util_Taxi_Unavailable,Taxi - Unavailable,RideHail_available==0,,,,,,,,,,-999,,,,, +util_Taxi - In-vehicle time,Taxi - In-vehicle time,(s2_time_skims) * time_factor,,,,,,,,,,coef_ivt,,,,, +util_Taxi - Wait time,Taxi - Wait time,origTaxiWaitTime * time_factor,,,,,,,,,,coef_wait,,,,, +util_Taxi - Fare,Taxi - Fare,"@(((Taxi_baseFare + df.s2_dist_skims * Taxi_costPerMile + df.s2_time_skims * Taxi_costPerMinute) * 100 + df.s2_cost_skims)) / (np.maximum(df.income,1000)**df.income_exponent)",,,,,,,,,,coef_income,,,,, +#,TNC Single,,,,,,,,,,,,,,,, +util_TNC Single_Unavailable,TNC Single - Unavailable,RideHail_available==0,,,,,,,,,,,-999,,,, +util_TNC Single - In-vehicle time,TNC Single - In-vehicle time,(s2_time_skims) * time_factor,,,,,,,,,,,coef_ivt,,,, +util_TNC Single - Wait time,TNC Single - Wait time,origSingleTNCWaitTime * time_factor,,,,,,,,,,,coef_wait,,,, +util_TNC Single - Cost,TNC Single - Cost,"@((np.maximum(TNC_single_baseFare + df.s2_dist_skims * TNC_single_costPerMile + df.s2_time_skims * TNC_single_costPerMinute, TNC_single_costMinimum) * 100 + df.s2_cost_skims)) / (np.maximum(df.income,1000)**df.income_exponent)",,,,,,,,,,,coef_income,,,, +#,TNC Shared,,,,,,,,,,,,,,,, +util_TNC Shared_switch,TNC Shared - switch turn-off (depends on data availability),@(((~df.nev_available) & (~df.microtransit_available) & (scenarioYear==2022)) | (df.RideHail_available==0)),,,,,,,,,,,,-999,,, +util_TNC Shared - In-vehicle time,TNC Shared - In-vehicle time,"@(np.where(df.nev_available, df.nev_time, np.where(df.microtransit_available, df.microtransit_time, df.s3_time_skims * TNC_shared_IVTFactor))) * df.time_factor",,,,,,,,,,,,coef_ivt,,, +util_TNC Shared - Wait time,TNC Shared - Wait time,"@np.where(df.nev_available, nevWaitTime, np.where(df.microtransit_available, microtransitWaitTime, df.origSharedTNCWaitTime)) * df.time_factor",,,,,,,,,,,,coef_wait,,, +util_TNC Shared - Cost,TNC Shared - Cost,"@np.where(df.nev_available, nevCost, np.where(df.microtransit_available, microtransitCost, (np.maximum(TNC_shared_baseFare + df.s3_dist_skims * TNC_shared_costPerMile + df.s3_time_skims * TNC_shared_costPerMinute, TNC_shared_costMinimum) * 100 + df.s3_cost_skims))) / (np.maximum(df.income,1000)**df.income_exponent)",,,,,,,,,,,,coef_income,,, +util_calib_flexfleet,Calibration for flexible fleets,microtransit_available | nev_available,,,,,,,,,,,,coef_calib_flexfleet,,, +#,School bus,,,,,,,,,,,,,,,, +util_School Bus - Unavailable,School Bus - Unavailable,SCHBUS_available==0,,,,,,,,,,,,,-999,, +util_School Bus - In-vehicle Time at 20 miles per hour,School Bus - In-vehicle Time at 20 miles per hour,(da_dist_skims)* 3 * time_factor,,,,,,,,,,,,,coef_ivt,, +util_School Bus - Wait Time (asserted),School Bus - Wait Time (asserted),10 * time_factor,,,,,,,,,,,,,coef_wait,, +util_School Bus - Walk Time (asserted),School Bus - Walk Time (asserted),10 * time_factor,,,,,,,,,,,,,coef_acctime,, +util_School Bus - Age 6 to 12,School Bus - Age 6 to 12,@(df.age > 5) * (df.age < 13),,,,,,,,,,,,,coef_age6to12_schb,, +#,Escooter,,,,,,,,,,,,,,,, +util_Escooter - Unavailable,Escooter - Unavailable,Escooter_available==0,,,,,,,,,,,,,,,-999 +#,Ebike,,,,,,,,,,,,,,,, +util_Ebike - Unavailable,Ebike - Unavailable,Ebike_available==0,,,,,,,,,,,,,,-999, +#,indiv tour ASCs,,,,,,,,,,,,,,,, +util_calib_numberofparticipan,abm 2+ calibration,(numberOfParticipantsInJointTour<=2)&(jointTour==1),,,coef_calib_numberofparticipants_SHARED3,,,,,,,,,,,, +util_calib_KNR Transit - Distance Parameter,abm 2+ calibration,"@df.c_ivt*5*np.maximum((10-df.da_dist_skims),0)*0.5",,,,,,,,coef_calib_civt5max10sovdistski_KNR_TRANSIT,coef_calib_civt5max10sovdistski_TNC_TRANSIT,,,,,, +util_calib_Walk-Transit - Distance Parameter,abm 2+ calibration,"@df.c_ivt*np.maximum((200+(-133*df.da_dist_skims)),0)*0.5",,,,,,coef_calib_civtmax200133sovdist_WALK_TRANSIT,,,,,,,,, +util_calib_PNR-Transit - Distance Parameter,abm 2+ calibration,"@df.c_ivt*np.maximum((45+(-2.5*df.da_dist_skims)),0)*0.5",,,,,,,coef_calib_civtmax4525sovdistsk_PNR_TRANSIT,,,,,,,, +util_calib_esctour1,abm 2+ calibration,tour_type=='escort',,,,coef_calib_esctour1_WALK,,,,,,,,,,, +#,,,,,,,,,,,,,,,,, +util_calib_tourda,abm 2+ calibration,tourDA,,coef_calib_tourda_SHARED2,coef_calib_tourda_SHARED3,coef_calib_tourda_WALK,coef_calib_tourda_BIKE,coef_calib_tourda_WALK_TRANSIT,coef_calib_tourda_PNR_TRANSIT,coef_calib_tourda_KNR_TRANSIT,coef_calib_tourda_TNC_TRANSIT,coef_calib_tourda_TAXI,coef_calib_tourda_TNC_SINGLE,coef_calib_tourda_TNC_SHARED,coef_calib_tourda_SCH_BUS,-999,-999 +util_calib_tours2,abm 2+ calibration,tourS2,coef_calib_tours2_DRIVEALONE,coef_calib_tours2_SHARED2,coef_calib_tours2_SHARED3,coef_calib_tours2_WALK,coef_calib_tours2_BIKE,coef_calib_tours2_WALK_TRANSIT,coef_calib_tours2_PNR_TRANSIT,coef_calib_tours2_KNR_TRANSIT,coef_calib_tours2_TNC_TRANSIT,coef_calib_tours2_TAXI,coef_calib_tours2_TNC_SINGLE,coef_calib_tours2_TNC_SHARED,coef_calib_tours2_SCH_BUS,0,0 +util_calib_tours3,abm 2+ calibration,tourS3,coef_calib_tours3_DRIVEALONE,coef_calib_tours3_SHARED2,coef_calib_tours3_SHARED3,coef_calib_tours3_WALK,coef_calib_tours3_BIKE,coef_calib_tours3_WALK_TRANSIT,coef_calib_tours3_PNR_TRANSIT,coef_calib_tours3_KNR_TRANSIT,coef_calib_tours3_TNC_TRANSIT,coef_calib_tours3_TAXI,coef_calib_tours3_TNC_SINGLE,coef_calib_tours3_TNC_SHARED,coef_calib_tours3_SCH_BUS,0,0 +util_calib_tourwalk,abm 2+ calibration,tourWalk,coef_calib_tourwalk_DRIVEALONE,coef_calib_tourwalk_SHARED2,coef_calib_tourwalk_SHARED3,0,coef_calib_tourwalk_BIKE,coef_calib_tourwalk_WALK_TRANSIT,coef_calib_tourwalk_PNR_TRANSIT,coef_calib_tourwalk_KNR_TRANSIT,coef_calib_tourwalk_TNC_TRANSIT,coef_calib_tourwalk_TAXI,coef_calib_tourwalk_TNC_SINGLE,coef_calib_tourwalk_TNC_SHARED,coef_calib_tourwalk_SCH_BUS,-999,-999 +util_calib_tourbike,abm 2+ calibration,tourBike,coef_calib_tourbike_DRIVEALONE,coef_calib_tourbike_SHARED2,coef_calib_tourbike_SHARED3,coef_calib_tourbike_WALK,coef_calib_tourbike_BIKE,coef_calib_tourbike_WALK_TRANSIT,coef_calib_tourbike_PNR_TRANSIT,coef_calib_tourbike_KNR_TRANSIT,coef_calib_tourbike_TNC_TRANSIT,coef_calib_tourbike_TAXI,coef_calib_tourbike_TNC_SINGLE,coef_calib_tourbike_TNC_SHARED,coef_calib_tourbike_SCH_BUS,-999,-999 +util_calib_tourwtran,abm 2+ calibration,tourWTran,coef_calib_tourwtran_DRIVEALONE,coef_calib_tourwtran_SHARED2,coef_calib_tourwtran_SHARED3,coef_calib_tourwtran_WALK,coef_calib_tourwtran_BIKE,coef_calib_tourwtran_WALK_TRANSIT,coef_calib_tourwtran_PNR_TRANSIT,coef_calib_tourwtran_KNR_TRANSIT,coef_calib_tourwtran_TNC_TRANSIT,coef_calib_tourwtran_TAXI,coef_calib_tourwtran_TNC_SINGLE,coef_calib_tourwtran_TNC_SHARED,coef_calib_tourwtran_SCH_BUS,0,0 +util_calib_tourpnr,abm 2+ calibration,tourPNR,coef_calib_tourpnr_DRIVEALONE,coef_calib_tourpnr_SHARED2,coef_calib_tourpnr_SHARED3,coef_calib_tourpnr_WALK,coef_calib_tourpnr_BIKE,coef_calib_tourpnr_WALK_TRANSIT,coef_calib_tourpnr_PNR_TRANSIT,coef_calib_tourpnr_KNR_TRANSIT,coef_calib_tourpnr_TNC_TRANSIT,coef_calib_tourpnr_TAXI,coef_calib_tourpnr_TNC_SINGLE,coef_calib_tourpnr_TNC_SHARED,coef_calib_tourpnr_SCH_BUS,-999,-999 +util_calib_tourknr,abm 2+ calibration,tourKNR,coef_calib_tourknr_DRIVEALONE,coef_calib_tourknr_SHARED2,coef_calib_tourknr_SHARED3,coef_calib_tourknr_WALK,coef_calib_tourknr_BIKE,coef_calib_tourknr_WALK_TRANSIT,coef_calib_tourknr_PNR_TRANSIT,coef_calib_tourknr_KNR_TRANSIT,coef_calib_tourknr_TNC_TRANSIT,coef_calib_tourknr_TAXI,coef_calib_tourknr_TNC_SINGLE,coef_calib_tourknr_TNC_SHARED,coef_calib_tourknr_SCH_BUS,-999,-999 +util_calib_tourtnr,abm 2+ calibration,tourBNR,coef_calib_tourtnr_DRIVEALONE,0,0,0,coef_calib_tourtnr_BIKE,0,coef_calib_tourtnr_PNR_TRANSIT,0,0,0,0,0,coef_calib_tourtnr_SCH_BUS,-999,-999 +util_calib_tourmaas,abm 2+ calibration,tourMaaS,coef_calib_tourmaas_DRIVEALONE,coef_calib_tourmaas_SHARED2,coef_calib_tourmaas_SHARED3,coef_calib_tourmaas_WALK,coef_calib_tourmaas_BIKE,coef_calib_tourmaas_WALK_TRANSIT,coef_calib_tourmaas_PNR_TRANSIT,coef_calib_tourmaas_KNR_TRANSIT,coef_calib_tourmaas_TNC_TRANSIT,coef_calib_tourmaas_TAXI,coef_calib_tourmaas_TNC_SINGLE,coef_calib_tourmaas_TNC_SHARED,coef_calib_tourmaas_TNC_SCH_BUS,0,0 +util_calib_tourschbus,abm 2+ calibration,tourSchBus,coef_calib_tourschbus_DRIVEALONE,coef_calib_tourschbus_SHARED2,coef_calib_tourschbus_SHARED3,coef_calib_tourschbus_WALK,coef_calib_tourschbus_BIKE,coef_calib_tourschbus_WALK_TRANSIT,coef_calib_tourschbus_PNR_TRANSIT,coef_calib_tourschbus_KNR_TRANSIT,coef_calib_tourschbus_TNC_TRANSIT,coef_calib_tourschbus_TAXI,coef_calib_tourschbus_TNC_SINGLE,coef_calib_tourschbus_TNC_SHARED,0,-999,-999 +util_availability_tourebike,,tourEbike,-999,-999,-999,0,-999,-999,-999,-999,-999,-999,-999,-999,-999,0,-999 +util_availability_tourescooter,,tourEscooter,-999,-999,-999,0,-999,-999,-999,-999,-999,-999,-999,-999,-999,-999,0 +#,,,,,,,,,,,,,,,,, +util_calib_jointtour1,abm 2+ calibration,jointTour==1,0,0,0,coef_calib_jointtour1_WALK,0,coef_calib_jointtour1_WALK_TRANSIT,coef_calib_jointtour1_PNR_TRANSIT,coef_calib_jointtour1_KNR_TRANSIT,coef_calib_jointtour1_TNC_TRANSIT,0,0,0,0,, +util_calib_tourbikejointtour0,abm 2+ calibration,tourBike*(jointTour==0),coef_calib_tourbikejointtour0_DRIVEALONE,coef_calib_tourbikejointtour0_SHARED2,coef_calib_tourbikejointtour0_SHARED3,coef_calib_tourbikejointtour0_WALK,coef_calib_tourbikejointtour0_BIKE,coef_calib_tourbikejointtour0_WALK_TRANSIT,coef_calib_tourbikejointtour0_PNR_TRANSIT,coef_calib_tourbikejointtour0_KNR_TRANSIT,coef_calib_tourbikejointtour0_TNC_TRANSIT,coef_calib_tourbikejointtour0_TAXI,coef_calib_tourbikejointtour0_TNC_SINGLE,coef_calib_tourbikejointtour0_TNC_SHARED,0,, +util_calib_tourbikejointtour1,abm 2+ calibration,tourBike*(jointTour==1),0,0,0,0,coef_calib_tourbikejointtour1_BIKE,0,0,0,0,coef_calib_tourbikejointtour1_TAXI,coef_calib_tourbikejointtour1_TNC_SINGLE,coef_calib_tourbikejointtour1_TNC_SHARED,0,, +util_calib_tourdajointtour0,abm 2+ calibration,tourDA*(jointTour==0),coef_calib_tourdajointtour0_DRIVEALONE,coef_calib_tourdajointtour0_SHARED2,coef_calib_tourdajointtour0_SHARED3,coef_calib_tourdajointtour0_WALK,coef_calib_tourdajointtour0_BIKE,coef_calib_tourdajointtour0_WALK_TRANSIT,coef_calib_tourdajointtour0_PNR_TRANSIT,coef_calib_tourdajointtour0_KNR_TRANSIT,coef_calib_tourdajointtour0_TNC_TRANSIT,coef_calib_tourdajointtour0_TAXI,coef_calib_tourdajointtour0_TNC_SINGLE,coef_calib_tourdajointtour0_TNC_SHARED,0,, +util_calib_tourdajointtour1,abm 2+ calibration,tourDA*(jointTour==1),0,coef_calib_tourdajointtour1_SHARED2,coef_calib_tourdajointtour1_SHARED3,coef_calib_tourdajointtour1_WALK,coef_calib_tourdajointtour1_BIKE,coef_calib_tourdajointtour1_WALK_TRANSIT,coef_calib_tourdajointtour1_PNR_TRANSIT,coef_calib_tourdajointtour1_KNR_TRANSIT,coef_calib_tourdajointtour1_TNC_TRANSIT,coef_calib_tourdajointtour1_TAXI,coef_calib_tourdajointtour1_TNC_SINGLE,coef_calib_tourdajointtour1_TNC_SHARED,0,, +util_calib_tourknrjointtour0,abm 2+ calibration,tourKNR*(jointTour==0),coef_calib_tourknrjointtour0_DRIVEALONE,coef_calib_tourknrjointtour0_SHARED2,coef_calib_tourknrjointtour0_SHARED3,coef_calib_tourknrjointtour0_WALK,coef_calib_tourknrjointtour0_BIKE,coef_calib_tourknrjointtour0_WALK_TRANSIT,coef_calib_tourknrjointtour0_PNR_TRANSIT,coef_calib_tourknrjointtour0_KNR_TRANSIT,coef_calib_tourknrjointtour0_TNC_TRANSIT,coef_calib_tourknrjointtour0_TAXI,coef_calib_tourknrjointtour0_TNC_SINGLE,coef_calib_tourknrjointtour0_TNC_SHARED,0,, +util_calib_tourknrjointtour1,abm 2+ calibration,tourKNR*(jointTour==1),coef_calib_tourknrjointtour1_DRIVEALONE,0,0,0,coef_calib_tourknrjointtour1_BIKE,0,coef_calib_tourknrjointtour1_PNR_TRANSIT,0,coef_calib_tourknrjointtour1_TNC_TRANSIT,coef_calib_tourknrjointtour1_TAXI,coef_calib_tourknrjointtour1_TNC_SINGLE,coef_calib_tourknrjointtour1_TNC_SHARED,0,, +util_calib_tourknrtotstops0,abm 2+ calibration,tourKNR*(totStops==0),coef_calib_tourknrtotstops0_DRIVEALONE,coef_calib_tourknrtotstops0_SHARED2,coef_calib_tourknrtotstops0_SHARED3,coef_calib_tourknrtotstops0_WALK,coef_calib_tourknrtotstops0_BIKE,coef_calib_tourknrtotstops0_WALK_TRANSIT,coef_calib_tourknrtotstops0_PNR_TRANSIT,0,0,0,0,0,0,, +util_calib_tourknrtotstops>0,abm 2+ calibration,tourKNR*(totStops>0),coef_calib_tourknrtotstopsm0_DRIVEALONE,0,0,0,coef_calib_tourknrtotstopsm0_BIKE,0,coef_calib_tourknrtotstopsm0_PNR_TRANSIT,0,0,0,0,0,0,, +util_calib_tourknrfirstofmult,abm 2+ calibration,tourKNR*firstOfMultipleTrips,coef_calib_tourknrfirstofmultip_DRIVEALONE,0,0,0,coef_calib_tourknrfirstofmultip_BIKE,0,coef_calib_tourknrfirstofmultip_PNR_TRANSIT,0,0,0,0,0,0,, +util_calib_tourknrlastofmulti,abm 2+ calibration,tourKNR*lastofMultipleTrips,coef_calib_tourknrlastofmultipl_DRIVEALONE,0,0,0,coef_calib_tourknrlastofmultipl_BIKE,0,coef_calib_tourknrlastofmultipl_PNR_TRANSIT,0,0,0,0,0,0,, +util_calib_tourebikejointtour0,,tourEbike*(jointTour==0),,,,coef_calib_tourebikejointtour0_WALK,,,,,,,,,,coef_calib_tourebikejointtour0_EBIKE, +util_calib_tourebikejointtour1,,tourEbike*(jointTour==1),,,,coef_calib_tourebikejointtour1_WALK,,,,,,,,,,coef_calib_tourebikejointtour1_EBIKE, +util_calib_tourescooterjointtour0,,tourEscooter*(jointTour==0),,,,coef_calib_tourescooterjointtour0_WALK,,,,,,,,,,,coef_calib_tourescooterjointtour0_ESCOOTER +util_calib_tourescooterjointtour1,,tourEscooter*(jointTour==1),,,,coef_calib_tourescooterjointtour1_WALK,,,,,,,,,,,coef_calib_tourescooterjointtour1_ESCOOTER +#,,,,,,,,,,,,,,,,, +util_calib_tourmaasjointtour0,abm 2+ calibration,tourMaaS*(jointTour==0),0,coef_calib_tourmaasjointtour0_SHARED2,coef_calib_tourmaasjointtour0_SHARED3,coef_calib_tourmaasjointtour0_WALK,0,0,0,0,0,coef_calib_tourmaasjointtour0_TAXI,coef_calib_tourmaasjointtour0_TNC_SINGLE,coef_calib_tourmaasjointtour0_TNC_SHARED,0,, +util_calib_tourmaasjointtour1,abm 2+ calibration,tourMaaS*(jointTour==1),0,coef_calib_tourmaasjointtour1_SHARED2,coef_calib_tourmaasjointtour1_SHARED3,coef_calib_tourmaasjointtour1_WALK,0,0,0,0,0,0,coef_calib_tourmaasjointtour1_TNC_SINGLE,coef_calib_tourmaasjointtour1_TNC_SHARED,0,, +util_calib_tourpnrjointtour0,abm 2+ calibration,tourPNR*(jointTour==0),coef_calib_tourpnrjointtour0_DRIVEALONE,coef_calib_tourpnrjointtour0_SHARED2,coef_calib_tourpnrjointtour0_SHARED3,coef_calib_tourpnrjointtour0_WALK,coef_calib_tourpnrjointtour0_BIKE,coef_calib_tourpnrjointtour0_WALK_TRANSIT,coef_calib_tourpnrjointtour0_PNR_TRANSIT,coef_calib_tourpnrjointtour0_KNR_TRANSIT,coef_calib_tourpnrjointtour0_TNC_TRANSIT,coef_calib_tourpnrjointtour0_TAXI,coef_calib_tourpnrjointtour0_TNC_SINGLE,coef_calib_tourpnrjointtour0_TNC_SHARED,0,, +util_calib_tourpnrjointtour1,abm 2+ calibration,tourPNR*(jointTour==1),0,0,0,0,coef_calib_tourpnrjointtour1_BIKE,0,0,coef_calib_tourpnrjointtour1_KNR_TRANSIT,coef_calib_tourpnrjointtour1_TNC_TRANSIT,coef_calib_tourpnrjointtour1_TAXI,coef_calib_tourpnrjointtour1_TNC_SINGLE,coef_calib_tourpnrjointtour1_TNC_SHARED,0,, +util_calib_tourpnrtotstops0,abm 2+ calibration,tourPNR*(totStops==0),coef_calib_tourpnrtotstops0_DRIVEALONE,coef_calib_tourpnrtotstops0_SHARED2,coef_calib_tourpnrtotstops0_SHARED3,coef_calib_tourpnrtotstops0_WALK,coef_calib_tourpnrtotstops0_BIKE,coef_calib_tourpnrtotstops0_WALK_TRANSIT,0,coef_calib_tourpnrtotstops0_KNR_TRANSIT,coef_calib_tourpnrtotstops0_TNC_TRANSIT,0,0,0,0,, +util_calib_tourpnrtotstops>0,abm 2+ calibration,tourPNR*(totStops>0),0,0,0,0,coef_calib_tourpnrtotstopsm0_BIKE,coef_calib_tourpnrtotstopsm0_WALK_TRANSIT,0,coef_calib_tourpnrtotstopsm0_KNR_TRANSIT,coef_calib_tourpnrtotstopsm0_TNC_TRANSIT,0,0,0,0,, +util_calib_tourpnrfirstofmult,abm 2+ calibration,tourPNR*firstOfMultipleTrips,0,0,0,0,coef_calib_tourpnrfirstofmultip_BIKE,0,0,coef_calib_tourpnrfirstofmultip_KNR_TRANSIT,coef_calib_tourpnrfirstofmultip_TNC_TRANSIT,0,0,0,0,, +util_calib_tourpnrlastofmulti,abm 2+ calibration,tourPNR*lastofMultipleTrips,0,0,0,0,coef_calib_tourpnrlastofmultipl_BIKE,0,0,coef_calib_tourpnrlastofmultipl_KNR_TRANSIT,coef_calib_tourpnrlastofmultipl_TNC_TRANSIT,0,0,0,0,, +util_calib_tours2jointtour0,abm 2+ calibration,tourS2*(jointTour==0),coef_calib_tours2jointtour0_DRIVEALONE,coef_calib_tours2jointtour0_SHARED2,coef_calib_tours2jointtour0_SHARED3,coef_calib_tours2jointtour0_WALK,coef_calib_tours2jointtour0_BIKE,coef_calib_tours2jointtour0_WALK_TRANSIT,coef_calib_tours2jointtour0_PNR_TRANSIT,coef_calib_tours2jointtour0_KNR_TRANSIT,coef_calib_tours2jointtour0_TNC_TRANSIT,coef_calib_tours2jointtour0_TAXI,coef_calib_tours2jointtour0_TNC_SINGLE,coef_calib_tours2jointtour0_TNC_SHARED,0,, +util_calib_tours2jointtour1,abm 2+ calibration,tourS2*(jointTour==1),coef_calib_tours2jointtour1_DRIVEALONE,coef_calib_tours2jointtour1_SHARED2,coef_calib_tours2jointtour1_SHARED3,coef_calib_tours2jointtour1_WALK,coef_calib_tours2jointtour1_BIKE,coef_calib_tours2jointtour1_WALK_TRANSIT,coef_calib_tours2jointtour1_PNR_TRANSIT,coef_calib_tours2jointtour1_KNR_TRANSIT,coef_calib_tours2jointtour1_TNC_TRANSIT,coef_calib_tours2jointtour1_TAXI,coef_calib_tours2jointtour1_TNC_SINGLE,coef_calib_tours2jointtour1_TNC_SHARED,0,, +#,,,,,,,,,,,,,,,,, +util_calib_tours2totstops0,abm 2+ calibration,tourS2*(totStops==0),coef_calib_tours2totstops0_DRIVEALONE,0,coef_calib_tours2totstops0_SHARED3,coef_calib_tours2totstops0_WALK,coef_calib_tours2totstops0_BIKE,coef_calib_tours2totstops0_WALK_TRANSIT,coef_calib_tours2totstops0_PNR_TRANSIT,coef_calib_tours2totstops0_KNR_TRANSIT,coef_calib_tours2totstops0_TNC_TRANSIT,0,0,0,0,, +util_calib_tours2firstofmulti,abm 2+ calibration,tourS2*firstOfMultipleTrips,coef_calib_tours2firstofmultipl_DRIVEALONE,0,coef_calib_tours2firstofmultipl_SHARED3,coef_calib_tours2firstofmultipl_WALK,coef_calib_tours2firstofmultipl_BIKE,coef_calib_tours2firstofmultipl_WALK_TRANSIT,coef_calib_tours2firstofmultipl_PNR_TRANSIT,coef_calib_tours2firstofmultipl_KNR_TRANSIT,coef_calib_tours2firstofmultipl_TNC_TRANSIT,0,0,0,0,, +util_calib_tours2lastofmultip,abm 2+ calibration,tourS2*lastofMultipleTrips,coef_calib_tours2lastofmultiple_DRIVEALONE,0,coef_calib_tours2lastofmultiple_SHARED3,coef_calib_tours2lastofmultiple_WALK,coef_calib_tours2lastofmultiple_BIKE,coef_calib_tours2lastofmultiple_WALK_TRANSIT,coef_calib_tours2lastofmultiple_PNR_TRANSIT,coef_calib_tours2lastofmultiple_KNR_TRANSIT,coef_calib_tours2lastofmultiple_TNC_TRANSIT,0,0,0,0,, +util_calib_tours3jointtour0,abm 2+ calibration,tourS3*(jointTour==0),coef_calib_tours3jointtour0_DRIVEALONE,coef_calib_tours3jointtour0_SHARED2,coef_calib_tours3jointtour0_SHARED3,coef_calib_tours3jointtour0_WALK,coef_calib_tours3jointtour0_BIKE,coef_calib_tours3jointtour0_WALK_TRANSIT,coef_calib_tours3jointtour0_PNR_TRANSIT,coef_calib_tours3jointtour0_KNR_TRANSIT,coef_calib_tours3jointtour0_TNC_TRANSIT,coef_calib_tours3jointtour0_TAXI,coef_calib_tours3jointtour0_TNC_SINGLE,coef_calib_tours3jointtour0_TNC_SHARED,0,, +util_calib_tours3jointtour1,abm 2+ calibration,tourS3*(jointTour==1),coef_calib_tours3jointtour1_DRIVEALONE,coef_calib_tours3jointtour1_SHARED2,coef_calib_tours3jointtour1_SHARED3,coef_calib_tours3jointtour1_WALK,coef_calib_tours3jointtour1_BIKE,coef_calib_tours3jointtour1_WALK_TRANSIT,coef_calib_tours3jointtour1_PNR_TRANSIT,coef_calib_tours3jointtour1_KNR_TRANSIT,coef_calib_tours3jointtour1_TNC_TRANSIT,coef_calib_tours3jointtour1_TAXI,coef_calib_tours3jointtour1_TNC_SINGLE,coef_calib_tours3jointtour1_TNC_SHARED,0,, +util_calib_tours3totstops0,abm 2+ calibration,tourS3*(totStops==0),coef_calib_tours3totstops0_DRIVEALONE,coef_calib_tours3totstops0_SHARED2,0,coef_calib_tours3totstops0_WALK,coef_calib_tours3totstops0_BIKE,coef_calib_tours3totstops0_WALK_TRANSIT,coef_calib_tours3totstops0_PNR_TRANSIT,coef_calib_tours3totstops0_KNR_TRANSIT,coef_calib_tours3totstops0_TNC_TRANSIT,0,0,0,0,, +util_calib_tours3autodeficien,abm 2+ calibration,tourS3*autoDeficientHH,0,coef_calib_tours3autodeficienth_SHARED2,0,0,0,0,0,0,0,0,0,0,0,, +util_calib_tours3firstofmulti,abm 2+ calibration,tourS3*firstOfMultipleTrips,coef_calib_tours3firstofmultipl_DRIVEALONE,coef_calib_tours3firstofmultipl_SHARED2,0,coef_calib_tours3firstofmultipl_WALK,coef_calib_tours3firstofmultipl_BIKE,coef_calib_tours3firstofmultipl_WALK_TRANSIT,coef_calib_tours3firstofmultipl_PNR_TRANSIT,coef_calib_tours3firstofmultipl_KNR_TRANSIT,coef_calib_tours3firstofmultipl_TNC_TRANSIT,0,0,0,0,, +util_calib_tours3lastofmultip,abm 2+ calibration,tourS3*lastofMultipleTrips,coef_calib_tours3lastofmultiple_DRIVEALONE,coef_calib_tours3lastofmultiple_SHARED2,0,coef_calib_tours3lastofmultiple_WALK,coef_calib_tours3lastofmultiple_BIKE,coef_calib_tours3lastofmultiple_WALK_TRANSIT,coef_calib_tours3lastofmultiple_PNR_TRANSIT,coef_calib_tours3lastofmultiple_KNR_TRANSIT,coef_calib_tours3lastofmultiple_TNC_TRANSIT,0,0,0,0,, +util_calib_tours3zeroautohh,abm 2+ calibration,tourS3*zeroAutoHH,0,coef_calib_tours3zeroautohh_SHARED2,0,0,0,0,0,0,0,0,0,0,0,, +#,,,,,,,,,,,,,,,,, +util_calib_tourwtranjointtour0,abm 2+ calibration,tourWTran*(jointTour==0),coef_calib_tourwtranjointtour0_DRIVEALONE,coef_calib_tourwtranjointtour0_SHARED2,coef_calib_tourwtranjointtour0_SHARED3,coef_calib_tourwtranjointtour0_WALK,coef_calib_tourwtranjointtour0_BIKE,coef_calib_tourwtranjointtour0_WALK_TRANSIT,coef_calib_tourwtranjointtour0_PNR_TRANSIT,coef_calib_tourwtranjointtour0_KNR_TRANSIT,coef_calib_tourwtranjointtour0_TNC_TRANSIT,0,coef_calib_tourwtranjointtour0_TNC_SINGLE,coef_calib_tourwtranjointtour0_TNC_SHARED,0,, +util_calib_tourwtranjointtour1,abm 2+ calibration,tourWTran*(jointTour==1),coef_calib_tourwtranjointtour1_DRIVEALONE,coef_calib_tourwtranjointtour1_SHARED2,coef_calib_tourwtranjointtour1_SHARED3,coef_calib_tourwtranjointtour1_WALK,coef_calib_tourwtranjointtour1_BIKE,coef_calib_tourwtranjointtour1_WALK_TRANSIT,coef_calib_tourwtranjointtour1_PNR_TRANSIT,coef_calib_tourwtranjointtour1_KNR_TRANSIT,coef_calib_tourwtranjointtour1_TNC_TRANSIT,coef_calib_tourwtranjointtour1_TAXI,coef_calib_tourwtranjointtour1_TNC_SINGLE,coef_calib_tourwtranjointtour1_TNC_SHARED,0,, +util_calib_tourwtrantotstops0,abm 2+ calibration,tourWTran*(totStops==0),coef_calib_tourwtrantotstops0_DRIVEALONE,coef_calib_tourwtrantotstops0_SHARED2,coef_calib_tourwtrantotstops0_SHARED3,coef_calib_tourwtrantotstops0_WALK,coef_calib_tourwtrantotstops0_BIKE,0,coef_calib_tourwtrantotstops0_PNR_TRANSIT,coef_calib_tourwtrantotstops0_KNR_TRANSIT,coef_calib_tourwtrantotstops0_TNC_TRANSIT,0,0,0,coef_calib_tourwtrantotstops0_SCH_BUS,, +#,,,,,,,,,,,,,,,,, +util_calib_tourwtranfirstofmu,abm 2+ calibration,tourWTran*firstOfMultipleTrips,coef_calib_tourwtranfirstofmult_DRIVEALONE,coef_calib_tourwtranfirstofmult_SHARED2,coef_calib_tourwtranfirstofmult_SHARED3,coef_calib_tourwtranfirstofmult_WALK,coef_calib_tourwtranfirstofmult_BIKE,0,coef_calib_tourwtranfirstofmult_PNR_TRANSIT,coef_calib_tourwtranfirstofmult_KNR_TRANSIT,coef_calib_tourwtranfirstofmult_TNC_TRANSIT,0,0,0,coef_calib_tourwtranfirstofmult_SCH_BUS,, +util_calib_tourwtranlastofmul,abm 2+ calibration,tourWTran*lastofMultipleTrips,coef_calib_tourwtranlastofmulti_DRIVEALONE,coef_calib_tourwtranlastofmulti_SHARED2,coef_calib_tourwtranlastofmulti_SHARED3,coef_calib_tourwtranlastofmulti_WALK,coef_calib_tourwtranlastofmulti_BIKE,0,coef_calib_tourwtranlastofmulti_PNR_TRANSIT,coef_calib_tourwtranlastofmulti_KNR_TRANSIT,coef_calib_tourwtranlastofmulti_TNC_TRANSIT,0,0,0,coef_calib_tourwtranlastofmulti_SCH_BUS,, +util_calib_tourwtranzeroautoh,abm 2+ calibration,tourWTran*zeroAutoHH,0,0,0,coef_calib_tourwtranzeroautohh_WALK,0,0,0,0,0,0,0,0,0,, +util_calib_tourwalkjointtour0,abm 2+ calibration,tourWalk*(jointTour==0),coef_calib_tourwalkjointtour0_DRIVEALONE,coef_calib_tourwalkjointtour0_SHARED2,coef_calib_tourwalkjointtour0_SHARED3,coef_calib_tourwalkjointtour0_WALK,coef_calib_tourwalkjointtour0_BIKE,coef_calib_tourwalkjointtour0_WALK_TRANSIT,coef_calib_tourwalkjointtour0_PNR_TRANSIT,coef_calib_tourwalkjointtour0_KNR_TRANSIT,coef_calib_tourwalkjointtour0_TNC_TRANSIT,coef_calib_tourwalkjointtour0_TAXI,coef_calib_tourwalkjointtour0_TNC_SINGLE,coef_calib_tourwalkjointtour0_TNC_SHARED,0,, +util_calib_tourwalkjointtour1,abm 2+ calibration,tourWalk*(jointTour==1),coef_calib_tourwalkjointtour1_DRIVEALONE,coef_calib_tourwalkjointtour1_SHARED2,coef_calib_tourwalkjointtour1_SHARED3,0,coef_calib_tourwalkjointtour1_BIKE,coef_calib_tourwalkjointtour1_WALK_TRANSIT,coef_calib_tourwalkjointtour1_PNR_TRANSIT,coef_calib_tourwalkjointtour1_KNR_TRANSIT,coef_calib_tourwalkjointtour1_TNC_TRANSIT,coef_calib_tourwalkjointtour1_TAXI,coef_calib_tourwalkjointtour1_TNC_SINGLE,coef_calib_tourwalkjointtour1_TNC_SHARED,0,, +#,Micromobility,,,,,,,,,,,,,,,, +util_micromobility_long_access,Shut off micromobility if access time > threshold and not micromobility tour,@((df.MicroAccessTime > microAccessThreshold)& ~(df.tourEbike | df.tourEscooter)),,,,,,,,,,,,,,-999,-999 +util_ebike_long_access_microTour,Decrease ebike if access time > threshold but tour is micromobility,@((df.MicroAccessTime > microAccessThreshold) & (df.tourEbike | df.tourEscooter) & ((~df.ebike_owner) | (~df.tourEbike))),,,,,,,,,,,,,,-20, +util_escooter_long_access_microTour,Decrease escooter if access time > threshold but tour is micromobility,@((df.MicroAccessTime > microAccessThreshold) & (df.tourEbike | df.tourEscooter)),,,,,,,,,,,,,,,-20 +#util_micromobility_long_trip,Shut off ebike if distance > threshold,ebikeMaxDistance,,,,,,,,,,,,,,-999, +#util_micromobility_long_trip,Shut off escooter if distance > threshold,escooterMaxDistance,,,,,,,,,,,,,,,-999 +util_ebike_ivt,Ebike utility for in-vehicle time,@(df.ebike_time * df.time_factor),,,,,,,,,,,,,,coef_ivt, +util_ebike_access,Ebike utility for access time time,@((((~df.ebike_owner) | (~df.tourEbike))&(microRentTime + df.MicroAccessTime)))*df.time_factor,,,,,,,,,,,,,,coef_acctime, +util_ebike_cost_inb,Ebike utility for inbound cost,@(((~df.ebike_owner) | (~df.tourEbike)) & ((microFixedCost + microVarCost*df.ebike_time)/df.cost_sensitivity)),,,,,,,,,,,,,,coef_income, +util_escooter_ivt,Escooter utility for in-vehicle time,@(df.escooter_time)*df.time_factor,,,,,,,,,,,,,,,coef_ivt +util_escooter_access,Escooter utility for access time,@(microRentTime + df.MicroAccessTime) *df.time_factor,,,,,,,,,,,,,,,coef_acctime +util_escooter_cost_inb,Escooter utility for inbound cost,@(microFixedCost + microVarCost*df.escooter_time)/df.cost_sensitivity,,,,,,,,,,,,,,,coef_income diff --git a/resident/configs/trip_mode_choice.yaml b/resident/configs/trip_mode_choice.yaml new file mode 100644 index 0000000..7f87b22 --- /dev/null +++ b/resident/configs/trip_mode_choice.yaml @@ -0,0 +1,139 @@ +SPEC: trip_mode_choice.csv +COEFFICIENTS: trip_mode_choice_coefficients.csv +COEFFICIENT_TEMPLATE: trip_mode_choice_coefficients_template.csv + +LOGIT_TYPE: NL + +NESTS: + name: root + coefficient: coef_nest_root + alternatives: + - name: AUTO + coefficient: coef_nest_AUTO + alternatives: + - DRIVEALONE + - SHARED2 + - SHARED3 + - name: NONMOTORIZED + coefficient: coef_nest_NONMOTORIZED + alternatives: + - WALK + - BIKE + - name: MICROMOBILITY + coefficient: coef_nest_MICROMOBILITY + alternatives: + - EBIKE + - ESCOOTER + - name: TRANSIT + coefficient: coef_nest_TRANSIT + alternatives: + - WALK_TRANSIT + - PNR_TRANSIT + - KNR_TRANSIT + - BIKE_TRANSIT + - name: RIDEHAIL + coefficient: coef_nest_RIDEHAIL + alternatives: + - TAXI + - TNC_SINGLE + - TNC_SHARED + - name: SCHOOL_BUS + coefficient: coef_nest_SCHOOL_BUS + alternatives: + - SCH_BUS + +CONSTANTS: + orig_col_name: origin + dest_col_name: destination + + +# so far, we can use the same spec as for non-joint tours +preprocessor: + SPEC: trip_mode_choice_annotate_trips_preprocessor + DF: df + TABLES: + - land_use + - tours + - vehicles + - households + - persons + +# to reduce memory needs filter chooser table to these fields +TOURS_MERGED_CHOOSER_COLUMNS: + - hhsize + - age + - num_adults + - auto_ownership + - number_of_participants + - tour_category + - parent_tour_id + - tour_mode + - duration + - tour_type + - free_parking_at_work + - income_segment + - income + - SEX + - time_factor_work + - time_factor_nonwork + - ptype + - ebike_owner + - start + - end + - transit_pass_subsidy + - transit_pass_ownership + +MODE_CHOICE_LOGSUM_COLUMN_NAME: mode_choice_logsum + +CHOOSER_COLS_TO_KEEP: + - vot_da + - vot_s2 + - vot_s3 + - ebike_owner + - parkingCost + - auto_op_cost + - autoCPMFactor + - autoParkingCostFactor + - autoTermTimeFactor + - costFactorS2 + - costFactorS3 + - transitSubsidyPassDiscount + - origTaxiWaitTime + - origSingleTNCWaitTime + - destSingleTNCWaitTime + - origSharedTNCWaitTime + - da_dist_skims + - s2_time_skims + - s2_dist_skims + - s2_cost_skims + - s3_time_skims + - s3_dist_skims + - s3_cost_skims + - ebike_time + - escooter_time + - microtransit_orig + - microtransit_dest + - microtransit_operating + - microtransit_available + - microtransit_time + - nev_orig + - nev_dest + - nev_operating + - nev_available + - nev_time + - microtransit_local_access_available_out + - nev_local_access_available_out + - microtransit_local_egress_available_out + - nev_local_egress_available_out + - microtransit_local_access_available_in + - nev_local_access_available_in + - microtransit_local_egress_available_in + - nev_local_egress_available_in + - microtransit_local_access_time_out + - nev_local_access_time_out + - microtransit_local_egress_time_out + - nev_local_egress_time_out + - microtransit_local_access_time_in + - nev_local_access_time_in + - microtransit_local_egress_time_in + - nev_local_egress_time_in \ No newline at end of file diff --git a/resident/configs/trip_mode_choice_annotate_trips_preprocessor.csv b/resident/configs/trip_mode_choice_annotate_trips_preprocessor.csv new file mode 100644 index 0000000..5cd5420 --- /dev/null +++ b/resident/configs/trip_mode_choice_annotate_trips_preprocessor.csv @@ -0,0 +1,248 @@ +Description,Target,Expression +,is_joint,(df.number_of_participants > 1) +,is_indiv,(df.number_of_participants == 1) +#,, +,dest_density_index,"reindex(land_use.density_index, df[dest_col_name])" +#,, +,tour_type,"df.get('tour_type', default='work')" +# cost coef,, +,income_exponent,"np.where(df.tour_type == 'work', 0.6, 0.5)" +,c_cost,"(coef_income) /(df.income.clip(0,1000).pow(income_exponent))" +,cost_sensitivity,"np.maximum(df.income,1000).pow(income_exponent)" +# ivt coef,, +,time_factor,"np.where(df.tour_type=='work', df.time_factor_work, df.time_factor_nonwork)" +,c_ivt,coef_ivt * time_factor +,origin,df.origin if 'origin' in df.columns else df.home_zone_id +,destination,df.destination if 'destination' in df.columns else df.alt_dest +#,, +# cost coef,, +,income_exponent,"np.where(tour_type == 'work', 0.6, 0.5)" +,c_cost,"(coef_income) /(np.maximum(df.income,1000).pow(income_exponent))" +#,, +,vot_da,c_ivt / c_cost * 0.6 +,vot_s2,vot_da / cost_share_s2 +,vot_s3,vot_da / cost_share_s3 +,_vot_bin_da,"np.where(vot_da < vot_threshold_low, 1, np.where(vot_da < vot_threshold_med, 2, 3))" +,_vot_bin_s2,"np.where(vot_s2 < vot_threshold_low, 1, np.where(vot_s2 < vot_threshold_med, 2, 3))" +,_vot_bin_s3,"np.where(vot_s3 < vot_threshold_low, 1, np.where(vot_s3 < vot_threshold_med, 2, 3))" +#vot-indexed skims,, +,da_dist_skims,"(odt_skims['SOV_L_DIST'] * (_vot_bin_da == 1)) + (odt_skims['SOV_M_DIST'] * (_vot_bin_da == 2)) + (odt_skims['SOV_H_DIST'] * (_vot_bin_da == 3))" +,da_cost_skims,"(odt_skims['SOV_L_COST'] * (_vot_bin_da == 1)) + (odt_skims['SOV_M_COST'] * (_vot_bin_da == 2)) + (odt_skims['SOV_H_COST'] * (_vot_bin_da == 3))" +,da_time_skims,"(odt_skims['SOV_L_TIME'] * (_vot_bin_da == 1)) + (odt_skims['SOV_M_TIME'] * (_vot_bin_da == 2)) + (odt_skims['SOV_H_TIME'] * (_vot_bin_da == 3))" +,s2_dist_skims,(((odt_skims['SR2_L_DIST']) * (_vot_bin_s2 == 1)) + ((odt_skims['SR2_M_DIST']) * (_vot_bin_s2 == 2)) + ((odt_skims['SR2_H_DIST']) * (_vot_bin_s2 == 3))) +,s2_cost_skims,(((odt_skims['SR2_L_COST']) * (_vot_bin_s2 == 1)) + ((odt_skims['SR2_M_COST']) * (_vot_bin_s2 == 2)) + ((odt_skims['SR2_H_COST']) * (_vot_bin_s2 == 3))) +,s2_time_skims,(((odt_skims['SR2_L_TIME']) * (_vot_bin_s2 == 1)) + ((odt_skims['SR2_M_TIME']) * (_vot_bin_s2 == 2)) + ((odt_skims['SR2_H_TIME']) * (_vot_bin_s2 == 3))) +,s3_dist_skims,(((odt_skims['SR3_L_DIST']) * (_vot_bin_s3 == 1)) + ((odt_skims['SR3_M_DIST']) * (_vot_bin_s3 == 2)) + ((odt_skims['SR3_H_DIST']) * (_vot_bin_s3 == 3))) +,s3_cost_skims,(((odt_skims['SR3_L_COST']) * (_vot_bin_s3 == 1)) + ((odt_skims['SR3_M_COST']) * (_vot_bin_s3 == 2)) + ((odt_skims['SR3_H_COST']) * (_vot_bin_s3 == 3))) +,s3_time_skims,(((odt_skims['SR3_L_TIME']) * (_vot_bin_s3 == 1)) + ((odt_skims['SR3_M_TIME']) * (_vot_bin_s3 == 2)) + ((odt_skims['SR3_H_TIME']) * (_vot_bin_s3 == 3))) +,walk_time_skims,od_skims['DISTWALK'] / walkSpeed * 60 +#,, +# FIXME no transit subzones so all zones short walk to transit,, +,_walk_transit_origin,True +,_walk_transit_destination,True +,walk_transit_available,_walk_transit_origin & _walk_transit_destination +,drive_transit_available,"np.where(df.outbound, _walk_transit_destination, _walk_transit_origin) & (df.auto_ownership > 0)" +# RIDEHAIL,, +# FIXME population_density replaced PopDenPerSqMi which may not be calculated the same,, +household_density calculated in annotate_landuse in acres and is converted to sq miles here,_origin_density_measure,"reindex(land_use.population_density, df[orig_col_name])" +employment_density calculated in annotate_landuse in acres and is converted to sq miles here,_dest_density_measure,"reindex(land_use.population_density, df[dest_col_name])" +,origin_density,"pd.cut(_origin_density_measure, bins=[-np.inf, 500, 2000, 5000, 15000, np.inf], labels=[5, 4, 3, 2, 1]).astype(int)" +,dest_density,"pd.cut(_dest_density_measure, bins=[-np.inf, 500, 2000, 5000, 15000, np.inf], labels=[5, 4, 3, 2, 1]).astype(int)" +Origin MGRA Dwelling Unit Density,oMGRADUDen,"reindex(land_use.duden,df.origin)" +Origin MGRA Employment Density,oMGRAEmpDen,"reindex(land_use.empden,df.origin)" +Origin MGRA Total Intersections,oMGRATotInt,"reindex(land_use.totint,df.origin)" +Destination MGRA Dwelling Unit Density,dMGRADUDen,"reindex(land_use.duden,df.destination)" +Destination MGRA Employment Density,dMGRAEmpDen,"reindex(land_use.empden,df.destination)" +Destination MGRA Total Intersections,dMGRATotInt,"reindex(land_use.totint,df.destination)" +Origin MGRA Mix,oMGRAMix,"np.where(oMGRADUDen+oMGRAEmpDen > 0,(oMGRADUDen*oMGRAEmpDen)/(oMGRADUDen+oMGRAEmpDen),0)" +Destination MGRA Mix,dMGRAMix,"np.where(dMGRADUDen+dMGRAEmpDen > 0,(dMGRADUDen*dMGRAEmpDen)/(dMGRADUDen+dMGRAEmpDen),0)" +,origin_zone_taxi_wait_time_mean,"origin_density.map({k: v for k, v in Taxi_waitTime_mean.items()})" +,origin_zone_taxi_wait_time_sd,"origin_density.map({k: v for k, v in Taxi_waitTime_sd.items()})" +,dest_zone_taxi_wait_time_mean,"dest_density.map({k: v for k, v in Taxi_waitTime_mean.items()})" +,dest_zone_taxi_wait_time_sd,"dest_density.map({k: v for k, v in Taxi_waitTime_sd.items()})" +# ,, Note that the mean and standard deviation are not the values for the distribution itself +,origTaxiWaitTime,"rng.lognormal_for_df(df, mu=origin_zone_taxi_wait_time_mean, sigma=origin_zone_taxi_wait_time_sd, broadcast=True, scale=True).clip(min_waitTime, max_waitTime)" +,destTaxiWaitTime,"rng.lognormal_for_df(df, mu=dest_zone_taxi_wait_time_mean, sigma=dest_zone_taxi_wait_time_sd, broadcast=True, scale=True).clip(min_waitTime, max_waitTime)" +,origin_zone_singleTNC_wait_time_mean,"origin_density.map({k: v for k, v in TNC_single_waitTime_mean.items()})" +,origin_zone_singleTNC_wait_time_sd,"origin_density.map({k: v for k, v in TNC_single_waitTime_sd.items()})" +,dest_zone_singleTNC_wait_time_mean,"dest_density.map({k: v for k, v in TNC_single_waitTime_mean.items()})" +,dest_zone_singleTNC_wait_time_sd,"dest_density.map({k: v for k, v in TNC_single_waitTime_sd.items()})" +,origSingleTNCWaitTime,"rng.lognormal_for_df(df, mu=origin_zone_singleTNC_wait_time_mean, sigma=origin_zone_singleTNC_wait_time_sd, broadcast=True, scale=True).clip(min_waitTime, max_waitTime)" +,destSingleTNCWaitTime,"rng.lognormal_for_df(df, mu=dest_zone_singleTNC_wait_time_mean, sigma=dest_zone_singleTNC_wait_time_sd, broadcast=True, scale=True).clip(min_waitTime, max_waitTime)" +,origin_zone_sharedTNC_wait_time_mean,"origin_density.map({k: v for k, v in TNC_shared_waitTime_mean.items()})" +,origin_zone_sharedTNC_wait_time_sd,"origin_density.map({k: v for k, v in TNC_shared_waitTime_sd.items()})" +# wrong? producing bad probs error,dest_zone_sharedTNC_wait_time_mean,"np.maximum((200+(-133 * da_dist_skims)),0) * time_factor" +,dest_zone_sharedTNC_wait_time_mean,"dest_density.map({k: v for k, v in TNC_shared_waitTime_mean.items()})" +,dest_zone_sharedTNC_wait_time_sd,"dest_density.map({k: v for k, v in TNC_shared_waitTime_sd.items()})" +,origSharedTNCWaitTime,"rng.lognormal_for_df(df, mu=origin_zone_sharedTNC_wait_time_mean, sigma=origin_zone_sharedTNC_wait_time_sd, broadcast=True, scale=True).clip(min_waitTime, max_waitTime)" +,destSharedTNCWaitTime,"rng.lognormal_for_df(df, mu=dest_zone_sharedTNC_wait_time_mean, sigma=dest_zone_sharedTNC_wait_time_sd, broadcast=True, scale=True).clip(min_waitTime, max_waitTime)" +,totalWaitTaxi,origTaxiWaitTime + destTaxiWaitTime +,totalWaitSingleTNC,origSingleTNCWaitTime + destSingleTNCWaitTime +,totalWaitSharedTNC,origSharedTNCWaitTime + destSharedTNCWaitTime +#,, +Household autos,autos,df.auto_ownership +Number of adults (age 18+) in household,adults,df.num_adults +Household size (number of persons),hhSize,df.hhsize +Person age,age,df.age +Person is female,female,(df.SEX == 2) +# Auto operating costs,, +,selected_tour_vehicle,"reindex(tours.selected_vehicle, df.tour_id)" +,auto_op_cost,"reindex(vehicles.groupby('vehicle_type')['auto_operating_cost'].mean(), pd.Series(selected_tour_vehicle, df.index))" +,auto_op_cost,"np.where(pd.isna(auto_op_cost), costPerMile, auto_op_cost)" +#,, +Does tour use an AV,useAV,selected_tour_vehicle.str.contains('AV') +Auto IVT Factor,autoIVTFactor,"np.where(useAV,autoIVTFactorAV,1)" +Auto Parking Cost Factor,autoParkingCostFactor,"np.where(useAV,autoParkingCostFactorAV,1)" +Auto CPM Factor,autoCPMFactor,"np.where(useAV,autoCostPerMileFactorAV,1)" +Auto Terminal Time Factor,autoTermTimeFactor,"np.where(useAV,autoTerminalTimeFactorAV,1)" +MinimumAgeDriveAlone,minimumAgeDA,"np.where(useAV,minAgeDriveAloneAV,16)" +#,, +Zero auto households,zeroAutoHH,"np.where(autos==0,1,0)" +Auto deficient household (more adults than autos),autoDeficientHH,"np.where(autos=adults,1,0) * (np.where(zeroAutoHH,0,1))" +Joint tour,jointTour,(df.tour_category == 'joint') +Number of participants in joint tour,numberOfParticipantsInJointTour,df.number_of_participants * jointTour +Individual tour,indivTour,1 * (jointTour==0) +Number of stops - outbound direction,outStops,df.trip_count * df.outbound +Number of stops - return direction,retStops,df.trip_count * ~df.outbound +Total stops on tour,totStops,outStops + retStops +First trip of tour,firstTrip,df.trip_num == 1 +Last trip of tour,lastTrip,df.trip_num == df.trip_count +First leg of multi-stop outbound,firstOfMultipleTrips,firstTrip *(outStops>1) +Last leg of multi-stop return,lastofMultipleTrips,lastTrip *( retStops>1) +# Flag for setting availability of auto modes for drive-transit access/egress trip segments,autoAllowedForDriveTransit,df.autoModeAllowedForTripSegment +# Flag for setting availability of walk mode for drive-transit access/egress trip segments,walkAllowedForDriveTransit,df.walkModeAllowedForTripSegment +Tour mode is drive-alone,tourDA,(df.tour_mode == 'DRIVEALONE').astype(int) +Tour mode is shared-2,tourS2,(df.tour_mode == 'SHARED2').astype(int) +Tour mode is shared-3+,tourS3,(df.tour_mode == 'SHARED3').astype(int) +Tour mode is walk,tourWalk,(df.tour_mode == 'WALK').astype(int) +Tour mode is bike,tourBike,(df.tour_mode == 'BIKE').astype(int) +Tour mode is walk-transit,tourWTran,(df.tour_mode == 'WALK_TRANSIT').astype(int) +Tour mode is PNR-transit,tourPNR,(df.tour_mode == 'PNR_TRANSIT').astype(int) +Tour mode is KNR-transit,tourKNR,(df.tour_mode == 'KNR_TRANSIT').astype(int) +Tour mode is Bike-transit,tourBNR,"(df.tour_mode == 'BIKE_TRANSIT').astype(int)" +Tour mode is MaaS,tourMaaS,"(df.tour_mode.isin(['TAXI', 'TNC_SINGLE', 'TNC_SHARED'])).astype(int)" +Tour mode is school bus,tourSchBus,(df.tour_mode == 'SCH_BUS').astype(int) +Tour mode is Ebike,tourEbike,(df.tour_mode == 'EBIKE').astype(int) +Tour mode is Escooter,tourEscooter,(df.tour_mode == 'ESCOOTER').astype(int) +#,#, +,free_parking_available,(df.tour_type == 'work') & df.free_parking_at_work +person has free on-site parking at workplace,freeOnsite,"(free_parking_available)*np.where(is_indiv,1,0)" +new reimbursement amount,reimburseProportion,0 +tour primary destination,tour_dest,"reindex(tours.destination, df.tour_id)" +half tour duration,tourDuration,"reindex(tours.duration, df.tour_id)/2" +new daily parking cost with reimbursement,parkingCostDay,"reindex(land_use.PRKCST_DAY, tour_dest)" +new hourly parking cost with reimbursement,parkingCostHour,"reindex(land_use.PRKCST_HR, tour_dest)" +new monthly parking cost with reimbursement,parkingCostMonth,"reindex(land_use.PRKCST_MNTH, tour_dest)" +Tour parking cost for full-time workers and university students,_parkingCostBeforeReimb,"df.ptype.isin([1,3]) * is_indiv * np.minimum(parkingCostMonth/22, parkingCostDay)" +Tour parking cost for full-time workers and university students,_parkingCostBeforeReimb,"df.ptype.isin([1,3]) * is_indiv * np.minimum(_parkingCostBeforeReimb, parkingCostHour * tourDuration)" +Tour parking cost for other person types,parkingCostBeforeReimb,"np.where((~df.ptype.isin([1,3]) * is_indiv) | (is_joint), np.minimum(parkingCostDay, parkingCostHour * tourDuration), _parkingCostBeforeReimb)" +Reimbursement applies to this tour purpose,reimbursePurpose,"reindex(tours.tour_type, df.tour_id)=='work'" +Effective parking cost for free parkers,_parkingCost,"0 * np.where(reimbursePurpose*freeOnsite,1,0)" +Effective parking cost for reimbursed parkers,_parkingCost,"np.where(is_indiv*reimbursePurpose*(1-freeOnsite), np.maximum((1-reimburseProportion) * parkingCostBeforeReimb, 0),_parkingCost)" +Effective parking cost applied to tour purpose,parkingCostPrimDest,"np.where(is_joint+is_indiv*(1-reimbursePurpose), parkingCostBeforeReimb,_parkingCost)" +#,, +Total trips on tour,totalTrips,totStops+2 +Indicator for trip origin is tour prim. Destination,tripOrigIsTourDest,~df.outbound*df.trip_num==1 +Indicator for trip destination is tour prim. Destination,tripDestIsTourDest,df.outbound*df.trip_num==df.trip_count +Contribution to trip parking cost from primary destination,parkCostTourDestContrib,parkingCostPrimDest/(totalTrips+2) +Hourly parking cost at trip origin,parkCostTripOrig,"reindex(land_use.PRKCST_HR, origin)*100" +Hourly parking cost at trip destination,parkCostTripDest,"reindex(land_use.PRKCST_HR, destination)*100" +Contribution to trip parking cost from trip origin,parkCostTripOrigContrib,parkCostTripOrig*(1-firstTrip)*(1-tripOrigIsTourDest) +Contribution to trip parking cost from trip origin,parkCostTripOrigContrib,parkingCostPrimDest/(totalTrips+2) * tripOrigIsTourDest +Contribution to trip parking cost from trip destination,parkCostTripDestContrib,parkCostTripDest * (1-lastTrip)*(1-tripDestIsTourDest) +Contribution to trip parking cost from trip destination,parkCostTripDestContrib,parkingCostPrimDest/(totalTrips+2) * tripDestIsTourDest +Final parking cost,parkingCost,parkCostTourDestContrib + parkCostTripOrigContrib + parkCostTripDestContrib +#,, +Origin Terminal Time,oTermTime,"reindex(land_use.TERMINALTIME,origin).fillna(0)" +Destination Terminal Time,dTermTime,"reindex(land_use.TERMINALTIME,destination).fillna(0)" +#,, +# FIXME need actual bike logsums and times,, +#bike logsum,bikeLS,od_skims['BIKE_LOGSUM'] +#bike time,bike_time,od_skims['BIKE_TIME'] +bike logsum,bikeLS,0 +bike time,bike_time,odt_skims['SOV_L_DIST'] / bikeSpeed * 60 +#,, +"Cost factor for shared 2 tours, 1/(2^0.8)",costFactorS2,0.57 +"Cost factor for shared 3+ tours, 1/(3.5^0.8)",costFactorS3,0.37 +# no sov for 0 autos or age< min drving age,sov_available,(autos>0) * (age>=minimumAgeDA) +no sov for age < min drving age,sov_available,"(age>=minimumAgeDA) * is_indiv * np.where((tourPNR)|(tourKNR)|(tourBNR)|(tourMaaS)|(tourSchBus)|(tourEbike)|(tourEscooter),0,1)" +,sr2_available,"np.where((tourPNR)|(tourKNR)|(tourBNR)|(tourMaaS)|(tourSchBus)|(tourEbike)|(tourEscooter)|(df.number_of_participants>2),0,1)" +,sr3_available,"np.where((tourPNR)|(tourKNR)|(tourBNR)|(tourMaaS)|(tourSchBus)|(tourEbike)|(tourEscooter)|(df.number_of_participants==2),0,1)" +no long walks,walkAvailable,"np.where((walk_time_skims < max_walk_time),1,0) * np.where((tourPNR)|(tourKNR)|(tourBNR)|(tourMaaS)|(tourSchBus)|(tourEbike)|(tourEscooter),0,1)" +,Escooter_available,"(bike_time>0) * np.where((tourDA)|(tourS2)|(tourS3)|(tourWalk)|(tourBike)|(tourWTran)|(tourPNR)|(tourKNR)|(tourBNR)|(tourMaaS)|(tourSchBus)|(tourEbike),0,1)" +,Ebike_available,"(bike_time>0) * np.where((tourDA)|(tourS2)|(tourS3)|(tourWalk)|(tourBike)|(tourWTran)|(tourPNR)|(tourKNR)|(tourBNR)|(tourMaaS)|(tourSchBus)|(tourEscooter),0,1)" +,PNR_available,"(autos>0) * (age>15) * np.where((tourDA)|(tourS2)|(tourS3)|(tourWalk)|(tourBike)|(tourWTran)|(tourKNR)|(tourBNR)|(tourMaaS)|(tourSchBus)|(tourEbike)|(tourEscooter),0,1)" +,KNR_available,"np.where((tourDA)|(tourS2)|(tourS3)|(tourWalk)|(tourBike)|(tourWTran)|(tourPNR)|(tourBNR)|(tourMaaS)|(tourSchBus)|(tourEbike)|(tourEscooter),0,1)" +,BNR_available,"np.where((tourDA)|(tourS2)|(tourS3)|(tourWalk)|(tourBike)|(tourWTran)|(tourPNR)|(tourKNR)|(tourMaaS)|(tourSchBus)|(tourEbike)|(tourEscooter),0,1)" +,RideHail_available,"np.where((tourDA)|(tourS2)|(tourS3)|(tourWalk)|(tourBike)|(tourWTran)|(tourPNR)|(tourKNR)|(tourBNR)|(tourSchBus)|(tourEbike)|(tourEscooter),0,1)" +,SCHBUS_available,"(df.tour_type =='school') & (df.ptype!=3)*np.where((tourDA)|(tourS2)|(tourS3)|(tourWalk)|(tourBike)|(tourWTran)|(tourPNR)|(tourKNR)|(tourBNR)|(tourMaaS)|(tourEbike)|(tourEscooter),0,1)" +#,, +,walk_local_available,(odt_skims['WTW_TIV']>0)&(tourWTran) +# FIXME need updated PNR availability,, +,knr_local_available,((df.outbound & (odt_skims['KTW_TIV']>0)) | (~df.outbound & (odt_skims['WTK_TIV']>0)))&(tourKNR) +,bike_local_available,(odt_skims['WTW_TIV']>0)&(tourBNR) +#access egress distances,, +,origin_local_dist,"reindex(land_use.walk_dist_local_bus, origin)" +,origin_micro_local_dist,"reindex(land_use.micro_dist_local_bus, origin)" +,dest_local_dist,"reindex(land_use.walk_dist_local_bus, destination)" +,dest_micro_local_dist,"reindex(land_use.micro_dist_local_bus, destination)" +,micro_local_dist_tncout,"odt_skims['KTW_ACC']/60 * driveSpeed" +,micro_local_dist_tncin,"odt_skims['WTK_EGR']/60 * driveSpeed" +#access egress times,, +,origin_local_time,origin_local_dist * 60/walkSpeed +,dest_local_time,dest_local_dist * 60/walkSpeed +# added for school escorting model,, +Number of school children in vehicle on trip,num_escortees,df.escort_participants.fillna('').apply(lambda x: len(x.split('_')) if len(x)>0 else 0) +# Micromobility times,, +ebike time,ebike_time,bike_time * bikeSpeed / ebikeSpeed +escooter time,escooter_time,bike_time * bikeSpeed / escooterSpeed +Micromobility access Time,MicroAccessTime,"reindex(land_use.ESCOOACCTIME,origin)" +ebike max distance availability,ebikeMaxDistance,"(od_skims[('SOV_M_DIST', 'MD')] > ebikeMaxDist)" +escooter max distance availability,escooterMaxDistance,"(od_skims[('SOV_M_DIST', 'MD')] > escooterMaxDist)" +# Microtransit and NEV,, +microtransit available at origin,microtransit_orig,"reindex(land_use.microtransit, df[orig_col_name])" +microtransit available at destination,microtransit_dest,"reindex(land_use.microtransit, df[dest_col_name])" +microtransit operating at time of trip,microtransit_operating,True +microtransit available,microtransit_available,(microtransit_orig > 0) & (microtransit_orig == microtransit_dest) & (s3_dist_skims < microtransitMaxDist) & microtransit_operating +microtransit direct time,microtransit_direct_time,"np.maximum(s3_dist_skims/microtransitSpeed*60, s3_time_skims)" +microtransit total time,microtransit_time,"np.maximum(microtransit_direct_time + microtransitDiversionConstant, microtransitDiversionFactor*microtransit_direct_time)" +nev available at origin,nev_orig,"reindex(land_use.nev, df[orig_col_name])" +nev available at destination,nev_dest,"reindex(land_use.nev, df[dest_col_name])" +nev operating at time of trip,nev_operating,True +nev available,nev_available,(nev_orig > 0) & (nev_orig == nev_dest) & (s3_dist_skims < nevMaxDist) & nev_operating +nev direct time,nev_direct_time,"np.maximum(s3_dist_skims/nevSpeed*60, s3_time_skims)" +nev total time,nev_time,"np.maximum(nev_direct_time + nevDiversionConstant, nevDiversionFactor*nev_direct_time)" +# Microtransit and NEV access to transit,, +outbound microtransit access to local available,microtransit_local_access_available_out,df.outbound & (microtransit_orig>0) & (micro_local_dist_tncout0) & (micro_local_dist_tncin0) & (micro_local_dist_tncout0) & (micro_local_dist_tncin0) & (dest_micro_local_dist>maxWalkIfMTAccessAvailable) & (dest_micro_local_dist0) & (origin_micro_local_dist>maxWalkIfMTAccessAvailable) & (origin_micro_local_dist0) & (dest_micro_local_dist>maxWalkIfMTAccessAvailable) & (dest_micro_local_dist0) & (origin_micro_local_dist>maxWalkIfMTAccessAvailable) & (origin_micro_local_dist= 25) & (df['outbound'])) | ((periods_left >= 34) & (~df['outbound'])), 47, periods_left)" +,tour_purpose,"reindex(tours.tour_type, df.tour_id)" +,tour_purpose_grouped,"np.where(tour_purpose.isin(['work','school','univ']), 'mand', 'non_mand')" +,half_tour_stops_remaining_grouped,(df.trip_count - df.trip_num).clip(upper=1) \ No newline at end of file diff --git a/resident/configs/trip_scheduling_probs_purpose_stops.csv b/resident/configs/trip_scheduling_probs_purpose_stops.csv new file mode 100644 index 0000000..411be00 --- /dev/null +++ b/resident/configs/trip_scheduling_probs_purpose_stops.csv @@ -0,0 +1,245 @@ +periods_left_min,periods_left_max,outbound,tour_purpose_grouped,half_tour_stops_remaining_grouped,0,1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,All +0,0,True,non_mand,0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0 +0,0,True,non_mand,1,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0 +0,0,True,mand,0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0 +0,0,True,mand,1,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0 +1,1,True,non_mand,0,0.8189802736716861,0.18101972632831478,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0 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+31,31,False,non_mand,1,0.0,0.0,0.0,0.01833810824001649,0.0,0.0,0.0,0.0,0.011516290886021865,0.16308529577680958,0.0,0.0,0.0,0.02547074507105579,0.01652281484738158,0.0,0.04452351185239044,0.009317122261056536,0.19971424002149518,0.013045312623538401,0.0075661778015869464,0.3102328065307183,0.0,0.025003003703219125,0.035055083246462164,0.030069783720618536,0.018526881151892546,0.034928530599363024,0.037084291666373344,0.0,0.0,0.0,0.0,0.0,0.0,1.0 +31,31,False,mand,0,0.0,0.0,0.0,0.0,0.0,0.011660697134411879,0.08359356906747217,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0074351633681512585,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.053515133972031996,0.0,0.046642788537647514,0.06362339255281085,0.004389112388089102,0.04017428022808142,0.4817135990383709,0.20725226371293273,0.0,0.0,0.0,1.0 +31,31,False,mand,1,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.014153420408759243,0.0,0.0,0.0,0.0,0.023248141345199345,0.008890938931074744,0.05285349575062624,0.04990553163058654,0.16677175839150424,0.11331994669851629,0.1181638409115989,0.07387812106291494,0.15865917983104275,0.031235937800509726,0.06596532918651592,0.035468217376292764,0.013865230438545558,0.033059807910658684,0.03141248412869757,0.006106628878031803,0.003041989318924492,0.0,0.0,0.0,0.0,1.0 +32,32,False,non_mand,0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.09030713454422928,0.10225330344102772,0.0,0.0,0.0,0.0,0.34201165614137685,0.10656854103943249,0.35885936483393366,0.0,0.0,1.0 +32,32,False,non_mand,1,0.0,0.03615936143527503,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.11870838866384083,0.0,0.0,0.030664587449557346,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.026266199236874226,0.0,0.0,0.08761524377109123,0.0,0.0,0.7005862194433614,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0 +32,32,False,mand,0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.05789616370086815,0.1344598373883522,0.3375637056076247,0.2230108052807423,0.24706948802241244,0.0,0.0,1.0 +32,32,False,mand,1,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.009238420497671889,0.0,0.0,0.0,0.0,0.0,0.0,0.027952902624459142,0.004745183696099721,0.041750935844626774,0.03560104623729895,0.019208273165269348,0.039862390831722126,0.2445054537893148,0.1472889280930601,0.10677793164025022,0.07872994398102684,0.04261569901118799,0.005675051098870241,0.0815413323203732,0.019486477408908912,0.0,0.09502002975985951,0.0,0.0,0.0,0.0,0.0,1.0 +33,33,False,non_mand,0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.3937868145193479,0.4486503063939636,0.15756287908668853,0.0,1.0 +33,33,False,non_mand,1,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.020230772694571117,0.0,0.13595146003291136,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.09279893744389224,0.0,0.0,0.07918749737876385,0.0,0.0,0.12358922634696451,0.07179448760198139,0.0,0.0,0.1394804713644242,0.0,0.2868037587900601,0.0,0.0,0.05016338834643131,0.0,0.0,1.0 +33,33,False,mand,0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.21087359210326426,0.35509399657354956,0.28936975269124166,0.14466265863194447,0.0,1.0 +33,33,False,mand,1,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.023247760476100464,0.0,0.024677767802522798,0.0,0.009363181643807422,0.029519156456030052,0.11193010731182607,0.02285317247116739,0.051385963088573655,0.04473753625556975,0.14095762264166245,0.055024985843556244,0.17384148255260748,0.029384651649520036,0.16325933704399054,0.03425760908549876,0.013563487427945331,0.015926159431522906,0.007948502073252565,0.0,0.022039827009254335,0.005005115351559031,0.013479183790602863,0.0,0.007597390593429946,0.0,0.0,1.0 +34,47,False,non_mand,0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.018384368573649112,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.5577089454113409,0.0,0.4239066860150099,1.0 +34,47,False,non_mand,1,0.016657687671303212,0.02183836474581605,0.0,0.021670440655999233,0.0,0.10173930098123364,0.02498724707017388,0.0,0.14226492691657533,0.004760495563960537,0.0,0.0,0.0,0.0,0.0,0.0,0.02638875495711764,0.0,0.0,0.05929170885647927,0.0,0.0,0.0,0.0,0.0,0.008986741490552959,0.0,0.0,0.22252478651398835,0.033523970436925156,0.0,0.010929820731753599,0.017516567897537586,0.28691918551058354,0.0,1.0 +34,47,False,mand,0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0778976250021037,0.030793465479170962,0.0,0.0,0.012379085610171855,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.07427570742104288,0.0,0.0,0.0,0.0,0.10023495355353525,0.26524960444707757,0.1397015192237292,0.29946803926316845,1.0 +34,47,False,mand,1,0.0,0.0,0.0,0.0,0.1018436706346972,0.0,0.0,0.0,0.0,0.002263878168597051,0.0,0.000983293615706626,0.0,0.008218649939220985,0.023737052572553673,0.021264126033222547,0.05518389314660721,0.010993620292826722,0.20415745418381434,0.07475058698068834,0.07116274568401747,0.07424603443354091,0.06495972196633373,0.03217245961981454,0.08352494702875252,0.03514324066332105,0.026534322261271913,0.04879548794140681,0.004334841005982029,0.02812637911830777,0.0,0.0,0.015797438844245334,0.0,0.011806155865071414,1.0 diff --git a/resident/configs/vehicle_allocation.csv b/resident/configs/vehicle_allocation.csv new file mode 100644 index 0000000..7a4bd77 --- /dev/null +++ b/resident/configs/vehicle_allocation.csv @@ -0,0 +1,174 @@ +Label,Description,Expression,veh_num1,veh_num2,veh_num3,veh_num4,non_hh_veh +#,Availability Conditions,,,, +util_alt1_unavail,Household does not own vehicle,"veh_num1.isna() | (veh_num1 == '')",coef_unavail,0,0,0,0 +util_alt2_unavail,Household does not own vehicle,"veh_num2.isna() | (veh_num2 == '')",0,coef_unavail,0,0,0 +util_alt3_unavail,Household does not own vehicle,"veh_num3.isna() | (veh_num3 == '')",0,0,coef_unavail,0,0 +util_alt4_unavail,Household does not own vehicle,"veh_num4.isna() | (veh_num4 == '')",0,0,0,coef_unavail,0 +#,BEV Range,,,, +util_dstgtrng1,Round trip tour distance > BEV range,"@np.where((df.tot_tour_dist > df.Range_1) & (df.fuel_type_1 == 'BEV'), 1, 0)",coef_dstgtrng,0,0,0,0 +util_dstgtrng2,Round trip tour distance > BEV range,"@np.where((df.tot_tour_dist > df.Range_2) & (df.fuel_type_2 == 'BEV'), 1, 0)",0,coef_dstgtrng,0,0,0 +util_dstgtrng3,Round trip tour distance > BEV range,"@np.where((df.tot_tour_dist > df.Range_3) & (df.fuel_type_3 == 'BEV'), 1, 0)",0,0,coef_dstgtrng,0,0 +util_dstgtrng4,Round trip tour distance > BEV range,"@np.where((df.tot_tour_dist > df.Range_4) & (df.fuel_type_4 == 'BEV'), 1, 0)",0,0,0,coef_dstgtrng,0 +#,Vehicles & Driver interactions,,,, +util_vehltdr_nh,Vehicles < Drivers -- Non-Household Vehicle,"hh_veh_lt_drivers",0,0,0,0,coef_vehltdr_nh +util_vehltdr_nh,Vehicles > Drivers -- Non-Household Vehicle,"hh_veh_gt_drivers",0,0,0,0,coef_vehgtdr_nh +util_vehltdr_van1,Vehicles > Drivers -- Van alt 1,"hh_veh_gt_drivers * (body_type_1 == 'Van')",coef_vehltdr_van,0,0,0,0 +util_vehltdr_van2,Vehicles > Drivers -- Van alt 2,"hh_veh_gt_drivers * (body_type_2 == 'Van')",0,coef_vehltdr_van,0,0,0 +util_vehltdr_van3,Vehicles > Drivers -- Van alt 3,"hh_veh_gt_drivers * (body_type_3 == 'Van')",0,0,coef_vehltdr_van,0,0 +util_vehltdr_van4,Vehicles > Drivers -- Van alt 4,"hh_veh_gt_drivers * (body_type_4 == 'Van')",0,0,0,coef_vehltdr_van,0 +util_vehltdr_suv1,Vehicles > Drivers -- SUV alt 1,"hh_veh_gt_drivers * (body_type_1 == 'SUV')",coef_vehltdr_suv,0,0,0,0 +util_vehltdr_suv2,Vehicles > Drivers -- SUV alt 2,"hh_veh_gt_drivers * (body_type_2 == 'SUV')",0,coef_vehltdr_suv,0,0,0 +util_vehltdr_suv3,Vehicles > Drivers -- SUV alt 3,"hh_veh_gt_drivers * (body_type_3 == 'SUV')",0,0,coef_vehltdr_suv,0,0 +util_vehltdr_suv4,Vehicles > Drivers -- SUV alt 4,"hh_veh_gt_drivers * (body_type_4 == 'SUV')",0,0,0,coef_vehltdr_suv,0 +util_vehltdr_pu1,Vehicles > Drivers -- Pickup alt 1,"hh_veh_gt_drivers * (body_type_1 == 'Pickup')",coef_vehltdr_pu,0,0,0,0 +util_vehltdr_pu2,Vehicles > Drivers -- Pickup alt 2,"hh_veh_gt_drivers * (body_type_2 == 'Pickup')",0,coef_vehltdr_pu,0,0,0 +util_vehltdr_pu3,Vehicles > Drivers -- Pickup alt 3,"hh_veh_gt_drivers * (body_type_3 == 'Pickup')",0,0,coef_vehltdr_pu,0,0 +util_vehltdr_pu4,Vehicles > Drivers -- Pickup alt 4,"hh_veh_gt_drivers * (body_type_4 == 'Pickup')",0,0,0,coef_vehltdr_pu,0 +util_vehltdr_mc1,Vehicles > Drivers -- Motorcycle alt 1,"hh_veh_gt_drivers * (body_type_1 == 'Motorcycle')",coef_vehltdr_mc,0,0,0,0 +util_vehltdr_mc2,Vehicles > Drivers -- Motorcycle alt 2,"hh_veh_gt_drivers * (body_type_2 == 'Motorcycle')",0,coef_vehltdr_mc,0,0,0 +util_vehltdr_mc3,Vehicles > Drivers -- Motorcycle alt 3,"hh_veh_gt_drivers * (body_type_3 == 'Motorcycle')",0,0,coef_vehltdr_mc,0,0 +util_vehltdr_mc4,Vehicles > Drivers -- Motorcycle alt 4,"hh_veh_gt_drivers * (body_type_4 == 'Motorcycle')",0,0,0,coef_vehltdr_mc,0 +util_vehltdr_hyb1,Vehicles > Drivers -- Hybrid alt 1,"hh_veh_gt_drivers * (fuel_type_1 == 'Hybrid')",coef_vehltdr_hyb,0,0,0,0 +util_vehltdr_hyb2,Vehicles > Drivers -- Hybrid alt 2,"hh_veh_gt_drivers * (fuel_type_2 == 'Hybrid')",0,coef_vehltdr_hyb,0,0,0 +util_vehltdr_hyb3,Vehicles > Drivers -- Hybrid alt 3,"hh_veh_gt_drivers * (fuel_type_3 == 'Hybrid')",0,0,coef_vehltdr_hyb,0,0 +util_vehltdr_hyb4,Vehicles > Drivers -- Hybrid alt 4,"hh_veh_gt_drivers * (fuel_type_4 == 'Hybrid')",0,0,0,coef_vehltdr_hyb,0 +util_vehltdr_ev1,Vehicles > Drivers -- Hybrid alt 1,"hh_veh_gt_drivers * ((fuel_type_1=='PEV') | (fuel_type_1=='BEV'))",coef_vehltdr_ev,0,0,0,0 +util_vehltdr_ev2,Vehicles > Drivers -- Hybrid alt 2,"hh_veh_gt_drivers * ((fuel_type_2=='PEV') | (fuel_type_2=='BEV'))",0,coef_vehltdr_ev,0,0,0 +util_vehltdr_ev3,Vehicles > Drivers -- Hybrid alt 3,"hh_veh_gt_drivers * ((fuel_type_3=='PEV') | (fuel_type_3=='BEV'))",0,0,coef_vehltdr_ev,0,0 +util_vehltdr_ev4,Vehicles > Drivers -- Hybrid alt 4,"hh_veh_gt_drivers * ((fuel_type_4=='PEV') | (fuel_type_4=='BEV'))",0,0,0,coef_vehltdr_ev,0 +util_vehltdr_age1,Vehicles > Drivers -- Age alt 1,"hh_veh_gt_drivers * age_1",coef_vehltdr_age,0,0,0,0 +util_vehltdr_age2,Vehicles > Drivers -- Age alt 2,"hh_veh_gt_drivers * age_2",0,coef_vehltdr_age,0,0,0 +util_vehltdr_age3,Vehicles > Drivers -- Age alt 3,"hh_veh_gt_drivers * age_3",0,0,coef_vehltdr_age,0,0 +util_vehltdr_age4,Vehicles > Drivers -- Age alt 4,"hh_veh_gt_drivers * age_4",0,0,0,coef_vehltdr_age,0 +#,Occupancy interactions,,,, +util_maxocc_van1,Maximum Occupancy -- Van alt 1,"@occup * (df.body_type_1 == 'Van')",coef_maxocc_van,0,0,0,0 +util_maxocc_van2,Maximum Occupancy -- Van alt 2,"@occup * (df.body_type_2 == 'Van')",0,coef_maxocc_van,0,0,0 +util_maxocc_van3,Maximum Occupancy -- Van alt 3,"@occup * (df.body_type_3 == 'Van')",0,0,coef_maxocc_van,0,0 +util_maxocc_van4,Maximum Occupancy -- Van alt 4,"@occup * (df.body_type_4 == 'Van')",0,0,0,coef_maxocc_van,0 +util_maxocc_suv1,Maximum Occupancy -- SUV alt 1,"@occup * (df.body_type_1 == 'SUV')",coef_maxocc_suv,0,0,0,0 +util_maxocc_suv2,Maximum Occupancy -- SUV alt 2,"@occup * (df.body_type_2 == 'SUV')",0,coef_maxocc_suv,0,0,0 +util_maxocc_suv3,Maximum Occupancy -- SUV alt 3,"@occup * (df.body_type_3 == 'SUV')",0,0,coef_maxocc_suv,0,0 +util_maxocc_suv4,Maximum Occupancy -- SUV alt 4,"@occup * (df.body_type_4 == 'SUV')",0,0,0,coef_maxocc_suv,0 +util_maxocc_pu1,Maximum Occupancy -- Pickup alt 1,"@occup * (df.body_type_1 == 'Pickup')",coef_maxocc_pu,0,0,0,0 +util_maxocc_pu2,Maximum Occupancy -- Pickup alt 2,"@occup * (df.body_type_2 == 'Pickup')",0,coef_maxocc_pu,0,0,0 +util_maxocc_pu3,Maximum Occupancy -- Pickup alt 3,"@occup * (df.body_type_3 == 'Pickup')",0,0,coef_maxocc_pu,0,0 +util_maxocc_pu4,Maximum Occupancy -- Pickup alt 4,"@occup * (df.body_type_4 == 'Pickup')",0,0,0,coef_maxocc_pu,0 +util_maxocc_mc1,Maximum Occupancy -- Motorcycle alt 1,"@occup * (df.body_type_1 == 'Motorcycle')",coef_maxocc_mc,0,0,0,0 +util_maxocc_mc2,Maximum Occupancy -- Motorcycle alt 2,"@occup * (df.body_type_2 == 'Motorcycle')",0,coef_maxocc_mc,0,0,0 +util_maxocc_mc3,Maximum Occupancy -- Motorcycle alt 3,"@occup * (df.body_type_3 == 'Motorcycle')",0,0,coef_maxocc_mc,0,0 +util_maxocc_mc4,Maximum Occupancy -- Motorcycle alt 4,"@occup * (df.body_type_4 == 'Motorcycle')",0,0,0,coef_maxocc_mc,0 +util_maxocc_age1,Maximum Occupancy -- Age alt 1,"@occup * df.age_1",coef_maxocc_age,0,0,0,0 +util_maxocc_age2,Maximum Occupancy -- Age alt 2,"@occup * df.age_2",0,coef_maxocc_age,0,0,0 +util_maxocc_age3,Maximum Occupancy -- Age alt 3,"@occup * df.age_3",0,0,coef_maxocc_age,0,0 +util_maxocc_age4,Maximum Occupancy -- Age alt 4,"@occup * df.age_4",0,0,0,coef_maxocc_age,0 +util_maxocc_nh,Maximum Occupancy -- Age alt 1,"@occup",0,0,0,0,util_maxocc_nh +#,Alternative Specific Constants,,,, +util_non_hh,Non-Household Vehicle Constant,"1",0,0,0,0,coef_non_hh +util_van1,Van ASC alt 1,"(body_type_1 == 'Van')",coef_van,0,0,0,0 +util_van2,Van ASC alt 2,"(body_type_2 == 'Van')",0,coef_van,0,0,0 +util_van3,Van ASC alt 3,"(body_type_3 == 'Van')",0,0,coef_van,0,0 +util_van4,Van ASC alt 4,"(body_type_4 == 'Van')",0,0,0,coef_van,0 +util_suv1,SUV ASC alt 1,"(body_type_1 == 'SUV')",coef_suv,0,0,0,0 +util_suv2,SUV ASC alt 2,"(body_type_2 == 'SUV')",0,coef_suv,0,0,0 +util_suv3,SUV ASC alt 3,"(body_type_3 == 'SUV')",0,0,coef_suv,0,0 +util_suv4,SUV ASC alt 4,"(body_type_4 == 'SUV')",0,0,0,coef_suv,0 +util_pu1,Pickup ASC alt 1,"(body_type_1 == 'Pickup')",coef_pu,0,0,0,0 +util_pu2,Pickup ASC alt 2,"(body_type_2 == 'Pickup')",0,coef_pu,0,0,0 +util_pu3,Pickup ASC alt 3,"(body_type_3 == 'Pickup')",0,0,coef_pu,0,0 +util_pu4,Pickup ASC alt 4,"(body_type_4 == 'Pickup')",0,0,0,coef_pu,0 +util_mc1,Motorcycle ASC alt 1,"(body_type_1 == 'Motorcycle')",coef_mc,0,0,0,0 +util_mc2,Motorcycle ASC alt 2,"(body_type_2 == 'Motorcycle')",0,coef_mc,0,0,0 +util_mc3,Motorcycle ASC alt 3,"(body_type_3 == 'Motorcycle')",0,0,coef_mc,0,0 +util_mc4,Motorcycle ASC alt 4,"(body_type_4 == 'Motorcycle')",0,0,0,coef_mc,0 +util_dsl1,Diesel ASC alt 1,"(fuel_type_1 == 'Diesel')",coef_dsl,0,0,0,0 +util_dsl2,Diesel ASC alt 2,"(fuel_type_2 == 'Diesel')",0,coef_dsl,0,0,0 +util_dsl3,Diesel ASC alt 3,"(fuel_type_3 == 'Diesel')",0,0,coef_dsl,0,0 +util_dsl4,Diesel ASC alt 4,"(fuel_type_4 == 'Diesel')",0,0,0,coef_dsl,0 +util_hyb1,Hybrid ASC alt 1,"(fuel_type_1 == 'Hybrid')",coef_hyb,0,0,0,0 +util_hyb2,Hybrid ASC alt 2,"(fuel_type_2 == 'Hybrid')",0,coef_hyb,0,0,0 +util_hyb3,Hybrid ASC alt 3,"(fuel_type_3 == 'Hybrid')",0,0,coef_hyb,0,0 +util_hyb4,Hybrid ASC alt 4,"(fuel_type_4 == 'Hybrid')",0,0,0,coef_hyb,0 +util_pev1,PEV ASC alt 1,"(fuel_type_1 == 'PEV')",coef_pev,0,0,0,0 +util_pev2,PEV ASC alt 2,"(fuel_type_2 == 'PEV')",0,coef_pev,0,0,0 +util_pev3,PEV ASC alt 3,"(fuel_type_3 == 'PEV')",0,0,coef_pev,0,0 +util_pev4,PEV ASC alt 4,"(fuel_type_4 == 'PEV')",0,0,0,coef_pev,0 +util_bev1,BEV ASC alt 1,"(fuel_type_1 == 'BEV')",coef_bev,0,0,0,0 +util_bev2,BEV ASC alt 2,"(fuel_type_2 == 'BEV')",0,coef_bev,0,0,0 +util_bev3,BEV ASC alt 3,"(fuel_type_3 == 'BEV')",0,0,coef_bev,0,0 +util_bev4,BEV ASC alt 4,"(fuel_type_4 == 'BEV')",0,0,0,coef_bev,0 +util_age2_1,Age 1 alt 1,"(age_1 == 2)",coef_age2,0,0,0,0 +util_age2_2,Age 1 alt 2,"(age_2 == 2)",0,coef_age2,0,0,0 +util_age2_3,Age 1 alt 3,"(age_3 == 2)",0,0,coef_age2,0,0 +util_age2_4,Age 1 alt 4,"(age_4 == 2)",0,0,0,coef_age2,0 +util_age3_1,Age 3 alt 1,"(age_1 == 3)",coef_age3,0,0,0,0 +util_age3_2,Age 3 alt 2,"(age_2 == 3)",0,coef_age3,0,0,0 +util_age3_3,Age 3 alt 3,"(age_3 == 3)",0,0,coef_age3,0,0 +util_age3_4,Age 4 alt 4,"(age_4 == 3)",0,0,0,coef_age3,0 +util_age4_1,Age 4 alt 1,"(age_1 == 4)",coef_age4,0,0,0,0 +util_age4_2,Age 4 alt 2,"(age_2 == 4)",0,coef_age4,0,0,0 +util_age4_3,Age 4 alt 3,"(age_3 == 4)",0,0,coef_age4,0,0 +util_age4_4,Age 3 alt 4,"(age_4 == 4)",0,0,0,coef_age4,0 +util_age5_1,Age 5 alt 1,"(age_1 == 5)",coef_age5,0,0,0,0 +util_age5_2,Age 5 alt 2,"(age_2 == 5)",0,coef_age5,0,0,0 +util_age5_3,Age 5 alt 3,"(age_3 == 5)",0,0,coef_age5,0,0 +util_age5_4,Age 5 alt 4,"(age_4 == 5)",0,0,0,coef_age5,0 +util_age6_1,Age 6 alt 1,"(age_1 == 6)",coef_age6,0,0,0,0 +util_age6_2,Age 6 alt 2,"(age_2 == 6)",0,coef_age6,0,0,0 +util_age6_3,Age 6 alt 3,"(age_3 == 6)",0,0,coef_age6,0,0 +util_age6_4,Age 6 alt 4,"(age_4 == 6)",0,0,0,coef_age6,0 +util_age7_1,Age 7 alt 1,"(age_1 == 7)",coef_age7,0,0,0,0 +util_age7_2,Age 7 alt 2,"(age_2 == 7)",0,coef_age7,0,0,0 +util_age7_3,Age 7 alt 3,"(age_3 == 7)",0,0,coef_age7,0,0 +util_age7_4,Age 7 alt 4,"(age_4 == 7)",0,0,0,coef_age7,0 +util_age8_1,Age 8 alt 1,"(age_1 == 8)",coef_age8,0,0,0,0 +util_age8_2,Age 8 alt 2,"(age_2 == 8)",0,coef_age8,0,0,0 +util_age8_3,Age 8 alt 3,"(age_3 == 8)",0,0,coef_age8,0,0 +util_age8_4,Age 8 alt 4,"(age_4 == 8)",0,0,0,coef_age8,0 +util_age9_1,Age 9 alt 1,"(age_1 == 9)",coef_age9,0,0,0,0 +util_age9_2,Age 9 alt 2,"(age_2 == 9)",0,coef_age9,0,0,0 +util_age9_3,Age 9 alt 3,"(age_3 == 9)",0,0,coef_age9,0,0 +util_age9_4,Age 9 alt 4,"(age_4 == 9)",0,0,0,coef_age9,0 +util_age10_1,Age 10 alt 1,"(age_1 == 10)",coef_age10,0,0,0,0 +util_age10_2,Age 10 alt 2,"(age_2 == 10)",0,coef_age10,0,0,0 +util_age10_3,Age 10 alt 3,"(age_3 == 10)",0,0,coef_age10,0,0 +util_age10_4,Age 10 alt 4,"(age_4 == 10)",0,0,0,coef_age10,0 +util_age11_1,Age 11 alt 1,"(age_1 == 11)",coef_age11,0,0,0,0 +util_age11_2,Age 11 alt 2,"(age_2 == 11)",0,coef_age11,0,0,0 +util_age11_3,Age 11 alt 3,"(age_3 == 11)",0,0,coef_age11,0,0 +util_age11_4,Age 11 alt 4,"(age_4 == 11)",0,0,0,coef_age11,0 +util_age12_1,Age 12 alt 1,"(age_1 == 12)",coef_age12,0,0,0,0 +util_age12_2,Age 12 alt 2,"(age_2 == 12)",0,coef_age12,0,0,0 +util_age12_3,Age 12 alt 3,"(age_3 == 12)",0,0,coef_age12,0,0 +util_age12_4,Age 12 alt 4,"(age_4 == 12)",0,0,0,coef_age12,0 +util_age13_1,Age 13 alt 1,"(age_1 == 13)",coef_age13,0,0,0,0 +util_age13_2,Age 13 alt 2,"(age_2 == 13)",0,coef_age13,0,0,0 +util_age13_3,Age 13 alt 3,"(age_3 == 13)",0,0,coef_age13,0,0 +util_age13_4,Age 13 alt 4,"(age_4 == 13)",0,0,0,coef_age13,0 +util_age14_1,Age 14 alt 1,"(age_1 == 14)",coef_age14,0,0,0,0 +util_age14_2,Age 14 alt 2,"(age_2 == 14)",0,coef_age14,0,0,0 +util_age14_3,Age 14 alt 3,"(age_3 == 14)",0,0,coef_age14,0,0 +util_age14_4,Age 14 alt 4,"(age_4 == 14)",0,0,0,coef_age14,0 +util_age15_1,Age 15 alt 1,"(age_1 == 15)",coef_age15,0,0,0,0 +util_age15_2,Age 15 alt 2,"(age_2 == 15)",0,coef_age15,0,0,0 +util_age15_3,Age 15 alt 3,"(age_3 == 15)",0,0,coef_age15,0,0 +util_age15_4,Age 15 alt 4,"(age_4 == 15)",0,0,0,coef_age15,0 +util_age16_1,Age 16 alt 1,"(age_1 == 16)",coef_age16,0,0,0,0 +util_age16_2,Age 16 alt 2,"(age_2 == 16)",0,coef_age16,0,0,0 +util_age16_3,Age 16 alt 3,"(age_3 == 16)",0,0,coef_age16,0,0 +util_age16_4,Age 16 alt 4,"(age_4 == 16)",0,0,0,coef_age16,0 +util_age17_1,Age 17 alt 1,"(age_1 == 17)",coef_age17,0,0,0,0 +util_age17_2,Age 17 alt 2,"(age_2 == 17)",0,coef_age17,0,0,0 +util_age17_3,Age 17 alt 3,"(age_3 == 17)",0,0,coef_age17,0,0 +util_age17_4,Age 17 alt 4,"(age_4 == 17)",0,0,0,coef_age17,0 +util_age18_1,Age 18 alt 1,"(age_1 == 18)",coef_age18,0,0,0,0 +util_age18_2,Age 18 alt 2,"(age_2 == 18)",0,coef_age18,0,0,0 +util_age18_3,Age 18 alt 3,"(age_3 == 18)",0,0,coef_age18,0,0 +util_age18_4,Age 18 alt 4,"(age_4 == 18)",0,0,0,coef_age18,0 +util_age19_1,Age 19 alt 1,"(age_1 == 19)",coef_age19,0,0,0,0 +util_age19_2,Age 19 alt 2,"(age_2 == 19)",0,coef_age19,0,0,0 +util_age19_3,Age 19 alt 3,"(age_3 == 19)",0,0,coef_age19,0,0 +util_age19_4,Age 19 alt 4,"(age_4 == 19)",0,0,0,coef_age19,0 +util_age20_1,Age 20 alt 1,"(age_1 == 20)",coef_age20,0,0,0,0 +util_age20_2,Age 20 alt 2,"(age_2 == 20)",0,coef_age20,0,0,0 +util_age20_3,Age 20 alt 3,"(age_3 == 20)",0,0,coef_age20,0,0 +util_age20_4,Age 20 alt 4,"(age_4 == 20)",0,0,0,coef_age20,0 diff --git a/resident/configs/vehicle_allocation.yaml b/resident/configs/vehicle_allocation.yaml new file mode 100644 index 0000000..23222fd --- /dev/null +++ b/resident/configs/vehicle_allocation.yaml @@ -0,0 +1,28 @@ +# vehicle_choice.yaml + +# last column in spec needs to be non_hh_veh +SPEC: vehicle_allocation.csv +COEFFICIENTS: vehicle_allocation_coefficients.csv + +LOGIT_TYPE: MNL + +preprocessor: + SPEC: vehicle_allocation_annotate_choosers_preprocessor + DF: choosers + TABLES: + - vehicles + +# will create columns in the tour table selecting a vehicle for each of the following +# occupancy levels. They are named vehicle_occup_1, vehicle_occup_2, ... +# if not supplied, will default to only one occupancy level of 1 +OCCUPANCY_LEVELS: + - 1 + - 2 + - 3.5 + +# optional annotate tours +# annotate_tours: +# SPEC: annotate_tours_vehicle_allocation +# DF: tours +# TABLES: +# - vehicles diff --git a/resident/configs/vehicle_allocation_annotate_choosers_preprocessor.csv b/resident/configs/vehicle_allocation_annotate_choosers_preprocessor.csv new file mode 100644 index 0000000..5113460 --- /dev/null +++ b/resident/configs/vehicle_allocation_annotate_choosers_preprocessor.csv @@ -0,0 +1,20 @@ +Description,Target,Expression +Round Trip Tour Distance,tot_tour_dist,"(odt_skims['SOV_M_DIST'] + dot_skims['SOV_M_DIST'])" +number of vehicles is greater than the number of drivers,hh_veh_gt_drivers,"np.where(df.auto_ownership > df.num_drivers, 1, 0)" +number of vehicles is less than the number of drivers,hh_veh_lt_drivers,"np.where(df.auto_ownership < df.num_drivers, 1, 0)" +Age of vehicle alternative 1,age_1,"df.veh_num1.astype(str).str.split('_').str[1].fillna(0).astype(int)" +Age of vehicle alternative 2,age_2,"df.veh_num2.astype(str).str.split('_').str[1].fillna(0).astype(int)" +Age of vehicle alternative 3,age_3,"df.veh_num3.astype(str).str.split('_').str[1].fillna(0).astype(int)" +Age of vehicle alternative 4,age_4,"df.veh_num4.astype(str).str.split('_').str[1].fillna(0).astype(int)" +body type of vehicle alternative 1,body_type_1,"df.veh_num1.astype(str).str.split('_').str[0]" +body type of vehicle alternative 2,body_type_2,"df.veh_num2.astype(str).str.split('_').str[0]" +body type of vehicle alternative 3,body_type_3,"df.veh_num3.astype(str).str.split('_').str[0]" +body type of vehicle alternative 4,body_type_4,"df.veh_num4.astype(str).str.split('_').str[0]" +fuel type of vehicle alternative 1,fuel_type_1,"df.veh_num1.astype(str).str.split('_').str[2]" +fuel type of vehicle alternative 2,fuel_type_2,"df.veh_num2.astype(str).str.split('_').str[2]" +fuel type of vehicle alternative 3,fuel_type_3,"df.veh_num3.astype(str).str.split('_').str[2]" +fuel type of vehicle alternative 4,fuel_type_4,"df.veh_num4.astype(str).str.split('_').str[2]" +Range of vehicle alternative 1,Range_1,"reindex(vehicles.groupby('vehicle_type')['Range'].mean(), df.veh_num1)" +Range of vehicle alternative 2,Range_2,"reindex(vehicles.groupby('vehicle_type')['Range'].mean(), df.veh_num2)" +Range of vehicle alternative 3,Range_3,"reindex(vehicles.groupby('vehicle_type')['Range'].mean(), df.veh_num3)" +Range of vehicle alternative 4,Range_4,"reindex(vehicles.groupby('vehicle_type')['Range'].mean(), df.veh_num4)" diff --git a/resident/configs/vehicle_allocation_coefficients.csv b/resident/configs/vehicle_allocation_coefficients.csv new file mode 100644 index 0000000..3a1a8fa --- /dev/null +++ b/resident/configs/vehicle_allocation_coefficients.csv @@ -0,0 +1,46 @@ +coefficient_name,value,constrain +coef_unavail,-999,F +coef_dstgtrng,-3.454,F +coef_vehltdr_nh,0.172,F +coef_vehgtdr_nh,-0.212,F +coef_vehltdr_van,0.048,F +coef_vehltdr_suv,0.104,F +coef_vehltdr_pu,-0.011,F +coef_vehltdr_mc,-0.749,F +coef_vehltdr_hyb,0.156,F +coef_vehltdr_ev,0.207,F +coef_vehltdr_age,-0.048,F +coef_maxocc_van,0.570,F +coef_maxocc_suv,0.316,F +coef_maxocc_pu,-0.077,F +coef_maxocc_mc,-0.938,F +coef_maxocc_age,-0.026,F +util_maxocc_nh,-0.212,F +coef_non_hh,-4.549,F +coef_van,-0.942,F +coef_suv,-0.429,F +coef_pu,-0.048,F +coef_mc,-0.470,F +coef_dsl,-0.227,F +coef_hyb,0.024,F +coef_pev,-0.168,F +coef_bev,-0.105,F +coef_age2,0.070,F +coef_age3,0.104,F +coef_age4,0.136,F +coef_age5,0.149,F +coef_age6,0.161,F +coef_age7,0.223,F +coef_age8,0.272,F +coef_age9,0.291,F +coef_age10,0.256,F +coef_age11,0.318,F +coef_age12,0.334,F +coef_age13,0.338,F +coef_age14,0.348,F +coef_age15,0.307,F +coef_age16,0.377,F +coef_age17,0.324,F +coef_age18,0.237,F +coef_age19,0.292,F +coef_age20,-0.181,F diff --git a/resident/configs/vehicle_type_choice.yaml b/resident/configs/vehicle_type_choice.yaml new file mode 100644 index 0000000..010da1f --- /dev/null +++ b/resident/configs/vehicle_type_choice.yaml @@ -0,0 +1,133 @@ +# vehicle_type_choice.yaml + +SPEC: vehicle_type_choice_op4.csv +COEFFICIENTS: vehicle_type_choice_op4_coefficients.csv + +# SPEC: vehicle_type_choice_op2.csv +# COEFFICIENTS: vehicle_type_choice_op2_coefficients.csv +# ALTS: vehicle_type_choice_op2_alternatives.csv +# # probs must have body_type and vehicle_year columns +# # probs spec has vehicle age calculated from FLEET_YEAR +# PROBS_SPEC: vehicle_type_choice_op2_fuel_type_probs.csv + +LOGIT_TYPE: MNL + +# options: simple_simulate or interaction_simulate +# if interaction_simulate, will build alteratives from combinatorial_alts below +# if simple_simulate, alternatives need to be specified in the columns of the model spec +SIMULATION_TYPE: interaction_simulate + +# additional vehicle_type_data merged to the alternatives for use in utility expressions +# need to have columns body_type, fuel_type, and vehicle_year +# entries in the data need to match the combinations in combinatorial_alts below +# VEHICLE_TYPE_DATA_FILE: vehicle_type_data.csv +VEHICLE_TYPE_DATA_FILE: vehicle_type_data_extended.csv + +REQUIRE_DATA_FOR_ALL_ALTS: False + +# age is computed as (1 + FLEET_YEAR - vehicle_year) +# FLEET_YEAR: 2017 +FLEET_YEAR: 2022 + +# if PROBS_SPEC is supplied, auto operating cost will not be +# merged until after a fuel type is selected +COLS_TO_INCLUDE_IN_VEHICLE_TABLE: + - auto_operating_cost + - Range + - MPG + - vehicle_year + +# If PROBS_SPEC is supplied, fuel_type will be ignored +combinatorial_alts: + body_type: + - Car + - Car-AV + - Van + - Van-AV + - SUV + - SUV-AV + - Pickup + - Pickup-AV + - Motorcycle + age: + - 1 + - 2 + - 3 + - 4 + - 5 + - 6 + - 7 + - 8 + - 9 + - 10 + - 11 + - 12 + - 13 + - 14 + - 15 + - 16 + - 17 + - 18 + - 19 + - 20 + fuel_type: + - Gas + - Diesel + - Hybrid + - PEV + - BEV + +# will write out the created vehicle_type_model_alternatives.csv file +# to the base configs directory. Helpful for debugging alternaties. +WRITE_OUT_ALTS_FILE: False + +preprocessor: + SPEC: vehicle_type_choice_annotate_choosers_preprocessor + DF: choosers + TABLES: + - land_use + - vehicles + - persons + - households + +alts_preprocessor: + SPEC: vehicle_type_choice_annotate_alts_preprocessor + DF: alts_wide + +COLS_TO_INCLUDE_IN_ALTS_TABLE: + - age + - fuel_type_num_coded + - body_type_num_coded + - Range + - MPG + - is_av + - logged_models + - logged_makes + - logged_chargers_per_capita + - SAN + - NewPrice + +# annotate_persons: +# SPEC: annotate_persons_vehicle_type +# DF: persons +# TABLES: +# - vehicles +# +annotate_households: + SPEC: annotate_households_vehicle_type + DF: households + TABLES: + - vehicles +# +# annotate_vehicles: +# SPEC: annotate_vehicles_vehicle_type +# DF: vehicles +# TABLES: + +CONSTANTS: + # calculated using charger count / population + CHARGERS_PER_CAP: 0.00034592 + # chargers per cap used in vehicle type model estimation + # CHARGERS_PER_CAP: 0.000721205 + scenarioYear: 2022 + chargerSensitivityDecayFactor: -0.08245202 diff --git a/resident/configs/vehicle_type_choice_annotate_alts_preprocessor.csv b/resident/configs/vehicle_type_choice_annotate_alts_preprocessor.csv new file mode 100644 index 0000000..bcee198 --- /dev/null +++ b/resident/configs/vehicle_type_choice_annotate_alts_preprocessor.csv @@ -0,0 +1,9 @@ +Description,Target,Expression +mapping fuel type to integers,fuel_type_num_coded,"df.fuel_type.map({'BEV': 1, 'Diesel': 2, 'Gas': 3, 'Hybrid': 4, 'PEV': 5}).astype(int)" +mapping body type to integers,body_type_num_coded,"df.body_type.map({'Car': 1, 'Motorcycle': 2, 'Pickup': 3, 'SUV': 4, 'Van': 5, 'Car-AV': 1, 'Motorcycle-AV': 2, 'Pickup-AV': 3, 'SUV-AV': 4, 'Van-AV': 5}).astype(int)" +flag indicating if vehicle is AV,is_av,"df.body_type.str.contains('AV')" +flag for SAN-specific contants,SAN,True +# moving log terms to preprocessor to avoid expensive log calculations,, +log number of models available,logged_models,"np.log(1 + df.NumModels)" +log number of makes available,logged_makes,"np.log(1 + df.NumMakes)" +logged chargers per capita,logged_chargers_per_capita,"np.log(1 + CHARGERS_PER_CAP)" \ No newline at end of file diff --git a/resident/configs/vehicle_type_choice_annotate_choosers_preprocessor.csv b/resident/configs/vehicle_type_choice_annotate_choosers_preprocessor.csv new file mode 100644 index 0000000..e55e502 --- /dev/null +++ b/resident/configs/vehicle_type_choice_annotate_choosers_preprocessor.csv @@ -0,0 +1,16 @@ +Description,Target,Expression +total household distance to work,total_hh_dist_to_work,persons.groupby('household_id')['distance_to_work'].sum().reindex(df.household_id).fillna(0) +total household distance to work capped at 100 mi per worker,total_hh_dist_to_work_cap,"np.where(total_hh_dist_to_work > (100*df.num_workers.values), 100*df.num_workers.values, total_hh_dist_to_work)" +average household distance to work,avg_hh_dist_to_work,persons.groupby('household_id')['distance_to_work'].mean().reindex(df.household_id).fillna(0) +number of vehicles is greater than the number of drivers,hh_veh_gt_drivers,"np.where(df.auto_ownership > df.num_drivers, 1, 0)" +number of household vehicles owned,num_hh_veh_owned,df[df['already_owned_veh'].str.len() > 0].groupby('household_id').size().reindex(df.household_id).fillna(0) +number of household Vans,num_hh_Van,"df[df['already_owned_veh'].str.contains('Van', na=False)].groupby('household_id').size().reindex(df.household_id).fillna(0)" +number of household SUVs,num_hh_SUV,"df[df['already_owned_veh'].str.contains('SUV', na=False)].groupby('household_id').size().reindex(df.household_id).fillna(0)" +number of household Pickups,num_hh_Pickup,"df[df['already_owned_veh'].str.contains('Pickup', na=False)].groupby('household_id').size().reindex(df.household_id).fillna(0)" +number of household Motorcycles,num_hh_Motorcycle,"df[df['already_owned_veh'].str.contains('Motorcycle', na=False)].groupby('household_id').size().reindex(df.household_id).fillna(0)" +number of household Hybrid,num_hh_Hybrid,"df[df['already_owned_veh'].str.contains('Hybrid', na=False)].groupby('household_id').size().reindex(df.household_id).fillna(0)" +number of household BEV,num_hh_BEV,"df[df['already_owned_veh'].str.contains('BEV', na=False)].groupby('household_id').size().reindex(df.household_id).fillna(0)" +number of household PEV,num_hh_PEV,"df[df['already_owned_veh'].str.contains('PEV', na=False)].groupby('household_id').size().reindex(df.household_id).fillna(0)" +number of household EVs,num_hh_EV,"num_hh_BEV + num_hh_PEV" +# FIXME is this correction factor correct for Metro base year?? -- should probably move to constants file,, +household income adjusted to 2017$,income17,"np.where(df.income < 0, df.income, df.income/1.217)" \ No newline at end of file diff --git a/resident/configs/vehicle_type_choice_op4.csv b/resident/configs/vehicle_type_choice_op4.csv new file mode 100644 index 0000000..c6f54c7 --- /dev/null +++ b/resident/configs/vehicle_type_choice_op4.csv @@ -0,0 +1,151 @@ +Label,Description,Expression,Coefficient +util_ln_nmods,number of models available,"logged_models",coef_ln_nmods +util_ln_nmakes,number of makes available,"logged_makes",coef_ln_nmakes +util_mpg,miles per gallon (or equivalent),"@df.MPG",coef_mpg +util_crange,Range for BEV (mi),"@df.Range",coef_crange +util_crangeltwk,range less than average round trip distance to work,"(Range < (avg_hh_dist_to_work * 2)) & (fuel_type_num_coded==1)",coef_crangeltwk +util_ln_chpc_ev,ln(1+number of chargers per capita in MSA/state),"@df.logged_chargers_per_capita * ((df.fuel_type_num_coded==5) | (df.fuel_type_num_coded==1)) * np.exp(chargerSensitivityDecayFactor*(scenarioYear-2022))",coef_ln_chpc_ev +#,autonomous vehicle related variables,, +util_must_select_av,Must select autonomous vehicle if hh owns one,av_ownership & ~is_av & (num_hh_veh_owned == 0),coef_unavail +util_must_select_av,Cannot select AV if hh does not own one,~av_ownership & is_av,coef_unavail +#,income related variables,, +util_cprice0,New Purchase Price (2017$) Segmented by Income,"((income17 < 25000) & (income17 > -1)) * NewPrice",coef_cprice0 +util_cprice25,New Purchase Price (2017$) Segmented by Income,"((income17 < 50000) & (income17 >= 25000)) * NewPrice",coef_cprice25 +util_cprice50,New Purchase Price (2017$) Segmented by Income,"((income17 < 100000) & (income17 >=50000)) * NewPrice",coef_cprice50 +util_cprice100,New Purchase Price (2017$) Segmented by Income,"((income17 < 150000) & (income17 >= 100000)) * NewPrice",coef_cprice100 +util_cprice150,New Purchase Price (2017$) Segmented by Income,"(income17 > 150000) * NewPrice",coef_cprice150 +util_cpricem,New Purchase Price (2017$) Missing Income,"((income17 < 0) | income17.isna()) * NewPrice",coef_cpricem +util_i025_age,Vehicle Age Segmented by Income,"((income17 < 25000) & (income17 > -1)) * age",coef_i025_age +util_i2550_age,Vehicle Age Segmented by Income,"((income17 < 50000) & (income17 >= 25000)) * age",coef_i2550_age +util_i100_age,Vehicle Age Segmented by Income,"((income17 < 150000) & (income17 >= 100000)) * age",coef_i100_age +util_i150p_age,Vehicle Age Segmented by Income,"((income17 < 150000) & (income17 >= 100000)) * age",coef_i150p_age +util_imiss_age,Vehicle Age Missing Income,"((income17 < 0) | income17.isna()) * age",coef_imiss_age +#,household density variables,, +util_den3_van,500-999 HH / sq mi Van,"((household_density < 1000) & (household_density >= 500)) & (body_type_num_coded==5)",coef_den3_van +util_den4_van,1000-1999 HH / sq mi Van,"((household_density < 2000) & (household_density >= 1000)) & (body_type_num_coded==5)",coef_den4_van +util_den5_van,2000-3999 HH / sq mi Van,"((household_density < 4000) & (household_density >= 2000)) & (body_type_num_coded==5)",coef_den5_van +util_den6_van,4000-9999 HH / sq mi Van,"((household_density < 10000) & (household_density >= 4000)) & (body_type_num_coded==5)",coef_den6_van +util_den7_van,10000-24999 HH / sq mi Van,"((household_density < 25000) & (household_density >= 10000)) & (body_type_num_coded==5)",coef_den7_van +util_den8_van,25000+ HH / sq mi Van,"(household_density >= 25000) & (body_type_num_coded==5)",coef_den8_van +util_den3_suv,500-999 HH / sq mi SUV,"((household_density < 1000) & (household_density >= 500)) & (body_type_num_coded==4)",coef_den3_suv +util_den4_suv,1000-1999 HH / sq mi SUV,"((household_density < 2000) & (household_density >= 1000)) & (body_type_num_coded==4)",coef_den4_suv +util_den5_suv,2000-3999 HH / sq mi SUV,"((household_density < 4000) & (household_density >= 2000)) & (body_type_num_coded==4)",coef_den5_suv +util_den6_suv,4000-9999 HH / sq mi SUV,"((household_density < 10000) & (household_density >= 4000)) & (body_type_num_coded==4)",coef_den6_suv +util_den7_suv,10000-24999 HH / sq mi SUV,"((household_density < 25000) & (household_density >= 10000)) & (body_type_num_coded==4)",coef_den7_suv +util_den8_suv,25000+ HH / sq mi SUV,"(household_density >= 25000) & (body_type_num_coded==4)",coef_den8_suv +util_den3_pu,500-999 HH / sq mi Pickup,"((household_density < 1000) & (household_density >= 500)) & (body_type_num_coded==3)",coef_den3_pu +util_den4_pu,1000-1999 HH / sq mi Pickup,"((household_density < 2000) & (household_density >= 1000)) & (body_type_num_coded==3)",coef_den4_pu +util_den5_pu,2000-3999 HH / sq mi Pickup,"((household_density < 4000) & (household_density >= 2000)) & (body_type_num_coded==3)",coef_den5_pu +util_den6_pu,4000-9999 HH / sq mi Pickup,"((household_density < 10000) & (household_density >= 4000)) & (body_type_num_coded==3)",coef_den6_pu +util_den7_pu,10000-24999 HH / sq mi Pickup,"((household_density < 25000) & (household_density >= 10000)) & (body_type_num_coded==3)",coef_den7_pu +util_den8_mc,25000+ HH / sq mi Pickup,"(household_density >= 25000) & (body_type_num_coded==3)",coef_den8_pu +util_den3_mc,500-999 HH / sq mi Motorcycle,"((household_density < 1000) & (household_density >= 500)) & (body_type_num_coded==2)",coef_den3_mc +util_den4_mc,1000-1999 HH / sq mi Motorcycle,"((household_density < 2000) & (household_density >= 1000)) & (body_type_num_coded==2)",coef_den4_mc +util_den5_mc,2000-3999 HH / sq mi Motorcycle,"((household_density < 4000) & (household_density >= 2000)) & (body_type_num_coded==2)",coef_den5_mc +util_den6_mc,4000-9999 HH / sq mi Motorcycle,"((household_density < 10000) & (household_density >= 4000)) & (body_type_num_coded==2)",coef_den6_mc +util_den7_mc,10000-24999 HH / sq mi Motorcycle,"((household_density < 25000) & (household_density >= 10000)) & (body_type_num_coded==2)",coef_den7_mc +util_den8_mc,25000+ HH / sq mi Motorcycle,"(household_density >= 25000) & (body_type_num_coded==2)",coef_den8_mc +util_den3_hyb,500-999 HH / sq mi Hybrid,"((household_density < 1000) & (household_density >= 500)) & (fuel_type_num_coded==4)",coef_den3_hyb +util_den4_hyb,1000-1999 HH / sq mi Hybrid,"((household_density < 2000) & (household_density >= 1000)) & (fuel_type_num_coded==4)",coef_den4_hyb +util_den5_hyb,2000-3999 HH / sq mi Hybrid,"((household_density < 4000) & (household_density >= 2000)) & (fuel_type_num_coded==4)",coef_den5_hyb +util_den6_hyb,4000-9999 HH / sq mi Hybrid,"((household_density < 10000) & (household_density >= 4000)) & (fuel_type_num_coded==4)",coef_den6_hyb +util_den7_hyb,10000-24999 HH / sq mi Hybrid,"((household_density < 25000) & (household_density >= 10000)) & (fuel_type_num_coded==4)",coef_den7_hyb +util_den8_hyb,25000+ HH / sq mi Hybrid,"(household_density >= 25000) & (fuel_type_num_coded==4)",coef_den8_hyb +util_den34_ev,500-1999 HH / sq mi Electric,"((household_density < 2000) & (household_density >= 500)) & ((fuel_type_num_coded==5) | (fuel_type_num_coded==1))",coef_den34_ev +util_den5_ev,2000-3999 HH / sq mi Electric,"((household_density < 4000) & (household_density >= 2000)) & ((fuel_type_num_coded==5) | (fuel_type_num_coded==1))",coef_den5_ev +util_den6_ev,4000-9999 HH / sq mi Electric,"((household_density < 10000) & (household_density >= 4000)) & ((fuel_type_num_coded==5) | (fuel_type_num_coded==1))",coef_den6_ev +util_den78_ev,10000+ HH / sq mi Electric,"(household_density >= 10000) & ((fuel_type_num_coded==5) | (fuel_type_num_coded==1))",coef_den78_ev +#,household drivers,, +util_oneveh_age,Household owns only one vehicle * vehicle age,"(auto_ownership == 1) * age",coef_oneveh_age +util_vhgtdr_van,Household vehicles gt drivers for Van,hh_veh_gt_drivers * (body_type_num_coded==5),coef_vhgtdr_van +util_vhgtdr_suv,Household vehicles gt drivers for SUV,hh_veh_gt_drivers * (body_type_num_coded==4),coef_vhgtdr_suv +util_vhgtdr_pu,Household vehicles gt drivers for Pickup,hh_veh_gt_drivers * (body_type_num_coded==3),coef_vhgtdr_pu +util_vhgtdr_mc,Household vehicles gt drivers for Motorcycle,hh_veh_gt_drivers * (body_type_num_coded==2),coef_vhgtdr_mc +util_vhgtdr_hy,Household vehicles gt drivers for Hybrid,hh_veh_gt_drivers * (fuel_type_num_coded==4),coef_vhgtdr_hy +util_vhgtdr_ev,Household vehicles gt drivers for EVs,hh_veh_gt_drivers * ((fuel_type_num_coded==5) | (fuel_type_num_coded==1)),coef_vhgtdr_ev +util_vhgtdr_age,Household vehicles gt drivers for age,hh_veh_gt_drivers * age,coef_vhgtdr_age +#,interacting with number of children,, +util_nchld_van,Number of children max 3 * Van,num_children.clip(upper=3) * (body_type_num_coded==5),coef_nchld_van +util_nchld_suv,Number of children max 3 * SUV,num_children.clip(upper=3) * (body_type_num_coded==4),coef_nchld_suv +util_nchld_pu,Number of children max 3 * Pickup,num_children.clip(upper=3) * (body_type_num_coded==3),coef_nchld_pu +util_nchld_mc,Number of children max 3 * Motorcycle,num_children.clip(upper=3) * (body_type_num_coded==2),coef_nchld_mc +util_nchld_hy,Number of children max 3 * Hybrid,num_children.clip(upper=3) * (fuel_type_num_coded==4),coef_nchld_hy +util_nchld_ev,Number of children max 3 * EVs,num_children.clip(upper=3) * ((fuel_type_num_coded==5) | (fuel_type_num_coded==1)),coef_nchld_ev +util_nchld_age,Number of children max 3 * age,num_children.clip(upper=3) * age,coef_nchld_age +util_dstwkt_age,Total distance to work * age,total_hh_dist_to_work_cap * age,coef_dstwkt_age +#,household already owning vehicles,, +util_van_van,Household already owns a Van -- Van,"(num_hh_Van > 0) & (body_type_num_coded==5)",coef_van_van +util_van_suv,Household already owns a Van -- SUV,"(num_hh_Van > 0) & (body_type_num_coded==4)",coef_van_suv +util_van_pu,Household already owns a Van -- Pickup,"(num_hh_Van > 0) & (body_type_num_coded==3)",coef_van_pu +util_van_mc,Household already owns a Van -- Motorcycle,"(num_hh_Van > 0) & (body_type_num_coded==2)",coef_van_mc +util_van_suv,Household already owns an SUV -- Van (symmetrical with above),"(num_hh_SUV > 0) & (body_type_num_coded==5)",coef_van_suv +util_suv_suv,Household already owns an SUV -- SUV,"(num_hh_SUV > 0) & (body_type_num_coded==4)",coef_suv_suv +util_suv_pu,Household already owns an SUV -- Pickup,"(num_hh_SUV > 0) & (body_type_num_coded==3)",coef_suv_pu +util_suv_mc,Household already owns an SUV -- Motorcycle,"(num_hh_SUV > 0) & (body_type_num_coded==2)",coef_suv_mc +util_van_pu,Household already owns a Pickup -- Van (symmetrical with above),"(num_hh_Pickup > 0) & (body_type_num_coded==5)",coef_van_pu +util_suv_pu,Household already owns a Pickup -- SUV (symmetrical with above),"(num_hh_Pickup > 0) & (body_type_num_coded==4)",coef_suv_pu +util_pu_pu,Household already owns a Pickup -- Pickup,"(num_hh_Pickup > 0) & (body_type_num_coded==3)",coef_pu_pu +util_pu_mc,Household already owns a Pickup -- Motorcycle,"(num_hh_Pickup > 0) & (body_type_num_coded==2)",coef_pu_mc +util_van_mc,Household already owns a Motorcycle -- Van (symmetrical with above),"(num_hh_Motorcycle > 0) & (body_type_num_coded==5)",coef_van_mc +util_suv_mc,Household already owns a Motorcycle -- SUV (symmetrical with above),"(num_hh_Motorcycle > 0) & (body_type_num_coded==4)",coef_suv_mc +util_pu_mc,Household already owns a Motorcycle -- Pickup (symmetrical with above),"(num_hh_Motorcycle > 0) & (body_type_num_coded==3)",coef_pu_mc +util_mc_mc,Household already owns a Motorcycle -- Motorcycle,"(num_hh_Motorcycle > 0) & (body_type_num_coded==2)",coef_mc_mc +util_hyb_hyb,Houeshold already owns a Hybrid -- Hybrid,"(num_hh_Hybrid > 0) & (fuel_type_num_coded==4)",coef_hyb_hyb +util_hyb_ev,Houeshold already owns a Hybrid -- EV,"(num_hh_Hybrid > 0) & ((fuel_type_num_coded==5) | (fuel_type_num_coded==1))",coef_hyb_ev +util_hyb_ev,Houeshold already owns an EV -- Hybrid (symmetrical with above),"(num_hh_EV> 0) & (fuel_type_num_coded==4)",coef_hyb_ev +util_only_van,Household only owns Vans,"(num_hh_Van > 0) & (num_hh_Van == num_hh_veh_owned) & (body_type_num_coded==5)",coef_only_van +util_only_suv,Household only owns SUVs,"(num_hh_SUV > 0) & (num_hh_SUV == num_hh_veh_owned) & (body_type_num_coded==4)",coef_only_suv +util_only_pu,Household only owns Pickups,"(num_hh_Pickup > 0) & (num_hh_Pickup == num_hh_veh_owned) & (body_type_num_coded==3)",coef_only_pu +util_only_mc,Household only owns Motorcycles,"(num_hh_Motorcycle > 0) & (num_hh_Motorcycle == num_hh_veh_owned) & (body_type_num_coded==2)",coef_only_mc +util_only_bev,Household only owns BEVs,"(num_hh_BEV > 0) & (num_hh_BEV == num_hh_veh_owned) & (fuel_type_num_coded==1)",coef_only_bev +#,constants,, +util_van,Van ASC,(body_type_num_coded==5),coef_van +util_suv,SUV ASC,(body_type_num_coded==4),coef_suv +util_pu,Pickup ASC,(body_type_num_coded==3),coef_pu +util_mc,Motorcycle ASC,(body_type_num_coded==2),coef_mc +util_dsl,Diesel ASC,(fuel_type_num_coded==2),coef_dsl +util_hyb,Hybrid ASC,(fuel_type_num_coded==4),coef_hyb +util_pev,PEV ASC,(fuel_type_num_coded==5),coef_pev +util_bev,BEV ASC,(fuel_type_num_coded==1),coef_bev +util_age2,Age 2 ASC,(age==2),coef_age2 +util_age3,Age 3 ASC,(age==3),coef_age3 +util_age4,Age 4 ASC,(age==4),coef_age4 +util_age5,Age 5 ASC,(age==5),coef_age5 +util_age6,Age 6 ASC,(age==6),coef_age6 +util_age7,Age 7 ASC,(age==7),coef_age7 +util_age8,Age 8 ASC,(age==8),coef_age8 +util_age9,Age 9 ASC,(age==9),coef_age9 +util_age10,Age 10 ASC,(age==10),coef_age10 +util_age11,Age 11 ASC,(age==11),coef_age11 +util_age12,Age 12 ASC,(age==12),coef_age12 +util_age13,Age 13 ASC,(age==13),coef_age13 +util_age14,Age 14 ASC,(age==14),coef_age14 +util_age15,Age 15 ASC,(age==15),coef_age15 +util_age16,Age 16 ASC,(age==16),coef_age16 +util_age17,Age 17 ASC,(age==17),coef_age17 +util_age18,Age 18 ASC,(age==18),coef_age18 +util_age19,Age 19 ASC,(age==19),coef_age19 +util_age20,Age 20 ASC,(age==20),coef_age20 +#,rural household coefficients,, +util_rural_van,Household is in Rural Area - Van,"home_is_rural & (body_type_num_coded==5)",coef_rural_van +util_rural_suv,Household is in Rural Area - SUV,"home_is_rural & (body_type_num_coded==4)",coef_rural_suv +util_rural_pu,Household is in Rural Area - Pickup,"home_is_rural & (body_type_num_coded==3)",coef_rural_pu +util_rural_mc,Household is in Rural Area - Motorcycle,"home_is_rural & (body_type_num_coded==2)",coef_rural_mc +util_rural_hyb,Household is in Rural Area - Hybrid,"home_is_rural & (fuel_type_num_coded==4)",coef_rural_hyb +util_rural_ev,Household is in Rural Area - Electric,"home_is_rural & ((fuel_type_num_coded==5) | (fuel_type_num_coded==1))",coef_rural_ev +util_rural_age,Household is in Rural Area - Age,"home_is_rural * age",coef_rural_age +#,Region Specific Constants,, +util_san_van,San Diego - Van,"@(df.SAN) & (df.body_type_num_coded==5)",coef_san_van +util_san_suv,San Diego - SUV,"@(df.SAN) & (df.body_type_num_coded==4)",coef_san_suv +util_san_pu,San Diego - Pickup,"@(df.SAN) & (df.body_type_num_coded==3)",coef_san_pu +util_san_mc,San Diego - Motorcycle,"@(df.SAN) & (df.body_type_num_coded==2)",coef_san_mc +util_san_hyb,San Diego - Hybrid,"@(df.SAN) & (df.fuel_type_num_coded==4)",coef_san_hyb +util_san_ev,San Diego - Electric,"@(df.SAN) & ((df.fuel_type_num_coded==5) | (df.fuel_type_num_coded==1))",coef_san_ev +util_san_age,San Diego - Age,"@(df.SAN) * df.age",coef_san_age +#,EV calibration constants,, +util_calib_pev_2022,PEV calibration factor for 2022,"@(df.fuel_type_num_coded==5)*np.maximum(1 + (2022-scenarioYear)/(2035-2022), 0)",coef_calib_pev_2022 +util_calib_bev_2022,BEV calibration factor for 2022,"@(df.fuel_type_num_coded==1)*np.maximum(1 + (2022-scenarioYear)/(2035-2022), 0)",coef_calib_bev_2022 +util_calib_pev_2035,PEV calibration factor for 2035,"@(df.fuel_type_num_coded==5)*np.minimum((scenarioYear-2022)/(2035-2022), 1 + (2035-scenarioYear)/(2050-2035))",coef_calib_pev_2035 +util_calib_bev_2035,BEV calibration factor for 2035,"@(df.fuel_type_num_coded==1)*np.minimum((scenarioYear-2022)/(2035-2022), 1 + (2035-scenarioYear)/(2050-2035))",coef_calib_bev_2035 +util_calib_pev_2050,PEV calibration factor for 2050,"@(df.fuel_type_num_coded==5)*np.maximum((scenarioYear-2035)/(2050-2035), 0)",coef_calib_pev_2050 +util_calib_bev_2050,BEV calibration factor for 2050,"@(df.fuel_type_num_coded==1)*np.maximum((scenarioYear-2035)/(2050-2035), 0)",coef_calib_bev_2050 diff --git a/resident/configs/vehicle_type_choice_op4_coefficients.csv b/resident/configs/vehicle_type_choice_op4_coefficients.csv new file mode 100644 index 0000000..1b70509 --- /dev/null +++ b/resident/configs/vehicle_type_choice_op4_coefficients.csv @@ -0,0 +1,196 @@ +coefficient_name,value,constrain +coef_unavail,-999,F +coef_ln_nmods,.612,F +coef_ln_nmakes,.247,F +coef_mpg,0.007,F +coef_crange,0.018,F +coef_crangeltwk,-0.781,F +coef_ln_chpc_ev,354.347,F +coef_cprice0,-0.00010,F +coef_cprice25,-0.00009,F +coef_cprice50,-0.00008,F +coef_cprice100,-0.00007,F +coef_cprice150,-0.00005,F +coef_cpricem,-0.00007,F +coef_i025_age,0.107,F +coef_i2550_age,0.047,F +coef_i100_age,-0.037,F +coef_i150p_age,-0.071,F +coef_imiss_age,0.001,F +coef_den3_van,-0.019,F +coef_den4_van,-0.089,F +coef_den5_van,-0.193,F +coef_den6_van,-0.413,F +coef_den7_van,-0.710,F +coef_den8_van,-0.509,F +coef_den3_suv,0.005,F +coef_den4_suv,-0.084,F +coef_den5_suv,-0.208,F +coef_den6_suv,-0.422,F +coef_den7_suv,-0.465,F +coef_den8_suv,-0.463,F +coef_den3_pu,-0.167,F +coef_den4_pu,-0.219,F +coef_den5_pu,-0.313,F +coef_den6_pu,-0.720,F +coef_den7_pu,-1.140,F +coef_den8_pu,-2.006,F +coef_den3_mc,-0.030,F +coef_den4_mc,0.157,F +coef_den5_mc,0.185,F +coef_den6_mc,0.436,F +coef_den7_mc,1.106,F +coef_den8_mc,1.533,F +coef_den3_hyb,-0.018,F +coef_den4_hyb,0.070,F +coef_den5_hyb,0.239,F +coef_den6_hyb,0.290,F +coef_den7_hyb,0.406,F +coef_den8_hyb,0.445,F +coef_den34_ev,0.107,F +coef_den5_ev,0.323,F +coef_den6_ev,0.272,F +coef_den78_ev,0.030,F +coef_oneveh_age,-0.028,F +coef_vhgtdr_van,-.108,F +coef_vhgtdr_suv,-0.126,F +coef_vhgtdr_pu,0.287,F +coef_vhgtdr_mc,2.408,F +coef_vhgtdr_hy,-0.168,F +coef_vhgtdr_ev,0.165,F +coef_vhgtdr_age,0.068,F +coef_nchld_van,0.560,F +coef_nchld_suv,0.193,F +coef_nchld_pu,0.016,F +coef_nchld_mc,0.028,F +coef_nchld_hy,-0.045,F +coef_nchld_ev,0.174,F +coef_nchld_age,-0.001,F +coef_dstwkt_age,-0.00013,F +coef_van_van,0.028,F +coef_van_suv,-0.477,F +coef_van_pu,-0.075,F +coef_van_mc,0.527,F +coef_suv_suv,-0.115,F +coef_suv_pu,0.194,F +coef_suv_mc,1.048,F +coef_pu_pu,-0.164,F +coef_pu_mc,1.360,F +coef_mc_mc,3.469,F +coef_hyb_hyb,1.611,F +coef_hyb_ev,0.940,F +coef_only_van,-0.259,F +coef_only_suv,-0.102,F +coef_only_pu,-0.781,F +coef_only_mc,-1.233,F +coef_only_bev,-0.491,F +coef_van,-0.629,F +coef_suv,0.413,F +coef_pu,0.224,F +coef_mc,-5.259,F +coef_dsl,-0.649,F +coef_hyb,-1.055,F +coef_pev,-1.406,F +coef_bev,-4.640,F +coef_age2,0.163,F +coef_age3,0.107,F +coef_age4,0.009,F +coef_age5,-0.079,F +coef_age6,-0.307,F +coef_age7,-0.216,F +coef_age8,-0.346,F +coef_age9,-0.133,F +coef_age10,-0.163,F +coef_age11,-0.060,F +coef_age12,-0.032,F +coef_age13,-0.248,F +coef_age14,-0.350,F +coef_age15,-0.510,F +coef_age16,-0.658,F +coef_age17,-0.772,F +coef_age18,-0.885,F +coef_age19,-1.187,F +coef_age20,0.484,F +coef_rural_van,0.234,F +coef_rural_suv,0.150,F +coef_rural_pu,0.824,F +coef_rural_mc,-0.150,F +coef_rural_hyb,-0.280,F +coef_rural_ev,-0.483,F +coef_rural_age,0.004,F +coef_smsa_van,0.130,F +coef_smsa_suv,0.003,F +coef_smsa_pu,0.357,F +coef_smsa_mc,0.011,F +coef_smsa_hyb,-0.063,F +coef_smsa_ev,-0.341,F +coef_smsa_age,0.011,F +coef_sfo_van,0.047,F +coef_sfo_suv,-0.386,F +coef_sfo_pu,-0.527,F +coef_sfo_mc,-0.021,F +coef_sfo_hyb,0.942,F +coef_sfo_ev,1.497,F +coef_sfo_age,0.058,F +coef_san_van,0.055,F +coef_san_suv,-0.132,F +coef_san_pu,-0.187,F +coef_san_mc,-0.129,F +coef_san_hyb,0.0629,F +coef_san_ev,0.0922,F +coef_san_age,0.030,F +coef_atl_van,-0.158,F +coef_atl_suv,-0.072,F +coef_atl_pu,-0.107,F +coef_atl_mc,-0.284,F +coef_atl_hyb,-0.180,F +coef_atl_ev,0.547,F +coef_atl_age,0.016,F +coef_sea_van,0.088,F +coef_sea_suv,-0.134,F +coef_sea_pu,-0.131,F +coef_sea_mc,-0.275,F +coef_sea_hyb,0.421,F +coef_sea_ev,0.706,F +coef_sea_age,0.060,F +coef_det_van,0.703,F +coef_det_suv,0.260,F +coef_det_pu,-0.150,F +coef_det_mc,0.427,F +coef_det_hyb,-0.982,F +coef_det_ev,0.531,F +coef_det_age,-0.045,F +coef_msp_van,0.263,F +coef_msp_suv,-0.052,F +coef_msp_pu,-0.087,F +coef_msp_mc,0.083,F +coef_msp_hyb,-0.111,F +coef_msp_ev,-0.733,F +coef_msp_age,0.008,F +coef_dca_van,0.062,F +coef_dca_suv,0.273,F +coef_dca_pu,-0.411,F +coef_dca_mc,0.062,F +coef_dca_hyb,-0.428,F +coef_dca_ev,-1.605,F +coef_dca_age,-0.040,F +coef_oreg_van,0.037,F +coef_oreg_suv,-0.121,F +coef_oreg_pu,0.185,F +coef_oreg_mc,0.044,F +coef_oreg_hyb,0.819,F +coef_oreg_ev,0.278,F +coef_oreg_age,0.056,F +coef_ohio_van,0.170,F +coef_ohio_suv,-0.147,F +coef_ohio_pu,-0.355,F +coef_ohio_mc,0.140,F +coef_ohio_hyb,-0.142,F +coef_ohio_ev,-1.590,F +coef_ohio_age,-0.008,F +coef_calib_pev_2022,0.56468,F +coef_calib_bev_2022,1.42228,F +coef_calib_pev_2035,0.86656,F +coef_calib_bev_2035,0.98343,F +coef_calib_pev_2050,0.36963,F +coef_calib_bev_2050,-0.64752,F diff --git a/resident/configs/vehicle_type_data.csv b/resident/configs/vehicle_type_data.csv new file mode 100644 index 0000000..7831b52 --- /dev/null +++ b/resident/configs/vehicle_type_data.csv @@ -0,0 +1,501 @@ +body_type,fuel_type,vehicle_year,NumMakes,NumModels,MPG,Range,NewPrice,auto_operating_cost,co2gpm +Car,Gas,2017,39,738,24,0,32926.61346,20.17,388.85 +Car,Gas,2016,39,734,23.7,0,33383.61537,20.32822785,392.27 +Car,Gas,2015,39,740,23.4,0,33369.12233,20.49051282,398.43 +Car,Gas,2014,41,717,23.2,0,32571.33539,20.60103448,402.86 +Car,Gas,2013,40,702,23.1,0,32976.62731,20.65701299,402.94 +Car,Gas,2012,42,668,22.4,0,32959.56219,21.06285714,417.59 +Car,Gas,2011,42,617,21.7,0,34409.88113,21.49488479,428.48 +Car,Gas,2010,44,641,21.5,0,35279.71318,21.62348837,434.37 +Car,Gas,2009,45,637,21,0,35638.52401,21.95571429,444.46 +Car,Gas,2008,46,649,20.5,0,35745.78309,22.30414634,454.02 +Car,Gas,2007,43,594,20.3,0,37613.76774,22.44832512,454.23 +Car,Gas,2006,43,595,20.2,0,38581.65596,22.52148515,456.18 +Car,Gas,2005,41,661,20.4,0,37537.36453,22.37588235,452.05 +Car,Gas,2004,43,628,20.6,0,37988.14511,22.2331068,445.25 +Car,Gas,2003,42,573,20.5,0,37597.37624,22.30414634,450.5 +Car,Gas,2002,42,543,20.8,0,37380.8733,22.09307692,439.76 +Car,Gas,2001,39,505,21,0,37361.09207,21.95571429,437.72 +Car,Gas,2000,39,473,21,0,38856.86312,21.95571429,437.7 +Car,Gas,1999,40,483,21.2,0,38447.21363,21.8209434,435.35 +Car,Gas,1998,42,464,21.3,0,38952.55256,21.75450704,433.86 +Car,Diesel,2017,3,9,35.2,0,38787.1746,16.19272727,290 +Car,Diesel,2016,2,8,31.4,0,39325.51763,17.22414013,325.13 +Car,Diesel,2015,4,23,32.8,0,39308.445,16.81634146,311.57 +Car,Diesel,2014,4,20,31.5,0,38368.66109,17.19380952,322.3 +Car,Diesel,2013,2,12,31.8,0,38846.09034,17.10396226,319.17 +Car,Diesel,2012,2,8,32.3,0,38825.98783,16.9579257,314.55 +Car,Diesel,2011,3,8,31.4,0,40534.44697,17.22414013,324.25 +Car,Diesel,2010,3,8,31.4,0,41559.0992,17.22414013,324.25 +Car,Diesel,2009,3,6,30.2,0,41981.77425,17.60377483,339.1 +Car,Diesel,2008,1,1,26,0,42108.12422,19.20846154,391.54 +Car,Diesel,2007,1,1,26,0,44308.58881,19.20846154,391.54 +Car,Diesel,2006,2,7,32.3,0,45448.75007,16.9579257,317.06 +Car,Diesel,2005,2,13,32,0,44218.5867,17.045,321.07 +Car,Diesel,2004,1,10,32.6,0,44749.60108,16.87245399,315.61 +Car,Diesel,2003,1,8,35.6,0,44289.27981,16.09696629,287.33 +Car,Diesel,2002,1,8,35.6,0,44034.24182,16.09696629,287.33 +Car,Diesel,2001,1,6,35.5,0,44010.93975,16.12070423,288.19 +Car,Diesel,2000,1,6,35.5,0,45772.94097,16.12070423,288.19 +Car,Diesel,1999,2,8,34.5,0,45290.37855,16.36565217,299.48 +Car,Diesel,1998,2,8,33.8,0,45885.66203,16.54573964,306.26 +Car,Hybrid,2017,11,29,40.4,0,34769.7517,15.09574257,242.55 +Car,Hybrid,2016,14,31,36.8,0,35252.33528,15.82217391,253.65 +Car,Hybrid,2015,12,33,35.5,0,35237.03096,16.12070423,261.42 +Car,Hybrid,2014,15,40,34.8,0,34394.58617,16.29068966,270.95 +Car,Hybrid,2013,15,34,35.3,0,34822.56518,16.16858357,265.21 +Car,Hybrid,2012,12,25,34.2,0,34804.54481,16.44192982,280.1 +Car,Hybrid,2011,11,20,33.6,0,36336.04847,16.59857143,285.2 +Car,Hybrid,2010,8,13,34.5,0,37254.57126,16.36565217,276.5 +Car,Hybrid,2009,6,8,32.3,0,37633.4673,16.9579257,293.75 +Car,Hybrid,2008,6,8,31.8,0,37746.73043,17.10396226,299.43 +Car,Hybrid,2007,5,7,33.3,0,39719.27956,16.67900901,281.75 +Car,Hybrid,2006,2,5,42.4,0,40741.3474,14.7454717,224.05 +Car,Hybrid,2005,2,8,41.9,0,39638.59951,14.82990453,218.55 +Car,Hybrid,2004,2,7,43.9,0,40114.61351,14.50371298,204.44 +Car,Hybrid,2003,2,7,43.3,0,39701.9705,14.59840647,207.34 +Car,Hybrid,2002,2,3,47,0,39473.34834,14.05297872,191.17 +Car,Hybrid,2001,2,4,47,0,39452.45982,14.05297872,190.65 +Car,Hybrid,2000,1,1,53,0,41031.95988,13.33037736,167.68 +Car,Hybrid,1999,0,0,0,0,40599.37938,13.33037736,0 +Car,Hybrid,1998,0,0,0,0,41133.00574,13.33037736,0 +Car,PEV,2017,9,14,35.1,0,56882.64178,16.21700855,138.64 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+Motorcycle,BEV,2009,1,1,240,80,11382.15714,8.08,0 +Motorcycle,BEV,2008,1,1,240,80,11382.15714,8.08,0 +Motorcycle,BEV,2007,0,0,0,0,11382.15714,8.08,0 +Motorcycle,BEV,2006,0,0,0,0,11382.15714,8.08,0 +Motorcycle,BEV,2005,0,0,0,0,11382.15714,8.08,0 +Motorcycle,BEV,2004,0,0,0,0,11382.15714,8.08,0 +Motorcycle,BEV,2003,0,0,0,0,11382.15714,8.08,0 +Motorcycle,BEV,2002,0,0,0,0,11382.15714,8.08,0 +Motorcycle,BEV,2001,0,0,0,0,11382.15714,8.08,0 +Motorcycle,BEV,2000,0,0,0,0,11382.15714,8.08,0 +Motorcycle,BEV,1999,0,0,0,0,11382.15714,8.08,0 +Motorcycle,BEV,1998,0,0,0,0,11382.15714,8.08,0 diff --git a/resident/configs/vehicle_type_data_extended.csv b/resident/configs/vehicle_type_data_extended.csv new file mode 100644 index 0000000..c996ee7 --- /dev/null +++ b/resident/configs/vehicle_type_data_extended.csv @@ -0,0 +1,1116 @@ +body_type,fuel_type,vehicle_year,MPG,NewPrice,NumMakes,NumModels,Range,auto_operating_cost,co2gpm +Car,BEV,2011,93.8,44331.89559,3.0,4.0,73.0,10.86829424,0.0 +Car,BEV,2012,95.6,54291.33743,5.0,5.0,113.0,10.80807531,0.0 +Car,BEV,2013,104.3,59928.48966,9.0,12.0,105.0,10.54631831,0.0 +Car,BEV,2014,106.1,50547.30465,10.0,13.0,115.0,10.49752121,0.0 +Car,BEV,2015,103.2,63109.22167,10.0,18.0,157.0,10.57697674,0.0 +Car,BEV,2016,103.1,74787.61065,11.0,25.0,173.0,10.57979631,0.0 +Car,BEV,2017,108.7,65091.20275,13.0,23.0,169.0,10.4298896,0.0 +Car,BEV,2018,115.03098584261784,65202.70828428572,13.923076923076923,44.30769230769231,145.639398695536,10.36546280178571, +Car,BEV,2019,116.75645063025709,65202.70828428572,14.846153846153847,65.61538461538461,147.82398967596902,10.302922147738087, +Car,BEV,2020,118.50779738971093,65202.70828428572,15.76923076923077,86.92307692307692,150.04134952110854,10.240381493690478, +Car,BEV,2021,120.28541435055658,65202.70828428572,16.692307692307693,108.23076923076923,152.29196976392515,10.177840839642855, +Car,BEV,2022,122.08969556581492,65202.70828428572,17.615384615384617,129.53846153846155,154.57634931038402,10.115300185595231, +Car,BEV,2023,123.92104099930214,65202.70828428572,18.53846153846154,150.84615384615384,156.89499455003977,10.052759531547622, +Car,BEV,2024,125.77985661429163,65202.70828428572,19.46153846153846,172.15384615384613,159.24841946829034,9.990218877499998, +Car,BEV,2025,127.66655446350602,65202.70828428572,20.384615384615387,193.46153846153845,161.6371457603147,9.927678223452375, +Car,BEV,2026,129.5815527804586,65202.70828428572,21.307692307692307,214.76923076923077,164.06170294671938,9.865137569404752, +Car,BEV,2027,131.52527607216544,65202.70828428572,22.230769230769234,236.07692307692307,166.52262849092014,9.802596915357142, +Car,BEV,2028,133.4981552132479,65202.70828428572,23.153846153846153,257.38461538461536,169.02046791828394,9.740056261309519, +Car,BEV,2029,135.50062754144662,65202.70828428572,24.076923076923077,278.6923076923077,171.55577493705817,9.677515607261896, +Car,BEV,2030,137.53313695456833,65202.70828428572,25.0,300.0,174.12911156111406,9.614974953214286, +Car,BEV,2031,139.59613400888682,65202.70828428572,25.25,297.5,176.74104823453075,9.552434299166663, +Car,BEV,2032,141.6900760190201,65202.70828428572,25.5,295.0,179.39216395804866,9.48989364511904, +Car,BEV,2033,143.8154271593054,65202.70828428572,25.75,292.5,182.08304641741938,9.42735299107143, +Car,BEV,2034,145.97265856669497,65202.70828428572,26.0,290.0,184.81429211368066,9.364812337023807, +Car,BEV,2035,148.16224844519536,65202.70828428572,26.25,287.5,187.58650649538583,9.302271682976183, +Car,BEV,2036,150.3846821718733,65202.70828428572,26.5,285.0,190.4003040928166,9.23973102892856, +Car,BEV,2037,152.64045240445137,65202.70828428572,26.75,282.5,193.25630865420885,9.17719037488095, +Car,BEV,2038,154.93005919051814,65202.70828428572,27.0,280.0,196.15515328402196,9.114649720833327, +Car,BEV,2039,157.2540100783759,65202.70828428572,27.25,277.5,199.09748058328228,9.052109066785704, +Car,BEV,2040,159.61282022955152,65202.70828428572,27.5,275.0,202.08394279203148,8.989568412738095, +Car,BEV,2041,162.00701253299476,65202.70828428572,27.75,272.5,205.11520193391195,8.927027758690471, +Car,BEV,2042,164.43711772098968,65202.70828428572,28.0,270.0,208.1919299629206,8.864487104642848, +Car,BEV,2043,166.9036744868045,65202.70828428572,28.25,267.5,211.3148089123644,8.801946450595239, +Car,BEV,2044,169.40722960410656,65202.70828428572,28.5,265.0,214.48453104604982,8.739405796547615, +Car,BEV,2045,171.94833804816815,65202.70828428572,28.75,262.5,217.70179901174058,8.676865142499992, +Car,BEV,2046,174.52756311889064,65202.70828428572,29.0,260.0,220.96732599691666,8.614324488452368, +Car,BEV,2047,177.145476565674,65202.70828428572,29.25,257.5,224.28183588687037,8.551783834404745, +Car,BEV,2048,179.8026587141591,65202.70828428572,29.5,255.0,227.64606342517342,8.489243180357136, +Car,BEV,2049,182.49969859487146,65202.70828428572,29.75,252.5,231.060754376551,8.426702526309526, +Car,BEV,2050,185.2371940737945,65202.70828428572,30.0,250.0,234.52666569219926,8.364161872261889, +Car,Diesel,1998,33.8,45885.66203,2.0,8.0,0.0,16.54573964,306.26 +Car,Diesel,1999,34.5,45290.37855,2.0,8.0,0.0,16.36565217,299.48 +Car,Diesel,2000,35.5,45772.94097,1.0,6.0,0.0,16.12070423,288.19 +Car,Diesel,2001,35.5,44010.93975,1.0,6.0,0.0,16.12070423,288.19 +Car,Diesel,2002,35.6,44034.24182,1.0,8.0,0.0,16.09696629,287.33 +Car,Diesel,2003,35.6,44289.27981,1.0,8.0,0.0,16.09696629,287.33 +Car,Diesel,2004,32.6,44749.60108,1.0,10.0,0.0,16.87245399,315.61 +Car,Diesel,2005,32.0,44218.5867,2.0,13.0,0.0,17.045,321.07 +Car,Diesel,2006,32.3,45448.75007,2.0,7.0,0.0,16.9579257,317.06 +Car,Diesel,2007,26.0,44308.58881,1.0,1.0,0.0,19.20846154,391.54 +Car,Diesel,2008,26.0,42108.12422,1.0,1.0,0.0,19.20846154,391.54 +Car,Diesel,2009,30.2,41981.77425,3.0,6.0,0.0,17.60377483,339.1 +Car,Diesel,2010,31.4,41559.0992,3.0,8.0,0.0,17.22414013,324.25 +Car,Diesel,2011,31.4,40534.44697,3.0,8.0,0.0,17.22414013,324.25 +Car,Diesel,2012,32.3,38825.98783,2.0,8.0,0.0,16.9579257,314.55 +Car,Diesel,2013,31.8,38846.09034,2.0,12.0,0.0,17.10396226,319.17 +Car,Diesel,2014,31.5,38368.66109,4.0,20.0,0.0,17.19380952,322.3 +Car,Diesel,2015,32.8,39308.445,4.0,23.0,0.0,16.81634146,311.57 +Car,Diesel,2016,31.4,39325.51763,2.0,8.0,0.0,17.22414013,325.13 +Car,Diesel,2017,35.2,38787.1746,3.0,9.0,0.0,16.19272727,290.0 +Car,Diesel,2018,41.48711268918141,45079.34609428571,2.769230769230769,8.307692307692308,0.0,16.5874271385714, +Car,Diesel,2019,42.109419379519125,45079.34609428571,2.5384615384615383,7.615384615384615,0.0,16.497489097142847, +Car,Diesel,2020,42.74106067021191,45079.34609428571,2.3076923076923075,6.923076923076923,0.0,16.407551055714265, +Car,Diesel,2021,43.38217658026508,45079.34609428571,2.0769230769230766,6.230769230769231,0.0,16.317613014285683, +Car,Diesel,2022,44.032909228969054,45079.34609428571,1.846153846153846,5.538461538461538,0.0,16.22767497285713, +Car,Diesel,2023,44.69340286740359,45079.34609428571,1.6153846153846154,4.846153846153847,0.0,16.13773693142855, +Car,Diesel,2024,45.36380391041463,45079.34609428571,1.3846153846153846,4.153846153846154,0.0,16.047798889999967, +Car,Diesel,2025,46.044260969070855,45079.34609428571,1.1538461538461537,3.4615384615384617,0.0,15.957860848571414, +Car,Diesel,2026,46.73492488360691,45079.34609428571,0.9230769230769229,2.769230769230769,0.0,15.867922807142833, +Car,Diesel,2027,47.435948756861,45079.34609428571,0.6923076923076921,2.0769230769230766,0.0,15.77798476571428, +Car,Diesel,2028,48.14748798821392,45079.34609428571,0.46153846153846123,1.384615384615385,0.0,15.688046724285698, +Car,Diesel,2029,48.86970030803712,45079.34609428571,0.23076923076923084,0.6923076923076934,0.0,15.598108682857116, +Car,Gas,1998,21.3,38952.55256,42.0,464.0,0.0,21.75450704,433.86 +Car,Gas,1999,21.2,38447.21363,40.0,483.0,0.0,21.8209434,435.35 +Car,Gas,2000,21.0,38856.86312,39.0,473.0,0.0,21.95571429,437.7 +Car,Gas,2001,21.0,37361.09207,39.0,505.0,0.0,21.95571429,437.72 +Car,Gas,2002,20.8,37380.8733,42.0,543.0,0.0,22.09307692,439.76 +Car,Gas,2003,20.5,37597.37624,42.0,573.0,0.0,22.30414634,450.5 +Car,Gas,2004,20.6,37988.14511,43.0,628.0,0.0,22.2331068,445.25 +Car,Gas,2005,20.4,37537.36453,41.0,661.0,0.0,22.37588235,452.05 +Car,Gas,2006,20.2,38581.65596,43.0,595.0,0.0,22.52148515,456.18 +Car,Gas,2007,20.3,37613.76774,43.0,594.0,0.0,22.44832512,454.23 +Car,Gas,2008,20.5,35745.78309,46.0,649.0,0.0,22.30414634,454.02 +Car,Gas,2009,21.0,35638.52401,45.0,637.0,0.0,21.95571429,444.46 +Car,Gas,2010,21.5,35279.71318,44.0,641.0,0.0,21.62348837,434.37 +Car,Gas,2011,21.7,34409.88113,42.0,617.0,0.0,21.49488479,428.48 +Car,Gas,2012,22.4,32959.56219,42.0,668.0,0.0,21.06285714,417.59 +Car,Gas,2013,23.1,32976.62731,40.0,702.0,0.0,20.65701299,402.94 +Car,Gas,2014,23.2,32571.33539,41.0,717.0,0.0,20.60103448,402.86 +Car,Gas,2015,23.4,33369.12233,39.0,740.0,0.0,20.49051282,398.43 +Car,Gas,2016,23.7,33383.61537,39.0,734.0,0.0,20.32822785,392.27 +Car,Gas,2017,24.0,32926.61346,39.0,738.0,0.0,20.17,388.85 +Car,Gas,2018,29.449734763848717,38268.06719428572,36.94444444444444,699.7777777777778,0.0,19.85096401428575, +Car,Gas,2019,29.891480785306445,38268.06719428572,34.888888888888886,661.5555555555555,0.0,19.639288838571474, +Car,Gas,2020,30.339852997086037,38268.06719428572,32.833333333333336,623.3333333333334,0.0,19.42761366285714, +Car,Gas,2021,30.794950792042325,38268.06719428572,30.77777777777778,585.1111111111111,0.0,19.215938487142864, 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+Car,Gas,2031,35.73879759880567,38268.06719428572,10.222222222222225,202.8888888888889,0.0,17.099186730000042, +Car,Gas,2032,36.274879562787746,38268.06719428572,8.166666666666671,164.66666666666663,0.0,16.887511554285766, +Car,Gas,2033,36.81900275622956,38268.06719428572,6.111111111111114,126.44444444444446,0.0,16.675836378571432, +Car,Gas,2034,37.371287797573,38268.06719428572,4.055555555555557,88.22222222222229,0.0,16.464161202857156, +Car,Gas,2035,37.931857114536584,38268.06719428572,2.0,50.0,0.0,16.25248602714288, +Car,Gas,2036,38.50083497125463,38268.06719428572,2.0,50.0,0.0,16.040810851428603, +Car,Gas,2037,39.07834749582345,38268.06719428572,2.0,50.0,0.0,15.829135675714326, +Car,Gas,2038,39.6645227082608,38268.06719428572,2.0,50.0,0.0,15.61746050000005, +Car,Gas,2039,40.259490548884706,38268.06719428572,2.0,50.0,0.0,15.405785324285716, +Car,Gas,2040,40.86338290711797,38268.06719428572,2.0,50.0,0.0,15.19411014857144, 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+Pickup,Diesel,2014,22.5,39406.01202,1.0,2.0,0.0,21.66333333,450.5 +Pickup,Diesel,2015,23.0,40371.20431,1.0,3.0,0.0,21.37347826,443.67 +Pickup,Diesel,2016,23.6,40388.73853,3.0,7.0,0.0,21.04186441,434.71 +Pickup,Diesel,2017,23.0,39835.84063,3.0,7.0,0.0,21.37347826,448.43 +Pickup,Diesel,2018,18.039974136761973,46298.12986714286,2.9444444444444446,6.888888888888889,0.0,20.536679650357087, +Pickup,Diesel,2019,18.3105737488134,46298.12986714286,2.888888888888889,6.777777777777778,0.0,20.227085658214264, +Pickup,Diesel,2020,18.585232355045598,46298.12986714286,2.8333333333333335,6.666666666666667,0.0,19.91749166607144, +Pickup,Diesel,2021,18.86401084037128,46298.12986714286,2.7777777777777777,6.555555555555555,0.0,19.607897673928505, +Pickup,Diesel,2022,19.146971002976848,46298.12986714286,2.7222222222222223,6.444444444444445,0.0,19.298303681785683, +Pickup,Diesel,2023,19.434175568021498,46298.12986714286,2.6666666666666665,6.333333333333333,0.0,18.98870968964286, 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+Pickup,Gas,2001,16.0,37667.36471,8.0,126.0,0.0,27.08,566.42 +Pickup,Gas,2002,15.7,37687.3081,9.0,127.0,0.0,27.43828025,577.81 +Pickup,Gas,2003,15.8,37905.58585,8.0,127.0,0.0,27.31734177,571.99 +Pickup,Gas,2004,16.3,38299.55811,7.0,140.0,0.0,26.73490798,557.48 +Pickup,Gas,2005,16.4,37845.08219,7.0,127.0,0.0,26.62268293,556.04 +Pickup,Gas,2006,16.2,38897.93434,12.0,151.0,0.0,26.84851852,561.0 +Pickup,Gas,2007,16.1,37922.11173,12.0,162.0,0.0,26.96354037,558.22 +Pickup,Gas,2008,16.3,36038.81401,14.0,148.0,0.0,26.73490798,556.44 +Pickup,Gas,2009,16.8,35930.67567,11.0,138.0,0.0,26.18714286,534.88 +Pickup,Gas,2010,17.2,35568.92344,10.0,108.0,0.0,25.77186047,525.8 +Pickup,Gas,2011,17.3,34691.96081,8.0,110.0,0.0,25.67104046,522.85 +Pickup,Gas,2012,17.3,33229.7527,8.0,100.0,0.0,25.67104046,520.49 +Pickup,Gas,2013,16.9,33246.9577,7.0,69.0,0.0,26.08147929,532.93 +Pickup,Gas,2014,17.1,32838.34335,8.0,65.0,0.0,25.87385965,531.12 +Pickup,Gas,2015,18.6,33642.67026,6.0,66.0,0.0,24.45903226,485.79 +Pickup,Gas,2016,18.5,33657.28211,7.0,72.0,0.0,24.54621622,488.18 +Pickup,Gas,2017,18.5,33196.53386,8.0,84.0,0.0,24.54621622,488.67 +Pickup,Gas,2018,22.755150249225185,38581.77489,7.722222222222222,80.44444444444444,0.0,24.360774814999957, +Pickup,Gas,2019,23.09647750296356,38581.77489,7.444444444444445,76.88888888888889,0.0,24.14592630083331, +Pickup,Gas,2020,23.44292466550801,38581.77489,7.166666666666667,73.33333333333333,0.0,23.931077786666663, +Pickup,Gas,2021,23.794568535490626,38581.77489,6.888888888888889,69.77777777777777,0.0,23.71622927249996, +Pickup,Gas,2022,24.151487063522985,38581.77489,6.611111111111111,66.22222222222223,0.0,23.501380758333312, +Pickup,Gas,2023,24.513759369475828,38581.77489,6.333333333333333,62.66666666666667,0.0,23.286532244166665, +Pickup,Gas,2024,24.88146576001796,38581.77489,6.055555555555555,59.111111111111114,0.0,23.07168372999996, +Pickup,Gas,2025,25.254687746418227,38581.77489,5.777777777777778,55.55555555555556,0.0,22.856835215833314, 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+Pickup,Gas,2035,29.30909615298073,38581.77489,3.0,20.0,0.0,20.708350074166617, +Pickup,Gas,2036,29.74873259527544,38581.77489,3.0,20.0,0.0,20.49350155999997, +Pickup,Gas,2037,30.194963584204565,38581.77489,3.0,20.0,0.0,20.278653045833323, +Pickup,Gas,2038,30.64788803796763,38581.77489,3.0,20.0,0.0,20.06380453166662, +Pickup,Gas,2039,31.107606358537144,38581.77489,3.0,20.0,0.0,19.848956017499972, +Pickup,Gas,2040,31.574220453915196,38581.77489,3.0,20.0,0.0,19.634107503333325, +Pickup,Gas,2041,32.04783376072392,38581.77489,3.0,20.0,0.0,19.41925898916662, +Pickup,Gas,2042,32.52855126713478,38581.77489,3.0,20.0,0.0,19.204410474999975, +Pickup,Gas,2043,33.0164795361418,38581.77489,3.0,20.0,0.0,18.989561960833328, +Pickup,Gas,2044,33.51172672918392,38581.77489,3.0,20.0,0.0,18.774713446666624, +Pickup,Gas,2045,34.01440263012168,38581.77489,3.0,20.0,0.0,18.559864932499977, +Pickup,Gas,2046,34.5246186695735,38581.77489,3.0,20.0,0.0,18.34501641833333, +Pickup,Gas,2047,35.0424879496171,38581.77489,3.0,20.0,0.0,18.130167904166626, +Pickup,Gas,2048,35.56812526886135,38581.77489,3.0,20.0,0.0,17.91531938999998, +Pickup,Gas,2049,36.10164714789427,38581.77489,3.0,20.0,0.0,17.700470875833332, +Pickup,Gas,2050,36.643171855112676,38581.77489,3.0,20.0,0.0,17.48562236166663, +Pickup,Hybrid,2004,16.5,42129.51392,2.0,4.0,0.0,26.51181818,539.1 +Pickup,Hybrid,2005,16.5,41629.5904,2.0,4.0,0.0,26.51181818,539.1 +Pickup,Hybrid,2006,16.5,42787.72777,2.0,4.0,0.0,26.51181818,539.1 +Pickup,Hybrid,2007,16.5,41714.3229,2.0,4.0,0.0,26.51181818,539.1 +Pickup,Hybrid,2009,20.5,39523.74323,2.0,4.0,0.0,22.96414634,433.77 +Pickup,Hybrid,2010,21.5,39125.81578,2.0,4.0,0.0,22.28348837,413.57 +Pickup,Hybrid,2011,21.0,38161.1569,2.0,4.0,0.0,22.61571429,423.19 +Pickup,Hybrid,2012,21.0,36552.72796,2.0,4.0,0.0,22.61571429,423.19 +Pickup,Hybrid,2013,21.0,36571.65347,2.0,4.0,0.0,22.61571429,420.5 +Pickup,Hybrid,2016,19.0,37023.01032,2.0,4.0,0.0,24.11947368,468.0 +Pickup,Hybrid,2017,19.0,36516.18724,2.0,4.0,0.0,24.11947368,467.5 +Pickup,Hybrid,2018,19.71362026541786,42439.95237571428,2.0,4.461538461538462,0.0,24.73958708500004, +Pickup,Hybrid,2019,20.009324569399123,42439.95237571428,2.0,4.923076923076923,0.0,25.053702938333345, +Pickup,Hybrid,2020,20.309464437940107,42439.95237571428,2.0,5.384615384615385,0.0,25.36781879166665, +Pickup,Hybrid,2021,20.614106404509208,42439.95237571428,2.0,5.846153846153847,0.0,25.681934644999956, +Pickup,Hybrid,2022,20.923318000576845,42439.95237571428,2.0,6.307692307692308,0.0,25.996050498333375, +Pickup,Hybrid,2023,21.237167770585494,42439.95237571428,2.0,6.769230769230769,0.0,26.31016635166668, +Pickup,Hybrid,2024,21.55572528714427,42439.95237571428,2.0,7.230769230769231,0.0,26.624282204999986, +Pickup,Hybrid,2025,21.879061166451436,42439.95237571428,2.0,7.6923076923076925,0.0,26.93839805833329, +Pickup,Hybrid,2026,22.207247083948204,42439.95237571428,2.0,8.153846153846153,0.0,27.25251391166671, 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+Van-AV,PEV,2049,53.61630764538753,800000.0,2.9000000000000004,3.85,0.0,17.0, +Van-AV,PEV,2050,54.42055226006834,800000.0,3.0,4.0,0.0,17.0, diff --git a/resident/configs/work_from_home.csv b/resident/configs/work_from_home.csv new file mode 100644 index 0000000..0b9fa07 --- /dev/null +++ b/resident/configs/work_from_home.csv @@ -0,0 +1,24 @@ +Label,Description,Expression,work_at_home,work_away_from_home +util_work_from_home_constant,Constant for Working from home,1,coef_work_from_home_constant, +util_part_time_worker,Part time worker,@df.ptype==2,coef_part_time_worker, +util_access_to_workplaces,Accessibility to workplaces of the home mgra,@df.workplace_location_accessibility,,coef_access_to_workplaces +util_2016,Year 2016 survey,@PRE_COVID,coef_2016, +util_age_35_to_44,Age Group - 35 yrs to 44 yrs,"@df.age.between(35, 44)",coef_age_35_to_44, +util_age_45_to_54,Age Group - 45 yrs to 54 yrs,"@df.age.between(45, 54)",coef_age_45_to_54, +util_age_55_to_64,Age Group - 55 yrs to 64 yrs,"@df.age.between(55, 64)",coef_age_55_to_64, +util_age_65_79,Age 65-79,"@df.age.between(65, 79)",coef_age_65_79, +util_age_80_plus,Age 80 plus,@df.age > 79,coef_age_80_plus, +util_inc_lt_15,Household income less than 15k,@df.income<15000,coef_inc_lt_15, +util_inc_150_250,Household income between 150k-249999k,"@df.income.between(150000,249999)",coef_inc_150_250, +util_ind_accom,Industry type is accomodation,@df.naics_code==721,coef_ind_accom, +util_ind_bus_srv,Industry type is business services,@df.naics_code==54,coef_ind_bus_srv, +util_ind_construct,Industry type is construction,@df.naics_code==23,coef_ind_construct, +util_ind_edu,Industry type is education,@df.naics_code==61,coef_ind_edu, +util_ind_enter,Industry type is entertainment,@df.naics_code==71,coef_ind_enter, +util_ind_food_srv,Industry type is food services,@df.naics_code==722,coef_ind_food_srv, +util_ind_gov,Industry type is government,@df.naics_code==92,coef_ind_gov, +util_ind_health,Industry type is healthcare,@df.naics_code==62,coef_ind_health, +util_ind_manu,Industry type is manufacturing,"@df.naics_code.isin([31,32,33])",coef_ind_manu, +util_ind_mgmt_srv,Industry type is management services,@df.naics_code==55,coef_ind_mgmt_srv, +util_ind_mil,Industry type is military,@df.naics_code==9000,coef_ind_mil, +util_regional_calibration, Regional calibration factor,1,coef_regional_calibration, diff --git a/resident/configs/work_from_home.yaml b/resident/configs/work_from_home.yaml new file mode 100644 index 0000000..c4f265c --- /dev/null +++ b/resident/configs/work_from_home.yaml @@ -0,0 +1,12 @@ + +# borrowed from free parking model + +SPEC: work_from_home.csv +COEFFICIENTS: work_from_home_coeffs.csv + +#LOGIT_TYPE: NL +LOGIT_TYPE: MNL + +WORK_FROM_HOME_ALT: 0 + +DEST_CHOICE_COLUMN_NAME: workplace_zone_id diff --git a/resident/configs/work_from_home_coeffs.csv b/resident/configs/work_from_home_coeffs.csv new file mode 100644 index 0000000..31ed2a9 --- /dev/null +++ b/resident/configs/work_from_home_coeffs.csv @@ -0,0 +1,31 @@ +coefficient_name,value,constrain +coef_work_from_home_constant,-0.386629856,F +coef_part_time_worker,-1.4513809,F +coef_access_to_workplaces,0.1,T +coef_age_35_to_44,0.353530241,F +coef_age_45_to_54,0.468133972,F +coef_age_55_to_64,0.495172418,F +coef_2016,-1.06294934,F +coef_age_65_79,1.243578944,F +coef_age_80_plus,0.770722144,F +coef_inc_lt_15,0.613583177,F +coef_inc_150_250,0.419020695,F +coef_ind_accom,-1.639185004,F +coef_ind_bus_srv,0.721333596,F +coef_ind_construct,-0.738399459,F +coef_ind_edu,-0.508510677,F +coef_ind_enter,1.027744147,F +coef_ind_food_srv,-1.204685952,F +coef_ind_gov,-0.75445486,F +coef_ind_health,-0.441707723,F +coef_ind_manu,-0.566600437,F +coef_ind_mgmt_srv,0.386847827,F +coef_ind_mil,-2.257002339,F +coef_external_worker_identification,-999.0,T +coef_regional_calibration,-1.03,T +coef_cbd,0.8653139314986638,F +coef_central,0.45025084346579664,F +coef_SSuburb,-0.4,F +coef_NWCounty,-0.38900940018141394,F +coef_NCounty,-0.7103700573089826,F +coef_ECounty,-5.0,F diff --git a/resident/configs/workplace_location.csv b/resident/configs/workplace_location.csv new file mode 100644 index 0000000..a976ade --- /dev/null +++ b/resident/configs/workplace_location.csv @@ -0,0 +1,31 @@ +Label,Description,Expression,coefficient +local_dist,,"_DIST@skims[('SOV_M_DIST', 'MD')]",1 +alt_dest_pmsa,,"_ALT_PMSA@reindex(land_use.pseudomsa, df.alt_dest)",1 +#,,, +util_dist,Distance,@_DIST,coef_dist +util_dist_sqrt,Square root of distance,@_DIST**0.5,coef_dist_sqrt +util_dist_sqrd,Distance squared,@_DIST**2,coef_dist_sqrd +util_dist_cubed,Distance cubed,@_DIST**3,coef_dist_cubed +util_dist_lowincome,Distance - low income,@_DIST * (df.income<=2),coef_dist_lowincme +util_dist_sqrt_lowincome,Distance sqrt - low income,@(_DIST**0.5) * (df.income<=2),coef_dist_sqrt_lowincme +util_dist_sqrd_lowincome,Distance squared - low income,@(_DIST**2) * (df.income<=2),coef_dist_sqrd__lowincme +util_dist_highincome,Distance - high income,@_DIST * (df.income>=4),coef_dist_highincme +util_dist_sqrd_highincome,Distance squared - high income,@(_DIST**2) * (df.income>=4),coef_dist_sqrd_highincme +util_dist_pt,Distance - parttime worker,@_DIST * (df.ptype==2),coef_dist_ptworker +util_dist_sqrd_pt,Distance squared - parttime worker,@(_DIST**2) * (df.ptype==2),coef_dist_sqrd_ptworker +util_dist_femaleworker,Distance - female worker,@_DIST * (df.ptype<3) * (df.female==1),coef_dist_femaleworker +#,,, +util_size_variable,Size variable,@df['size_term'].apply(np.log1p),1 +util_size_unavailable,Size variable,@df['size_term']==0,-999 +util_sp_utility_adjustment,shadow price utility adjustment,@df['shadow_price_utility_adjustment'],1 +#,,, +util_mode_logsum,Mode choice logsum,mode_choice_logsum,coef_mode_logsum +#,,, +util_sample_of_corrections_factor,Sample of alternatives correction factor,"@np.minimum(np.log(df.pick_count/df.prob), 60)",1 +#calibration constants,,, +util_Calibration 0-2 - miles,Calibration 0-2 - miles,@_DIST<=2,coef_distance_0_2miles +util_Calibration 2-5 - miles,Calibration 2-5 - miles,@(_DIST>2) * (_DIST<=5),coef_distance_2_5miles +util_Calibration 5-10 - miles,Calibration 5-10 - miles,@(_DIST>5) * (_DIST<=10),coef_distance_5_10miles +util_ABM2 calibration 10-20 - miles,ABM2 calibration 10-20 - miles,@(_DIST>10) * (_DIST<=20),coef_distance_10_20miles +util_ABM2 calibration 20-30 - miles,ABM2 calibration 20-30 - miles,@(_DIST>20) * (_DIST<=30),coef_distance_20_30miles +util_ABM2 calibration >30 - miles,ABM2 calibration >30 - miles,@(_DIST>30),coef_distance_30plusmiles diff --git a/resident/configs/workplace_location.yaml b/resident/configs/workplace_location.yaml new file mode 100644 index 0000000..59dcc0b --- /dev/null +++ b/resident/configs/workplace_location.yaml @@ -0,0 +1,77 @@ +SAMPLE_SIZE: 30 +ESTIMATION_SAMPLE_SIZE: 10 + +SIMULATE_CHOOSER_COLUMNS: + - income_segment + - home_zone_id + - income + - age + - ptype + - female + +SAMPLE_SPEC: workplace_location_sample.csv +SPEC: workplace_location.csv +COEFFICIENTS: workplace_location_coefficients.csv + +LOGSUM_SETTINGS: tour_mode_choice.yaml +LOGSUM_PREPROCESSOR: nontour_preprocessor +LOGSUM_TOUR_PURPOSE: work + +# model-specific logsum-related settings +CHOOSER_ORIG_COL_NAME: home_zone_id +ALT_DEST_COL_NAME: alt_dest +IN_PERIOD: 17 +OUT_PERIOD: 8 + +DEST_CHOICE_COLUMN_NAME: workplace_zone_id +# comment out DEST_CHOICE_LOGSUM_COLUMN_NAME if not desired in persons table +DEST_CHOICE_LOGSUM_COLUMN_NAME: workplace_location_logsum +# comment out MODE_CHOICE_LOGSUM_COLUMN_NAME if not desired in persons table +MODE_CHOICE_LOGSUM_COLUMN_NAME: workplace_modechoice_logsum + +# comment out DEST_CHOICE_LOGSUM_COLUMN_NAME if saved alt logsum table +DEST_CHOICE_SAMPLE_TABLE_NAME: workplace_location_sample + + +annotate_persons: + SPEC: annotate_persons_workplace + DF: persons + TABLES: + - land_use + +# - shadow pricing + +# income_segment is in households, but we want to count persons +CHOOSER_TABLE_NAME: persons_merged + +# size_terms model_selector +MODEL_SELECTOR: workplace + +# we can't use use household income_segment as this will also be set for non-workers +CHOOSER_SEGMENT_COLUMN_NAME: occupation #income_segment + +# boolean column to filter choosers (True means keep) +CHOOSER_FILTER_COLUMN_NAME: is_internal_worker + +# FIXME - these are assigned to persons in annotate_persons. we need a better way to manage this +# FIXME - these are not needed for this model and should be re/factored out +SEGMENT_IDS: + mngt_busi_scic_arts: mngt_busi_scic_arts + services: services + health: health + sales_office: sales_office + prod_trans_move: prod_trans_move + constr_maint: constr_maint + military: military + # work_low: 1 # INCOME_SEGMENT_LOW + # work_med: 2 # INCOME_SEGMENT_MED + # work_high: 3 # INCOME_SEGMENT_HIGH + # work_veryhigh: 4 # INCOME_SEGMENT_VERYHIGH + + +# model adds these tables (informational - not added if commented out) +SHADOW_PRICE_TABLE: workplace_shadow_prices +MODELED_SIZE_TABLE: workplace_modeled_size + +# not loaded if commented out +SAVED_SHADOW_PRICE_TABLE_NAME: workplace_shadow_prices.csv diff --git a/resident/configs/workplace_location_coefficients.csv b/resident/configs/workplace_location_coefficients.csv new file mode 100644 index 0000000..26d9dcf --- /dev/null +++ b/resident/configs/workplace_location_coefficients.csv @@ -0,0 +1,21 @@ +coefficient_name,value,constrain +coef_dist,0.285,F +coef_dist_sqrt,-1.604,F +coef_dist_sqrd,-0.004362,F +coef_dist_cubed,0.00002365,F +coef_dist_lowincme,0.1941,F +coef_dist_sqrt_lowincme,-0.8721,F +coef_dist_sqrd__lowincme,-0.002409,F +coef_dist_highincme,0,F +coef_dist_sqrd_highincme,0.0001919,F +coef_dist_ptworker,-0.116,F +coef_dist_sqrd_ptworker,0.0003934,F +coef_dist_femaleworker,-0.02515,F +coef_mode_logsum,0.5468,F +#work distance calibration,, +coef_distance_0_2miles,-0.770,F +coef_distance_2_5miles,-0.870,F +coef_distance_5_10miles,0.0270,F +coef_distance_10_20miles,0.720,F +coef_distance_20_30miles,0.852,F +coef_distance_30plusmiles,0.231,F diff --git a/resident/configs/workplace_location_sample.csv b/resident/configs/workplace_location_sample.csv new file mode 100644 index 0000000..f844721 --- /dev/null +++ b/resident/configs/workplace_location_sample.csv @@ -0,0 +1,19 @@ +Label,Description,Expression,coefficient +local_dist,,"_DIST@skims[('SOV_M_DIST', 'MD')]",1 +#,,, +util_dist,Distance,@_DIST,coef_dist +util_dist_sqrt,Square root of distance,@_DIST**0.5,coef_dist_sqrt +util_dist_sqrd,Distance squared,@_DIST**2,coef_dist_sqrd +util_dist_cubed,Distance cubed,@_DIST**3,coef_dist_cubed +util_dist_lowincome,Distance - low income,@_DIST * (df.income<=2),coef_dist_lowincme +util_dist_sqrt_lowincome,Distance sqrt - low income,@(_DIST**0.5) * (df.income<=2),coef_dist_sqrt_lowincme +util_dist_sqrd_lowincome,Distance squared - low income,@(_DIST**2) * (df.income<=2),coef_dist_sqrd__lowincme +util_dist_highincome,Distance - high income,@_DIST * (df.income>=4),coef_dist_highincme +util_dist_sqrd_highincome,Distance squared - high income,@(_DIST**2) * (df.income>=4),coef_dist_sqrd_highincme +util_dist_pt,Distance - parttime worker,@_DIST * (df.ptype==2),coef_dist_ptworker +util_dist_sqrd_pt,Distance squared - parttime worker,@(_DIST**2) * (df.ptype==2),coef_dist_sqrd_ptworker +util_dist_femaleworker,Distance - female worker,@_DIST * (df.ptype<3) * (df.female==1),coef_dist_femaleworker +#,,, +util_size_variable,Size variable,@df['size_term'].apply(np.log1p),1 +util_size_unavailable,Size variable,@df['size_term']==0,-999 +util_sp_utility_adjustment,shadow price utility adjustment,@df['shadow_price_utility_adjustment'],1 \ No newline at end of file diff --git a/resident/configs/write_data_dictionary.yaml b/resident/configs/write_data_dictionary.yaml new file mode 100644 index 0000000..6d6ed39 --- /dev/null +++ b/resident/configs/write_data_dictionary.yaml @@ -0,0 +1,13 @@ + + +txt_format: data_dict.txt +csv_format: data_dict.csv + +tables: + - land_use + - accessibility + - households + - persons + - tours + - trips + - joint_tour_participants diff --git a/resident/configs/write_trip_matrices.yaml b/resident/configs/write_trip_matrices.yaml new file mode 100644 index 0000000..b99145b --- /dev/null +++ b/resident/configs/write_trip_matrices.yaml @@ -0,0 +1,406 @@ +# read trips table post preprocessor and run expressions to code +# additional data fields, with one data fields for each matrix specified below + +preprocessor: + SPEC: write_trip_matrices_annotate_trips_preprocessor + DF: trips + TABLES: + - tours + - households + - land_use + +# divide trip counts by household expansion factor +HH_EXPANSION_WEIGHT_COL: sample_rate # added when households read in + +# save preprocessed trips table to pipeline if desired +SAVE_TRIPS_TABLE: True + +MATRICES: + - file_name: autotrips_EA_low.omx + tables: + - name: SOV_EA + data_field: DRIVEALONE_EA_LOW + - name: SR2_EA + data_field: SHARED2_EA_LOW + - name: SR3_EA + data_field: SHARED3_EA_LOW + - file_name: autotrips_EA_med.omx + tables: + - name: SOV_EA + data_field: DRIVEALONE_EA_MED + - name: SR2_EA + data_field: SHARED2_EA_MED + - name: SR3_EA + data_field: SHARED3_EA_MED + - file_name: autotrips_EA_high.omx + tables: + - name: SOV_EA + data_field: DRIVEALONE_EA_HIGH + - name: SR2_EA + data_field: SHARED2_EA_HIGH + - name: SR3_EA + data_field: SHARED3_EA_HIGH + - file_name: autotrips_AM_low.omx + tables: + - name: SOV_AM + data_field: DRIVEALONE_AM_LOW + - name: SR2_AM + data_field: SHARED2_AM_LOW + - name: SR3_AM + data_field: SHARED3_AM_LOW + - file_name: autotrips_AM_med.omx + tables: + - name: SOV_AM + data_field: DRIVEALONE_AM_MED + - name: SR2_AM + data_field: SHARED2_AM_MED + - name: SR3_AM + data_field: SHARED3_AM_MED + - file_name: autotrips_AM_high.omx + tables: + - name: SOV_AM + data_field: DRIVEALONE_AM_HIGH + - name: SR2_AM + data_field: SHARED2_AM_HIGH + - name: SR3_AM + data_field: SHARED3_AM_HIGH + - file_name: autotrips_MD_low.omx + tables: + - name: SOV_MD + data_field: DRIVEALONE_MD_LOW + - name: SR2_MD + data_field: SHARED2_MD_LOW + - name: SR3_MD + data_field: SHARED3_MD_LOW + - file_name: autotrips_MD_med.omx + tables: + - name: SOV_MD + data_field: DRIVEALONE_MD_MED + - name: SR2_MD + data_field: SHARED2_MD_MED + - name: SR3_MD + data_field: SHARED3_MD_MED + - file_name: autotrips_MD_high.omx + tables: + - name: SOV_MD + data_field: DRIVEALONE_MD_HIGH + - name: SR2_MD + data_field: SHARED2_MD_HIGH + - name: SR3_MD + data_field: SHARED3_MD_HIGH + - file_name: autotrips_PM_low.omx + tables: + - name: SOV_PM + data_field: DRIVEALONE_PM_LOW + - name: SR2_PM + data_field: SHARED2_PM_LOW + - name: SR3_PM + data_field: SHARED3_PM_LOW + - file_name: autotrips_PM_med.omx + tables: + - name: SOV_PM + data_field: DRIVEALONE_PM_MED + - name: SR2_PM + data_field: SHARED2_PM_MED + - name: SR3_PM + data_field: SHARED3_PM_MED + - file_name: autotrips_PM_high.omx + tables: + - name: SOV_PM + data_field: DRIVEALONE_PM_HIGH + - name: SR2_PM + data_field: SHARED2_PM_HIGH + - name: SR3_PM + data_field: SHARED3_PM_HIGH + - file_name: autotrips_EV_low.omx + tables: + - name: SOV_EV + data_field: DRIVEALONE_EV_LOW + - name: SR2_EV + data_field: SHARED2_EV_LOW + - name: SR3_EV + data_field: SHARED3_EV_LOW + - file_name: autotrips_EV_med.omx + tables: + - name: SOV_EV + data_field: DRIVEALONE_EV_MED + - name: SR2_EV + data_field: SHARED2_EV_MED + - name: SR3_EV + data_field: SHARED3_EV_MED + - file_name: autotrips_EV_high.omx + tables: + - name: SOV_EV + data_field: DRIVEALONE_EV_HIGH + - name: SR2_EV + data_field: SHARED2_EV_HIGH + - name: SR3_EV + data_field: SHARED3_EV_HIGH + - file_name: trantrips_EA.omx + tables: + - name: WALK_SET_set1_EA + data_field: WALK_LOC_EA + - name: KNROUT_SET_set1_EA + data_field: KNR_LOCOUT_EA + - name: PNROUT_SET_set1_EA + data_field: PNR_LOCOUT_EA + - name: TNCOUT_SET_set1_EA + data_field: TNC_LOCOUT_EA + - name: KNRIN_SET_set1_EA + data_field: KNR_LOCIN_EA + - name: PNRIN_SET_set1_EA + data_field: PNR_LOCIN_EA + - name: TNCIN_SET_set1_EA + data_field: TNC_LOCIN_EA + - name: WALK_SET_set2_EA + data_field: WALK_PRM_EA + - name: KNROUT_SET_set2_EA + data_field: KNR_PRMOUT_EA + - name: PNROUT_SET_set2_EA + data_field: PNR_PRMOUT_EA + - name: TNCOUT_SET_set2_EA + data_field: TNC_PRMOUT_EA + - name: KNRIN_SET_set2_EA + data_field: KNR_PRMIN_EA + - name: PNRIN_SET_set2_EA + data_field: PNR_PRMIN_EA + - name: TNCIN_SET_set2_EA + data_field: TNC_PRMIN_EA + - name: WALK_SET_set3_EA + data_field: WALK_MIX_EA + - name: KNROUT_SET_set3_EA + data_field: KNR_MIXOUT_EA + - name: PNROUT_SET_set3_EA + data_field: PNR_MIXOUT_EA + - name: TNCOUT_SET_set3_EA + data_field: TNC_MIXOUT_EA + - name: KNRIN_SET_set3_EA + data_field: KNR_MIXIN_EA + - name: PNRIN_SET_set3_EA + data_field: PNR_MIXIN_EA + - name: TNCIN_SET_set3_EA + data_field: TNC_MIXIN_EA + - file_name: trantrips_AM.omx + tables: + - name: WALK_SET_set1_AM + data_field: WALK_LOC_AM + - name: KNROUT_SET_set1_AM + data_field: KNR_LOCOUT_AM + - name: PNROUT_SET_set1_AM + data_field: PNR_LOCOUT_AM + - name: TNCOUT_SET_set1_AM + data_field: TNC_LOCOUT_AM + - name: KNRIN_SET_set1_AM + data_field: KNR_LOCIN_AM + - name: PNRIN_SET_set1_AM + data_field: PNR_LOCIN_AM + - name: TNCIN_SET_set1_AM + data_field: TNC_LOCIN_AM + - name: WALK_SET_set2_AM + data_field: WALK_PRM_AM + - name: KNROUT_SET_set2_AM + data_field: KNR_PRMOUT_AM + - name: PNROUT_SET_set2_AM + data_field: PNR_PRMOUT_AM + - name: TNCOUT_SET_set2_AM + data_field: TNC_PRMOUT_AM + - name: KNRIN_SET_set2_AM + data_field: KNR_PRMIN_AM + - name: PNRIN_SET_set2_AM + data_field: PNR_PRMIN_AM + - name: TNCIN_SET_set2_AM + data_field: TNC_PRMIN_AM + - name: WALK_SET_set3_AM + data_field: WALK_MIX_AM + - name: KNROUT_SET_set3_AM + data_field: KNR_MIXOUT_AM + - name: PNROUT_SET_set3_AM + data_field: PNR_MIXOUT_AM + - name: TNCOUT_SET_set3_AM + data_field: TNC_MIXOUT_AM + - name: KNRIN_SET_set3_AM + data_field: KNR_MIXIN_AM + - name: PNRIN_SET_set3_AM + data_field: PNR_MIXIN_AM + - name: TNCIN_SET_set3_AM + data_field: TNC_MIXIN_AM + - file_name: trantrips_MD.omx + tables: + - name: WALK_SET_set1_MD + data_field: WALK_LOC_MD + - name: KNROUT_SET_set1_MD + data_field: KNR_LOCOUT_MD + - name: PNROUT_SET_set1_MD + data_field: PNR_LOCOUT_MD + - name: TNCOUT_SET_set1_MD + data_field: TNC_LOCOUT_MD + - name: KNRIN_SET_set1_MD + data_field: KNR_LOCIN_MD + - name: PNRIN_SET_set1_MD + data_field: PNR_LOCIN_MD + - name: TNCIN_SET_set1_MD + data_field: TNC_LOCIN_MD + - name: WALK_SET_set2_MD + data_field: WALK_PRM_MD + - name: KNROUT_SET_set2_MD + data_field: KNR_PRMOUT_MD + - name: PNROUT_SET_set2_MD + data_field: PNR_PRMOUT_MD + - name: TNCOUT_SET_set2_MD + data_field: TNC_PRMOUT_MD + - name: KNRIN_SET_set2_MD + data_field: KNR_PRMIN_MD + - name: PNRIN_SET_set2_MD + data_field: PNR_PRMIN_MD + - name: TNCIN_SET_set2_MD + data_field: TNC_PRMIN_MD + - name: WALK_SET_set3_MD + data_field: WALK_MIX_MD + - name: KNROUT_SET_set3_MD + data_field: KNR_MIXOUT_MD + - name: PNROUT_SET_set3_MD + data_field: PNR_MIXOUT_MD + - name: TNCOUT_SET_set3_MD + data_field: TNC_MIXOUT_MD + - name: KNRIN_SET_set3_MD + data_field: KNR_MIXIN_MD + - name: PNRIN_SET_set3_MD + data_field: PNR_MIXIN_MD + - name: TNCIN_SET_set3_MD + data_field: TNC_MIXIN_MD + - file_name: trantrips_PM.omx + tables: + - name: WALK_SET_set1_PM + data_field: WALK_LOC_PM + - name: KNROUT_SET_set1_PM + data_field: KNR_LOCOUT_PM + - name: PNROUT_SET_set1_PM + data_field: PNR_LOCOUT_PM + - name: TNCOUT_SET_set1_PM + data_field: TNC_LOCOUT_PM + - name: KNRIN_SET_set1_PM + data_field: KNR_LOCIN_PM + - name: PNRIN_SET_set1_PM + data_field: PNR_LOCIN_PM + - name: TNCIN_SET_set1_PM + data_field: TNC_LOCIN_PM + - name: WALK_SET_set2_PM + data_field: WALK_PRM_PM + - name: KNROUT_SET_set2_PM + data_field: KNR_PRMOUT_PM + - name: PNROUT_SET_set2_PM + data_field: PNR_PRMOUT_PM + - name: TNCOUT_SET_set2_PM + data_field: TNC_PRMOUT_PM + - name: KNRIN_SET_set2_PM + data_field: KNR_PRMIN_PM + - name: PNRIN_SET_set2_PM + data_field: PNR_PRMIN_PM + - name: TNCIN_SET_set2_PM + data_field: TNC_PRMIN_PM + - name: WALK_SET_set3_PM + data_field: WALK_MIX_PM + - name: KNROUT_SET_set3_PM + data_field: KNR_MIXOUT_PM + - name: PNROUT_SET_set3_PM + data_field: PNR_MIXOUT_PM + - name: TNCOUT_SET_set3_PM + data_field: TNC_MIXOUT_PM + - name: KNRIN_SET_set3_PM + data_field: KNR_MIXIN_PM + - name: PNRIN_SET_set3_PM + data_field: PNR_MIXIN_PM + - name: TNCIN_SET_set3_PM + data_field: TNC_MIXIN_PM + - file_name: trantrips_EV.omx + tables: + - name: WALK_SET_set1_EV + data_field: WALK_LOC_EV + - name: KNROUT_SET_set1_EV + data_field: KNR_LOCOUT_EV + - name: PNROUT_SET_set1_EV + data_field: PNR_LOCOUT_EV + - name: TNCOUT_SET_set1_EV + data_field: TNC_LOCOUT_EV + - name: KNRIN_SET_set1_EV + data_field: KNR_LOCIN_EV + - name: PNRIN_SET_set1_EV + data_field: PNR_LOCIN_EV + - name: TNCIN_SET_set1_EV + data_field: TNC_LOCIN_EV + - name: WALK_SET_set2_EV + data_field: WALK_PRM_EV + - name: KNROUT_SET_set2_EV + data_field: KNR_PRMOUT_EV + - name: PNROUT_SET_set2_EV + data_field: PNR_PRMOUT_EV + - name: TNCOUT_SET_set2_EV + data_field: TNC_PRMOUT_EV + - name: KNRIN_SET_set2_EV + data_field: KNR_PRMIN_EV + - name: PNRIN_SET_set2_EV + data_field: PNR_PRMIN_EV + - name: TNCIN_SET_set2_EV + data_field: TNC_PRMIN_EV + - name: WALK_SET_set3_EV + data_field: WALK_MIX_EV + - name: KNROUT_SET_set3_EV + data_field: KNR_MIXOUT_EV + - name: PNROUT_SET_set3_EV + data_field: PNR_MIXOUT_EV + - name: TNCOUT_SET_set3_EV + data_field: TNC_MIXOUT_EV + - name: KNRIN_SET_set3_EV + data_field: KNR_MIXIN_EV + - name: PNRIN_SET_set3_EV + data_field: PNR_MIXIN_EV + - name: TNCIN_SET_set3_EV + data_field: TNC_MIXIN_EV + - file_name: nmottrips_EA.omx + tables: + - name: WALK_EA + data_field: WALK_EA + - name: BIKE_EA + data_field: BIKE_EA + - file_name: nmottrips_AM.omx + tables: + - name: WALK_AM + data_field: WALK_AM + - name: BIKE_AM + data_field: BIKE_AM + - file_name: nmottrips_MD.omx + tables: + - name: WALK_MD + data_field: WALK_MD + - name: BIKE_MD + data_field: BIKE_MD + - file_name: nmottrips_PM.omx + tables: + - name: WALK_PM + data_field: WALK_PM + - name: BIKE_PM + data_field: BIKE_PM + - file_name: nmottrips_EV.omx + tables: + - name: WALK_EV + data_field: WALK_EV + - name: BIKE_EV + data_field: BIKE_EV + +CONSTANTS: + time_periods: + EA: + first_hour: 1 + last_hour: 6 + AM: + first_hour: 7 + last_hour: 12 + MD: + first_hour: 13 + last_hour: 25 + PM: + first_hour: 26 + last_hour: 32 + EV: + first_hour: 33 + last_hour: 48 \ No newline at end of file diff --git a/resident/configs/write_trip_matrices_annotate_trips_preprocessor.csv b/resident/configs/write_trip_matrices_annotate_trips_preprocessor.csv new file mode 100644 index 0000000..2857d4e --- /dev/null +++ b/resident/configs/write_trip_matrices_annotate_trips_preprocessor.csv @@ -0,0 +1,182 @@ +Description,Target,Expression +# add additional fields,, +,tour_participants,trips.tour_id.map(tours.number_of_participants) +# code time periods,, +,is_ea,"trips.depart.between(time_periods['EA']['first_hour'], time_periods['EA']['last_hour'])" +,is_am,"trips.depart.between(time_periods['AM']['first_hour'], time_periods['AM']['last_hour'])" +,is_md,"trips.depart.between(time_periods['MD']['first_hour'], time_periods['MD']['last_hour'])" +,is_pm,"trips.depart.between(time_periods['PM']['first_hour'], time_periods['PM']['last_hour'])" +,is_ev,"trips.depart.between(time_periods['EV']['first_hour'], time_periods['EV']['last_hour'])" +,_vot_bin_da,"np.where(trips.trip_mode.isin(['DRIVEALONE']),np.where(trips.vot_da < vot_threshold_low, 1,np.where(trips.vot_da < vot_threshold_med, 2, 3)),0)" +,_vot_bin_s2,"np.where(trips.trip_mode.isin(['SHARED2','TAXI','TNC_SINGLE']),np.where(trips.vot_s2 < vot_threshold_low,1,np.where(trips.vot_s2 < vot_threshold_med, 2, 3)),0)" +,_vot_bin_s3,"np.where(trips.trip_mode.isin(['SHARED3','TNC_SHARED']),np.where(trips.vot_s3 < vot_threshold_low,1,np.where(trips.vot_s3 < vot_threshold_med, 2, 3)),0)" +,vot1,"np.where((_vot_bin_da == 1) |( _vot_bin_s2 == 1) |( _vot_bin_s3 ==1),1,0)" +,vot2,"np.where((_vot_bin_da == 2 )|( _vot_bin_s2 == 2) |( _vot_bin_s3 ==2),1,0)" +,vot3,"np.where((_vot_bin_da == 3) | (_vot_bin_s2 == 3) | (_vot_bin_s3 ==3),1,0)" +,outbound,trips.outbound +,inbound,~trips.outbound +# ea trips,, +,DRIVEALONE_EA_LOW,((trips.trip_mode == 'DRIVEALONE') & is_ea & vot1) * tour_participants +,SHARED2_EA_LOW,(trips.trip_mode.isin(['SHARED2'])/OCC_SHARED2)* is_ea * vot1 * tour_participants +,SHARED3_EA_LOW,(trips.trip_mode.isin(['SHARED3'])/OCC_SHARED3)* is_ea * vot1 * tour_participants +,DRIVEALONE_EA_MED,((trips.trip_mode == 'DRIVEALONE') & is_ea & vot2) * tour_participants +,SHARED2_EA_MED,(trips.trip_mode.isin(['SHARED2'])/OCC_SHARED2)* is_ea * vot2 * tour_participants +,SHARED3_EA_MED,(trips.trip_mode.isin(['SHARED3'])/OCC_SHARED3)* is_ea * vot2 * tour_participants +,DRIVEALONE_EA_HIGH,((trips.trip_mode == 'DRIVEALONE') & is_ea & vot3) * tour_participants +,SHARED2_EA_HIGH,(trips.trip_mode.isin(['SHARED2'])/OCC_SHARED2)* is_ea * vot3 * tour_participants +,SHARED3_EA_HIGH,(trips.trip_mode.isin(['SHARED3'])/OCC_SHARED3)* is_ea * vot3 * tour_participants +,WALK_LOC_EA,((trips.trip_mode == 'WALK_LOC') & is_ea) * tour_participants +,WALK_PRM_EA,((trips.trip_mode == 'WALK_PRM') & is_ea) * tour_participants +,WALK_MIX_EA,((trips.trip_mode == 'WALK_MIX') & is_ea) * tour_participants +,PNR_LOCOUT_EA,((trips.trip_mode == 'PNR_LOC') & is_ea) * tour_participants * outbound +,PNR_PRMOUT_EA,((trips.trip_mode == 'PNR_PRM') & is_ea) * tour_participants * outbound +,PNR_MIXOUT_EA,((trips.trip_mode == 'PNR_MIX') & is_ea) * tour_participants * outbound +,KNR_LOCOUT_EA,((trips.trip_mode == 'KNR_LOC') & is_ea) * tour_participants * outbound +,KNR_PRMOUT_EA,((trips.trip_mode == 'KNR_PRM') & is_ea) * tour_participants * outbound +,KNR_MIXOUT_EA,((trips.trip_mode == 'KNR_MIX') & is_ea) * tour_participants * outbound +,TNC_LOCOUT_EA,((trips.trip_mode == 'TNC_LOC') & is_ea) * tour_participants * outbound +,TNC_PRMOUT_EA,((trips.trip_mode == 'TNC_PRM') & is_ea) * tour_participants * outbound +,TNC_MIXOUT_EA,((trips.trip_mode == 'TNC_MIX') & is_ea) * tour_participants * outbound +,PNR_LOCIN_EA,((trips.trip_mode == 'PNR_LOC') & is_ea) * tour_participants * inbound +,PNR_PRMIN_EA,((trips.trip_mode == 'PNR_PRM') & is_ea) * tour_participants * inbound +,PNR_MIXIN_EA,((trips.trip_mode == 'PNR_MIX') & is_ea) * tour_participants * inbound +,KNR_LOCIN_EA,((trips.trip_mode == 'KNR_LOC') & is_ea) * tour_participants * inbound +,KNR_PRMIN_EA,((trips.trip_mode == 'KNR_PRM') & is_ea) * tour_participants * inbound +,KNR_MIXIN_EA,((trips.trip_mode == 'KNR_MIX') & is_ea) * tour_participants * inbound +,TNC_LOCIN_EA,((trips.trip_mode == 'TNC_LOC') & is_ea) * tour_participants * inbound +,TNC_PRMIN_EA,((trips.trip_mode == 'TNC_PRM') & is_ea) * tour_participants * inbound +,TNC_MIXIN_EA,((trips.trip_mode == 'TNC_MIX') & is_ea) * tour_participants * inbound +,BIKE_EA,((trips.trip_mode == 'BIKE') & is_ea) * tour_participants +,WALK_EA,((trips.trip_mode == 'WALK') & is_ea) * tour_participants +# am trips,, +,DRIVEALONE_AM_LOW,((trips.trip_mode == 'DRIVEALONE') & is_am & vot1) * tour_participants +,SHARED2_AM_LOW,(trips.trip_mode.isin(['SHARED2'])/OCC_SHARED2)* is_am * vot1 * tour_participants +,SHARED3_AM_LOW,(trips.trip_mode.isin(['SHARED3'])/OCC_SHARED3)* is_am * vot1 * tour_participants +,DRIVEALONE_AM_MED,((trips.trip_mode == 'DRIVEALONE') & is_am & vot2) * tour_participants +,SHARED2_AM_MED,(trips.trip_mode.isin(['SHARED2'])/OCC_SHARED2)* is_am * vot2 * tour_participants +,SHARED3_AM_MED,(trips.trip_mode.isin(['SHARED3'])/OCC_SHARED3)* is_am * vot2 * tour_participants +,DRIVEALONE_AM_HIGH,((trips.trip_mode == 'DRIVEALONE') & is_am & vot3) * tour_participants +,SHARED2_AM_HIGH,(trips.trip_mode.isin(['SHARED2'])/OCC_SHARED2)* is_am * vot3 * tour_participants +,SHARED3_AM_HIGH,(trips.trip_mode.isin(['SHARED3'])/OCC_SHARED3)* is_am * vot3 * tour_participants +,WALK_LOC_AM,((trips.trip_mode == 'WALK_LOC') & is_am) * tour_participants +,WALK_PRM_AM,((trips.trip_mode == 'WALK_PRM') & is_am) * tour_participants +,WALK_MIX_AM,((trips.trip_mode == 'WALK_MIX') & is_am) * tour_participants +,PNR_LOCOUT_AM,((trips.trip_mode == 'PNR_LOC') & is_am) * tour_participants * outbound +,PNR_PRMOUT_AM,((trips.trip_mode == 'PNR_PRM') & is_am) * tour_participants * outbound +,PNR_MIXOUT_AM,((trips.trip_mode == 'PNR_MIX') & is_am) * tour_participants * outbound +,KNR_LOCOUT_AM,((trips.trip_mode == 'KNR_LOC') & is_am) * tour_participants * outbound +,KNR_PRMOUT_AM,((trips.trip_mode == 'KNR_PRM') & is_am) * tour_participants * outbound +,KNR_MIXOUT_AM,((trips.trip_mode == 'KNR_MIX') & is_am) * tour_participants * outbound +,TNC_LOCOUT_AM,((trips.trip_mode == 'TNC_LOC') & is_am) * tour_participants * outbound +,TNC_PRMOUT_AM,((trips.trip_mode == 'TNC_PRM') & is_am) * tour_participants * outbound +,TNC_MIXOUT_AM,((trips.trip_mode == 'TNC_MIX') & is_am) * tour_participants * outbound +,PNR_LOCIN_AM,((trips.trip_mode == 'PNR_LOC') & is_am) * tour_participants * inbound +,PNR_PRMIN_AM,((trips.trip_mode == 'PNR_PRM') & is_am) * tour_participants * inbound +,PNR_MIXIN_AM,((trips.trip_mode == 'PNR_MIX') & is_am) * tour_participants * inbound +,KNR_LOCIN_AM,((trips.trip_mode == 'KNR_LOC') & is_am) * tour_participants * inbound +,KNR_PRMIN_AM,((trips.trip_mode == 'KNR_PRM') & is_am) * tour_participants * inbound +,KNR_MIXIN_AM,((trips.trip_mode == 'KNR_MIX') & is_am) * tour_participants * inbound +,TNC_LOCIN_AM,((trips.trip_mode == 'TNC_LOC') & is_am) * tour_participants * inbound +,TNC_PRMIN_AM,((trips.trip_mode == 'TNC_PRM') & is_am) * tour_participants * inbound +,TNC_MIXIN_AM,((trips.trip_mode == 'TNC_MIX') & is_am) * tour_participants * inbound +,BIKE_AM,((trips.trip_mode == 'BIKE') & is_am) * tour_participants +,WALK_AM,((trips.trip_mode == 'WALK') & is_am) * tour_participants +# md trips,, +,DRIVEALONE_MD_LOW,((trips.trip_mode == 'DRIVEALONE') & is_md & vot1) * tour_participants +,SHARED2_MD_LOW,(trips.trip_mode.isin(['SHARED2'])/OCC_SHARED2)* is_md * vot1 * tour_participants +,SHARED3_MD_LOW,(trips.trip_mode.isin(['SHARED3'])/OCC_SHARED3)* is_md * vot1 * tour_participants +,DRIVEALONE_MD_MED,((trips.trip_mode == 'DRIVEALONE') & is_md & vot2) * tour_participants +,SHARED2_MD_MED,(trips.trip_mode.isin(['SHARED2'])/OCC_SHARED2)* is_md * vot2 * tour_participants +,SHARED3_MD_MED,(trips.trip_mode.isin(['SHARED3'])/OCC_SHARED3)* is_md * vot2 * tour_participants +,DRIVEALONE_MD_HIGH,((trips.trip_mode == 'DRIVEALONE') & is_md & vot3) * tour_participants +,SHARED2_MD_HIGH,(trips.trip_mode.isin(['SHARED2'])/OCC_SHARED2)* is_md * vot3 * tour_participants +,SHARED3_MD_HIGH,(trips.trip_mode.isin(['SHARED3'])/OCC_SHARED3)* is_md * vot3 * tour_participants +,WALK_LOC_MD,((trips.trip_mode == 'WALK_LOC') & is_md) * tour_participants +,WALK_PRM_MD,((trips.trip_mode == 'WALK_PRM') & is_md) * tour_participants +,WALK_MIX_MD,((trips.trip_mode == 'WALK_MIX') & is_md) * tour_participants +,PNR_LOCOUT_MD,((trips.trip_mode == 'PNR_LOC') & is_md) * tour_participants * outbound +,PNR_PRMOUT_MD,((trips.trip_mode == 'PNR_PRM') & is_md) * tour_participants * outbound +,PNR_MIXOUT_MD,((trips.trip_mode == 'PNR_MIX') & is_md) * tour_participants * outbound +,KNR_LOCOUT_MD,((trips.trip_mode == 'KNR_LOC') & is_md) * tour_participants * outbound +,KNR_PRMOUT_MD,((trips.trip_mode == 'KNR_PRM') & is_md) * tour_participants * outbound +,KNR_MIXOUT_MD,((trips.trip_mode == 'KNR_MIX') & is_md) * tour_participants * outbound +,TNC_LOCOUT_MD,((trips.trip_mode == 'TNC_LOC') & is_md) * tour_participants * outbound +,TNC_PRMOUT_MD,((trips.trip_mode == 'TNC_PRM') & is_md) * tour_participants * outbound +,TNC_MIXOUT_MD,((trips.trip_mode == 'TNC_MIX') & is_md) * tour_participants * outbound +,PNR_LOCIN_MD,((trips.trip_mode == 'PNR_LOC') & is_md) * tour_participants * inbound +,PNR_PRMIN_MD,((trips.trip_mode == 'PNR_PRM') & is_md) * tour_participants * inbound +,PNR_MIXIN_MD,((trips.trip_mode == 'PNR_MIX') & is_md) * tour_participants * inbound +,KNR_LOCIN_MD,((trips.trip_mode == 'KNR_LOC') & is_md) * tour_participants * inbound +,KNR_PRMIN_MD,((trips.trip_mode == 'KNR_PRM') & is_md) * tour_participants * inbound +,KNR_MIXIN_MD,((trips.trip_mode == 'KNR_MIX') & is_md) * tour_participants * inbound +,TNC_LOCIN_MD,((trips.trip_mode == 'TNC_LOC') & is_md) * tour_participants * inbound +,TNC_PRMIN_MD,((trips.trip_mode == 'TNC_PRM') & is_md) * tour_participants * inbound +,TNC_MIXIN_MD,((trips.trip_mode == 'TNC_MIX') & is_md) * tour_participants * inbound +,BIKE_MD,((trips.trip_mode == 'BIKE') & is_md) * tour_participants +,WALK_MD,((trips.trip_mode == 'WALK') & is_md) * tour_participants +# pm trips,, +,DRIVEALONE_PM_LOW,((trips.trip_mode == 'DRIVEALONE') & is_pm & vot1) * tour_participants +,SHARED2_PM_LOW,(trips.trip_mode.isin(['SHARED2'])/OCC_SHARED2)* is_pm * vot1 * tour_participants +,SHARED3_PM_LOW,(trips.trip_mode.isin(['SHARED3'])/OCC_SHARED3)* is_pm * vot1 * tour_participants +,DRIVEALONE_PM_MED,((trips.trip_mode == 'DRIVEALONE') & is_pm & vot2) * tour_participants +,SHARED2_PM_MED,(trips.trip_mode.isin(['SHARED2'])/OCC_SHARED2)* is_pm * vot2 * tour_participants +,SHARED3_PM_MED,(trips.trip_mode.isin(['SHARED3'])/OCC_SHARED3)* is_pm * vot2 * tour_participants +,DRIVEALONE_PM_HIGH,((trips.trip_mode == 'DRIVEALONE') & is_pm & vot3) * tour_participants +,SHARED2_PM_HIGH,(trips.trip_mode.isin(['SHARED2'])/OCC_SHARED2)* is_pm * vot3 * tour_participants +,SHARED3_PM_HIGH,(trips.trip_mode.isin(['SHARED3'])/OCC_SHARED3)* is_pm * vot3 * tour_participants +,WALK_LOC_PM,((trips.trip_mode == 'WALK_LOC') & is_pm) * tour_participants +,WALK_PRM_PM,((trips.trip_mode == 'WALK_PRM') & is_pm) * tour_participants +,WALK_MIX_PM,((trips.trip_mode == 'WALK_MIX') & is_pm) * tour_participants +,PNR_LOCOUT_PM,((trips.trip_mode == 'PNR_LOC') & is_pm) * tour_participants * outbound +,PNR_PRMOUT_PM,((trips.trip_mode == 'PNR_PRM') & is_pm) * tour_participants * outbound +,PNR_MIXOUT_PM,((trips.trip_mode == 'PNR_MIX') & is_pm) * tour_participants * outbound +,KNR_LOCOUT_PM,((trips.trip_mode == 'KNR_LOC') & is_pm) * tour_participants * outbound +,KNR_PRMOUT_PM,((trips.trip_mode == 'KNR_PRM') & is_pm) * tour_participants * outbound +,KNR_MIXOUT_PM,((trips.trip_mode == 'KNR_MIX') & is_pm) * tour_participants * outbound +,TNC_LOCOUT_PM,((trips.trip_mode == 'TNC_LOC') & is_pm) * tour_participants * outbound +,TNC_PRMOUT_PM,((trips.trip_mode == 'TNC_PRM') & is_pm) * tour_participants * outbound +,TNC_MIXOUT_PM,((trips.trip_mode == 'TNC_MIX') & is_pm) * tour_participants * outbound +,PNR_LOCIN_PM,((trips.trip_mode == 'PNR_LOC') & is_pm) * tour_participants * inbound +,PNR_PRMIN_PM,((trips.trip_mode == 'PNR_PRM') & is_pm) * tour_participants * inbound +,PNR_MIXIN_PM,((trips.trip_mode == 'PNR_MIX') & is_pm) * tour_participants * inbound +,KNR_LOCIN_PM,((trips.trip_mode == 'KNR_LOC') & is_pm) * tour_participants * inbound +,KNR_PRMIN_PM,((trips.trip_mode == 'KNR_PRM') & is_pm) * tour_participants * inbound +,KNR_MIXIN_PM,((trips.trip_mode == 'KNR_MIX') & is_pm) * tour_participants * inbound +,TNC_LOCIN_PM,((trips.trip_mode == 'TNC_LOC') & is_pm) * tour_participants * inbound +,TNC_PRMIN_PM,((trips.trip_mode == 'TNC_PRM') & is_pm) * tour_participants * inbound +,TNC_MIXIN_PM,((trips.trip_mode == 'TNC_MIX') & is_pm) * tour_participants * inbound +,BIKE_PM,((trips.trip_mode == 'BIKE') & is_pm) * tour_participants +,WALK_PM,((trips.trip_mode == 'WALK') & is_pm) * tour_participants +# ev trips,, +,DRIVEALONE_EV_LOW,((trips.trip_mode == 'DRIVEALONE') & is_ev & vot1) * tour_participants +,SHARED2_EV_LOW,(trips.trip_mode.isin(['SHARED2'])/OCC_SHARED2)* is_ev * vot1 * tour_participants +,SHARED3_EV_LOW,(trips.trip_mode.isin(['SHARED3'])/OCC_SHARED3)* is_ev * vot1 * tour_participants +,DRIVEALONE_EV_MED,((trips.trip_mode == 'DRIVEALONE') & is_ev & vot2) * tour_participants +,SHARED2_EV_MED,(trips.trip_mode.isin(['SHARED2'])/OCC_SHARED2)* is_ev * vot2 * tour_participants +,SHARED3_EV_MED,(trips.trip_mode.isin(['SHARED3'])/OCC_SHARED3)* is_ev * vot2 * tour_participants +,DRIVEALONE_EV_HIGH,((trips.trip_mode == 'DRIVEALONE') & is_ev & vot3) * tour_participants +,SHARED2_EV_HIGH,(trips.trip_mode.isin(['SHARED2'])/OCC_SHARED2)* is_ev * vot3 * tour_participants +,SHARED3_EV_HIGH,(trips.trip_mode.isin(['SHARED3'])/OCC_SHARED3)* is_ev * vot3 * tour_participants +,WALK_LOC_EV,((trips.trip_mode == 'WALK_LOC') & is_ev) * tour_participants +,WALK_PRM_EV,((trips.trip_mode == 'WALK_PRM') & is_ev) * tour_participants +,WALK_MIX_EV,((trips.trip_mode == 'WALK_MIX') & is_ev) * tour_participants +,PNR_LOCOUT_EV,((trips.trip_mode == 'PNR_LOC') & is_ev) * tour_participants * outbound +,PNR_PRMOUT_EV,((trips.trip_mode == 'PNR_PRM') & is_ev) * tour_participants * outbound +,PNR_MIXOUT_EV,((trips.trip_mode == 'PNR_MIX') & is_ev) * tour_participants * outbound +,KNR_LOCOUT_EV,((trips.trip_mode == 'KNR_LOC') & is_ev) * tour_participants * outbound +,KNR_PRMOUT_EV,((trips.trip_mode == 'KNR_PRM') & is_ev) * tour_participants * outbound +,KNR_MIXOUT_EV,((trips.trip_mode == 'KNR_MIX') & is_ev) * tour_participants * outbound +,TNC_LOCOUT_EV,((trips.trip_mode == 'TNC_LOC') & is_ev) * tour_participants * outbound +,TNC_PRMOUT_EV,((trips.trip_mode == 'TNC_PRM') & is_ev) * tour_participants * outbound +,TNC_MIXOUT_EV,((trips.trip_mode == 'TNC_MIX') & is_ev) * tour_participants * outbound +,PNR_LOCIN_EV,((trips.trip_mode == 'PNR_LOC') & is_ev) * tour_participants * inbound +,PNR_PRMIN_EV,((trips.trip_mode == 'PNR_PRM') & is_ev) * tour_participants * inbound +,PNR_MIXIN_EV,((trips.trip_mode == 'PNR_MIX') & is_ev) * tour_participants * inbound +,KNR_LOCIN_EV,((trips.trip_mode == 'KNR_LOC') & is_ev) * tour_participants * inbound +,KNR_PRMIN_EV,((trips.trip_mode == 'KNR_PRM') & is_ev) * tour_participants * inbound +,KNR_MIXIN_EV,((trips.trip_mode == 'KNR_MIX') & is_ev) * tour_participants * inbound +,TNC_LOCIN_EV,((trips.trip_mode == 'TNC_LOC') & is_ev) * tour_participants * inbound +,TNC_PRMIN_EV,((trips.trip_mode == 'TNC_PRM') & is_ev) * tour_participants * inbound +,TNC_MIXIN_EV,((trips.trip_mode == 'TNC_MIX') & is_ev) * tour_participants * inbound +,BIKE_EV,((trips.trip_mode == 'BIKE') & is_ev) * tour_participants +,WALK_EV,((trips.trip_mode == 'WALK') & is_ev) * tour_participants \ No newline at end of file diff --git a/resident/extensions/__init__.py b/resident/extensions/__init__.py new file mode 100644 index 0000000..d8496bf --- /dev/null +++ b/resident/extensions/__init__.py @@ -0,0 +1,7 @@ +from . import av_ownership +from . import external_identification +from . import external_location_choice +from . import adjust_auto_operating_cost +from . import license_holding_status +from . import bike_comfort +from . import transit_lot_connectivity diff --git a/resident/extensions/adjust_auto_operating_cost.py b/resident/extensions/adjust_auto_operating_cost.py new file mode 100644 index 0000000..0020258 --- /dev/null +++ b/resident/extensions/adjust_auto_operating_cost.py @@ -0,0 +1,34 @@ +import logging + +import numpy as np +import pandas as pd + +from activitysim.core import workflow + +logger = logging.getLogger(__name__) + + +@workflow.step +def adjust_auto_operating_cost(state: workflow.State, vehicles: pd.DataFrame): + """ + Adjusts the `auto_operating_cost` field in the vehicles table + so that the average is a desired value set as costPerMile in the + settings + + Parameters + ---------- + vehicles : pd.DataFrame + """ + target_auto_operating_cost = state.get_global_constants()["costPerMile"] + + adjustment_factor = ( + target_auto_operating_cost / vehicles["auto_operating_cost"].mean() + ) + logger.info( + "Adjusting auto operating costs in vehicles table by a factor of {}".format( + adjustment_factor + ) + ) + vehicles["auto_operating_cost"] *= adjustment_factor + + state.add_table("vehicles", vehicles) diff --git a/resident/extensions/av_ownership.py b/resident/extensions/av_ownership.py new file mode 100644 index 0000000..7b7d74c --- /dev/null +++ b/resident/extensions/av_ownership.py @@ -0,0 +1,207 @@ +# ActivitySim +# See full license in LICENSE.txt. +import logging + +import numpy as np +import pandas as pd + +from activitysim.core import ( + config, + expressions, + estimation, + simulate, + tracing, + workflow, +) +from activitysim.core.configuration.base import PreprocessorSettings, PydanticReadable +from activitysim.core.configuration.logit import LogitComponentSettings + +logger = logging.getLogger("activitysim") + + +class AVOwnershipSettings(LogitComponentSettings, extra="forbid"): + """ + Settings for the `transit_pass_subsidy` component. + """ + + preprocessor: PreprocessorSettings | None = None + """Setting for the preprocessor.""" + + AV_OWNERSHIP_ALT: int = 0 + """The column index number of the spec file for owning an autonomous vehicle.""" + + # iterative what-if analysis example + # omit these settings to not iterate + AV_OWNERSHIP_ITERATIONS: int | None = 1 + """Maximum number of auto-calibration iterations to run.""" + AV_OWNERSHIP_TARGET_PERCENT: float | None = 0.0 + """Target percent of households owning an autonomous vehicle.""" + AV_OWNERSHIP_TARGET_PERCENT_TOLERANCE: float | None = 0.01 + """ + Tolerance for the target percent of households owning an autonomous vehicle. + Auto-calibration iterations will stop after achieving tolerance or hitting the max number. + """ + AV_OWNERSHIP_COEFFICIENT_CONSTANT: str | None = "coef_av_target_share" + """Name of the coefficient to adjust in each auto-calibration iteration.""" + + +@workflow.step +def av_ownership( + state: workflow.State, + households_merged: pd.DataFrame, + households: pd.DataFrame, + model_settings: AVOwnershipSettings | None = None, + model_settings_file_name: str = "av_ownership.yaml", + trace_label: str = "av_ownership", + trace_hh_id: bool = False, +) -> None: + """ + This model predicts whether a household owns an autonomous vehicle. + The output from this model is TRUE or FALSE. + """ + + if model_settings is None: + model_settings = AVOwnershipSettings.read_settings_file( + state.filesystem, + model_settings_file_name, + ) + + choosers = households_merged + logger.info("Running %s with %d households", trace_label, len(choosers)) + + estimator = estimation.manager.begin_estimation(state, "av_ownership") + + constants = config.get_model_constants(model_settings) + av_ownership_alt = model_settings.AV_OWNERSHIP_ALT + + # - preprocessor + preprocessor_settings = model_settings.preprocessor + if preprocessor_settings: + + locals_d = {} + if constants is not None: + locals_d.update(constants) + + expressions.assign_columns( + state, + df=choosers, + model_settings=preprocessor_settings, + locals_dict=locals_d, + trace_label=trace_label, + ) + + model_spec = state.filesystem.read_model_spec(file_name=model_settings.SPEC) + coefficients_df = state.filesystem.read_model_coefficients(model_settings) + nest_spec = config.get_logit_model_settings(model_settings) + + if estimator: + estimator.write_model_settings(model_settings, model_settings_file_name) + estimator.write_spec(model_settings) + estimator.write_coefficients(coefficients_df) + estimator.write_choosers(choosers) + + # - iterative single process what-if adjustment if specified + iterations = model_settings.AV_OWNERSHIP_ITERATIONS + iterations_coefficient_constant = model_settings.AV_OWNERSHIP_COEFFICIENT_CONSTANT + iterations_target_percent = model_settings.AV_OWNERSHIP_TARGET_PERCENT + iterations_target_percent_tolerance = ( + model_settings.AV_OWNERSHIP_TARGET_PERCENT_TOLERANCE + ) + + # check to make sure all required settings are specified + assert ( + iterations_coefficient_constant is not None if (iterations > 0) else True + ), "AV_OWNERSHIP_COEFFICIENT_CONSTANT required if AV_OWNERSHIP_ITERATIONS is specified" + assert ( + iterations_target_percent is not None if (iterations > 0) else True + ), "AV_OWNERSHIP_TARGET_PERCENT required if AV_OWNERSHIP_ITERATIONS is specified" + assert ( + iterations_target_percent_tolerance is not None if (iterations > 0) else True + ), "AV_OWNERSHIP_TARGET_PERCENT_TOLERANCE required if AV_OWNERSHIP_ITERATIONS is specified" + + for iteration in range(iterations): + + logger.info( + "Running %s with %d households iteration %d", + trace_label, + len(choosers), + iteration, + ) + + # re-read spec to reset substitution + model_spec = state.filesystem.read_model_spec(file_name=model_settings.SPEC) + model_spec = simulate.eval_coefficients( + state, model_spec, coefficients_df, estimator + ) + + choices = simulate.simple_simulate( + state, + choosers=choosers, + spec=model_spec, + nest_spec=nest_spec, + locals_d=constants, + trace_label=trace_label, + trace_choice_name="av_ownership", + estimator=estimator, + compute_settings=model_settings.compute_settings, + ) + + if iterations_target_percent is not None: + + current_percent = (choices == av_ownership_alt).sum() / len(choosers) + logger.info( + "Running %s iteration %i choosers %i current percent %f target percent %f", + trace_label, + iteration, + len(choosers), + current_percent, + iterations_target_percent, + ) + + if current_percent <= ( + iterations_target_percent + iterations_target_percent_tolerance + ) and current_percent >= ( + iterations_target_percent - iterations_target_percent_tolerance + ): + logger.info( + "Running %s iteration %i converged with coefficient %f", + trace_label, + iteration, + coefficients_df.value[iterations_coefficient_constant], + ) + break + + else: + new_value = ( + np.log( + iterations_target_percent / np.maximum(current_percent, 0.0001) + ) + + coefficients_df.value[iterations_coefficient_constant] + ) + coefficients_df.value[iterations_coefficient_constant] = new_value + logger.info( + "Running %s iteration %i new coefficient for next iteration %f", + trace_label, + iteration, + new_value, + ) + iteration = iteration + 1 + + choices = choices == av_ownership_alt + + if estimator: + estimator.write_choices(choices) + choices = estimator.get_survey_values(choices, "households", "av_ownership") + estimator.write_override_choices(choices) + estimator.end_estimation() + + households["av_ownership"] = ( + choices.reindex(households.index).fillna(0).astype(bool) + ) + + state.add_table("households", households) + + tracing.print_summary("av_ownership", households.av_ownership, value_counts=True) + + if trace_hh_id: + state.tracing.trace_df(households, label=trace_label, warn_if_empty=True) diff --git a/resident/extensions/bike_comfort.py b/resident/extensions/bike_comfort.py new file mode 100644 index 0000000..ae0d8ef --- /dev/null +++ b/resident/extensions/bike_comfort.py @@ -0,0 +1,125 @@ +# ActivitySim +# See full license in LICENSE.txt. +from __future__ import annotations + +import logging + +import pandas as pd + +from activitysim.core import ( + config, + estimation, + expressions, + simulate, + tracing, + workflow, +) +from activitysim.core.configuration.base import PreprocessorSettings +from activitysim.core.configuration.logit import LogitComponentSettings + +logger = logging.getLogger("activitysim") + + +class BikeComfortSettings(LogitComponentSettings, extra="forbid"): + """ + Settings for the 'bike_comfort' model. + """ + + CHOOSE_FILTER_COLUMN_NAME: str = "adult" + """Column name in the dataframe to filter persons eligible for license holding status model.""" + + +@workflow.step +def bike_comfort( + state: workflow.State, + persons_merged: pd.DataFrame, + persons: pd.DataFrame, + model_settings: BikeComfortSettings | None = None, + model_settings_file_name: str = "bike_comfort.yaml", + trace_label: str = "bike_comfort", +) -> None: + """ + This model predicts the bike comfort level for each person. + The alternatives of this model are NoWyNoHow, InterestedButConcerned, EnthsuedAndConfident, StrongAndFearless + """ + + if model_settings is None: + model_settings = BikeComfortSettings.read_settings_file( + state.filesystem, + model_settings_file_name, + ) + + choosers = persons_merged + chooser_filter_columun_name = model_settings.CHOOSE_FILTER_COLUMN_NAME + choosers = choosers[(choosers[chooser_filter_columun_name])] + logger.info("Running %s with %d persons", trace_label, len(choosers)) + + estimator = estimation.manager.begin_estimation(state, "bike_comfort") + + constants = config.get_model_constants(model_settings) + + # - preprocessor + expressions.annotate_preprocessors( + state, + df=choosers, + locals_dict=constants, + skims=None, + model_settings=model_settings, + trace_label=trace_label, + ) + + model_spec = state.filesystem.read_model_spec(file_name=model_settings.SPEC) + coefficients_df = state.filesystem.read_model_coefficients(model_settings) + model_spec = simulate.eval_coefficients( + state, model_spec, coefficients_df, estimator + ) + nest_spec = config.get_logit_model_settings(model_settings) + + if estimator: + estimator.write_model_settings(model_settings, model_settings_file_name) + estimator.write_spec(model_settings) + estimator.write_coefficients(coefficients_df, model_settings) + estimator.write_choosers(choosers) + + choices = simulate.simple_simulate( + state, + choosers=choosers, + spec=model_spec, + nest_spec=nest_spec, + locals_d=constants, + trace_label=trace_label, + trace_choice_name="bike_comfort", + estimator=estimator, + compute_settings=model_settings.compute_settings, + ) + + choices = pd.Series(model_spec.columns[choices.values], index=choices.index) + bike_comfort_cat = pd.api.types.CategoricalDtype( + model_spec.columns.tolist() + [""], + ordered=False, + ) + + choices = choices.astype(bike_comfort_cat) + + if estimator: + estimator.write_choices(choices) + choices = estimator.get_survey_values(choices, "persons", "bike_comfort") + estimator.write_override_choices(choices) + estimator.end_estimation() + + persons["bike_comfort"] = choices.reindex(persons.index).fillna("") + + state.add_table("persons", persons) + + tracing.print_summary("bike_comfort", persons.bike_comfort, value_counts=True) + + if state.settings.trace_hh_id: + state.tracing.trace_df(persons, label=trace_label, warn_if_empty=True) + + expressions.annotate_tables( + state, + locals_dict=constants, + skims=None, + model_settings=model_settings, + trace_label=trace_label, + ) diff --git a/resident/extensions/external_identification.py b/resident/extensions/external_identification.py new file mode 100644 index 0000000..3800a04 --- /dev/null +++ b/resident/extensions/external_identification.py @@ -0,0 +1,420 @@ +# ActivitySim +# See full license in LICENSE.txt. +import logging + +import numpy as np +import pandas as pd + +from pydantic import validator + +from activitysim.core import ( + config, + expressions, + los, + estimation, + simulate, + tracing, + workflow, +) +from activitysim.core.configuration.logit import LogitComponentSettings +from activitysim.core.configuration.base import PreprocessorSettings + +logger = logging.getLogger(__name__) + + +class ExternalIdentificationSettings(LogitComponentSettings, extra="forbid"): + """ + Settings for the `external_identification` component. + """ + + CHOOSER_FILTER_COLUMN_NAME: str | None = None + """Column name which selects choosers.""" + + EXTERNAL_COL_NAME: str | None = None + """Adds this column and set to True if model selects external""" + + INTERNAL_COL_NAME: str | None = None + """Column name set to True if not external but CHOOSER_FILTER_COLUMN_NAME is True""" + + preprocessor: PreprocessorSettings | None = None + + +def determine_closest_external_station( + state, choosers, skim_dict, origin_col="home_zone_id" +): + + unique_origin_zones = choosers[origin_col].unique() + landuse = state.get_table("land_use") + ext_zones = landuse[landuse.EXTERNAL > 0].index.to_numpy() + + choosers["closest_external_zone"] = -1 + choosers["dist_to_external_zone"] = 0.0 + + for origin_zone in unique_origin_zones: + # in_skim check in skim_dictionary.py requires orig and dests to be the same shape in lookup + orig_zones = np.full(shape=len(ext_zones), fill_value=origin_zone, dtype=int) + zone_distances = skim_dict.lookup(orig_zones, ext_zones, ("SOV_M_DIST", "MD")) + + closest_zone_idx = np.argmin(zone_distances) + closest_zone = int(ext_zones[closest_zone_idx]) + dist_to_closest_zone = zone_distances[closest_zone_idx] + + choosers.loc[ + choosers[origin_col] == origin_zone, "closest_external_zone" + ] = closest_zone + choosers.loc[ + choosers[origin_col] == origin_zone, "dist_to_external_zone" + ] = dist_to_closest_zone + + return choosers + + +def external_identification( + state, + model_settings, + estimator, + choosers, + network_los, + model_settings_file_name, + trace_label, +): + + constants = config.get_model_constants(model_settings) + + locals_d = {} + if constants is not None: + locals_d.update(constants) + locals_d.update({"land_use": state.get_table("land_use")}) + + skim_dict = network_los.get_default_skim_dict() + choosers = determine_closest_external_station(state, choosers, skim_dict) + + # - preprocessor + preprocessor_settings = model_settings.preprocessor + if preprocessor_settings: + expressions.assign_columns( + state, + df=choosers, + model_settings=preprocessor_settings, + locals_dict=locals_d, + trace_label=trace_label, + ) + + model_spec = state.filesystem.read_model_spec(file_name=model_settings.SPEC) + coefficients_df = state.filesystem.read_model_coefficients(model_settings) + model_spec = simulate.eval_coefficients( + state, model_spec, coefficients_df, estimator + ) + + nest_spec = config.get_logit_model_settings(model_settings) + + if estimator: + estimator.write_model_settings(model_settings, model_settings_file_name) + estimator.write_spec(model_settings) + estimator.write_coefficients(coefficients_df, model_settings) + estimator.write_choosers(choosers) + + choices = simulate.simple_simulate( + state, + choosers=choosers, + spec=model_spec, + nest_spec=nest_spec, + locals_d=locals_d, + trace_label=trace_label, + trace_choice_name=trace_label, + estimator=estimator, + compute_settings=model_settings.compute_settings, + ) + + return choices + + +@workflow.step +def external_worker_identification( + state: workflow.State, + persons: pd.DataFrame, + persons_merged: pd.DataFrame, + network_los: los.Network_LOS, + model_settings: ExternalIdentificationSettings | None = None, + model_settings_file_name: str = "external_worker_identification.yaml", + trace_label: str = "external_worker_identification", + trace_hh_id: bool = False, +) -> None: + """ + This model predicts the whether a worker has an external work location. + The output from this model is TRUE (if external) or FALSE (if internal). + + The main interface to the external worker model is the external_worker_identification() function. + This function is registered as an orca step in the example Pipeline. + """ + if model_settings is None: + model_settings = ExternalIdentificationSettings.read_settings_file( + state.filesystem, + model_settings_file_name, + ) + + estimator = estimation.manager.begin_estimation(state, trace_label) + + filter_col = model_settings.CHOOSER_FILTER_COLUMN_NAME + if filter_col is None: + choosers = persons_merged + else: + choosers = persons_merged[persons_merged[filter_col]] + logger.info("Running %s with %d persons", trace_label, len(choosers)) + + choices = external_identification( + state, + model_settings, + estimator, + choosers, + network_los, + model_settings_file_name, + trace_label, + ) + + external_col_name = model_settings.EXTERNAL_COL_NAME + internal_col_name = model_settings.INTERNAL_COL_NAME + + if estimator: + estimator.write_choices(choices) + choices = estimator.get_survey_values(choices, "persons", trace_label) + estimator.write_override_choices(choices) + estimator.end_estimation() + + if external_col_name is not None: + persons[external_col_name] = ( + (choices == 0).reindex(persons.index).fillna(False).astype(bool) + ) + if internal_col_name is not None: + persons[internal_col_name] = persons[filter_col] & ~persons[external_col_name] + + state.add_table("persons", persons) + + tracing.print_summary( + external_col_name, persons[external_col_name], value_counts=True + ) + + if trace_hh_id: + state.tracing.trace_df(persons, label=trace_label, warn_if_empty=True) + + +@workflow.step +def external_student_identification( + state: workflow.State, + persons: pd.DataFrame, + persons_merged: pd.DataFrame, + network_los: los.Network_LOS, + model_settings: ExternalIdentificationSettings | None = None, + model_settings_file_name: str = "external_student_identification.yaml", + trace_label: str = "external_student_identification", + trace_hh_id: bool = False, +) -> None: + """ + This model predicts the whether a student has an external work location. + The output from this model is TRUE (if external) or FALSE (if internal). + + The main interface to the external student model is the external_student_identification() function. + This function is registered as an orca step in the example Pipeline. + """ + + if model_settings is None: + model_settings = ExternalIdentificationSettings.read_settings_file( + state.filesystem, + model_settings_file_name, + ) + + estimator = estimation.manager.begin_estimation(state, trace_label) + + filter_col = model_settings.CHOOSER_FILTER_COLUMN_NAME + if filter_col is None: + choosers = persons_merged + else: + choosers = persons_merged[persons_merged[filter_col]] + logger.info("Running %s with %d persons", trace_label, len(choosers)) + + choices = external_identification( + state, + model_settings, + estimator, + choosers, + network_los, + model_settings_file_name, + trace_label, + ) + + external_col_name = model_settings.EXTERNAL_COL_NAME + internal_col_name = model_settings.INTERNAL_COL_NAME + + if estimator: + estimator.write_choices(choices) + choices = estimator.get_survey_values(choices, "persons", trace_label) + estimator.write_override_choices(choices) + estimator.end_estimation() + + if external_col_name is not None: + persons[external_col_name] = ( + (choices == 0).reindex(persons.index).fillna(False).astype(bool) + ) + if internal_col_name is not None: + persons[internal_col_name] = persons[filter_col] & ~persons[external_col_name] + + state.add_table("persons", persons) + + tracing.print_summary( + external_col_name, persons[external_col_name], value_counts=True + ) + + if trace_hh_id: + state.tracing.trace_df(persons, label=trace_label, warn_if_empty=True) + + +def set_external_tour_variables(state, tours, choices, model_settings, trace_label): + """ + Set the internal and external tour indicator columns in the tours file + """ + external_col_name = model_settings.EXTERNAL_COL_NAME + internal_col_name = model_settings.INTERNAL_COL_NAME + + if external_col_name is not None: + tours[external_col_name] = ( + (choices == 0).reindex(tours.index).fillna(False).astype(bool) + ) + if internal_col_name is not None: + tours[internal_col_name] = ( + (choices == 1).reindex(tours.index).fillna(True).astype(bool) + ) + + # - annotate tours table + if "annotate_tours" in model_settings: + expressions.assign_columns( + state, + df=tours, + model_settings=model_settings.get("annotate_tours"), + trace_label=tracing.extend_trace_label(trace_label, "annotate_tours"), + ) + + return tours + + +@workflow.step +def external_non_mandatory_identification( + state: workflow.State, + tours: pd.DataFrame, + tours_merged: pd.DataFrame, + network_los: los.Network_LOS, + model_settings: ExternalIdentificationSettings | None = None, + model_settings_file_name: str = "external_non_mandatory_identification.yaml", + trace_label: str = "external_non_mandatory_identification", + trace_hh_id: bool = False, +) -> None: + """ + This model predicts the whether a non-mandatory tour is external. + The output from this model is TRUE (if external) or FALSE (if internal). + + The main interface to the external student model is the external_nonmandatory_identification() function. + This function is registered as an orca step in the example Pipeline. + """ + if model_settings is None: + model_settings = ExternalIdentificationSettings.read_settings_file( + state.filesystem, + model_settings_file_name, + ) + + estimator = estimation.manager.begin_estimation(state, trace_label) + + choosers = tours_merged[tours_merged["tour_category"] == "non_mandatory"] + + choices = external_identification( + state, + model_settings, + estimator, + choosers, + network_los, + model_settings_file_name, + trace_label, + ) + + if estimator: + estimator.write_choices(choices) + choices = estimator.get_survey_values(choices, "tours", trace_label) + estimator.write_override_choices(choices) + estimator.end_estimation() + + tours = set_external_tour_variables( + state, tours, choices, model_settings, trace_label + ) + + state.add_table("tours", tours) + + external_col_name = model_settings.EXTERNAL_COL_NAME + tracing.print_summary( + external_col_name, tours[external_col_name], value_counts=True + ) + + if trace_hh_id: + state.tracing.trace_df(tours, label=trace_label, warn_if_empty=True) + + +@workflow.step +def external_joint_tour_identification( + state: workflow.State, + tours: pd.DataFrame, + tours_merged: pd.DataFrame, + network_los: los.Network_LOS, + model_settings: ExternalIdentificationSettings | None = None, + model_settings_file_name: str = "external_joint_tour_identification.yaml", + trace_label: str = "external_joint_tour_identification", + trace_hh_id: bool = False, +) -> None: + """ + This model predicts the whether a joint tour is external. + The output from this model is TRUE (if external) or FALSE (if internal). + + The main interface to the external student model is the external_nonmandatory_identification() function. + This function is registered as an orca step in the example Pipeline. + """ + if model_settings is None: + model_settings = ExternalIdentificationSettings.read_settings_file( + state.filesystem, + model_settings_file_name, + ) + + estimator = estimation.manager.begin_estimation(state, trace_label) + + choosers = tours_merged[tours_merged["tour_category"] == "joint"] + + # - if no choosers + if choosers.shape[0] > 0: + choices = external_identification( + state, + model_settings, + estimator, + choosers, + network_los, + model_settings_file_name, + trace_label, + ) + else: + # everything is internal, still want to set internal or external columns in df + choices = pd.Series(1, index=choosers.index) + tracing.no_results(trace_label) + + if estimator: + estimator.write_choices(choices) + choices = estimator.get_survey_values(choices, "tours", trace_label) + estimator.write_override_choices(choices) + estimator.end_estimation() + + tours = set_external_tour_variables( + state, tours, choices, model_settings, trace_label + ) + + state.add_table("tours", tours) + + external_col_name = model_settings.EXTERNAL_COL_NAME + tracing.print_summary( + external_col_name, tours[external_col_name], value_counts=True + ) + + if trace_hh_id: + state.tracing.trace_df(tours, label=trace_label, warn_if_empty=True) diff --git a/resident/extensions/external_location_choice.py b/resident/extensions/external_location_choice.py new file mode 100644 index 0000000..0b3ee8e --- /dev/null +++ b/resident/extensions/external_location_choice.py @@ -0,0 +1,325 @@ +# ActivitySim +# See full license in LICENSE.txt. +from __future__ import annotations + +import logging + +import numpy as np +import pandas as pd + +from activitysim.abm.models.util import logsums as logsum +from activitysim.abm.models.util import tour_destination +from activitysim.abm.tables import shadow_pricing +from activitysim.core import ( + config, + expressions, + los, + estimation, + simulate, + tracing, + workflow, +) +from activitysim.core.configuration.logit import ( + TourLocationComponentSettings, + TourModeComponentSettings, +) +from activitysim.abm.models.location_choice import ( + write_estimation_specs, + iterate_location_choice, +) +from activitysim.core.util import assign_in_place + +logger = logging.getLogger(__name__) + + +@workflow.step +def external_school_location( + state: workflow.State, + persons_merged: pd.DataFrame, + persons: pd.DataFrame, + households: pd.DataFrame, + network_los: los.Network_LOS, + locutor: bool, + model_settings: TourLocationComponentSettings | None = None, + model_settings_file_name: str = "external_school_location.yaml", + trace_label: str = "external_school_location", +): + """ + External school location choice model + + iterate_location_choice adds location choice column and annotations to persons table + """ + if model_settings is None: + model_settings = TourLocationComponentSettings.read_settings_file( + state.filesystem, + model_settings_file_name, + ) + + estimator = estimation.manager.begin_estimation(state, "external_school_location") + if estimator: + write_estimation_specs(estimator, model_settings, model_settings_file_name) + + persons_df = iterate_location_choice( + state=state, + model_settings=model_settings, + persons_merged=persons_merged, + persons=persons, + households=households, + network_los=network_los, + estimator=estimator, + chunk_size=state.settings.chunk_size, + locutor=locutor, + trace_label=trace_label, + ) + + state.add_table("persons", persons_df) + + if estimator: + estimator.end_estimation() + + +@workflow.step +def external_workplace_location( + state: workflow.State, + persons_merged: pd.DataFrame, + persons: pd.DataFrame, + households: pd.DataFrame, + network_los: los.Network_LOS, + locutor: bool, + model_settings: TourLocationComponentSettings | None = None, + model_settings_file_name: str = "external_workplace_location.yaml", + trace_label: str = "external_workplace_location", +): + """ + External workplace location choice model + + iterate_location_choice adds location choice column and annotations to persons table + """ + + if model_settings is None: + model_settings = TourLocationComponentSettings.read_settings_file( + state.filesystem, + model_settings_file_name, + ) + + estimator = estimation.manager.begin_estimation( + state, "external_workplace_location" + ) + if estimator: + write_estimation_specs(estimator, model_settings, model_settings_file_name) + + persons_df = iterate_location_choice( + state=state, + model_settings=model_settings, + persons_merged=persons_merged, + persons=persons, + households=households, + network_los=network_los, + estimator=estimator, + chunk_size=state.settings.chunk_size, + locutor=locutor, + trace_label=trace_label, + ) + + state.add_table("persons", persons_df) + + if estimator: + estimator.end_estimation() + + +@workflow.step +def external_non_mandatory_destination( + state: workflow.State, + tours: pd.DataFrame, + persons_merged: pd.DataFrame, + network_los: los.Network_LOS, + model_settings: TourLocationComponentSettings | None = None, + model_settings_file_name: str = "external_non_mandatory_destination.yaml", + trace_label: str = "external_non_mandatory_destination", +): + """ + Given the tour generation from the above, each tour needs to have a + destination, so in this case tours are the choosers (with the associated + person that's making the tour) + """ + if model_settings is None: + model_settings = TourLocationComponentSettings.read_settings_file( + state.filesystem, + model_settings_file_name, + ) + + logsum_column_name = model_settings.DEST_CHOICE_LOGSUM_COLUMN_NAME + want_logsums = logsum_column_name is not None + + sample_table_name = model_settings.DEST_CHOICE_SAMPLE_TABLE_NAME + want_sample_table = ( + state.settings.want_dest_choice_sample_tables and sample_table_name is not None + ) + + # choosers are tours - in a sense tours are choosing their destination + non_mandatory_ext_tours = tours[ + (tours.tour_category == "non_mandatory") & (tours.is_external_tour) + ] + + # if no external non-mandatory tours + if non_mandatory_ext_tours.shape[0] == 0: + tracing.no_results(trace_label) + return + + estimator = estimation.manager.begin_estimation( + state, "external_non_mandatory_destination" + ) + if estimator: + estimator.write_coefficients(model_settings=model_settings) + # estimator.write_spec(model_settings, tag='SAMPLE_SPEC') + estimator.write_spec(model_settings, tag="SPEC") + estimator.set_alt_id(model_settings.ALT_DEST_COL_NAME) + estimator.write_table( + state.get_injectable("size_terms"), "size_terms", append=False + ) + estimator.write_table(state.get_table("land_use"), "landuse", append=False) + estimator.write_model_settings(model_settings, model_settings_file_name) + + choices_df, save_sample_df = tour_destination.run_tour_destination( + state, + non_mandatory_ext_tours, + persons_merged, + want_logsums, + want_sample_table, + model_settings, + network_los, + estimator, + trace_label, + ) + + if estimator: + estimator.write_choices(choices_df.choice) + choices_df.choice = estimator.get_survey_values( + choices_df.choice, "tours", "destination" + ) + estimator.write_override_choices(choices_df.choice) + estimator.end_estimation() + + non_mandatory_ext_tours["destination"] = choices_df.choice + + assign_in_place(tours, non_mandatory_ext_tours[["destination"]]) + + if want_logsums: + non_mandatory_ext_tours[logsum_column_name] = choices_df["logsum"] + assign_in_place(tours, non_mandatory_ext_tours[[logsum_column_name]]) + + state.add_table("tours", tours) + + if want_sample_table: + assert len(save_sample_df.index.get_level_values(0).unique()) == len(choices_df) + # save_sample_df.set_index(model_settings['ALT_DEST_COL_NAME'], append=True, inplace=True) + state.extend_table(sample_table_name, save_sample_df) + + if state.settings.trace_hh_id: + state.tracing.trace_df( + tours[tours.tour_category == "non_mandatory"], + label="external_non_mandatory_destination", + slicer="person_id", + index_label="tour", + columns=None, + warn_if_empty=True, + ) + + +@workflow.step +def external_joint_tour_destination( + state: workflow.State, + tours: pd.DataFrame, + persons_merged: pd.DataFrame, + network_los: los.Network_LOS, + model_settings: TourLocationComponentSettings | None = None, + model_settings_file_name: str = "external_joint_tour_destination.yaml", + trace_label: str = "external_joint_tour_destination", +): + """ + Given the tour generation from the above, each tour needs to have a + destination, so in this case tours are the choosers (with the associated + person that's making the tour) + """ + if model_settings is None: + model_settings = TourLocationComponentSettings.read_settings_file( + state.filesystem, + model_settings_file_name, + ) + + logsum_column_name = model_settings.DEST_CHOICE_LOGSUM_COLUMN_NAME + want_logsums = logsum_column_name is not None + + sample_table_name = model_settings.DEST_CHOICE_SAMPLE_TABLE_NAME + want_sample_table = ( + state.settings.want_dest_choice_sample_tables and sample_table_name is not None + ) + + joint_ext_tours = tours[ + (tours.tour_category == "joint") + & (tours.get("is_external_tour", False) == True) + ] # get needed incase no joint tours + + # if no external joint tours + if joint_ext_tours.shape[0] == 0: + tracing.no_results(trace_label) + return + + estimator = estimation.manager.begin_estimation( + state, "external_joint_tour_destination" + ) + if estimator: + estimator.write_coefficients(model_settings=model_settings) + # estimator.write_spec(model_settings, tag='SAMPLE_SPEC') + estimator.write_spec(model_settings, tag="SPEC") + estimator.set_alt_id(model_settings.ALT_DEST_COL_NAME) + estimator.write_table( + state.get_injectable("size_terms"), "size_terms", append=False + ) + estimator.write_table(state.get_table("land_use"), "landuse", append=False) + estimator.write_model_settings(model_settings, model_settings_file_name) + + choices_df, save_sample_df = tour_destination.run_tour_destination( + state, + joint_ext_tours, + persons_merged, + want_logsums, + want_sample_table, + model_settings, + network_los, + estimator, + trace_label, + ) + + if estimator: + estimator.write_choices(choices_df.choice) + choices_df.choice = estimator.get_survey_values( + choices_df.choice, "tours", "destination" + ) + estimator.write_override_choices(choices_df.choice) + estimator.end_estimation() + + joint_ext_tours["destination"] = choices_df.choice + + assign_in_place(tours, joint_ext_tours[["destination"]]) + + if want_logsums: + joint_ext_tours[logsum_column_name] = choices_df["logsum"] + assign_in_place(tours, joint_ext_tours[[logsum_column_name]]) + + state.add_table("tours", tours) + + if want_sample_table: + assert len(save_sample_df.index.get_level_values(0).unique()) == len(choices_df) + # save_sample_df.set_index(model_settings['ALT_DEST_COL_NAME'], append=True, inplace=True) + state.extend_table(sample_table_name, save_sample_df) + + if state.settings.trace_hh_id: + state.tracing.trace_df( + tours[tours.tour_category == "non_mandatory"], + label="external_joint_tour_destination", + slicer="person_id", + index_label="tour", + columns=None, + warn_if_empty=True, + ) diff --git a/resident/extensions/license_holding_status.py b/resident/extensions/license_holding_status.py new file mode 100644 index 0000000..cd2fc90 --- /dev/null +++ b/resident/extensions/license_holding_status.py @@ -0,0 +1,124 @@ +# ActivitySim +# See full license in LICENSE.txt. +from __future__ import annotations + +import logging +import pandas as pd + +from activitysim.core import ( + config, + estimation, + expressions, + simulate, + tracing, + workflow, +) + +from activitysim.core.configuration.base import PreprocessorSettings, PydanticReadable +from activitysim.core.configuration.logit import LogitComponentSettings + +logger = logging.getLogger("activitysim") + + +class LicenseHoldingStatusSettings(LogitComponentSettings, extra="forbid"): + """ + Settings for the 'license_holding_status' model. + """ + + LICENSE_STATUS_ALT: int = 0 + """Value that specifies if the person has a driver's license (can drive).""" + + CHOOSE_FILTER_COLUMN_NAME: str | None = None + """Column name in the dataframe to filter persons eligible for license holding status model.""" + + +@workflow.step +def license_holding_status( + state: workflow.State, + persons_merged: pd.DataFrame, + persons: pd.DataFrame, + model_settings: LicenseHoldingStatusSettings | None = None, + model_settings_file_name: str = "license_holding_status.yaml", + trace_label: str = "license_holding_status", +) -> None: + """ + This model predicts whether a person holds a driver's license or not. + The output from this model is TRUE (if the person holds a license) or FALSE (if not). + """ + if model_settings is None: + model_settings = LicenseHoldingStatusSettings.read_settings_file( + state.filesystem, model_settings_file_name + ) + + choosers = persons_merged + chooser_filter_columun_name = model_settings.CHOOSE_FILTER_COLUMN_NAME + if chooser_filter_columun_name: + choosers = choosers[(choosers[chooser_filter_columun_name])] + logger.info("Running %s with %d persons", trace_label, len(choosers)) + + estimator = estimation.manager.begin_estimation(state, "license_holding_status") + + constants = config.get_model_constants(model_settings) + + # - preprocessor + expressions.annotate_preprocessors( + state, + df=choosers, + locals_dict=constants, + skims=None, + model_settings=model_settings, + trace_label=trace_label, + ) + + model_spec = state.filesystem.read_model_spec(file_name=model_settings.SPEC) + coefficients_df = state.filesystem.read_model_coefficients(model_settings) + model_spec = simulate.eval_coefficients( + state, model_spec, coefficients_df, estimator + ) + nest_spec = config.get_logit_model_settings(model_settings) + + if estimator: + estimator.write_model_settings(model_settings, model_settings_file_name) + estimator.write_spec(model_settings) + estimator.write_coefficients(coefficients_df, model_settings) + estimator.write_choosers(choosers) + + choices = simulate.simple_simulate( + state, + choosers=choosers, + spec=model_spec, + nest_spec=nest_spec, + locals_d=constants, + trace_label=trace_label, + trace_choice_name="has_license", + estimator=estimator, + compute_settings=model_settings.compute_settings, + ) + + has_license = model_settings.LICENSE_STATUS_ALT + choices = choices == has_license + + if estimator: + estimator.write_choices(choices) + choices = estimator.get_survey_values(choices, "persons", "has_license") + estimator.write_override_choices(choices) + estimator.end_estimation() + + persons["has_license"] = choices.reindex(persons.index).fillna(0).astype(bool) + + state.add_table("persons", persons) + + tracing.print_summary( + "license_holding_status", persons.has_license, value_counts=True + ) + + if state.settings.trace_hh_id: + state.tracing.trace_df(persons, label=trace_label, warn_if_empty=True) + + expressions.annotate_tables( + state, + locals_dict=constants, + skims=None, + model_settings=model_settings, + trace_label=trace_label, + ) diff --git a/resident/extensions/transit_lot_connectivity.py b/resident/extensions/transit_lot_connectivity.py new file mode 100644 index 0000000..06473fe --- /dev/null +++ b/resident/extensions/transit_lot_connectivity.py @@ -0,0 +1,73 @@ +import logging + +import numpy as np +import pandas as pd + +from activitysim.core import ( + los, + workflow, +) + +from activitysim.abm.models.park_and_ride_lot_choice import ( + ParkAndRideLotChoiceSettings +) + +logger = logging.getLogger(__name__) + + +@workflow.step +def transit_lot_connectivity( + state: workflow.State, + land_use: pd.DataFrame, + network_los: los.Network_LOS, + model_settings: ParkAndRideLotChoiceSettings | None = None, + model_settings_file_name: str = "park_and_ride_lot_choice.yaml", +) -> None: + """ + This model flags landuse zones that have transit access. + + This output is used by the park and ride lot choice model to limit + the choice set to only tours that have transit access at their destination. + """ + if model_settings is None: + model_settings = ParkAndRideLotChoiceSettings.read_settings_file( + state.filesystem, + model_settings_file_name, + ) + + skim_dict = network_los.get_default_skim_dict() + unique_destinations = land_use.index.values + unique_lot_locations = land_use[ + land_use[model_settings.LANDUSE_PNR_SPACES_COLUMN] > 0 + ].index.values + transit_accessible = np.full( + shape=len(unique_destinations), fill_value=False, dtype=bool + ) + + for skim_name in model_settings.TRANSIT_SKIMS_FOR_ELIGIBILITY: + if "__" in skim_name: + # If the skim name contains '__', it is a 3D skim + # we need to pass the skim name as a tuple to the lookup method, e.g. ('WALK_TRANSIT_IVTT', 'MD') + skim_name = tuple(skim_name.split("__")) + + # Filter choosers to only those with destinations that have transit access + # want to check whether ANY of the lot locations have transit access to EVERY destination + transit_accessible_i = [ + ( + skim_dict.lookup( + unique_lot_locations, + np.full(shape=len(unique_lot_locations), fill_value=dest), + skim_name, + ) + > 0 + ).any() + for dest in unique_destinations + ] + transit_accessible = np.logical_or(transit_accessible, transit_accessible_i) + + land_use[model_settings.LANDUSE_COL_FOR_PNR_ELIGIBLE_DEST] = transit_accessible + + logger.info( + f"Marking {transit_accessible.sum()} of {len(land_use)} zones as transit accessible for PNR/ KNR/ TNCNR lot choice" + ) + state.add_table("land_use", land_use) \ No newline at end of file diff --git a/resident/model_data/metro/crop_metro_data.ipynb b/resident/model_data/metro/crop_metro_data.ipynb new file mode 100644 index 0000000..cf2d828 --- /dev/null +++ b/resident/model_data/metro/crop_metro_data.ipynb @@ -0,0 +1,617 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "id": "93a5ee20", + "metadata": {}, + "outputs": [], + "source": [ + "import pandas as pd\n", + "import numpy as np\n", + "import os\n", + "import secrets\n", + "import openmatrix as omx" + ] + }, + { + "cell_type": "markdown", + "id": "4de81361", + "metadata": {}, + "source": [ + "## File cropping for testing\n", + "The idea here is for us to be able to quickly iterate through the model using a small subset of the data for easier development and testing. Set the files and zones below and run the notebook." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "b9c8494b", + "metadata": {}, + "outputs": [], + "source": [ + "# read full input data\n", + "input_folder = r\".\\data_full\"\n", + "output_folder = r\".\\data_cropped\"\n", + "full_households = pd.read_csv(os.path.join(input_folder, 'households.csv'))\n", + "full_persons = pd.read_csv(os.path.join(input_folder, 'persons.csv'))\n", + "full_landuse = pd.read_csv(os.path.join(input_folder, 'land_use.csv'))\n", + "\n", + "input_skim_folder = r\".\\data_full\"\n", + "skim_list = [\n", + " \"autoSkims__EA.omx\",\n", + " \"autoSkims__AM.omx\",\n", + " \"autoSkims__MD.omx\",\n", + " \"autoSkims__PM.omx\",\n", + " \"autoSkims__EV.omx\",\n", + " \"transitSkims__EA.omx\",\n", + " \"transitSkims__AM.omx\",\n", + " \"transitSkims__MD.omx\",\n", + " \"transitSkims__PM.omx\",\n", + " \"transitSkims__EV.omx\",\n", + " \"walkSkim.omx\"\n", + "]\n", + "maz_skim_file = \"maz_maz_walk.csv\"\n", + "\n", + "# optional since this can take a while (actually only about 1 minute!)\n", + "CROP_SKIMS = True\n", + "# downtown? + some externals\n", + "tazs_to_crop = list(range(1, 51)) + list(range(2100, 2106))" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "9bf1237a", + "metadata": {}, + "outputs": [], + "source": [ + "cropped_landuse = full_landuse[full_landuse['TAZ'].isin(tazs_to_crop)].copy()\n", + "cropped_households = full_households[full_households['TAZ'].isin(tazs_to_crop)].copy()\n", + "cropped_persons = full_persons[full_persons['household_id'].isin(cropped_households['household_id'])].copy()" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "0400758d", + "metadata": {}, + "outputs": [], + "source": [ + "# obfuscate the employment data\n", + "emp_colummns = [col for col in cropped_landuse.columns if col.startswith('EMP')]\n", + "for col in emp_colummns:\n", + " # scale employment by +/- 0-50% \n", + " cropped_landuse[col] = cropped_landuse[col].apply(\n", + " lambda x: x * (0.5 + secrets.randbelow(10000) / 10000)\n", + " ).fillna(secrets.randbelow(100)).astype(int) " + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "cf90dca7", + "metadata": {}, + "outputs": [], + "source": [ + "# check there is some enrollment and external counts in the cropped dataset\n", + "for col in ['ENROLLGRADEKto8','ENROLLGRADE9to12','COLLEGEENROLL', \"EXTERNAL\"]:\n", + " if cropped_landuse[col].sum() == 0:\n", + " raise ValueError(f\"Column {col} has no data in the cropped land use dataset.\")" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "b362c8d0", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Cropped land use dataset has 652 MAZs\n", + "Cropped households dataset has 25380 households\n", + "Cropped persons dataset has 58957 persons\n" + ] + } + ], + "source": [ + "print(f\"Cropped land use dataset has {len(cropped_landuse)} MAZs\")\n", + "print(f\"Cropped households dataset has {len(cropped_households)} households\")\n", + "print(f\"Cropped persons dataset has {len(cropped_persons)} persons\")\n", + "# save the cropped datasets\n", + "cropped_landuse.to_csv(os.path.join(output_folder, 'land_use.csv'), index=False)\n", + "cropped_households.to_csv(os.path.join(output_folder, 'households.csv'), index=False)\n", + "cropped_persons.to_csv(os.path.join(output_folder, 'persons.csv'), index=False)" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "516172fc", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "omx_in shape (2106, 2106)\n", + "SOV_H_COST__EA (56, 56)\n", + "SOV_H_DIST__EA (56, 56)\n", + "SOV_H_TIME__EA (56, 56)\n", + "SOV_L_COST__EA (56, 56)\n", + "SOV_L_DIST__EA (56, 56)\n", + "SOV_L_TIME__EA (56, 56)\n", + "SOV_M_COST__EA (56, 56)\n", + "SOV_M_DIST__EA (56, 56)\n", + "SOV_M_TIME__EA (56, 56)\n", + "SR2_H_COST__EA (56, 56)\n", + "SR2_H_DIST__EA (56, 56)\n", + "SR2_H_TIME__EA (56, 56)\n", + "SR2_L_COST__EA (56, 56)\n", + "SR2_L_DIST__EA (56, 56)\n", + "SR2_L_TIME__EA (56, 56)\n", + "SR2_M_COST__EA (56, 56)\n", + "SR2_M_DIST__EA (56, 56)\n", + "SR2_M_TIME__EA (56, 56)\n", + "SR3_H_COST__EA (56, 56)\n", + "SR3_H_DIST__EA (56, 56)\n", + "SR3_H_TIME__EA (56, 56)\n", + "SR3_L_COST__EA (56, 56)\n", + "SR3_L_DIST__EA (56, 56)\n", + "SR3_L_TIME__EA (56, 56)\n", + "SR3_M_COST__EA (56, 56)\n", + "SR3_M_DIST__EA (56, 56)\n", + "SR3_M_TIME__EA (56, 56)\n", + "omx_in shape (2106, 2106)\n", + "SOV_H_COST__AM (56, 56)\n", + "SOV_H_DIST__AM (56, 56)\n", + "SOV_H_TIME__AM (56, 56)\n", + "SOV_L_COST__AM (56, 56)\n", + "SOV_L_DIST__AM (56, 56)\n", + "SOV_L_TIME__AM (56, 56)\n", + "SOV_M_COST__AM (56, 56)\n", + "SOV_M_DIST__AM (56, 56)\n", + "SOV_M_TIME__AM (56, 56)\n", + "SR2_H_COST__AM (56, 56)\n", + "SR2_H_DIST__AM (56, 56)\n", + "SR2_H_TIME__AM (56, 56)\n", + "SR2_L_COST__AM (56, 56)\n", + 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(56, 56)\n", + "WTK_TIV__EV (56, 56)\n", + "WTK_VTC__EV (56, 56)\n", + "WTK_XFR__EV (56, 56)\n", + "WTK_XWT__EV (56, 56)\n", + "WTW_ACC__EV (56, 56)\n", + "WTW_AST__EV (56, 56)\n", + "WTW_AUX__EV (56, 56)\n", + "WTW_BRT__EV (56, 56)\n", + "WTW_BUS__EV (56, 56)\n", + "WTW_CRT__EV (56, 56)\n", + "WTW_EGR__EV (56, 56)\n", + "WTW_FWT__EV (56, 56)\n", + "WTW_LRT__EV (56, 56)\n", + "WTW_RST__EV (56, 56)\n", + "WTW_SCR__EV (56, 56)\n", + "WTW_TIV__EV (56, 56)\n", + "WTW_VTC__EV (56, 56)\n", + "WTW_XFR__EV (56, 56)\n", + "WTW_XWT__EV (56, 56)\n", + "omx_in shape (2106, 2106)\n", + "WLK_DIST (56, 56)\n" + ] + } + ], + "source": [ + "if CROP_SKIMS:\n", + " # crop omx skim files\n", + " taz_labels = cropped_landuse.sort_values(\"TAZ\")['TAZ'].unique() # TAZ zone_ids in omx index order\n", + " tazs_indexes = taz_labels - 1 # index of TAZ in skim (zero-based, no mapping)\n", + "\n", + " for omx_infile_name in skim_list:\n", + " skim_data_type = np.float32\n", + "\n", + " omx_in = omx.open_file(os.path.join(input_skim_folder, omx_infile_name))\n", + " omx_out = omx.open_file(os.path.join(output_folder, omx_infile_name), \"w\")\n", + " print(f\"omx_in shape {omx_in.shape()}\")\n", + "\n", + " omx_out.create_mapping(\"ZONE\", taz_labels)\n", + "\n", + " for mat_name in omx_in.list_matrices():\n", + "\n", + " # make sure we have a vanilla numpy array, not a CArray\n", + " m = np.asanyarray(omx_in[mat_name]).astype(skim_data_type)\n", + " m = m[tazs_indexes, :][:, tazs_indexes]\n", + " print(f\"{mat_name} {m.shape}\")\n", + "\n", + " omx_out[mat_name] = m\n", + "\n", + " omx_in.close()\n", + " omx_out.close()" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [], + "source": [ + "if CROP_SKIMS:\n", + " # crop the maz-maz walk skim\n", + " maz_maz_walk = pd.read_csv(os.path.join(input_skim_folder, maz_skim_file))\n", + " cropped_maz_maz_walk = maz_maz_walk[maz_maz_walk['OMAZ'].isin(cropped_landuse.MAZ) & maz_maz_walk['DMAZ'].isin(cropped_landuse.MAZ)]\n", + " # FIXME this should be renamed in the maz-maz walk creation script\n", + " maz_maz_walk.rename(columns={'DISTWALK': 'WLK_DIST'}, inplace=True)\n", + " cropped_maz_maz_walk.to_csv(os.path.join(output_folder, maz_skim_file), index=False)" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "bc86f9a1", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "['KTW_ACC__AM', 'KTW_AST__AM', 'KTW_AUX__AM', 'KTW_BRT__AM', 'KTW_BUS__AM', 'KTW_CRT__AM', 'KTW_EGR__AM', 'KTW_FWT__AM', 'KTW_LRT__AM', 'KTW_RST__AM', 'KTW_SCR__AM', 'KTW_TIV__AM', 'KTW_VTC__AM', 'KTW_XFR__AM', 'KTW_XWT__AM', 'WTK_ACC__AM', 'WTK_AST__AM', 'WTK_AUX__AM', 'WTK_BRT__AM', 'WTK_BUS__AM', 'WTK_CRT__AM', 'WTK_EGR__AM', 'WTK_FWT__AM', 'WTK_LRT__AM', 'WTK_RST__AM', 'WTK_SCR__AM', 'WTK_TIV__AM', 'WTK_VTC__AM', 'WTK_XFR__AM', 'WTK_XWT__AM', 'WTW_ACC__AM', 'WTW_AST__AM', 'WTW_AUX__AM', 'WTW_BRT__AM', 'WTW_BUS__AM', 'WTW_CRT__AM', 'WTW_EGR__AM', 'WTW_FWT__AM', 'WTW_LRT__AM', 'WTW_RST__AM', 'WTW_SCR__AM', 'WTW_TIV__AM', 'WTW_VTC__AM', 'WTW_XFR__AM', 'WTW_XWT__AM']\n" + ] + } + ], + "source": [ + "skm = omx.open_file(os.path.join(output_folder, 'transitSkims__AM.omx'), \"r\")\n", + "print(skm.list_matrices())\n", + "skm.close()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "2a837f63", + "metadata": {}, + "outputs": [], + "source": [ + "tazs[tazs['NO'].isin(tazs_to_crop)].explore()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "c5ae32df", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "activitysim", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.14" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/resident/model_data/metro/ctramp_to_asim_conversion/MAZ_Transformation_Walkacross.xlsx b/resident/model_data/metro/ctramp_to_asim_conversion/MAZ_Transformation_Walkacross.xlsx new file mode 100644 index 0000000..54949f0 Binary files /dev/null and b/resident/model_data/metro/ctramp_to_asim_conversion/MAZ_Transformation_Walkacross.xlsx differ diff --git a/resident/model_data/metro/ctramp_to_asim_conversion/SyntheticPop_Processing.ipynb b/resident/model_data/metro/ctramp_to_asim_conversion/SyntheticPop_Processing.ipynb new file mode 100644 index 0000000..64dbd48 --- /dev/null +++ b/resident/model_data/metro/ctramp_to_asim_conversion/SyntheticPop_Processing.ipynb @@ -0,0 +1,885 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 20, + "id": "5e394cdd", + "metadata": {}, + "outputs": [], + "source": [ + "import os\n", + "import pandas as pd\n", + "import numpy as np\n", + "import openmatrix as omx\n", + "import geopandas as gpd" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "id": "fc2d5cd4", + "metadata": {}, + "outputs": [], + "source": [ + "input_data_folder = r'C:\\projects\\odot_joint_estimation\\metro_data_prep\\PopulationSim_to_RSG\\HH_GQ_combined'\n", + "# output_data_folder = r'C:\\projects\\odot_joint_estimation\\pnr\\SimOR\\resident\\model_data\\metro\\data_full'\n", + "output_data_folder = r'C:\\projects\\odot_joint_estimation\\pnr\\tasks\\run_dir_new_zones\\input\\SyntheticPopulation'\n", + "\n", + "assert os.path.exists(input_data_folder), f\"Input data folder {input_data_folder} does not exist.\"\n", + "assert os.path.exists(output_data_folder), f\"Output data folder {output_data_folder} does not exist.\"\n", + "\n", + "maz_xwalk_new = pd.read_excel(os.path.join(input_data_folder, 'Metro_Block_MAZ_Xwalk-2025-12-16.xlsx'), sheet_name = \"NEW - BlockWalk - 12-26-25\")\n", + "maz_xwalk_old = pd.read_excel(os.path.join(input_data_folder, 'Metro_Block_MAZ_Xwalk-2025-12-16.xlsx'), sheet_name = \"OLD - BlockWalk\")" + ] + }, + { + "cell_type": "markdown", + "id": "411e2af0", + "metadata": {}, + "source": [ + "## Households Data Processing" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "id": "e953c8c2", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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household_idMAZTAZTYPENPHHTHHINCADJWORKERS
019524.0502.0431141550.2809762
129524.0502.0425186591.7110152
239524.0502.0421140747.6133930
349524.0502.042150294.2617292
459524.0502.041476542.2081791
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" + ], + "text/plain": [ + " household_id MAZ TAZ TYPE NP HHT HHINCADJ WORKERS\n", + "0 1 9524.0 502.0 4 3 1 141550.280976 2\n", + "1 2 9524.0 502.0 4 2 5 186591.711015 2\n", + "2 3 9524.0 502.0 4 2 1 140747.613393 0\n", + "3 4 9524.0 502.0 4 2 1 50294.261729 2\n", + "4 5 9524.0 502.0 4 1 4 76542.208179 1" + ] + }, + "execution_count": 23, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "in_households = pd.read_csv(os.path.join(input_data_folder, 'households_all.csv'))\n", + "\n", + "# Update MAZ/TAZ to new zone system\n", + "maz_xwalk_old_dedup = maz_xwalk_old.drop_duplicates(subset=['MAZ ID'], keep='first')\n", + "\n", + "in_households = in_households.merge(\n", + " maz_xwalk_old_dedup[['MAZ ID', 'FIPS (GEOID20)']],\n", + " how='left',\n", + " left_on='maz',\n", + " right_on='MAZ ID'\n", + ")\n", + "\n", + "in_households = in_households.merge(\n", + " maz_xwalk_new[['FIPS (GEOID20)', 'MAZ NO', 'TAZ NO']],\n", + " on = 'FIPS (GEOID20)',\n", + " how = 'left'\n", + ")\n", + "\n", + "hh_renaming_dict = {\n", + " 'hhid': 'household_id',\n", + " # 'serialno': 'SERIALNO', # (Optional, excluding for now)\n", + " 'MAZ NO': 'MAZ',\n", + " 'TAZ NO': 'TAZ',\n", + " 'htype': 'TYPE',\n", + " 'np': 'NP',\n", + " 'hht': 'HHT',\n", + " 'hhincadj': 'HHINCADJ',\n", + " 'nwrkrs_esr': 'WORKERS'\n", + "}\n", + "\n", + "out_households = in_households[hh_renaming_dict.keys()].copy()\n", + "out_households.rename(columns=hh_renaming_dict, inplace=True)\n", + "out_households = out_households[~out_households['MAZ'].isna()] # drop houesholds with no MAZ match in new zone system\n", + "\n", + "out_households.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "id": "bfb74ef6", + "metadata": {}, + "outputs": [], + "source": [ + "assert out_households['household_id'].is_unique, \"household_id is not unique after processing!\"" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "id": "b9261619", + "metadata": {}, + "outputs": [], + "source": [ + "# out_households.to_csv(os.path.join(output_data_folder, 'households.csv'), index=False)" + ] + }, + { + "cell_type": "markdown", + "id": "d066739c", + "metadata": {}, + "source": [ + "## Persons Data Processing" + ] + }, + { + "cell_type": "code", + "execution_count": 46, + "id": "6ec763e5", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Filling missing columns in out_persons with empty string: {'DOUT', 'NAICSP', 'OCCP', 'DPHY', 'INDP', 'TOLLFACTOR', 'DEAR', 'DEYE', 'DDRS', 'FAREFACTOR', 'DREM'}\n" + ] + }, + { + "data": { + "text/html": [ + "
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household_idPERSON_NUMperson_idPUMA_GEOIDTAZMAZAGEPSEXWKHPWKW...OCCPOCCCATDDRSDEARDEYEDOUTDPHYDREMTOLLFACTORFAREFACTOR
01111303502.09524.0421401...2
11221303502.09524.0292501...6
21331303502.09524.011-8-8...999
32141303502.09524.0571401...2
42251303502.09524.0521401...1
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5 rows × 25 columns

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" + ], + "text/plain": [ + " household_id PERSON_NUM person_id PUMA_GEOID TAZ MAZ AGEP SEX \\\n", + "0 1 1 1 1303 502.0 9524.0 42 1 \n", + "1 1 2 2 1303 502.0 9524.0 29 2 \n", + "2 1 3 3 1303 502.0 9524.0 1 1 \n", + "3 2 1 4 1303 502.0 9524.0 57 1 \n", + "4 2 2 5 1303 502.0 9524.0 52 1 \n", + "\n", + " WKHP WKW ... OCCP OCCCAT DDRS DEAR DEYE DOUT DPHY DREM TOLLFACTOR \\\n", + "0 40 1 ... 2 \n", + "1 50 1 ... 6 \n", + "2 -8 -8 ... 999 \n", + "3 40 1 ... 2 \n", + "4 40 1 ... 1 \n", + "\n", + " FAREFACTOR \n", + "0 \n", + "1 \n", + "2 \n", + "3 \n", + "4 \n", + "\n", + "[5 rows x 25 columns]" + ] + }, + "execution_count": 46, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Import Persons Table\n", + "in_persons = pd.read_csv(os.path.join(input_data_folder, 'persons_all.csv'))\n", + "\n", + "# Update MAZ/TAZ to new zone system\n", + "in_persons = in_persons.merge(\n", + " maz_xwalk_old_dedup[['MAZ ID', 'FIPS (GEOID20)']],\n", + " how='left',\n", + " left_on='maz',\n", + " right_on='MAZ ID'\n", + ")\n", + "\n", + "in_persons = in_persons.merge(\n", + " maz_xwalk_new[['FIPS (GEOID20)', 'MAZ NO', 'TAZ NO']],\n", + " on = 'FIPS (GEOID20)',\n", + " how = 'left'\n", + ")\n", + "\n", + "renaming_dict = {\n", + " 'hhid': 'household_id',\n", + " 'sporder': 'PERSON_NUM',\n", + " 'PERID': 'person_id',\n", + " 'PUMA': 'PUMA_GEOID',\n", + " 'TAZ NO': 'TAZ', # optional, including for now\n", + " 'MAZ NO': 'MAZ', # optional, including for now\n", + " 'agep': 'AGEP',\n", + " 'sex': 'SEX',\n", + " 'wkhp': 'WKHP',\n", + " 'wkw': 'WKW',\n", + " 'esr': 'ESR',\n", + " 'schg': 'SCHG',\n", + " 'mil': 'MIL',\n", + " 'occp': 'OCCCAT'\n", + "}\n", + "\n", + "final_order = ['household_id','PERSON_NUM','person_id','PUMA_GEOID', 'TAZ', 'MAZ', 'AGEP','SEX','WKHP','WKW','ESR','SCHG','MIL','NAICSP','INDP',\n", + "'OCCP','OCCCAT','DDRS','DEAR','DEYE','DOUT','DPHY','DREM','TOLLFACTOR','FAREFACTOR']\n", + "\n", + "# Grab data and rename columns\n", + "out_persons = in_persons[renaming_dict.keys()].copy()\n", + "\n", + "out_persons.rename(columns=renaming_dict, inplace=True)\n", + "\n", + "# fill in missing columns with empty strings\n", + "missing_cols = set(final_order) - set(out_persons.columns)\n", + "print(f\"Filling missing columns in out_persons with empty string: {missing_cols}\")\n", + "\n", + "# Add new column headers to ActivitySim\n", + "out_persons[list(missing_cols)] = ''\n", + "\n", + "# Reorder fields again\n", + "out_persons = out_persons[final_order]\n", + "\n", + "# Drop persons with no MAZ match in new zone system\n", + "out_persons = out_persons[~out_persons['MAZ'].isna()]\n", + "\n", + "out_persons.head()\n" + ] + }, + { + "cell_type": "code", + "execution_count": 47, + "id": "58232f6f", + "metadata": {}, + "outputs": [], + "source": [ + "assert out_persons.person_id.is_unique, \"person_id is not unique after processing!\"" + ] + }, + { + "cell_type": "code", + "execution_count": 48, + "id": "e1a035c9", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "0" + ] + }, + "execution_count": 48, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "(~out_persons.household_id.isin(out_households.household_id)).sum()" + ] + }, + { + "cell_type": "markdown", + "id": "fea3a764", + "metadata": {}, + "source": [ + "## Land Use Processing" + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "id": "0ab211be", + "metadata": {}, + "outputs": [], + "source": [ + "landuse = pd.read_excel(os.path.join(input_data_folder, 'MAZ_23333_ActivitySim_Inputs_2025-11-20.xlsx'), sheet_name='ActivitySim_MAZ_inputs')\n", + "\n", + "landuse_rename = {\n", + " 'MAZ_NO': 'MAZ',\n", + " 'TAZ_NO': 'TAZ',\n", + "}\n", + "\n", + "# Rename columns\n", + "landuse = landuse.rename(columns=landuse_rename)" + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "id": "cd2acf86", + "metadata": {}, + "outputs": [], + "source": [ + "# need to read in maz to stop file and merge with landuse\n", + "# maz_stop_walk = pd.read_csv(os.path.join(output_data_folder, 'maz_stop_walk.csv')).rename(columns={'maz': 'MAZ'})\n", + "# maz_stop_walk.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "id": "f0233278", + "metadata": {}, + "outputs": [], + "source": [ + "# landuse = landuse.merge(maz_stop_walk, on='MAZ', how='left', validate='one_to_one')" + ] + }, + { + "cell_type": "markdown", + "id": "af0053b3", + "metadata": {}, + "source": [ + "#### Need to have landuse numbers match the skim numbers" + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "id": "7d0c7233", + "metadata": {}, + "outputs": [], + "source": [ + "# skim_file = omx.open_file(os.path.join(output_data_folder, 'autoSkims__AM.omx'), 'r')\n", + "# skim_taz_numbers = range(1, skim_file.shape()[0] + 1)\n", + "# print(len(skim_taz_numbers))\n", + "# skim_file.close()\n" + ] + }, + { + "cell_type": "code", + "execution_count": 33, + "id": "64966ead", + "metadata": {}, + "outputs": [], + "source": [ + "# landuse.TAZ.nunique()" + ] + }, + { + "cell_type": "code", + "execution_count": 34, + "id": "4d06fbd6", + "metadata": {}, + "outputs": [], + "source": [ + "# creating additional landuse rows for the missing TAZs\n", + "# missing_tazs = list(set(skim_taz_numbers) - set(landuse.TAZ))\n", + "\n", + "# missing_landuse = pd.DataFrame(columns=landuse.columns, index=missing_tazs).fillna(0)\n", + "# missing_landuse['TAZ'] = missing_landuse.index\n", + "# missing_landuse['MAZ'] = landuse.MAZ.max() + range(len(missing_landuse)) # Giving dummy MAZ numbers for these TAZs\n", + "# missing_landuse['EXTERNAL'] = 1 # Assuming these missing TAZs are external, set EXTERNAL to 1\n", + "# landuse = pd.concat([landuse, missing_landuse], ignore_index=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 35, + "id": "5fcbc355", + "metadata": {}, + "outputs": [], + "source": [ + "# FIXME additional columns needed for current SANDAG implementation\n", + "# expect these to be removed or changed in future versions\n", + "# landuse['micro_dist_local_bus'] = landuse['walk_dist_local_bus'] \n", + "# landuse['micro_dist_premium_transit'] = landuse['walk_dist_premium_transit']\n", + "# landuse['MicroAccessTime'] = 0.5\n", + "# landuse['nev'] = 0\n", + "# landuse['microtransit'] = 0\n", + "# landuse['external_work'] = np.where(landuse['EXTERNAL'], 10, 0) # adding non-zero size for external models so model won't crash\n", + "# landuse['external_nonwork'] = np.where(landuse['EXTERNAL'], 10, 0) # adding non-zero size for external models so model won't crash\n", + "# landuse['external_MAZ'] = np.where(landuse['EXTERNAL'], 1, 0)\n", + "# landuse['totint'] = 10 # total intersections" + ] + }, + { + "cell_type": "code", + "execution_count": 36, + "id": "527e057b", + "metadata": {}, + "outputs": [], + "source": [ + "# assert landuse.TAZ.nunique() == len(skim_taz_numbers), \"Landuse and skim file TAZ counts do not match.\"" + ] + }, + { + "cell_type": "code", + "execution_count": 37, + "id": "8b6960f0", + "metadata": {}, + "outputs": [], + "source": [ + "# len(skim_taz_numbers)" + ] + }, + { + "cell_type": "markdown", + "id": "124d9822", + "metadata": {}, + "source": [ + "#### Park and Ride Data" + ] + }, + { + "cell_type": "code", + "execution_count": 38, + "id": "f25c8748", + "metadata": {}, + "outputs": [], + "source": [ + "# pnr = pd.read_excel(os.path.join(input_data_folder, '../../2025_PnR_2162_TAZs.xlsx'), sheet_name=0)\n", + "\n", + "# # number of official parking spaces provided by the transit agency in PNR zones in the MAZ\n", + "# # since the data was available only at the TAZ level, the parking spaces are all assigned to the first MAZ listed in land_use.csv within the TAZ\n", + "# landuse_unique_taz = landuse.drop_duplicates(subset=['TAZ'], keep='first')\n", + "# landuse_unique_taz = landuse_unique_taz.merge(pnr[['TAZ', 'CAPACITY']], on='TAZ', how='left')\n", + "# landuse = landuse.merge(landuse_unique_taz[['MAZ', 'CAPACITY']], on='MAZ', how='left')\n", + "# landuse['PNR_CAP_FORMAL'] = landuse['CAPACITY'].fillna(0).astype(int)\n", + "# landuse.drop(columns=['CAPACITY'], inplace=True)\n", + "\n", + "# # number of informal parking spaces in PNR zones in the MAZ, e.g., surface street\n", + "# # sample a random 10 percent of zones with PNR lots\n", + "# landuse['PNR_CAP_INFORMAL'] = np.where(np.random.rand(len(landuse)) < 0.1, (landuse['PNR_CAP_FORMAL'] > 0) * np.random.randint(50, 200), 0)\n", + "\n", + "# # cost of parking in the formal PNR lot (daily in 2024 dollars)\n", + "# # sample a random 50 percent of zones\n", + "# landuse['PNR_PRKCST'] = np.where(np.random.rand(len(landuse)) < 0.5, (landuse['PNR_CAP_FORMAL'] > 0) * np.random.randint(5, 20), 0)\n", + "\n", + "# # average time in minutes to get from the formal parking lot to the transit stop\n", + "# # add dummy values to all PNR lots\n", + "# landuse['PNR_TERMINALTIME'] = (landuse['PNR_CAP_FORMAL'] > 0) * np.round(landuse['PNR_CAP_FORMAL'] / 50, decimals=0)" + ] + }, + { + "cell_type": "markdown", + "id": "124d9822", + "metadata": {}, + "source": [ + "#### Park and Ride Data" + ] + }, + { + "cell_type": "code", + "execution_count": 39, + "id": "f25c8748", + "metadata": {}, + "outputs": [], + "source": [ + "# pnr = pd.read_excel(os.path.join(input_data_folder, '../../2025_PnR_2162_TAZs.xlsx'), sheet_name=0)\n", + "\n", + "# # number of official parking spaces provided by the transit agency in PNR zones in the MAZ\n", + "# # since the data was available only at the TAZ level, the parking spaces are all assigned to the first MAZ listed in land_use.csv within the TAZ\n", + "# landuse_unique_taz = landuse.drop_duplicates(subset=['TAZ'], keep='first')\n", + "# landuse_unique_taz = landuse_unique_taz.merge(pnr[['TAZ', 'CAPACITY']], on='TAZ', how='left')\n", + "# landuse = landuse.merge(landuse_unique_taz[['MAZ', 'CAPACITY']], on='MAZ', how='left')\n", + "# landuse['PNR_CAP_FORMAL'] = landuse['CAPACITY'].fillna(0).astype(int)\n", + "# landuse.drop(columns=['CAPACITY'], inplace=True)\n", + "\n", + "# # number of informal parking spaces in PNR zones in the MAZ, e.g., surface street\n", + "# # sample a random 10 percent of zones with PNR lots\n", + "# landuse['PNR_CAP_INFORMAL'] = np.where(np.random.rand(len(landuse)) < 0.1, (landuse['PNR_CAP_FORMAL'] > 0) * np.random.randint(50, 200), 0)\n", + "\n", + "# # cost of parking in the formal PNR lot (daily in 2024 dollars)\n", + "# # sample a random 50 percent of zones\n", + "# landuse['PNR_PRKCST'] = np.where(np.random.rand(len(landuse)) < 0.5, (landuse['PNR_CAP_FORMAL'] > 0) * np.random.randint(5, 20), 0)\n", + "\n", + "# # average time in minutes to get from the formal parking lot to the transit stop\n", + "# # add dummy values to all PNR lots\n", + "# landuse['PNR_TERMINALTIME'] = (landuse['PNR_CAP_FORMAL'] > 0) * np.round(landuse['PNR_CAP_FORMAL'] / 50, decimals=0)" + ] + }, + { + "cell_type": "markdown", + "id": "b414e208", + "metadata": {}, + "source": [ + "### Checking for consistency and saving to CSV" + ] + }, + { + "cell_type": "code", + "execution_count": 40, + "id": "baa9c278", + "metadata": {}, + "outputs": [], + "source": [ + "# removing duplicated MAZ rows, keeping the first occurrence\n", + "landuse = landuse.drop_duplicates(subset=['MAZ'], keep='last')" + ] + }, + { + "cell_type": "code", + "execution_count": 41, + "id": "a59bac70", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Removing 0 households with invalid MAZs not found in landuse data.\n", + "MAZs and TAZ of these households:\n", + "Empty DataFrame\n", + "Columns: [MAZ, TAZ]\n", + "Index: []\n" + ] + } + ], + "source": [ + "# checking to make sure all households have a valid MAZ in landuse\n", + "invalid_maz_hh = out_households[~out_households.MAZ.isin(landuse.MAZ)]\n", + "print(f\"Removing {len(invalid_maz_hh)} households with invalid MAZs not found in landuse data.\")\n", + "print(f\"MAZs and TAZ of these households:\\n{invalid_maz_hh[['MAZ', 'TAZ']].drop_duplicates().to_string(index=False)}\")\n", + "out_households = out_households[out_households.MAZ.isin(landuse.MAZ)]\n" + ] + }, + { + "cell_type": "code", + "execution_count": 42, + "id": "516fb68b", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Removing 0 persons with invalid household_id not found in households data.\n" + ] + } + ], + "source": [ + "# checking to make sure all persons have a valid household_id in households\n", + "invalid_hh_persons = out_persons[~out_persons.household_id.isin(out_households.household_id)]\n", + "print(f\"Removing {len(invalid_hh_persons)} persons with invalid household_id not found in households data.\")\n", + "out_persons = out_persons[out_persons.household_id.isin(out_households.household_id)]" + ] + }, + { + "cell_type": "code", + "execution_count": 43, + "id": "a744d439", + "metadata": {}, + "outputs": [], + "source": [ + "# assert landuse.TAZ.nunique() == len(skim_taz_numbers), \"Landuse and skim file TAZ counts do not match.\"" + ] + }, + { + "cell_type": "code", + "execution_count": 49, + "id": "cdd7aae8", + "metadata": {}, + "outputs": [], + "source": [ + "# Save to CSV\n", + "landuse.to_csv(os.path.join(output_data_folder, 'land_use.csv'), index=False)\n", + "out_persons.to_csv(os.path.join(output_data_folder, 'persons.csv'), index=False)\n", + "out_households.to_csv(os.path.join(output_data_folder, 'households.csv'), index=False)" + ] + }, + { + "cell_type": "code", + "execution_count": 56, + "id": "36f01d9b", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 56, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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+24,3,0,0,0,0,168,0,0,0,0,0,0,0,0,0,98,0,0,PORTLAND,PORTLAND,0,2.4,0.0,0.0,,302,0,,,,0,1,15,40,0.5326422455357304,0.017,0.048 +25,3,0,0,0,0,0,57,0,0,0,0,0,0,0,0,114,0,0,PORTLAND,PORTLAND,0,2.4,13.0,11.773,,285,0,,,,0,1,20,50,0.7772867394426929,0.0,0.031 +26,3,0,0,0,0,0,26,0,0,0,0,0,0,0,0,13,0,0,PORTLAND,PORTLAND,0,2.4,0.0,0.0,,321,0,,,,0,1,0,0,1.1400551553685587,0.032,0.205 +27,3,0,0,0,0,21,0,0,0,10,0,0,0,0,0,21,0,0,PORTLAND,PORTLAND,0,2.4,0.0,0.0,,297,0,,,,0,1,14,30,1.3220287299372866,0.0,0.0 +28,3,0,0,0,211,4,193,0,0,0,0,8,13,0,0,500,0,0,PORTLAND,PORTLAND,0,2.4,14.0,11.773,,304,0,,,,0,1,18,47,1.6717393482713658,0.0,0.0 +29,3,0,45,0,38,2,571,0,11,0,0,0,0,0,1,462,0,0,PORTLAND,PORTLAND,0,2.4,0.0,0.0,,316,0,,,,0,1,6,14,1.6724402986393905,0.0,0.101 +30,3,0,8,0,0,35,28,0,17,0,0,0,0,0,10,125,0,0,PORTLAND,PORTLAND,0,2.4,0.0,0.0,,293,0,,,,0,1,12,27,1.6676032817286577,0.0,0.0 +31,3,0,0,0,0,5,184,60,2,0,0,0,22,0,0,241,0,0,PORTLAND,PORTLAND,0,2.4,12.0,9.769,,319,0,,,,0,1,11,33,1.6793523852455514,0.027,0.151 +32,3,0,66,0,0,0,1560,0,20,0,0,0,0,0,0,725,0,0,PORTLAND,PORTLAND,0,2.4,0.0,0.0,,300,0,,,,0,1,11,26,1.5708424484498729,0.0,0.0 +33,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,PORTLAND,PORTLAND,0,2.4,0.0,0.0,,324,3,,,,0,1,17,44,1.6866192139632936,0.0,0.138 +34,3,0,1,0,0,0,122,0,2,0,0,0,69,0,0,185,0,0,PORTLAND,PORTLAND,0,2.4,12.0,9.769,,313,0,,,,0,1,17,38,0.9350760684896252,0.049,0.051 +35,3,0,0,0,533,0,0,0,202,0,0,0,0,0,0,1393,0,0,PORTLAND,PORTLAND,0,2.4,0.0,0.0,,277,0,,,,0,1,13,30,1.678629840951222,0.0,0.0 +36,3,0,115,0,0,0,6,0,2,0,0,0,0,0,0,181,0,0,PORTLAND,PORTLAND,0,2.4,0.0,0.0,,317,0,,,,0,1,6,14,1.6669725727203633,0.1,0.102 +37,3,0,0,0,0,16,901,0,0,0,0,0,0,0,0,661,0,0,PORTLAND,PORTLAND,0,2.4,20.0,16.2809999999999,,260,0,,,,0,1,14,37,1.5775658358927631,0.0,0.0 +38,3,0,0,0,0,44,0,0,0,0,0,0,0,0,0,57,0,23,PORTLAND,PORTLAND,0,2.4,0.0,0.0,,320,0,,,,0,1,9,21,1.4300813615620849,0.049,0.129 +39,3,0,9,0,0,0,33,0,46,0,0,0,0,0,0,90,0,0,PORTLAND,PORTLAND,0,2.4,14.0,12.25,,297,0,,,,0,1,25,66,1.228577635855726,0.0,0.031 +40,3,0,24,0,4,10,152,0,0,10,0,0,3,0,13,147,0,0,PORTLAND,PORTLAND,0,2.4,0.0,0.0,,314,0,,,,0,1,9,22,1.6012271136344274,0.056,0.12 +41,3,0,34,0,0,0,114,0,0,0,0,0,4,0,0,167,0,0,PORTLAND,PORTLAND,0,2.4,12.0,13.182,,272,0,,,,0,1,161,430,1.1857354812730807,0.083,0.094 +42,3,0,60,11,0,0,158,0,13,6,0,0,0,2,0,327,0,0,PORTLAND,PORTLAND,0,2.4,0.0,0.0,,285,0,,,,0,1,93,236,1.6068912711633316,0.0,0.051 +43,3,0,20,0,0,0,3,0,0,4,0,0,0,0,0,30,0,0,PORTLAND,PORTLAND,0,2.4,20.0,16.2809999999999,,269,0,,,,0,1,8,18,1.6702659668977653,0.048,0.11 +44,3,0,0,8,0,0,39,0,96,3,0,0,0,0,0,75,0,0,PORTLAND,PORTLAND,0,2.4,15.0,12.211,,277,0,,,,0,1,34,101,1.6125165242017716,0.048,0.1 +45,3,0,0,0,0,0,79,0,0,0,0,0,0,0,0,60,0,0,PORTLAND,PORTLAND,0,2.4,10.25,10.773,,259,0,,,,0,1,24,64,1.67243743223037,0.0,0.104 +46,3,0,0,0,0,0,270,0,0,5,0,4,0,0,2,294,0,0,PORTLAND,PORTLAND,0,2.4,15.31,11.364,,268,0,,,,0,1,14,34,1.6296306290641254,0.0,0.052 +47,3,0,18,0,0,0,33,0,14,0,0,0,0,3,0,45,0,0,PORTLAND,PORTLAND,0,2.4,0.0,0.0,,243,0,,,,0,1,15,36,1.6529541198668205,0.049,0.104 +48,3,0,142,0,0,0,11,0,5,0,0,0,0,0,0,170,0,0,PORTLAND,PORTLAND,0,2.4,0.0,0.0,,249,0,,,,0,1,180,451,1.6440680182969671,0.052,0.052 +49,3,0,51,0,0,0,20,0,12,4,8,0,0,0,0,182,0,0,PORTLAND,PORTLAND,0,2.4,0.0,0.0,,230,0,,,,0,1,13,28,1.6602172441801424,0.09,0.108 +50,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,PORTLAND,PORTLAND,0,2.4,0.0,0.0,,240,0,,,,0,1,9,19,1.0123463442414418,0.109,0.127 +51,3,0,12,0,0,0,0,0,0,0,0,0,0,0,0,7,0,0,PORTLAND,PORTLAND,0,2.4,9.0,7.326,,233,0,,,,0,1,39,114,0.3556276246452546,0.038,0.056 +52,3,0,41,0,0,0,0,0,0,2,0,0,0,0,0,38,0,0,PORTLAND,PORTLAND,0,2.4,0.0,0.0,,226,0,,,,0,1,13,30,1.2540853502904388,0.109,0.127 +53,4,0,0,0,0,0,5,0,0,0,0,0,0,0,0,5,0,0,PORTLAND,PORTLAND,0,2.4,0.0,0.0,,241,3,,,,0,1,11,26,1.2973569633627395,0.045,0.195 +54,4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,PORTLAND,PORTLAND,0,2.4,0.0,0.0,,251,0,,,,0,1,23,60,34.69835040929281,0.0,0.193 +55,4,0,0,0,0,0,0,0,0,4,0,0,0,0,0,6,0,0,PORTLAND,PORTLAND,0,2.4,12.0,12.682,,261,0,,,,0,1,11,23,2.1356078845791697,0.083,0.094 +56,4,0,0,0,0,0,65,214,0,0,0,0,5,0,0,283,0,0,PORTLAND,PORTLAND,0,2.4,11.0,8.955,,256,0,,,,0,1,29,80,1.7045783842577078,0.03,0.0 +57,4,0,6,89,1430,22,141,91,0,0,0,3,0,0,0,1088,0,0,PORTLAND,PORTLAND,0,2.4,0.0,0.0,,261,0,,,,0,1,881,1110,1.607878715073459,0.03,0.0 +58,4,0,94,0,0,0,33,0,54,1,0,0,0,0,0,103,0,0,PORTLAND,PORTLAND,0,2.4,0.0,0.0,,246,0,,,,0,1,17,43,1.5494571731129716,0.0,0.047 +59,4,0,25,0,0,0,25,0,0,0,0,0,0,0,0,89,0,0,PORTLAND,PORTLAND,0,2.4,12.0,9.769,,254,0,,,,0,1,24,64,1.591345950467043,0.0,0.03 +60,4,0,41,0,0,17,6,0,0,0,0,0,0,0,0,50,0,0,PORTLAND,PORTLAND,0,2.4,12.5,10.176,,237,0,,,,0,1,46,128,1.5494618764877872,0.049,0.096 +61,4,0,39,0,208,0,30,0,26,18,0,0,0,0,0,696,0,0,PORTLAND,PORTLAND,0,2.4,18.0,14.653,,241,0,,,,0,1,12,31,1.561606728062414,0.097,0.138 +62,4,0,0,0,103,0,0,0,0,0,0,0,0,0,0,115,0,0,PORTLAND,PORTLAND,0,2.4,0.0,0.0,,222,3,,,,0,1,449,449,1.5673886283521714,0.073,0.156 +63,4,0,7,0,0,0,1,0,0,5,0,0,0,1,0,12,0,0,PORTLAND,PORTLAND,0,2.4,0.0,0.0,,229,0,,,,0,1,85,217,1.946609515069344,0.073,0.156 +64,4,0,0,0,0,255,0,0,0,6,0,0,0,0,0,140,0,0,PORTLAND,PORTLAND,0,2.4,0.0,0.0,,216,0,,,,0,1,26,71,1.494185619695788,0.01,0.0 +65,4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,PORTLAND,PORTLAND,0,2.4,0.0,0.0,,217,0,,,,0,1,7,16,1.3053326591445602,0.01,0.0 +66,5,0,0,0,0,0,3,0,29,0,0,0,0,0,0,48,0,0,PORTLAND,PORTLAND,0,2.4,0.0,0.0,,279,0,,,,0,1,15,32,1.286830301654319,0.0,0.262 +67,5,0,0,0,0,0,4,0,6,0,0,0,0,0,0,17,0,0,PORTLAND,PORTLAND,0,2.4,11.75,12.6,,293,0,,,,0,1,8,17,1.604382897687055,0.0,0.262 +68,5,0,0,0,0,92,0,0,0,0,0,0,0,0,0,76,0,0,PORTLAND,PORTLAND,0,2.4,0.0,0.0,,303,0,,,,0,1,15,38,1.6453625802103908,0.048,0.332 +69,5,0,9,0,0,32,95,0,105,0,0,0,2,16,0,440,0,0,PORTLAND,PORTLAND,0,2.4,12.5,10.0,,309,0,,,,0,1,26,69,1.7094608487349583,0.097,0.194 +70,5,0,0,0,0,1,0,0,18,0,0,0,0,0,0,31,0,0,PORTLAND,PORTLAND,0,2.4,14.0,10.455,,308,0,,,,0,1,8,17,1.7236210700999297,0.0,0.242 +71,5,0,81,0,0,9,0,0,0,2,0,0,0,0,0,89,0,0,PORTLAND,PORTLAND,0,2.4,0.0,0.0,,311,0,,,,0,1,14,37,1.7388212687617306,0.0,0.292 +72,5,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,PORTLAND,PORTLAND,0,2.4,0.0,0.0,,306,0,,,,0,1,21,55,1.7129049524295037,0.035,0.347 +73,5,0,71,0,0,0,66,0,4,14,0,0,14,0,0,127,0,0,PORTLAND,PORTLAND,0,2.4,13.0,10.583,,304,0,,,,0,1,18,48,0.8730649113923236,0.0,0.312 +74,5,0,267,0,0,2,139,0,7,5,0,0,0,0,0,614,0,0,PORTLAND,PORTLAND,0,2.4,12.0,12.5,,310,0,,,,0,1,0,0,1.5474925860330944,0.051,0.311 +75,5,0,50,0,0,0,0,0,0,0,0,0,0,0,0,25,0,0,PORTLAND,PORTLAND,0,2.4,0.0,0.0,,308,0,,,,0,1,235,472,1.684945568356964,0.0,0.319 +76,5,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,PORTLAND,PORTLAND,0,2.4,0.0,0.0,,266,0,,,,0,1,7,16,0.4163023335276992,0.098,0.433 +77,5,0,12,0,0,113,15,0,0,0,0,0,0,0,0,169,0,0,PORTLAND,PORTLAND,0,2.4,0.0,0.0,,263,0,,,,0,1,95,192,3.086860877328012,0.0,0.335 +78,5,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,PORTLAND,PORTLAND,0,2.4,0.0,0.0,,306,0,,,,0,1,0,0,3.241959842377917,0.0,0.242 +79,5,0,0,0,0,0,1,0,0,0,0,0,0,0,0,1,2,30,PORTLAND,PORTLAND,0,2.4,0.0,0.0,,308,0,,,,0,1,14,37,1.1628766083353557,0.035,0.347 +80,5,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,PORTLAND,PORTLAND,0,2.4,0.0,0.0,,288,0,,,,0,1,50,118,1.5390963751755764,0.135,0.317 +81,5,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,PORTLAND,PORTLAND,0,2.4,0.0,0.0,,300,0,,,,0,1,22,54,1.011005406562363,0.135,0.317 +82,5,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,PORTLAND,PORTLAND,0,2.4,0.0,0.0,,303,0,,,,0,1,18,47,0.9908696847169872,0.097,0.194 +83,6,0,7,0,0,0,50,0,0,0,0,0,0,0,0,84,0,0,PORTLAND,PORTLAND,0,2.4,0.0,0.0,,311,0,,,,0,1,19,49,1.021717564604396,0.026,0.286 +84,6,0,44,0,0,0,10,0,3,38,0,0,0,0,0,106,0,0,PORTLAND,PORTLAND,0,2.4,0.0,0.0,,316,0,,,,0,1,25,54,1.0996451418744164,0.0,0.234 +85,6,0,77,0,0,2,16,0,14,0,0,1,0,0,0,59,0,0,PORTLAND,PORTLAND,0,2.4,0.0,0.0,,314,0,,,,0,1,20,42,1.7156532189277267,0.0,0.236 +86,6,0,24,28,111,2,86,0,29,22,64,0,0,4,0,427,0,0,PORTLAND,PORTLAND,0,2.4,0.0,0.0,,319,0,,,,0,1,17,41,0.715088302194791,0.039,0.0 +87,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,PORTLAND,PORTLAND,0,2.4,0.0,0.0,,309,0,,,,0,1,34,93,1.5629639073673838,0.024,0.063 +88,6,0,26,0,0,124,19,0,1,0,0,0,0,0,0,102,0,0,PORTLAND,PORTLAND,0,2.4,12.5,12.25,,309,0,,,,0,1,12,22,1.6545935532394729,0.098,0.192 +89,6,0,1,0,1,0,9,0,0,0,0,0,0,0,0,10,0,0,PORTLAND,PORTLAND,0,2.4,14.0,11.397,,321,0,,,,0,1,14,30,1.677437686140967,0.01,0.0 +90,6,0,107,0,0,31,56,0,2,19,0,0,0,0,0,202,0,0,PORTLAND,PORTLAND,0,2.4,0.0,0.0,,313,0,,,,0,1,10,20,1.5992760164294608,0.024,0.063 +91,6,0,0,0,0,21,64,0,0,0,0,15,0,0,0,175,0,0,PORTLAND,PORTLAND,0,2.4,0.0,0.0,,309,0,,,,0,1,12,24,1.6804430915809303,0.049,0.143 +92,6,0,0,0,0,0,0,0,1,0,0,0,0,0,0,1,0,0,PORTLAND,PORTLAND,0,2.4,0.0,0.0,,327,0,,,,0,1,4,9,1.684454234415983,0.01,0.0 +93,6,0,230,1,0,4,4,0,5,26,0,0,0,0,0,194,0,0,PORTLAND,PORTLAND,0,2.4,18.0,14.653,,318,0,,,,0,1,31,55,1.5928270886837017,0.0,0.164 +94,6,0,134,0,0,29,88,0,24,0,0,0,2,0,0,240,0,0,PORTLAND,PORTLAND,0,2.4,0.0,0.0,,314,0,,,,0,1,53,127,1.670256259968136,0.0,0.29 +95,6,0,194,1,0,0,93,0,0,0,0,0,0,0,0,513,0,0,PORTLAND,PORTLAND,0,2.4,0.0,0.0,,326,0,,,,0,1,0,0,1.6717291851203473,0.0,0.14 +96,6,0,19,5,0,229,41,0,1,0,0,1,0,0,0,142,0,0,PORTLAND,PORTLAND,0,2.4,18.0,13.182,,317,0,,,,0,1,11,25,1.5641402358415044,0.0,0.188 +97,6,0,20,310,0,0,164,0,0,0,0,1,0,0,0,278,0,0,PORTLAND,PORTLAND,0,2.4,11.0,12.955,,313,0,,,,0,1,8,19,1.6728477172187777,0.0,0.241 +98,6,0,1,0,0,0,433,0,0,11,6,0,1,0,0,695,0,0,PORTLAND,PORTLAND,0,2.4,0.0,0.0,,328,0,,,,0,1,12,29,1.6711223807379183,0.0,0.23 +99,6,0,5,0,0,0,28,0,0,18,0,0,0,13,0,127,0,0,PORTLAND,PORTLAND,0,2.4,0.0,0.0,,318,0,,,,0,1,11,23,1.597578647153853,0.0,0.236 +100,6,0,112,0,0,0,41,0,0,7,0,0,3,0,0,277,0,0,PORTLAND,PORTLAND,0,2.4,0.0,0.0,,311,0,,,,0,1,9,20,1.6346702389462562,0.051,0.311 +101,6,0,0,0,0,0,16,0,0,0,0,0,0,0,0,12,0,0,PORTLAND,PORTLAND,0,2.4,11.0,11.1359999999999,,321,0,,,,0,1,11,24,1.66881824031794,0.0,0.181 +102,6,0,63,0,0,0,0,0,12,3,0,0,0,0,0,51,0,0,PORTLAND,PORTLAND,0,2.4,11.795,10.0,,313,0,,,,0,1,20,47,1.670818161626772,0.0,0.137 +103,6,0,12,0,0,49,40,0,3,6,18,0,1,0,0,164,0,0,PORTLAND,PORTLAND,0,2.4,11.0,10.0,,300,0,,,,0,1,10,23,1.5981282020729266,0.0,0.189 +104,6,0,0,0,0,0,67,0,21,0,0,0,0,151,0,197,0,0,PORTLAND,PORTLAND,0,2.4,0.0,0.0,,300,0,,,,0,1,39,107,1.692732612134282,0.0,0.241 +105,7,0,38,0,45,10,988,0,10,11,77,0,5,35,0,1183,0,0,PORTLAND,PORTLAND,0,2.4,12.75,10.379,,311,0,,,,0,1,11,28,1.6794597155162525,0.024,0.011 +106,7,0,7,353,582,0,646,0,8,42,0,0,8,0,0,2196,0,0,PORTLAND,PORTLAND,0,2.4,14.0,13.955,,318,0,,,,0,1,36,99,1.6716876523242366,0.078,0.136 +107,7,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,PORTLAND,PORTLAND,0,2.4,0.0,0.0,,326,3,,,,0,1,4,9,1.5793027962664852,0.049,0.137 +108,7,0,48,0,7,0,49,0,0,0,0,0,0,0,0,120,0,0,PORTLAND,PORTLAND,0,2.4,0.0,0.0,,315,3,,,,0,1,14,30,0.8914068877352082,0.024,0.011 +109,7,0,0,0,0,0,2,0,0,364,0,0,0,0,0,417,0,0,PORTLAND,PORTLAND,0,2.4,0.0,0.0,,325,0,,,,0,1,23,57,1.6860095935479105,0.05,0.087 +110,7,0,0,0,0,0,547,0,0,2,0,2,0,0,0,574,0,0,PORTLAND,PORTLAND,0,2.4,14.0,11.397,,329,0,,,,0,1,39,109,1.5832840494152158,0.049,0.01 +111,7,0,1,4,0,6,262,0,56,17,0,0,0,29,9,647,0,0,PORTLAND,PORTLAND,0,2.4,0.0,0.0,,318,0,,,,0,1,16,29,0.8828961818100157,0.0,0.021 +112,7,0,52,0,0,0,61,0,11,0,0,0,18,0,0,90,0,0,PORTLAND,PORTLAND,0,2.4,0.0,0.0,,323,0,,,,0,1,11,24,1.651704007801476,0.0,0.069 +113,7,0,2,0,0,0,2,0,0,14,0,0,0,0,0,15,0,0,PORTLAND,PORTLAND,0,2.4,0.0,0.0,,325,0,,,,0,1,79,132,1.5814956219532377,0.0,0.041 +114,7,0,85,0,0,0,159,0,0,13,0,0,0,29,0,231,0,0,PORTLAND,PORTLAND,0,2.4,16.0,11.545,,315,0,,,,0,1,49,135,0.8965035714624098,0.0,0.053 +115,7,0,2,0,0,0,414,0,8,64,0,22,0,0,0,370,0,0,PORTLAND,PORTLAND,0,2.4,0.0,0.0,,329,0,,,,0,1,11,22,1.645333734336592,0.0,0.103 +116,7,0,172,0,0,0,45,0,4,11,0,0,0,0,0,152,0,0,PORTLAND,PORTLAND,0,2.4,0.0,0.0,,327,0,,,,0,1,22,37,1.583049387948184,0.0,0.09 +117,7,0,250,0,0,0,8,0,0,0,0,0,0,0,0,150,0,0,PORTLAND,PORTLAND,0,2.4,0.0,0.0,,305,0,,,,0,1,8,19,0.9253683943916642,0.0,0.052 +118,7,0,0,0,0,0,103,0,0,29,0,0,0,0,0,137,0,0,PORTLAND,PORTLAND,0,2.4,0.0,0.0,,312,0,,,,0,1,10,20,1.6705049425060954,0.0,0.102 +119,7,0,89,0,0,0,0,0,40,0,0,0,0,0,0,58,0,0,PORTLAND,PORTLAND,0,2.4,0.0,0.0,,321,0,,,,0,1,9,21,1.5875659658798251,0.0,0.13 +120,8,0,3,177,368,0,0,0,0,0,0,0,0,0,0,845,0,0,PORTLAND,PORTLAND,0,2.4,0.0,0.0,,227,3,,,,0,1,7,16,0.8709465374150231,0.041,0.325 +121,8,0,3,0,55,0,0,0,0,0,0,0,0,0,0,51,0,0,PORTLAND,PORTLAND,0,2.4,16.0,11.955,,239,0,,,,0,1,29,77,2.210298984196536,0.0,0.154 +122,8,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,PORTLAND,PORTLAND,0,2.4,0.0,0.0,,237,0,,,,0,1,11,28,1.5694319386879068,0.0,0.234 +123,8,0,5,0,5,0,415,0,57,0,0,0,0,0,0,363,0,0,PORTLAND,PORTLAND,0,2.4,25.0,11.818,,247,0,,,,0,1,12,30,1.7951610750219296,0.028,0.28 +124,8,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,PORTLAND,PORTLAND,0,2.4,0.0,0.0,,240,0,,,,0,1,28,73,1.5328716048983555,0.127,0.259 +125,8,0,5,14,361,10,395,0,25,0,0,1,2,0,13,687,0,0,PORTLAND,PORTLAND,0,2.4,19.0,15.467,,254,0,,,,0,1,13,33,1.7384206558138935,0.0,0.155 +126,8,0,0,0,0,0,0,0,7,0,0,0,0,0,0,8,0,0,PORTLAND,PORTLAND,0,2.4,14.0,11.1359999999999,,250,0,,,,0,1,8,18,1.5366625848895676,0.05,0.184 +127,8,0,7,1,82,125,1848,0,18,0,0,0,20,86,3,3161,0,0,PORTLAND,PORTLAND,0,2.4,0.0,0.0,,262,0,,,,0,1,0,0,1.6632248414140756,0.098,0.137 +128,8,0,36,21,0,140,179,0,1,0,0,0,0,0,0,461,0,0,PORTLAND,PORTLAND,0,2.4,15.0,12.211,,244,0,,,,0,1,38,95,1.5632104372843776,0.18,0.187 +129,8,0,29,0,0,0,130,0,11,0,0,12,0,2,0,183,0,0,PORTLAND,PORTLAND,0,2.4,0.0,0.0,,259,0,,,,0,1,14,24,1.689582569742657,0.125,0.0 +130,8,0,29,0,0,0,60,0,4,0,21,0,0,0,0,109,0,0,PORTLAND,PORTLAND,0,2.4,0.0,0.0,,251,0,,,,0,1,12,27,1.5525437328056253,0.157,0.032 +131,8,0,14,0,0,0,16,0,0,0,0,0,0,163,0,217,0,0,PORTLAND,PORTLAND,0,2.4,15.0,12.55,,259,0,,,,0,1,14,31,1.6583492204224135,0.125,0.0 +132,8,0,1,0,0,0,1,0,0,0,12,0,0,0,0,16,0,0,PORTLAND,PORTLAND,0,2.4,0.0,0.0,,233,3,,,,0,1,42,120,1.5538194634526732,0.246,0.207 +133,8,0,0,0,0,0,307,0,2,0,1,0,0,0,0,383,0,0,PORTLAND,PORTLAND,0,2.4,25.0,10.227,,263,0,,,,0,1,9,19,39.30413334612749,0.049,0.043 +134,8,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,PORTLAND,PORTLAND,0,2.4,0.0,0.0,,259,0,,,,0,1,13,34,1.5242915652443612,0.26,0.223 +135,8,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,PORTLAND,PORTLAND,0,2.4,15.0,11.5909999999999,,263,0,,,,0,1,34,99,2.788012127913644,0.0,0.15 +136,9,0,0,0,0,0,22,0,0,0,175,0,0,0,0,145,0,0,PORTLAND,PORTLAND,0,2.4,0.0,0.0,,284,0,,,,0,1,13,29,1.4118006245306958,0.096,0.282 +137,9,0,13,0,0,145,122,0,0,6,0,0,0,0,0,124,0,0,PORTLAND,PORTLAND,0,2.4,0.0,0.0,,274,0,,,,0,1,12,29,3.284074829132222,0.0,0.332 +138,9,0,5,14,0,5,27,0,0,0,0,0,0,106,0,156,136,118,PORTLAND,PORTLAND,0,2.4,0.0,0.0,,269,0,,,,0,1,8,19,3.334793938458561,0.0,0.332 +139,9,0,6,0,0,0,0,0,0,0,0,0,0,0,0,15,0,0,PORTLAND,PORTLAND,0,2.4,14.0,12.617,,301,0,,,,0,1,13,30,3.34060960102474,0.0,0.186 +140,9,0,0,0,0,16,56,0,32,0,0,0,0,0,0,120,0,0,PORTLAND,PORTLAND,0,2.4,11.0,12.617,,286,0,,,,0,1,20,46,1.6228551852691004,0.0,0.341 +141,9,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,PORTLAND,PORTLAND,0,2.4,9.25,12.0749999999999,,288,0,,,,0,1,13,32,1.6595806329335807,0.048,0.289 +142,10,0,0,0,0,0,2,0,22,0,0,0,0,0,0,22,0,0,PORTLAND,PORTLAND,0,2.4,0.0,0.0,,274,0,,,,0,1,7,16,1.6604339060726825,0.0,0.0 +143,10,0,24,0,0,0,5,0,11,0,74,0,0,0,0,185,0,0,PORTLAND,PORTLAND,0,2.4,0.0,0.0,,283,0,,,,0,1,21,51,1.6658515200157962,0.0,0.152 +144,10,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,PORTLAND,PORTLAND,0,2.4,0.0,0.0,,287,3,,,,0,1,14,32,1.5798648213566877,0.0,0.179 +145,10,0,0,0,35,0,11,0,16,0,1,0,0,12,0,118,0,0,PORTLAND,PORTLAND,0,2.4,0.0,0.0,,288,0,,,,0,1,18,47,0.822243542461405,0.0,0.053 +146,10,0,52,0,199,0,3,0,1,0,5,0,0,0,0,406,0,0,PORTLAND,PORTLAND,0,2.4,0.0,0.0,,292,0,,,,0,1,18,42,1.672790486419902,0.05,0.103 +147,10,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,PORTLAND,PORTLAND,0,2.4,0.0,0.0,,294,3,,,,0,1,12,30,1.5935505177889235,0.096,0.282 +148,10,0,0,0,1,0,324,0,4,2,7,518,0,0,0,1085,0,0,PORTLAND,PORTLAND,0,2.4,14.641,11.364,,294,0,,,,0,1,20,45,0.8356337906612008,0.0,0.102 +149,10,0,66,0,0,0,0,0,0,1,0,0,0,0,0,95,0,0,PORTLAND,PORTLAND,0,2.4,0.0,0.0,,300,0,,,,0,1,12,29,1.681316368037287,0.049,0.151 +150,10,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,PORTLAND,PORTLAND,0,2.4,0.0,0.0,,307,3,,,,0,1,13,32,1.5924103266506129,0.048,0.234 +151,11,0,0,0,243,0,24,0,0,0,0,3,0,0,0,253,0,0,PORTLAND,PORTLAND,0,2.4,0.0,0.0,,262,0,,,,0,1,14,36,0.8343126524947133,0.0,0.0 +152,11,0,9,0,0,0,653,0,0,0,2,0,35,0,0,1060,0,0,PORTLAND,PORTLAND,0,2.4,24.0,11.818,,267,0,,,,0,1,34,93,1.657625463163704,0.0,0.0 +153,11,0,0,0,1774,24,2,0,2,0,0,0,0,19,0,1121,0,0,PORTLAND,PORTLAND,0,2.4,0.0,0.0,,264,0,,,,0,1,26,68,1.652424970915803,0.023,0.074 +154,11,0,0,0,0,0,292,0,5,0,0,0,0,1,0,440,0,0,PORTLAND,PORTLAND,0,2.4,15.0,12.211,,279,0,,,,0,1,12,31,1.6708709358279408,0.0,0.025 +155,11,0,0,0,1,1,0,0,0,0,0,0,0,0,0,2,0,0,PORTLAND,PORTLAND,0,2.4,0.0,0.0,,275,0,,,,0,1,20,50,1.6790729961391286,0.0,0.0 +156,11,0,34,2,103,11,574,0,11,0,0,0,3,2,2,922,0,0,PORTLAND,PORTLAND,0,2.4,0.0,0.0,,286,0,,,,0,1,18,38,1.6664205070105678,0.0,0.025 +157,11,0,0,5,2237,0,2,0,2,0,0,0,0,0,1,2253,0,0,PORTLAND,PORTLAND,0,2.4,0.0,0.0,,249,0,,,,0,1,26,65,1.6679030126460834,0.0,0.102 +158,11,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,PORTLAND,PORTLAND,0,2.4,0.0,0.0,,251,3,,,,0,1,43,116,1.6726142716336034,0.0,0.051 +159,11,0,17,1,361,0,0,0,0,0,0,0,0,0,0,461,0,0,PORTLAND,PORTLAND,0,2.4,0.0,0.0,,252,0,,,,0,1,27,71,1.742631703295861,0.0,0.151 +160,11,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,PORTLAND,PORTLAND,0,2.4,0.0,0.0,,268,3,,,,0,1,10,22,1.548312582403614,0.0,0.051 +161,11,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,PORTLAND,PORTLAND,0,2.4,0.0,0.0,,257,3,,,,0,1,17,38,1.7299828258852996,0.023,0.074 +162,11,0,0,1,583,0,15,0,0,0,0,0,0,0,0,217,0,0,PORTLAND,PORTLAND,0,2.4,0.0,0.0,,259,0,,,,0,1,16,37,1.6569890732914174,0.0,0.103 +163,12,0,0,0,0,0,5,0,0,106,0,0,0,0,0,95,0,0,PORTLAND,PORTLAND,0,2.4,0.0,0.0,,255,0,,,,0,1,11,26,1.6239156979379128,0.0,0.232 +164,12,0,0,0,0,33,1,0,0,0,0,0,0,0,0,25,10,0,PORTLAND,PORTLAND,0,2.4,0.0,0.0,,261,0,,,,0,1,0,0,1.6681094378051915,0.0,0.18 +165,12,0,4,0,0,2,9,0,2,4,0,0,2,0,0,19,0,0,PORTLAND,PORTLAND,0,2.4,0.0,0.0,,241,0,,,,0,1,0,0,1.6290648981420428,0.0,0.18 +166,12,0,0,0,0,7,6,0,0,0,0,0,0,0,5,23,0,0,PORTLAND,PORTLAND,0,2.4,0.0,0.0,,232,0,,,,0,1,0,0,1.625728255469146,0.0,0.232 +167,12,0,6,0,0,1,0,0,3,0,0,0,0,1,0,7,0,0,PORTLAND,PORTLAND,0,2.4,11.75,10.682,,250,0,,,,0,1,77,151,1.6575914360530517,0.0,0.23 +168,12,0,0,0,0,0,1,0,0,0,19,0,0,0,0,26,0,0,PORTLAND,PORTLAND,0,2.4,12.5,10.227,,239,0,,,,0,1,21,56,1.6454443027820127,0.0,0.307 +169,13,0,0,0,0,0,0,0,0,7,0,0,0,0,0,5,0,0,PORTLAND,PORTLAND,0,2.4,0.0,0.0,,253,0,,,,0,1,388,898,1.6741936695923785,0.0,0.285 +170,13,0,0,0,0,0,8,0,1,0,0,0,0,0,0,19,0,0,PORTLAND,PORTLAND,0,2.4,0.0,0.0,,233,0,,,,0,1,585,1222,1.6691068766827404,0.052,0.284 +171,13,0,0,0,0,0,18,0,1,0,0,0,0,0,0,17,0,0,PORTLAND,PORTLAND,0,2.4,12.0,10.227,,235,0,,,,0,1,152,304,1.664580647044706,0.024,0.309 +172,14,0,0,0,33,0,0,0,0,0,0,0,0,0,0,16,0,0,PORTLAND,PORTLAND,0,2.4,0.0,0.0,,247,0,,,,0,1,29,68,1.6646419723996575,0.0,0.052 +173,14,0,0,0,357,0,38,0,4,0,1,0,1,235,0,389,0,0,PORTLAND,PORTLAND,0,2.4,0.0,0.0,,253,0,,,,0,1,181,607,1.6746437060176609,0.0,0.014 +174,14,0,1,0,0,0,85,0,7,3,0,0,1,9,0,147,0,0,PORTLAND,PORTLAND,0,2.4,13.25,10.682,,271,0,,,,0,1,184,570,1.6730766727762425,0.0,0.102 +175,14,0,0,0,0,0,166,0,0,0,0,0,0,0,0,151,0,0,PORTLAND,PORTLAND,0,2.4,0.0,0.0,,267,0,,,,0,1,49,166,1.60240393758065,0.0,0.052 +176,14,0,0,0,0,0,10,0,2,0,0,0,0,0,0,7,0,0,PORTLAND,PORTLAND,196,2.4,20.0,16.2809999999999,,251,0,,,,0,1,270,876,1.6739794645260369,0.0,0.0 +177,14,0,9,0,0,2,132,0,0,0,0,0,0,0,0,132,0,0,PORTLAND,PORTLAND,0,2.4,12.9499999999999,10.542,,259,0,,,,0,1,23,72,1.6960138061442938,0.0,0.0 +178,14,0,3,2,0,0,1930,46,0,1,0,0,103,0,0,1816,0,0,PORTLAND,PORTLAND,0,2.4,20.0,16.2809999999999,,256,0,,,,0,1,332,1113,1.673560068383579,0.0,0.0 +179,14,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,PORTLAND,PORTLAND,0,2.4,0.0,0.0,,247,3,,,,0,1,6,22,1.661970657283097,0.0,0.13 +180,14,0,0,0,0,2,5,0,0,0,0,0,1,0,2,10,0,0,PORTLAND,PORTLAND,0,2.4,0.0,0.0,,253,0,,,,0,1,7,21,0.8115878042538652,0.0,0.102 +181,14,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,PORTLAND,PORTLAND,0,2.4,0.0,0.0,,262,3,,,,0,1,9,28,1.5676870630090671,0.048,0.178 +182,14,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,PORTLAND,PORTLAND,0,2.4,0.0,0.0,,274,3,,,,0,1,336,1128,0.8433578317200401,0.0,0.131 +183,14,0,0,0,197,33,315,0,0,0,16,0,0,0,0,375,0,0,PORTLAND,PORTLAND,0,2.4,0.0,0.0,,255,0,,,,0,1,179,609,0.833288349029151,0.0,0.0 +184,14,0,57,0,0,0,15,0,0,0,3,0,0,0,0,53,0,0,PORTLAND,PORTLAND,0,2.4,0.0,0.0,,259,0,,,,0,1,7,17,1.6747949224009044,0.0,0.0 +185,14,0,0,0,0,52,1,0,0,0,10,0,0,0,0,44,0,0,PORTLAND,PORTLAND,0,2.4,12.0,9.769,,259,0,,,,0,1,20,74,1.669440445854454,0.0,0.0 +186,14,0,43,0,0,33,437,0,11,0,0,0,0,0,0,363,0,0,PORTLAND,PORTLAND,0,2.4,0.0,0.0,,262,0,,,,0,1,12,38,1.6628309661809624,0.049,0.151 +187,15,0,302,116,0,23,321,0,7,0,0,0,0,0,0,826,0,0,PORTLAND,PORTLAND,0,2.4,11.448,9.319,,163,3,,,,0,1,5,12,1.5943148357621986,0.155,0.508 +188,15,0,12,0,0,1,5,0,1,0,0,0,0,0,3,22,0,0,PORTLAND,PORTLAND,0,2.4,13.0,8.182,,180,0,,,,0,1,8,33,75.6600646586206,0.144,0.513 +189,15,0,0,0,0,13,104,0,1,0,0,0,0,0,0,143,0,0,PORTLAND,PORTLAND,0,2.4,0.0,0.0,,197,3,,,,0,1,188,570,3.7095640800324983,0.091,0.393 +190,15,0,33,0,0,1,25,0,8,0,0,0,0,98,0,116,286,0,PORTLAND,PORTLAND,0,2.4,0.0,0.0,,170,0,,,,0,1,384,1179,13.431728169780952,999999.0,999999.0 +191,16,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,PORTLAND,PORTLAND,0,2.4,0.0,0.0,,171,0,,,,0,1,23,55,20.12746735533597,0.16,0.311 +192,16,0,96,0,0,1,0,0,0,4,0,0,0,0,0,48,0,0,PORTLAND,PORTLAND,0,2.4,0.0,0.0,,184,0,,,,0,1,132,329,2.666666852248683,0.0,0.012 +193,16,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,PORTLAND,PORTLAND,0,2.4,0.0,0.0,,168,3,,,,0,1,44,93,1.6765748556311075,0.102,0.141 +194,16,0,94,0,0,0,3,0,0,0,0,0,0,0,0,68,0,0,PORTLAND,PORTLAND,0,2.4,0.0,0.0,,197,0,,,,0,1,150,379,0.9330048948613664,0.051,0.1 +195,16,0,0,0,0,9,0,0,0,0,0,0,0,4,0,18,0,0,PORTLAND,PORTLAND,0,2.4,14.0,11.397,,180,3,,,,0,1,44,103,1.673148198454919,0.149,0.188 +196,16,0,0,0,0,0,14,0,0,0,0,0,0,0,0,8,0,0,PORTLAND,PORTLAND,0,2.4,14.0,11.397,,208,0,,,,0,1,15,36,4.78031264150662,0.051,0.051 +197,16,0,0,0,0,0,9,0,0,0,0,0,0,0,0,8,0,0,PORTLAND,PORTLAND,0,2.4,0.0,0.0,,191,1,,,,0,1,21,49,1.6564868020545869,0.221,0.26 +198,16,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,PORTLAND,PORTLAND,0,2.4,0.0,0.0,,161,0,,,,0,1,59,133,15.577020063370796,0.102,0.141 +199,16,0,0,0,0,107,1,0,133,0,0,0,0,0,0,249,0,0,PORTLAND,PORTLAND,0,0.0,0.0,0.0,,158,0,,,,0,1,82,190,1.3356339815444214,0.166,0.205 +200,17,0,23,0,4,0,0,0,0,0,0,0,0,0,0,34,0,0,PORTLAND,PORTLAND,0,2.4,0.0,0.0,,228,0,,,,0,1,0,0,0.2619982053081672,0.03,0.0 +201,17,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,PORTLAND,PORTLAND,0,2.4,11.0,8.955,,214,0,,,,0,1,157,440,1.6708244735297544,0.149,0.381 +202,17,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,PORTLAND,PORTLAND,0,2.4,0.0,0.0,,202,0,,,,0,1,96,274,1.657331377355175,999999.0,999999.0 +203,17,0,2,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,PORTLAND,PORTLAND,0,2.4,11.8119999999999,9.616,,206,3,,,,0,1,13,35,3.341250588279187,0.048,0.323 +204,17,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,PORTLAND,PORTLAND,0,2.4,0.0,0.0,,227,0,,,,0,1,16,44,4.135859966362376,0.0,0.228 +205,17,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,PORTLAND,PORTLAND,0,2.4,0.0,0.0,,224,1,,,,0,1,0,0,1.652670838373726,0.186,0.418 +206,17,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,PORTLAND,PORTLAND,0,2.4,0.0,0.0,,217,1,,,,0,1,68,182,1.664774525748606,0.186,0.418 +207,17,0,8,0,0,0,0,0,0,0,0,0,0,1,0,20,0,0,PORTLAND,PORTLAND,0,2.4,0.0,0.0,,215,0,,,,0,1,22,60,1.6568597211911549,0.076,0.076 +208,17,0,1,0,0,0,0,0,2,0,0,0,0,0,0,5,40,10,PORTLAND,PORTLAND,21609,2.4,0.0,0.0,,219,3,,,,0,1,7,18,1.6718329641279477,0.051,0.051 +209,17,0,1,0,48,0,37,0,1,0,0,0,0,0,0,137,0,0,PORTLAND,PORTLAND,0,2.4,0.0,0.0,,228,0,,,,0,1,0,0,4.816302464085464,0.0,0.0 +210,17,0,74,0,0,0,6,0,15,3,0,1,0,0,0,129,0,0,PORTLAND,PORTLAND,0,2.4,0.0,0.0,,236,0,,,,0,1,42,123,1.76190750679946,0.0,0.004 +211,17,0,17,0,0,0,0,0,0,11,0,0,0,0,0,47,0,0,PORTLAND,PORTLAND,0,2.4,0.0,0.0,,240,0,,,,0,1,28,69,1.6561043454917388,0.0,0.02 +212,17,0,0,0,0,0,29,0,4,0,0,0,0,0,0,29,0,0,PORTLAND,PORTLAND,0,2.4,15.0,12.211,,228,0,,,,0,1,24,61,1.6713978245877117,0.0,0.0 +213,17,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,PORTLAND,PORTLAND,0,2.4,18.0,13.25,,241,0,,,,0,1,67,184,1.6756747016427287,0.0,0.0 +214,17,0,0,1,1,3,7,0,0,0,0,0,0,94,0,138,0,610,PORTLAND,PORTLAND,0,2.4,0.0,0.0,,243,0,,,,0,1,27,67,1.6892052257207752,0.0,0.014 +215,17,0,0,0,0,0,242,0,0,0,0,0,0,0,0,230,0,0,PORTLAND,PORTLAND,0,2.4,0.0,0.0,,239,0,,,,0,1,16,40,1.6769304013877104,0.1,0.1 +216,17,0,14,0,0,0,24,0,2,4,0,1,0,1,0,25,0,0,PORTLAND,PORTLAND,0,2.4,0.0,0.0,,210,0,,,,0,1,41,120,1.657046868260318,0.0,0.275 +217,18,0,0,0,0,12,0,0,0,0,0,0,0,0,0,20,0,0,PORTLAND,PORTLAND,0,2.4,0.0,0.0,,246,0,,,,0,1,127,281,1.635339732196699,0.0,0.336 +218,18,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,PORTLAND,PORTLAND,0,2.4,0.0,0.0,,209,2,,,,0,1,0,0,1.643688268449495,0.166,0.398 +219,18,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,20,0,PORTLAND,PORTLAND,0,2.4,11.0,8.955,,222,0,,,,0,1,0,0,3.3036853143019567,0.252,0.484 +220,18,0,0,0,0,34,46,0,3,0,0,0,0,0,0,57,0,0,PORTLAND,PORTLAND,0,2.4,0.0,0.0,,227,0,,,,0,1,0,0,3.188872642550055,0.104,0.336 +221,18,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,PORTLAND,PORTLAND,0,2.4,0.0,0.0,,220,0,,,,0,1,0,0,1.6904121262961682,0.252,0.484 +222,18,0,0,0,0,1,77,0,3,0,0,0,0,0,0,35,0,0,PORTLAND,PORTLAND,0,2.4,0.0,0.0,,238,0,,,,0,1,0,0,2.6176245585227504,0.024,0.309 +223,18,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,PORTLAND,PORTLAND,0,2.4,0.0,0.0,,242,0,,,,0,1,0,0,1.6590830909826018,0.0,0.336 +224,19,0,34,0,0,0,0,0,0,0,0,0,0,0,0,33,0,0,PORTLAND,PORTLAND,0,2.4,0.0,0.0,,197,0,,,,0,1,0,0,1.8831266886867457,0.027,0.022 +225,19,0,0,0,18,0,0,0,0,0,0,0,0,0,0,23,0,0,PORTLAND,PORTLAND,0,2.4,0.0,0.0,,202,0,,,,0,1,10,25,1.6454866538461603,0.027,0.027 +226,19,0,0,0,0,0,2,0,3,0,0,0,0,0,0,11,0,0,PORTLAND,PORTLAND,0,2.4,14.0,11.397,,217,0,,,,0,1,0,0,1.682903436267006,0.0,0.0 +227,19,0,3,0,0,0,0,0,0,0,0,0,0,10,0,11,0,0,PORTLAND,PORTLAND,0,2.4,0.0,0.0,,221,0,,,,0,1,0,0,1.6810609953140212,0.0,0.0 +228,19,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,PORTLAND,PORTLAND,0,2.4,0.0,0.0,,175,0,,,,0,1,12,39,1.696586347167039,0.0,0.0 +229,19,0,0,0,0,0,14,0,0,0,0,0,0,0,0,23,0,0,PORTLAND,PORTLAND,0,2.4,0.0,0.0,,187,0,,,,0,1,11,37,5.033548594871017,0.0,0.0 +230,19,0,109,0,0,0,0,0,0,9,0,0,0,0,0,118,0,0,PORTLAND,PORTLAND,0,2.4,13.0,10.583,,202,0,,,,0,1,13,53,1.677965745977898,0.0,0.0 +231,19,0,0,0,0,0,42,0,0,0,0,0,1,0,0,81,0,0,PORTLAND,PORTLAND,0,2.4,11.25,10.227,,213,0,,,,0,1,1,3,3.2854667121579637,0.0,0.0 +232,20,0,213,0,0,0,10,0,67,0,0,2,0,0,0,312,0,0,PORTLAND,PORTLAND,0,2.4,0.0,0.0,,244,0,,,,0,1,11,30,1.6867486656725346,0.0,0.15 +233,20,0,0,0,0,0,28,0,0,0,0,0,0,0,0,15,0,0,PORTLAND,PORTLAND,0,2.4,0.0,0.0,,249,0,,,,0,1,93,368,1.5894686276373309,0.0,0.1 +234,20,0,0,0,8,0,48,0,78,0,0,0,0,3686,0,2512,0,0,PORTLAND,PORTLAND,0,2.4,15.0,12.211,,235,0,,,,0,1,55,202,1.6470269114578355,0.0,0.053 +235,20,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,PORTLAND,PORTLAND,0,2.4,0.0,0.0,,240,3,,,,0,1,183,581,1.5266085264403504,0.098,0.101 +236,20,0,3,0,423,0,7,0,1,0,0,0,0,0,0,602,0,0,PORTLAND,PORTLAND,0,2.4,13.0,10.853,,214,3,,,,0,1,25,97,1.7004609245090243,0.0,0.0 +237,20,0,11,0,0,5,519,0,41,30,0,3,34,0,15,806,0,0,PORTLAND,PORTLAND,0,2.4,17.0,11.5909999999999,,224,3,,,,0,1,17,63,4.011558032339163,0.0,0.102 +238,20,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,PORTLAND,PORTLAND,0,2.4,0.0,0.0,,230,0,,,,0,1,3,11,6.468540919032868,0.153,0.262 +239,20,0,84,451,0,0,822,0,0,0,9,0,0,0,1,1516,0,0,PORTLAND,PORTLAND,0,2.4,17.0,13.839,,239,0,,,,0,1,118,330,1.7549946881265166,0.122,0.125 +240,20,0,0,0,0,0,0,0,5,0,0,0,0,0,0,2,0,0,PORTLAND,PORTLAND,0,2.4,14.0,11.397,,245,0,,,,0,1,172,514,1.610949408107777,0.122,0.125 +241,21,0,0,0,0,0,4,0,19,0,0,0,1,0,0,13,0,0,PORTLAND,PORTLAND,0,2.4,18.0,14.653,,187,3,,,,0,1,382,1192,1.6748015215303504,0.013,0.0 +242,21,0,0,0,0,8,61,0,0,5,10,0,0,52,0,207,0,0,PORTLAND,PORTLAND,0,2.4,0.0,0.0,,188,0,,,,0,1,2,7,14.387867616691212,0.0,0.0 +243,21,0,4,3,0,10,423,0,74,9,0,0,22,0,2,787,0,0,PORTLAND,PORTLAND,0,2.4,13.0,10.583,,200,3,,,,0,1,30,85,8.600230963540941,0.013,0.0 +244,22,0,2,17,170,5,794,0,16,6,0,1,4,0,0,734,0,0,PORTLAND,PORTLAND,0,2.4,14.0,11.397,,235,0,,,,0,1,5,12,8.35462070339791,0.0,0.203 +245,22,0,1,0,0,12,837,0,5,0,0,0,3,1,1,497,0,0,PORTLAND,PORTLAND,0,2.4,22.0,12.727,,228,0,,,,0,1,9,25,1.5194981316222578,0.0,0.272 +246,22,0,0,0,0,0,426,0,0,0,0,0,0,0,0,271,0,0,PORTLAND,PORTLAND,0,2.4,0.0,0.0,,201,0,,,,0,1,4,15,1.6746198805772308,0.0,0.066 +247,22,0,0,0,0,0,35,0,8,0,0,0,2,0,1,52,0,0,PORTLAND,PORTLAND,0,2.4,0.0,0.0,,212,3,,,,0,1,38,104,1.744144280295279,0.0,0.154 +248,22,0,0,0,0,2,1837,0,8,0,0,0,1,0,0,1077,0,0,PORTLAND,PORTLAND,0,2.4,14.0,11.397,,215,3,,,,0,1,23,61,3.0997638875987787,0.0,0.154 +249,22,0,0,0,73,1,290,0,59,42,0,0,0,0,0,723,0,0,PORTLAND,PORTLAND,0,2.4,14.694,11.962,,215,0,,,,0,1,8,24,5.724296419821107,0.02,0.234 +250,22,0,0,0,90,7,334,0,36,0,0,2,0,1,0,557,0,0,PORTLAND,PORTLAND,0,2.4,0.0,0.0,,213,0,,,,0,1,12,35,5.059776124904404,0.052,0.276 +251,22,0,7,0,0,0,0,0,1,0,0,0,0,0,0,7,0,0,PORTLAND,PORTLAND,0,2.4,14.0,12.5909999999999,,226,0,,,,0,1,11,31,1.7920397669484809,0.097,0.206 +252,22,0,228,0,0,0,56,0,21,0,0,0,0,0,0,249,0,0,PORTLAND,PORTLAND,0,2.4,0.0,0.0,,218,0,,,,0,1,15,45,1.5547367905468794,0.0,0.278 +253,22,0,0,0,0,0,15,0,0,0,0,0,0,0,0,14,0,0,PORTLAND,PORTLAND,0,2.4,0.0,0.0,,228,0,,,,0,1,24,68,1.6990615494543098,0.048,0.157 +254,23,0,0,0,0,0,0,0,40,0,0,0,0,0,0,25,0,0,PORTLAND,PORTLAND,0,2.4,0.0,0.0,,133,0,,,,0,1,153,442,1.552696613431927,0.113,0.952 +255,23,0,16,0,0,3,53,0,1,0,0,5,0,0,2,83,0,0,PORTLAND,PORTLAND,0,2.4,0.0,0.0,,213,0,,,,0,1,13,30,0.8012864442808678,0.0,0.551 +256,23,0,0,0,0,0,23,0,0,4,0,1,0,0,0,12,0,0,PORTLAND,PORTLAND,0,2.4,0.0,0.0,,219,0,,,,0,1,16,44,1.546438718571084,0.0,0.649 +257,23,0,0,0,0,0,142,0,0,137,0,0,20,0,0,369,0,0,PORTLAND,PORTLAND,0,2.4,16.0,13.025,,221,0,,,,0,1,11,28,1.61033759248163,0.0,0.598 +258,23,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,PORTLAND,PORTLAND,0,2.4,0.0,0.0,,223,0,,,,0,1,13,31,1.491313334945546,0.0,0.649 +259,23,0,22,0,0,35,18,0,3,0,0,0,0,0,0,75,0,0,PORTLAND,PORTLAND,0,2.4,0.0,0.0,,196,0,,,,0,1,19,54,1.04919895745097,0.048,0.552 +260,23,0,0,0,67,56,47,0,0,0,16,0,2,0,0,202,0,0,PORTLAND,PORTLAND,0,2.4,0.0,0.0,,206,0,,,,0,1,19,55,1.549596987501666,0.047,0.649 +261,23,0,0,21,0,0,1,0,0,0,0,0,0,0,0,17,0,0,PORTLAND,PORTLAND,0,2.4,0.0,0.0,,202,0,,,,0,1,19,51,1.5638572196582514,0.048,0.601 +262,23,0,0,0,66,0,0,0,0,0,0,0,0,0,0,65,0,0,PORTLAND,PORTLAND,0,2.4,0.0,0.0,,209,0,,,,0,1,19,58,1.5348580593106815,0.047,0.649 +263,23,0,0,0,0,0,40,0,1,0,0,0,0,0,0,40,0,0,PORTLAND,PORTLAND,0,2.4,0.0,0.0,,182,0,,,,0,1,19,52,1.0001861573025963,0.0,0.569 +264,23,0,0,0,0,0,0,0,0,0,0,0,6,0,0,3,0,0,PORTLAND,PORTLAND,0,2.4,15.0,12.211,,190,0,,,,0,1,15,37,1.5505664776320531,0.0,0.667 +265,23,0,0,0,0,0,10,0,0,0,0,0,0,0,0,17,0,0,PORTLAND,PORTLAND,0,2.4,0.0,0.0,,188,0,,,,0,1,20,52,1.5599978138858808,0.0,0.618 +266,23,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,PORTLAND,PORTLAND,0,2.4,0.0,0.0,,195,0,,,,0,1,16,49,1.5636897521094149,0.0,0.667 +267,23,0,0,0,0,0,0,0,1,0,0,0,1,0,0,1,0,0,PORTLAND,PORTLAND,0,2.4,0.0,0.0,,172,0,,,,0,1,13,36,0.9714175890231592,0.097,0.664 +268,23,0,0,3,76,0,5,0,6,0,0,0,0,0,0,124,0,0,PORTLAND,PORTLAND,0,2.4,0.0,0.0,,176,0,,,,0,1,13,33,1.5564924901787796,0.096,0.758 +269,23,0,0,0,0,0,24,0,1,2,0,0,5,0,0,36,0,0,PORTLAND,PORTLAND,0,2.4,0.0,0.0,,167,0,,,,0,1,36,110,1.540204825625615,0.049,0.614 +270,23,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,PORTLAND,PORTLAND,0,2.4,8.37599999999999,6.818,,181,0,,,,0,1,10,22,1.539182727510954,0.048,0.713 +271,23,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,PORTLAND,PORTLAND,0,2.4,10.9499999999999,6.591,,168,0,,,,0,1,25,66,0.979002278047731,0.096,0.758 +272,23,0,0,98,0,0,0,0,0,0,110,0,0,0,0,212,0,0,PORTLAND,PORTLAND,0,2.4,0.0,0.0,,163,0,,,,0,1,3,8,0.9661124051397512,0.096,0.758 +273,23,0,0,2,0,0,9,0,0,0,0,0,0,0,0,17,0,0,PORTLAND,PORTLAND,0,2.4,0.0,0.0,,153,0,,,,0,1,15,43,1.5516994147693337,0.146,0.808 +274,23,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,PORTLAND,PORTLAND,0,2.4,0.0,0.0,,157,0,,,,0,1,30,88,1.5607571319975708,0.146,0.808 +275,23,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,PORTLAND,PORTLAND,0,2.4,0.0,0.0,,142,0,,,,0,1,23,62,0.8957433512363279,0.145,0.856 +276,24,0,23,0,0,0,5,0,12,0,0,0,0,0,0,41,0,0,PORTLAND,PORTLAND,0,2.4,0.0,0.0,,198,0,,,,0,1,19,54,2.002379237504298,0.0,0.405 +277,24,0,0,2,0,0,6,0,14,1,0,0,0,0,0,21,0,0,PORTLAND,PORTLAND,0,2.4,0.0,0.0,,210,0,,,,0,1,20,58,1.5616484587869046,0.0,0.502 +278,24,0,0,1,0,78,13,0,1,0,4,0,4,0,0,91,0,0,PORTLAND,PORTLAND,0,2.4,0.0,0.0,,201,0,,,,0,1,19,48,1.552773534604741,0.0,0.453 +279,24,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,PORTLAND,PORTLAND,0,2.4,0.0,0.0,,185,3,,,,0,1,20,51,1.554670098868237,0.049,0.455 +280,24,0,0,0,0,19,2,0,1,0,0,0,0,0,0,29,0,0,PORTLAND,PORTLAND,0,2.4,0.0,0.0,,188,0,,,,0,1,19,51,1.5320540255870492,0.0,0.406 +281,24,0,10,0,0,8,5,0,2,0,0,0,0,0,0,29,0,0,PORTLAND,PORTLAND,0,2.4,0.0,0.0,,193,0,,,,0,1,10,25,1.5305052056753747,0.048,0.503 +282,24,0,7,20,0,4,9,0,25,0,0,0,0,0,0,64,0,0,PORTLAND,PORTLAND,0,2.4,0.0,0.0,,175,0,,,,0,1,13,34,1.5366380372747794,0.0,0.471 +283,24,0,11,0,0,0,12,0,0,7,0,0,1,6,0,57,0,0,PORTLAND,PORTLAND,0,2.4,0.0,0.0,,179,0,,,,0,1,19,55,1.5659406880151745,0.0,0.52 +284,25,0,0,0,0,47,34,0,4,0,0,0,0,0,0,69,0,0,PORTLAND,PORTLAND,0,2.4,12.0,9.09099999999999,,187,0,,,,0,1,18,48,1.5466840420253771,0.242,0.278 +285,25,0,73,0,0,0,0,0,4,0,0,0,0,0,0,66,0,0,PORTLAND,PORTLAND,0,2.4,0.0,0.0,,177,0,,,,0,1,18,45,3.795762831227481,0.114,0.292 +286,25,0,0,0,0,0,42,0,1,0,17,0,0,1,0,30,0,0,PORTLAND,PORTLAND,0,2.4,0.0,0.0,,176,0,,,,0,1,14,33,1.975582002737047,0.0,0.422 +287,25,0,0,0,5,0,377,0,0,281,0,0,0,0,0,502,0,0,PORTLAND,PORTLAND,0,2.4,0.0,0.0,,169,0,,,,0,1,16,37,1.444105868271445,0.114,0.292 +288,25,0,0,0,0,0,2,0,0,0,0,0,0,0,0,2,0,0,PORTLAND,PORTLAND,0,2.4,0.0,0.0,,152,3,,,,0,1,18,50,10.41109282072708,0.146,0.663 +289,25,0,0,0,2,93,96,4,8,0,0,0,20,0,0,418,0,0,PORTLAND,PORTLAND,0,2.4,0.0,0.0,,139,0,,,,0,1,20,51,10.09064423321757,0.0,0.471 +290,26,0,0,0,0,0,30,0,1,0,0,0,0,0,22,64,0,0,PORTLAND,PORTLAND,0,2.4,0.0,0.0,,167,0,,,,0,1,4,10,62.52125272196864,0.051,0.568 +291,26,0,0,0,0,1,1,0,0,0,0,0,0,0,0,6,0,0,PORTLAND,PORTLAND,0,2.4,0.0,0.0,,156,0,,,,0,1,19,54,1.5868087044464074,0.097,0.66 +292,26,0,7,0,0,1,1,0,0,0,0,0,0,0,0,18,0,0,PORTLAND,PORTLAND,0,2.4,0.0,0.0,,159,0,,,,0,1,23,66,1.5341001398924832,0.097,0.614 +293,26,0,0,0,0,0,17,0,0,1,0,0,1,0,1,27,0,0,PORTLAND,PORTLAND,0,2.4,0.0,0.0,,162,0,,,,0,1,2,6,1.5457422691012468,0.144,0.71 +294,26,0,0,0,0,27,0,0,1,0,0,0,0,18,0,19,0,0,PORTLAND,PORTLAND,0,2.4,0.0,0.0,,154,0,,,,0,1,45,70,1.551007740779258,0.195,0.759 +295,26,0,0,0,0,96,21,0,0,0,0,0,0,5,0,144,0,0,PORTLAND,PORTLAND,0,2.4,0.0,0.0,,135,0,,,,0,1,13,15,1.553948710219315,0.143,0.982 +296,27,0,19,0,0,20,28,0,0,6,0,7,0,0,0,67,0,0,PORTLAND,PORTLAND,0,2.4,0.0,0.0,,236,0,,,,0,1,19,48,8.265948674648202,0.049,0.465 +297,27,0,0,0,0,2,3,0,0,24,0,0,0,0,0,33,0,0,PORTLAND,PORTLAND,0,2.4,0.0,0.0,,225,3,,,,0,1,0,0,3.107220921789757,0.0,0.369 +298,27,0,5,0,0,5,24,0,0,12,0,0,0,0,0,37,0,0,PORTLAND,PORTLAND,0,2.4,0.0,0.0,,282,0,,,,0,1,0,0,3.1426873979699685,0.0,0.323 +299,27,0,7,1,0,0,8,0,0,39,0,7,6,3,0,95,0,0,PORTLAND,PORTLAND,0,2.4,0.0,0.0,,289,0,,,,0,1,7,10,1.5526790474792944,0.0,0.369 +300,27,0,17,0,0,0,1,0,0,0,0,0,0,0,0,11,0,0,PORTLAND,PORTLAND,0,2.4,0.0,0.0,,276,1,,,,0,1,21,48,1.5519639258251896,0.048,0.321 +301,27,0,0,0,0,0,2,0,0,0,0,0,0,0,0,2,0,0,PORTLAND,PORTLAND,0,2.4,0.0,0.0,,261,1,,,,0,1,0,0,1.5194578281496245,0.096,0.37 +302,27,0,42,0,0,0,5,0,9,7,0,0,0,0,0,52,0,0,PORTLAND,PORTLAND,0,2.4,0.0,0.0,,258,1,,,,0,1,0,0,1.523315676372967,0.0,0.272 +303,28,0,0,0,0,0,0,0,0,0,16,0,0,0,0,17,0,0,PORTLAND,PORTLAND,0,2.4,0.0,0.0,,258,0,,,,0,1,107,163,3.1112821452937043,0.098,0.419 +304,28,0,15,0,0,0,77,0,0,0,0,0,0,0,0,90,0,0,PORTLAND,PORTLAND,0,2.4,0.0,0.0,,248,0,,,,0,1,38,90,1.5629789341202844,0.097,0.466 +305,28,0,15,0,0,17,353,0,0,0,0,0,0,0,0,661,0,0,PORTLAND,PORTLAND,0,2.4,0.0,0.0,,232,0,,,,0,1,39,91,1.5537645273577494,0.048,0.514 +306,28,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,PORTLAND,PORTLAND,0,2.4,0.0,0.0,,243,0,,,,0,1,0,0,1.5724335680081485,0.05,0.611 +307,28,0,0,0,0,0,16,0,0,11,2,0,0,0,0,45,0,0,PORTLAND,PORTLAND,0,2.4,0.0,0.0,,238,0,,,,0,1,143,368,1.0396841300881157,0.05,0.611 +308,28,0,11,0,0,7,3,0,12,9,0,0,0,0,22,39,0,0,PORTLAND,PORTLAND,0,2.4,0.0,0.0,,240,0,,,,0,1,35,81,1.560517261160879,0.048,0.561 +309,28,0,0,0,0,0,36,0,4,0,0,0,0,0,0,18,0,0,PORTLAND,PORTLAND,0,2.4,0.0,0.0,,272,0,,,,0,1,29,44,1.5159762351004984,0.049,0.37 +310,28,0,0,2,0,1,6,0,10,2,0,1,0,0,0,25,0,0,PORTLAND,PORTLAND,0,2.4,0.0,0.0,,294,0,,,,0,1,156,253,1.550400250642004,0.048,0.371 +311,28,0,13,2,0,6,4,0,0,6,0,1,0,0,0,23,0,0,PORTLAND,PORTLAND,0,2.4,0.0,0.0,,285,0,,,,0,1,144,235,1.5527208808328763,0.0,0.419 +312,28,0,0,0,0,0,0,0,2,8,0,10,0,0,0,28,0,0,PORTLAND,PORTLAND,0,2.4,12.284,10.0,,301,0,,,,0,1,0,0,1.553691021459699,0.0,0.312 +313,28,0,0,0,0,0,82,0,0,74,0,0,4,0,0,215,0,0,PORTLAND,PORTLAND,0,2.4,0.0,0.0,,302,0,,,,0,1,0,0,1.5781580850888568,0.0,0.341 +314,28,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,PORTLAND,PORTLAND,0,2.4,0.0,0.0,,299,0,,,,0,1,0,0,1.5763620350945244,0.097,0.345 +315,28,0,28,0,0,27,67,0,7,19,0,0,0,0,0,194,0,0,PORTLAND,PORTLAND,0,2.4,0.0,0.0,,295,0,,,,0,1,26,38,1.413162847380132,0.097,0.373 +316,28,0,11,2,0,1,21,0,1,286,0,0,1,0,1,411,0,0,PORTLAND,PORTLAND,0,2.4,18.0,14.653,,300,0,,,,0,1,13,30,1.5092662472782954,0.049,0.39 +317,28,0,0,2,0,0,27,0,0,4,0,0,0,19,0,29,0,0,PORTLAND,PORTLAND,0,2.4,0.0,0.0,,297,0,,,,0,1,12,27,1.5561632300737749,0.044,0.42 +318,28,0,32,0,0,52,437,0,7,8,0,0,0,34,0,708,0,0,PORTLAND,PORTLAND,0,2.4,0.0,0.0,,306,0,,,,0,1,16,30,1.5359520897702306,0.049,0.371 +319,28,0,0,0,0,0,8,0,0,0,0,2,10,0,0,32,0,0,PORTLAND,PORTLAND,0,2.4,0.0,0.0,,292,0,,,,0,1,25,57,1.546350060091467,0.079,0.547 +320,28,0,51,152,0,0,12,0,13,39,0,0,0,0,0,448,0,0,PORTLAND,PORTLAND,0,2.4,0.0,0.0,,304,0,,,,0,1,17,35,4.642829853614706,0.0,0.37 +321,28,0,80,5,0,0,90,0,3,5,0,0,11,0,0,228,0,0,PORTLAND,PORTLAND,219,2.4,0.0,0.0,,301,0,,,,0,1,3,6,1.5498572800915795,0.0,0.419 +322,28,0,0,0,0,0,354,0,5,0,0,0,3,0,0,296,0,0,PORTLAND,PORTLAND,0,2.4,0.0,0.0,,291,0,,,,0,1,0,0,1.5571967404135325,0.0,0.468 +323,28,0,42,177,0,0,27,0,0,73,0,0,0,0,0,381,0,0,PORTLAND,PORTLAND,0,2.4,0.0,0.0,,297,0,,,,0,1,12,25,1.546338110595576,0.048,0.421 +324,28,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,PORTLAND,PORTLAND,0,2.4,0.0,0.0,,293,0,,,,0,1,103,170,1.5520138726547037,0.048,0.452 +325,28,0,47,1,0,83,47,0,2,0,0,17,0,0,0,120,0,0,PORTLAND,PORTLAND,0,2.4,0.0,0.0,,289,0,,,,0,1,65,128,1.9047007172891848,0.048,0.452 +326,28,0,88,0,0,6,200,0,27,10,16,0,2,17,0,278,0,0,PORTLAND,PORTLAND,0,2.4,0.0,0.0,,289,0,,,,0,1,15,32,1.563498054406683,0.0,0.468 +327,28,0,0,0,0,0,19,0,0,0,0,0,0,0,0,33,0,0,PORTLAND,PORTLAND,0,2.4,0.0,0.0,,283,0,,,,0,1,11,25,1.551952779319557,0.0,0.5 +328,28,0,75,0,0,0,68,0,0,0,0,0,0,17,0,149,0,0,PORTLAND,PORTLAND,0,2.4,0.0,0.0,,280,0,,,,0,1,30,82,1.544594166203995,0.098,0.419 +329,28,0,0,0,0,7,3,0,0,0,0,0,0,0,0,12,0,0,PORTLAND,PORTLAND,0,2.4,0.0,0.0,,268,0,,,,0,1,225,365,1.5516037333001471,0.146,0.468 +330,28,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,PORTLAND,PORTLAND,0,2.4,0.0,0.0,,275,0,,,,0,1,416,683,1.5506076307209908,0.149,0.518 +331,28,0,0,7,0,268,145,0,5,0,0,0,4,0,0,280,0,0,PORTLAND,PORTLAND,0,2.4,0.0,0.0,,269,0,,,,0,1,0,0,2.889848336734495,0.149,0.518 +332,28,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,PORTLAND,PORTLAND,0,2.4,0.0,0.0,,256,0,,,,0,1,402,658,3.1224849808070654,0.098,0.564 +333,28,0,0,2,0,0,10,0,3,103,0,0,0,0,0,144,0,0,PORTLAND,PORTLAND,0,2.4,14.0,12.045,,253,0,,,,0,1,6,9,1.0016450265914063,0.098,0.564 +334,28,0,0,0,0,0,40,0,0,0,0,0,0,0,0,16,0,0,PORTLAND,PORTLAND,0,2.4,0.0,0.0,,256,0,,,,0,1,18,46,1.55767630124524,0.048,0.561 +335,29,0,0,0,0,0,11,0,1,0,0,0,0,151,0,166,0,0,PORTLAND,PORTLAND,487,2.4,0.0,0.0,,248,3,,,,0,1,0,0,1.5433148394881215,0.031,0.205 +336,29,0,0,5,1234,0,22,0,3,0,0,0,0,0,5,872,0,0,PORTLAND,PORTLAND,0,2.4,0.0,0.0,,223,0,,,,0,1,0,0,1.5743540512173049,0.049,0.271 +337,29,0,16,0,0,11,20,8,51,14,0,0,0,0,0,77,0,0,PORTLAND,PORTLAND,0,2.4,0.0,0.0,,251,0,,,,0,1,0,0,15.537345468361051,0.0,0.272 +338,29,0,19,0,0,1,19,0,1,17,29,0,4,0,0,106,0,0,PORTLAND,PORTLAND,0,2.4,0.0,0.0,,222,0,,,,0,1,238,385,1.6018279622710538,0.0,0.32 +339,29,0,0,0,0,1,8,0,13,0,0,21,1,11,0,56,0,0,PORTLAND,PORTLAND,0,2.4,18.0,13.182,,281,0,,,,0,1,0,0,3.1703234123060158,0.0,0.272 +340,29,0,2,0,0,0,79,0,0,4,0,0,4,0,0,119,0,0,PORTLAND,PORTLAND,0,2.4,0.0,0.0,,267,0,,,,0,1,309,504,1.5519516627397598,0.0,0.223 +341,29,0,13,0,0,0,28,0,2,2,0,0,0,0,0,39,0,0,PORTLAND,PORTLAND,0,2.4,0.0,0.0,,272,0,,,,0,1,434,710,1.5673252035022132,0.0,0.223 +342,29,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,PORTLAND,PORTLAND,0,2.4,0.0,0.0,,255,3,,,,0,1,0,0,1.5542471823267747,0.0,0.174 +343,30,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,PORTLAND,PORTLAND,0,2.4,0.0,0.0,,199,0,,,,0,1,0,0,0.9303064429909071,0.0,0.096 +344,30,0,0,0,0,0,117,0,11,2,0,0,3,0,3,171,0,0,PORTLAND,PORTLAND,0,2.4,0.0,0.0,,167,0,,,,0,1,13,28,1.5390636388494108,0.256,0.475 +345,30,0,0,0,491,85,0,0,0,0,0,0,0,0,0,786,0,0,PORTLAND,PORTLAND,0,2.4,0.0,0.0,,223,0,,,,0,1,16,34,32.93314600333015,0.0,0.046 +346,30,0,3,0,0,0,0,0,0,0,0,0,0,0,1,8,0,0,PORTLAND,PORTLAND,0,2.4,0.0,0.0,,223,0,,,,0,1,23,61,1.5446287907541911,0.0,0.0 +347,30,0,0,0,0,25,0,0,0,0,0,0,0,0,0,37,0,0,PORTLAND,PORTLAND,0,2.4,7.0,5.698,,210,0,,,,0,1,1,2,4.699753398737107,0.0,0.0 +348,30,0,9,0,0,1,87,0,1,0,2,0,0,0,5,104,0,0,PORTLAND,PORTLAND,0,2.4,11.5,9.362,,195,2,,,,0,1,0,0,1.5565241459517711,0.099,0.099 +349,31,0,0,0,0,0,2,0,7,5,0,0,0,0,0,22,0,0,PORTLAND,PORTLAND,0,2.4,14.5,11.818,,307,0,,,,0,1,8,18,21.776157498335557,0.0,0.321 +350,31,0,0,2,0,0,48,0,2,12,0,0,3,2,0,98,0,0,PORTLAND,PORTLAND,0,2.4,0.0,0.0,,300,0,,,,0,1,0,0,1.5521823368598906,0.049,0.322 +351,31,0,91,5,0,0,23,0,0,10,0,0,0,0,0,154,0,0,PORTLAND,PORTLAND,0,2.4,0.0,0.0,,305,0,,,,0,1,72,126,1.5519500809160622,0.0,0.272 +352,31,0,48,183,0,0,28,0,20,3,0,0,0,0,0,446,0,0,PORTLAND,PORTLAND,0,2.4,0.0,0.0,,293,0,,,,0,1,25,54,1.5519748909330533,0.048,0.273 +353,31,0,13,0,0,0,707,0,0,17,0,0,1,0,0,368,0,0,PORTLAND,PORTLAND,0,2.4,0.0,0.0,,309,0,,,,0,1,21,43,1.5510941195274268,0.0,0.319 +354,31,0,0,0,0,0,0,0,0,143,0,0,0,0,0,171,0,0,PORTLAND,PORTLAND,0,2.4,0.0,0.0,,313,0,,,,0,1,21,45,1.5786563488306746,0.013,0.257 +355,31,0,91,0,0,0,357,0,0,37,0,0,2,0,0,618,0,0,PORTLAND,PORTLAND,0,2.4,0.0,0.0,,308,0,,,,0,1,19,40,1.5610092270239049,0.049,0.368 +356,31,0,135,0,0,0,2,0,0,2,0,0,0,0,0,124,0,0,PORTLAND,PORTLAND,0,2.4,10.75,10.682,,312,0,,,,0,1,20,33,1.585610986366301,0.048,0.322 +357,31,0,17,0,0,0,23,0,3,22,71,0,0,0,0,192,0,0,PORTLAND,PORTLAND,12,2.4,0.0,0.0,,317,0,,,,0,1,39,107,1.5515925186284285,0.0,0.319 +358,31,0,42,0,0,0,5,0,5,7,15,0,1,0,0,69,0,0,PORTLAND,PORTLAND,0,2.4,0.0,0.0,,315,0,,,,0,1,17,31,1.5415458993121542,0.0,0.273 +359,32,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,PORTLAND,PORTLAND,0,2.4,0.0,0.0,,313,3,,,,0,1,26,59,1.5470384409372473,0.0,0.124 +360,32,0,0,0,0,0,0,0,0,2,0,0,0,0,0,2,0,0,PORTLAND,PORTLAND,0,2.4,0.0,0.0,,324,0,,,,0,1,17,38,0.951464236114134,0.0,0.173 +361,32,0,0,0,10,75,0,0,0,0,0,1,0,0,0,142,0,0,PORTLAND,PORTLAND,0,2.4,0.0,0.0,,298,0,,,,0,1,0,0,1.5530047001566394,0.03,0.094 +362,32,0,5,0,0,0,90,0,0,26,11,0,126,0,0,392,0,0,PORTLAND,PORTLAND,0,2.4,12.75,10.0,,318,0,,,,0,1,0,0,1.5749121720250994,0.0,0.222 +363,32,0,0,0,0,0,160,0,0,2,21,0,0,0,0,118,0,0,PORTLAND,PORTLAND,0,2.4,15.0,12.211,,291,0,,,,0,1,258,378,1.5770758299872727,0.08,0.144 +364,32,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,PORTLAND,PORTLAND,0,2.4,0.0,0.0,,304,3,,,,0,1,262,409,1.538266286007197,0.098,0.273 +365,32,0,6,0,0,0,0,0,24,0,0,0,0,2,0,47,0,0,PORTLAND,PORTLAND,0,2.4,0.0,0.0,,315,0,,,,0,1,13,30,0.9462062904760932,0.047,0.222 +366,32,0,29,0,0,0,1,0,0,7,0,0,0,0,0,45,0,0,PORTLAND,PORTLAND,0,2.4,12.625,10.2769999999999,,323,0,,,,0,1,127,190,1.5509529211856516,0.0,0.274 +367,32,0,2,2,0,0,53,0,0,0,0,1,0,0,1,62,0,0,PORTLAND,PORTLAND,0,2.4,0.0,0.0,,280,0,,,,0,1,0,0,1.558770187177282,0.048,0.096 +368,32,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,PORTLAND,PORTLAND,0,2.4,0.0,0.0,,292,3,,,,0,1,37,86,1.5461328481451706,0.079,0.127 +369,32,0,0,0,0,0,57,0,0,0,5,0,0,0,0,54,0,0,PORTLAND,PORTLAND,0,2.4,0.0,0.0,,306,0,,,,0,1,0,0,0.9711651523737804,0.078,0.174 +370,32,0,64,0,0,0,8,0,0,0,0,0,0,0,0,67,0,0,PORTLAND,PORTLAND,0,2.4,0.0,0.0,,316,0,,,,0,1,20,46,1.5520074520274485,0.0,0.225 +371,32,0,4,0,0,37,3,0,0,5,0,0,1,0,0,74,0,0,PORTLAND,PORTLAND,0,2.4,0.0,0.0,,273,0,,,,0,1,0,0,1.556922722746608,0.0,0.096 +372,32,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,PORTLAND,PORTLAND,0,2.4,0.0,0.0,,284,3,,,,0,1,72,102,1.5537199150397287,0.03,0.126 +373,32,0,79,0,0,0,0,0,14,2,0,0,2,0,0,107,0,0,PORTLAND,PORTLAND,0,2.4,0.0,0.0,,295,0,,,,0,1,11,26,0.9620690136563372,0.078,0.174 +374,32,0,28,0,0,17,16,0,1,2,1,0,8,0,0,51,0,0,PORTLAND,PORTLAND,0,2.4,0.0,0.0,,308,0,,,,0,1,45,115,1.552775014468333,0.0,0.224 +375,32,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,PORTLAND,PORTLAND,0,2.4,0.0,0.0,,269,3,,,,0,1,17,38,1.5519734983881004,0.079,0.126 +376,32,0,29,0,0,8,21,0,3,18,0,0,0,0,0,46,0,0,PORTLAND,PORTLAND,0,2.4,0.0,0.0,,261,0,,,,0,1,99,266,0.9502467400885186,0.05,0.097 +377,32,0,0,0,0,0,47,0,36,8,0,0,0,0,0,119,0,0,PORTLAND,PORTLAND,0,2.4,12.0,9.769,,285,0,,,,0,1,40,98,1.553398328080572,0.049,0.176 +378,32,0,68,0,0,28,44,0,0,9,6,0,9,0,0,84,0,0,PORTLAND,PORTLAND,0,2.4,0.0,0.0,,295,0,,,,0,1,3,7,1.545338140579794,0.05,0.225 +379,33,0,33,0,0,0,112,0,0,0,32,8,0,0,0,106,0,0,PORTLAND,PORTLAND,0,2.4,0.0,0.0,,257,0,,,,0,1,12,28,1.5594180772862207,0.0,0.024 +380,33,0,23,0,0,1,7,0,3,5,0,0,0,0,0,74,0,0,PORTLAND,PORTLAND,0,2.4,0.0,0.0,,274,0,,,,0,1,73,197,1.5661177692006765,0.0,0.046 +381,33,0,46,0,0,91,1,0,0,0,0,0,0,0,0,175,0,0,PORTLAND,PORTLAND,0,2.4,0.0,0.0,,270,0,,,,0,1,9,9,1.5243610485025705,0.0,0.0 +382,33,0,10,0,0,152,240,0,0,0,20,0,0,0,0,272,0,0,PORTLAND,PORTLAND,0,2.4,13.0,11.9499999999999,,248,0,,,,0,1,6,10,1.5571226820157342,0.0,0.0 +383,33,0,0,0,0,52,102,0,0,1,0,0,0,0,9,126,0,0,PORTLAND,PORTLAND,0,2.4,13.0,9.773,,242,0,,,,0,1,27,64,1.549617565988507,0.0,0.027 +384,33,0,0,0,0,0,189,0,0,4,0,0,0,0,0,238,0,0,PORTLAND,PORTLAND,0,2.4,0.0,0.0,,263,0,,,,0,1,44,64,1.5486986868258814,0.0,0.048 +385,33,0,0,0,0,47,28,0,17,0,0,0,0,0,0,155,0,0,PORTLAND,PORTLAND,0,2.4,0.0,0.0,,234,0,,,,0,1,50,71,1.558127366382286,0.0,0.027 +386,33,0,0,0,0,12,1,0,0,12,0,63,0,0,0,39,0,0,PORTLAND,PORTLAND,0,2.4,0.0,0.0,,253,0,,,,0,1,35,45,1.551986772497011,0.0,0.048 +387,33,0,0,0,0,2,6,0,6,0,0,0,0,0,49,55,0,0,PORTLAND,PORTLAND,0,2.4,11.32,11.176,,231,0,,,,0,1,108,180,1.5519309106125618,0.0,0.028 +388,33,0,0,0,0,0,16,0,13,0,0,0,0,0,0,24,0,0,PORTLAND,PORTLAND,0,2.4,0.0,0.0,,248,0,,,,0,1,3,7,1.5261011418957364,0.0,0.047 +389,34,0,22,0,0,129,8,0,0,0,0,0,0,0,0,140,0,0,PORTLAND,PORTLAND,0,2.4,0.0,0.0,,210,0,,,,0,1,40,94,1.5489502808642452,0.0,0.057 +390,34,0,27,0,0,0,0,0,0,0,22,0,0,0,0,84,0,0,PORTLAND,PORTLAND,0,2.4,0.0,0.0,,216,0,,,,0,1,8,18,1.6335868398020643,0.0,0.098 +391,34,0,51,0,0,0,0,0,40,0,0,0,0,0,0,119,0,0,PORTLAND,PORTLAND,0,2.4,0.0,0.0,,224,0,,,,0,1,11,19,1.5395167233464264,0.0,0.048 +392,34,0,11,0,0,0,203,0,7,2,0,0,0,204,0,596,0,0,PORTLAND,PORTLAND,0,2.4,0.0,0.0,,206,0,,,,0,1,6,10,1.557692151425947,0.0,0.01 +393,34,0,0,0,0,0,69,0,2,0,0,0,13,0,0,84,0,0,PORTLAND,PORTLAND,0,2.4,12.75,10.682,,201,0,,,,0,1,7,15,1.8173341907877771,0.096,0.139 +394,34,0,0,10,0,0,137,0,12,0,0,0,0,73,0,92,0,0,PORTLAND,PORTLAND,160,2.4,0.0,0.0,,198,0,,,,0,1,1,2,1.5408680039025349,0.045,0.029 +395,34,0,40,0,0,13,23,0,10,8,27,0,10,8,0,135,0,0,PORTLAND,PORTLAND,0,2.4,0.0,0.0,,210,0,,,,0,1,14,23,1.7196806886380227,0.048,0.097 +396,34,0,1,0,0,19,17,0,0,1,0,14,0,0,0,45,0,0,PORTLAND,PORTLAND,0,2.4,0.0,0.0,,218,0,,,,0,1,14,33,1.5519678865790776,0.048,0.048 +397,34,0,0,0,3,107,188,0,8,0,0,0,0,0,0,335,0,0,PORTLAND,PORTLAND,0,2.4,12.0,10.227,,187,0,,,,0,1,9,19,1.5509072477455836,0.045,0.029 +398,34,0,24,0,0,0,0,0,1,0,0,0,0,8,0,45,0,0,PORTLAND,PORTLAND,0,2.4,0.0,0.0,,214,0,,,,0,1,1,2,1.7739257842282483,0.048,0.048 +399,34,0,32,0,0,0,61,0,70,0,0,0,0,0,0,110,0,0,PORTLAND,PORTLAND,0,2.4,10.0,10.0,,202,0,,,,0,1,17,32,1.5520053190332923,0.096,0.097 +400,34,0,0,37,0,0,26,0,2,0,19,0,0,0,0,147,0,0,PORTLAND,PORTLAND,0,2.4,15.0,9.773,,196,0,,,,0,1,0,0,1.5504431516316504,0.033,0.0 +401,34,0,0,0,0,0,0,0,0,0,0,0,0,237,0,217,0,0,PORTLAND,PORTLAND,0,2.4,0.0,0.0,,185,0,,,,0,1,0,0,1.5467781216991852,0.0,0.033 +402,34,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,PORTLAND,PORTLAND,0,2.4,0.0,0.0,,193,2,,,,0,1,0,0,1.4339853439193722,0.0,0.106 +403,34,0,0,0,0,0,0,0,0,0,0,11,0,0,0,5,0,0,PORTLAND,PORTLAND,0,2.4,0.0,0.0,,208,0,,,,0,1,0,0,1.5377298376331856,0.0,0.096 +404,34,0,0,0,0,14,0,0,0,0,0,0,0,0,0,21,0,0,PORTLAND,PORTLAND,0,2.4,11.8119999999999,9.545,,203,0,,,,0,1,16,37,1.5520264641245636,0.048,0.048 +405,34,0,0,0,0,26,0,0,0,0,0,0,0,0,0,45,0,0,PORTLAND,PORTLAND,0,2.4,11.726,9.545,,189,0,,,,0,1,8,15,1.5348510301466656,0.05,0.097 +406,34,0,0,12,397,0,10,0,0,0,0,0,0,0,0,424,0,0,PORTLAND,PORTLAND,0,2.4,0.0,0.0,,181,0,,,,0,1,18,38,1.5638502547555064,0.098,0.145 +407,34,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,PORTLAND,PORTLAND,0,2.4,0.0,0.0,,181,0,,,,0,1,19,41,1.4089828582762003,0.143,0.19 +408,34,0,46,0,0,0,56,0,4,22,0,4,0,0,0,176,0,0,PORTLAND,PORTLAND,0,2.4,0.0,0.0,,242,0,,,,0,1,15,31,2.7804181644194044,0.0,0.0 +409,34,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,PORTLAND,PORTLAND,0,2.4,10.0,9.045,,233,0,,,,0,1,0,0,1.5511968871830564,0.0,0.0 +410,34,0,0,0,0,0,53,0,0,0,0,0,0,0,0,27,0,0,PORTLAND,PORTLAND,0,2.4,10.0,10.455,,229,0,,,,0,1,9,14,1.5509082691057388,0.0,0.0 +411,34,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,PORTLAND,PORTLAND,0,2.4,0.0,0.0,,189,3,,,,0,1,0,0,1.5527345360237073,0.023,0.13 +412,34,0,18,0,0,0,184,0,12,0,0,0,0,0,0,217,0,0,PORTLAND,PORTLAND,0,2.4,0.0,0.0,,225,0,,,,0,1,43,76,27.15434893538027,0.0,0.0 +413,34,0,39,0,0,0,30,0,2,4,0,13,0,0,0,187,0,0,PORTLAND,PORTLAND,0,2.4,0.0,0.0,,217,0,,,,0,1,86,185,1.5527139739277347,0.0,0.0 +414,35,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,PORTLAND,PORTLAND,0,0.0,0.0,0.0,,124,0,,,,0,7,79,131,1.5486221839779255,0.041,0.887 +415,35,0,11,0,0,0,21,0,0,6,0,0,6,0,0,33,0,0,PORTLAND,PORTLAND,0,0.0,12.0,7.273,,130,0,,,,0,7,17,39,0.6735371972303735,0.098,0.956 +416,35,0,15,0,2,36,39,0,10,0,5,0,2,0,0,124,0,0,PORTLAND,PORTLAND,0,0.0,0.0,0.0,,74,0,,,,0,7,17,49,6.22926007107599,0.0,1.179 +417,35,0,0,0,0,0,47,2,0,2,0,0,0,0,0,89,0,0,PORTLAND,PORTLAND,0,0.0,0.0,0.0,,142,0,,,,0,7,9,20,84.73326249713818,0.0,0.952 +418,35,0,0,0,0,0,5,0,0,0,0,15,0,5,0,33,0,0,PORTLAND,PORTLAND,0,0.0,0.0,0.0,,137,0,,,,0,7,12,32,1.563180505754337,0.0,1.0 +419,35,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,PORTLAND,PORTLAND,0,0.0,0.0,0.0,,134,0,,,,0,7,0,0,1.5554033914748713,0.047,999999.0 +420,35,0,25,0,0,0,0,0,0,2,0,0,0,0,0,39,0,0,PORTLAND,PORTLAND,0,0.0,0.0,0.0,,135,0,,,,0,7,13,32,3.037235941405667,0.0,999999.0 +421,35,0,16,2,0,0,11,0,0,35,0,0,0,0,0,83,0,0,PORTLAND,PORTLAND,0,0.0,0.0,0.0,,152,0,,,,0,7,11,31,3.098568761054884,0.292,1.244 +422,35,0,1,26,26,669,615,0,0,0,0,0,0,6,0,1244,0,0,PORTLAND,PORTLAND,0,0.0,0.0,0.0,,95,0,,,,0,7,13,31,18.300479073845043,0.098,999999.0 +423,35,0,0,0,0,0,60,0,0,1,0,31,0,0,0,107,0,0,PORTLAND,PORTLAND,0,0.0,0.0,0.0,,120,0,,,,0,7,11,30,16.766906737079648,0.0,999999.0 +424,35,0,0,1,0,0,0,2,17,0,2,10,0,0,0,50,0,0,PORTLAND,PORTLAND,0,0.0,0.0,0.0,,132,0,,,,0,7,8,24,3.01143836032716,0.096,999999.0 +425,35,0,0,0,0,0,6,0,0,5,0,0,4,0,0,20,0,0,PORTLAND,PORTLAND,0,0.0,0.0,0.0,,133,0,,,,0,7,8,20,2.9999846378237143,0.049,1.001 +426,35,0,0,0,0,0,15,0,0,10,0,10,0,0,0,50,0,0,PORTLAND,PORTLAND,0,0.0,0.0,0.0,,141,0,,,,0,7,5,14,1.5296763327930865,0.197,1.149 +427,35,0,0,0,62,0,0,0,0,0,0,0,0,0,0,62,0,0,PORTLAND,PORTLAND,0,0.0,0.0,0.0,,138,0,,,,0,7,6,14,3.084847619655973,0.292,1.244 +428,35,0,31,0,0,0,280,0,0,0,0,0,0,0,0,142,0,0,PORTLAND,PORTLAND,0,0.0,10.0,8.14,,114,0,,,,0,7,5,13,3.1372017679082567,0.0,1.025 +429,35,0,0,0,0,0,3,0,0,0,0,12,157,0,0,120,0,0,PORTLAND,PORTLAND,0,0.0,0.0,0.0,,129,0,,,,0,7,8,19,2.6901987780411964,0.195,999999.0 +430,35,0,1,0,0,0,44,0,0,1,0,11,0,0,0,62,0,0,PORTLAND,PORTLAND,0,0.0,0.0,0.0,,126,0,,,,0,7,6,15,3.1437573737591324,0.049,999999.0 +431,35,0,0,0,0,0,0,0,0,0,0,0,17,0,0,15,0,0,PORTLAND,PORTLAND,0,0.0,0.0,0.0,,118,0,,,,0,7,5,12,3.1070392997252942,0.196,999999.0 +432,35,0,0,0,0,0,115,0,0,0,0,0,0,0,216,394,0,0,PORTLAND,PORTLAND,0,0.0,0.0,0.0,,116,0,,,,0,7,5,12,1.596053590434559,0.215,999999.0 +433,35,0,0,1199,0,0,6,0,0,0,0,1,0,0,0,1152,0,0,PORTLAND,PORTLAND,0,0.0,0.0,0.0,,122,0,,,,0,7,8,20,3.0999260632678194,0.213,999999.0 +434,35,0,28,0,0,0,0,0,0,0,2,1,13,0,0,39,0,0,PORTLAND,PORTLAND,0,0.0,0.0,0.0,,123,0,,,,0,7,5,15,6.219562848799783,0.099,999999.0 +435,35,0,4,94,0,0,7,0,4,0,0,0,0,0,0,88,0,0,PORTLAND,PORTLAND,0,0.0,0.0,0.0,,114,0,,,,0,7,5,11,4.619843065944305,0.193,999999.0 +436,35,0,0,52,0,0,10,0,0,0,0,0,0,0,0,89,0,0,PORTLAND,PORTLAND,0,0.0,0.0,0.0,,103,0,,,,0,7,7,17,3.0960551943987444,0.215,999999.0 +437,35,0,0,0,0,0,0,0,0,0,0,6,2,0,0,16,0,0,PORTLAND,PORTLAND,0,0.0,0.0,0.0,,103,0,,,,0,7,6,20,2.1514950118343057,0.246,999999.0 +438,35,0,0,12,0,0,0,0,0,4,0,7,21,0,0,59,0,0,PORTLAND,PORTLAND,0,0.0,0.0,0.0,,103,0,,,,0,7,6,15,2.1486591193652886,0.146,999999.0 +439,35,0,0,0,0,0,42,0,0,16,0,30,12,0,0,86,0,0,PORTLAND,PORTLAND,0,0.0,0.0,0.0,,104,0,,,,0,7,7,20,2.1141472697079022,0.193,999999.0 +440,35,0,0,62,0,0,133,0,15,8,0,12,1,0,31,327,0,0,PORTLAND,PORTLAND,0,0.0,0.0,0.0,,122,0,,,,0,7,26,96,2.184874102766029,0.244,1.196 +441,35,0,0,0,0,0,0,0,0,0,0,12,0,0,0,21,0,0,PORTLAND,PORTLAND,0,0.0,0.0,0.0,,114,0,,,,0,7,5,8,17.249521999850813,0.197,999999.0 +442,35,0,0,0,0,21,0,0,0,0,0,194,8,0,0,312,0,0,PORTLAND,PORTLAND,0,0.0,0.0,0.0,,117,0,,,,0,7,19,52,10.761010066224417,0.39,999999.0 +443,35,0,0,0,0,0,39,0,5,0,0,10,0,0,0,60,0,0,PORTLAND,PORTLAND,0,0.0,0.0,0.0,,93,0,,,,0,7,0,0,24.822385045579924,0.214,999999.0 +444,35,0,0,25,0,0,19,0,4,0,0,0,0,0,0,53,0,0,PORTLAND,PORTLAND,0,0.0,0.0,0.0,,92,0,,,,0,7,13,40,1.7868621530366826,0.314,999999.0 +445,35,0,0,0,0,0,2,0,0,0,0,125,0,0,14,83,0,0,PORTLAND,PORTLAND,0,0.0,0.0,0.0,,95,0,,,,0,7,5,12,1.9051087171195484,0.245,999999.0 +446,35,0,0,60,0,0,18,0,6,0,0,0,36,8,857,478,0,0,PORTLAND,PORTLAND,0,0.0,0.0,0.0,,103,0,,,,0,7,8,18,8.66407046190648,0.0,1.179 +447,35,0,0,0,0,0,18,0,0,0,0,0,3,0,0,27,0,0,PORTLAND,PORTLAND,0,0.0,0.0,0.0,,88,0,,,,0,7,14,39,11.542151290695042,0.148,999999.0 +448,35,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,PORTLAND,PORTLAND,0,0.0,0.0,0.0,,77,0,,,,0,7,15,53,4.160415386353442,0.205,999999.0 +449,36,0,0,0,0,0,0,0,0,7,0,0,0,0,0,14,0,0,PORTLAND,PORTLAND,0,0.0,0.0,0.0,,132,0,,,,0,7,9,31,4.004814549051559,0.061,0.907 +450,36,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,PORTLAND,PORTLAND,0,0.0,0.0,0.0,,222,0,,,,0,7,15,38,0.5905916911912001,0.0,0.697 +451,36,0,32,0,0,0,12,0,0,0,0,0,0,0,0,16,0,0,PORTLAND,PORTLAND,0,0.0,0.0,0.0,,224,0,,,,0,7,19,54,0.5290965466717529,0.0,0.649 +452,36,0,0,0,0,0,1,0,91,0,0,0,0,0,0,226,0,0,PORTLAND,PORTLAND,0,0.0,0.0,0.0,,231,0,,,,0,7,19,54,1.5292715971624211,0.0,0.611 +453,36,0,0,0,0,0,0,0,0,6,0,0,0,0,0,4,0,0,PORTLAND,PORTLAND,0,2.0,0.0,0.0,,228,0,,,,0,7,18,46,1.5521344577959872,0.0,0.562 +454,36,0,0,10,0,0,0,0,0,0,3,0,0,0,0,21,0,0,PORTLAND,PORTLAND,0,0.0,0.0,0.0,,213,0,,,,0,7,19,55,1.5529465041677388,0.048,0.698 +455,36,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,PORTLAND,PORTLAND,0,0.0,0.0,0.0,,207,0,,,,0,7,18,51,1.5351652766505324,0.048,0.698 +456,36,0,0,0,0,0,20,0,2,0,0,0,0,0,0,20,0,0,PORTLAND,PORTLAND,0,0.0,0.0,0.0,,221,0,,,,0,7,15,42,0.5530042062723014,0.049,0.66 +457,36,0,0,0,0,0,0,0,0,41,0,0,0,0,0,24,0,0,PORTLAND,PORTLAND,0,2.0,0.0,0.0,,217,0,,,,0,7,15,39,1.5499986699882993,0.049,0.611 +458,36,0,0,3,0,0,36,0,0,0,0,0,0,0,0,52,0,0,PORTLAND,PORTLAND,0,2.0,0.0,0.0,,200,0,,,,0,7,16,44,1.5526264157375949,0.0,0.746 +459,36,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,PORTLAND,PORTLAND,0,0.0,0.0,0.0,,194,0,,,,0,7,13,34,1.5357185895900076,0.0,0.715 +460,36,0,6,0,0,1,9,0,0,0,21,0,0,0,0,34,0,0,PORTLAND,PORTLAND,0,2.0,0.0,0.0,,208,0,,,,0,7,12,30,0.595586867940238,0.0,0.708 +461,36,0,0,0,0,0,5,0,2,17,0,0,0,0,0,25,0,0,PORTLAND,PORTLAND,0,2.0,0.0,0.0,,210,0,,,,0,7,10,25,1.551765152901137,0.0,0.66 +462,36,0,0,13,0,0,14,0,0,0,2,0,0,0,0,17,0,0,PORTLAND,PORTLAND,0,2.0,0.0,0.0,,197,0,,,,0,7,1,4,1.5526038904186985,0.048,0.756 +463,36,0,22,0,0,0,6,0,0,8,0,0,0,0,0,46,0,0,PORTLAND,PORTLAND,0,2.0,0.0,0.0,,194,0,,,,0,7,20,52,1.5549410912558628,0.0,0.708 +464,36,0,0,0,0,0,2,0,0,70,0,0,0,0,0,77,0,0,PORTLAND,PORTLAND,0,2.0,0.0,0.0,,186,0,,,,0,7,8,19,1.5375037168283407,0.097,0.843 +465,36,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,PORTLAND,PORTLAND,0,0.0,0.0,0.0,,180,0,,,,0,7,8,17,1.5380193470780454,0.049,0.759 +466,36,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,PORTLAND,PORTLAND,0,0.0,0.0,0.0,,168,0,,,,0,7,12,29,0.5911181745514296,0.097,0.804 +467,36,0,0,0,0,0,0,0,0,0,13,0,0,0,0,15,0,0,PORTLAND,PORTLAND,0,2.0,0.0,0.0,,175,0,,,,0,7,18,51,0.5708679573730354,0.097,0.843 +468,36,0,0,0,0,0,8,0,23,0,0,0,0,0,0,39,0,0,PORTLAND,PORTLAND,0,2.0,0.0,0.0,,185,0,,,,0,7,17,41,1.5122510921408274,0.05,0.805 +469,36,0,0,0,0,0,0,0,0,0,0,43,0,0,0,25,0,0,PORTLAND,PORTLAND,0,2.0,0.0,0.0,,186,0,,,,0,7,16,42,1.5637312139807744,0.049,0.757 +470,36,0,0,10,0,1,293,0,0,0,0,0,2,0,0,318,0,0,PORTLAND,PORTLAND,0,2.0,0.0,0.0,,186,0,,,,0,7,13,30,1.5491856859926765,0.096,0.853 +471,36,0,2,0,0,9,7,0,14,0,6,0,0,6,0,39,0,0,PORTLAND,PORTLAND,0,2.0,0.0,0.0,,171,0,,,,0,7,15,34,3.0921505526798057,0.0,0.855 +472,36,0,0,0,0,190,6,0,52,0,0,0,1,0,0,261,0,0,PORTLAND,PORTLAND,0,2.0,0.0,0.0,,173,0,,,,0,7,11,23,1.5375756954219864,0.0,0.806 +473,36,0,24,13,0,0,114,0,0,0,0,0,1,0,0,91,0,0,PORTLAND,PORTLAND,0,2.0,0.0,0.0,,178,0,,,,0,7,7,16,1.5463819127060303,0.0,0.952 +474,36,0,0,0,0,2,36,0,0,7,1,2,0,0,0,21,0,0,PORTLAND,PORTLAND,0,2.0,0.0,0.0,,189,0,,,,0,7,10,22,3.118224547597544,0.046,0.94 +475,36,0,0,0,0,0,6,0,0,0,0,0,0,0,0,9,0,0,PORTLAND,PORTLAND,0,2.0,0.0,0.0,,162,0,,,,0,7,17,44,6.1768701549808585,0.047,0.893 +476,36,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,PORTLAND,PORTLAND,0,0.0,0.0,0.0,,155,0,,,,0,7,21,59,1.588075241273872,0.1,0.853 +477,36,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,PORTLAND,PORTLAND,0,0.0,0.0,0.0,,152,0,,,,0,7,21,53,0.6723563906893335,0.098,0.903 +478,36,0,0,0,0,1,15,0,1,1,0,0,0,0,0,13,0,0,PORTLAND,PORTLAND,0,2.0,10.0,8.14,,177,0,,,,0,7,20,57,2.321788928937216,0.0,0.991 +479,36,0,0,0,0,0,0,0,4,0,0,0,0,0,0,5,0,0,PORTLAND,PORTLAND,0,2.0,0.0,0.0,,168,0,,,,0,7,21,61,3.102468097562304,0.046,0.998 +480,36,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,PORTLAND,PORTLAND,0,2.0,0.0,0.0,,163,0,,,,0,7,22,61,3.1511560323753933,0.05,0.904 +481,36,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,PORTLAND,PORTLAND,0,0.0,0.0,0.0,,159,0,,,,0,7,46,83,1.6253730282160623,0.048,0.95 +482,36,0,0,0,0,0,1,0,0,0,0,0,0,0,0,1,0,0,PORTLAND,PORTLAND,0,2.0,0.0,0.0,,161,0,,,,0,7,0,0,1.955648007889235,0.046,0.998 +483,36,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,PORTLAND,PORTLAND,0,0.0,0.0,0.0,,149,0,,,,0,7,7,14,2.8094865313246884,0.0,0.952 +484,36,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,PORTLAND,PORTLAND,0,0.0,0.0,0.0,,151,0,,,,0,7,17,43,1.0766780288538875,0.048,0.95 +485,36,0,0,0,0,0,2,0,0,3,0,0,8,0,0,8,0,0,PORTLAND,PORTLAND,0,0.0,0.0,0.0,,145,0,,,,0,7,13,31,1.7487933784647656,0.049,1.005 +486,36,0,0,0,0,0,0,0,0,0,0,0,0,65,0,44,206,0,PORTLAND,PORTLAND,0,0.0,0.0,0.0,,142,0,,,,0,7,19,57,1.5970207636762883,0.145,0.95 +487,36,0,0,0,0,11,237,0,0,0,0,0,20,0,477,358,0,0,PORTLAND,PORTLAND,0,2.0,0.0,0.0,,170,0,,,,0,7,19,52,1.7154113378302518,0.097,1.088 +488,37,0,34,0,0,8,50,0,34,1,15,0,0,0,0,123,0,0,PORTLAND,PORTLAND,0,2.0,0.0,0.0,,242,0,,,,0,7,21,62,2.962089683129452,0.0,0.66 +489,37,0,0,0,0,59,0,0,0,0,0,0,0,0,0,58,0,0,PORTLAND,PORTLAND,0,2.0,0.0,0.0,,249,0,,,,0,7,0,0,3.101617897571761,0.097,0.514 +490,37,0,2,0,0,0,4,0,2,0,0,0,1,0,0,11,0,0,PORTLAND,PORTLAND,0,0.0,0.0,0.0,,278,0,,,,0,7,13,33,1.5558299647173122,0.048,0.316 +491,37,0,35,0,0,0,6,0,0,0,0,0,0,0,0,65,0,0,PORTLAND,PORTLAND,0,2.0,0.0,0.0,,270,0,,,,0,7,32,83,1.550859981692053,0.0,0.268 +492,37,0,0,0,0,14,0,0,0,0,0,0,0,8,0,21,134,0,PORTLAND,PORTLAND,0,2.0,0.0,0.0,,261,0,,,,0,7,173,408,1.5527605823930175,0.096,0.392 +493,37,0,0,0,0,0,2,0,0,0,0,0,0,0,0,4,0,0,PORTLAND,PORTLAND,0,0.0,0.0,0.0,,275,0,,,,0,7,136,288,3.103974235038447,0.0,0.366 +494,37,0,0,0,0,0,8,0,0,3,0,0,0,0,0,5,0,0,PORTLAND,PORTLAND,0,2.0,0.0,0.0,,265,0,,,,0,7,103,207,1.5519366345107757,0.0,0.318 +495,37,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,PORTLAND,PORTLAND,0,2.0,0.0,0.0,,261,3,,,,0,7,31,75,1.5527400165137866,0.097,0.466 +496,37,0,0,0,0,31,0,0,3,0,0,0,0,0,0,25,0,0,PORTLAND,PORTLAND,0,2.0,0.0,0.0,,262,0,,,,0,7,67,156,3.1038544418554985,0.0,0.367 +497,37,0,0,0,0,0,15,0,0,0,0,0,1,0,0,39,0,0,PORTLAND,PORTLAND,0,2.0,0.0,0.0,,255,0,,,,0,7,19,44,1.55160491143139,0.096,0.514 +498,37,0,0,38,0,1,20,0,0,0,0,0,0,0,0,60,0,0,PORTLAND,PORTLAND,0,2.0,0.0,0.0,,260,0,,,,0,7,0,0,3.103896873524321,0.0,0.415 +499,37,0,0,0,0,0,8,0,1,0,0,0,1,0,0,16,0,0,PORTLAND,PORTLAND,0,2.0,0.0,0.0,,253,0,,,,0,7,79,172,1.55264618233984,0.096,0.514 +500,37,0,0,0,0,0,16,0,8,0,0,0,0,0,0,18,0,0,PORTLAND,PORTLAND,0,2.0,0.0,0.0,,256,0,,,,0,7,238,527,3.1016344521880272,0.049,0.464 +501,37,0,0,0,0,18,6,0,0,0,0,2,0,2,0,27,0,0,PORTLAND,PORTLAND,0,2.0,0.0,0.0,,253,0,,,,0,7,42,102,1.5418303101316786,0.098,0.563 +502,38,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,PORTLAND,PORTLAND,0,0.0,0.0,0.0,,243,0,,,,0,7,38,83,3.1038791147874094,0.097,0.648 +503,38,0,0,0,0,0,8,0,0,0,0,0,0,0,0,10,0,0,PORTLAND,PORTLAND,0,0.0,0.0,0.0,,299,0,,,,0,7,17,45,0.5425913397298703,0.0,0.248 +504,38,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,PORTLAND,PORTLAND,0,0.0,0.0,0.0,,290,0,,,,0,7,191,431,1.661154750877662,0.098,0.308 +505,38,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,PORTLAND,PORTLAND,0,0.0,0.0,0.0,,286,0,,,,0,7,58,106,1.718528737977584,0.047,0.404 +506,38,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,PORTLAND,PORTLAND,0,0.0,0.0,0.0,,273,0,,,,0,7,33,63,1.2160901308762595,0.049,0.502 +507,38,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,PORTLAND,PORTLAND,0,0.0,0.0,0.0,,259,0,,,,0,7,0,0,1.7473341782402774,0.146,0.599 +508,38,0,0,0,0,41,9,0,1,0,1,0,0,0,0,39,0,0,PORTLAND,PORTLAND,0,0.0,0.0,0.0,,248,0,,,,0,7,0,0,0.5838303908941146,0.048,0.563 +509,38,0,0,0,0,0,70,0,3,0,0,0,0,0,52,148,0,0,PORTLAND,PORTLAND,0,0.0,0.0,0.0,,241,0,,,,0,7,296,634,1.562710885834973,0.048,0.601 +510,38,0,0,0,0,0,4,0,0,0,0,0,0,0,0,4,0,0,PORTLAND,PORTLAND,0,0.0,0.0,0.0,,291,0,,,,0,7,77,182,1.5167380463105165,0.0,0.202 +511,38,0,0,0,0,0,0,0,0,0,0,0,0,23,0,40,225,0,PORTLAND,PORTLAND,0,0.0,0.0,0.0,,289,0,,,,0,7,96,228,1.4297459303273603,0.075,0.286 +512,38,0,0,14,0,0,5,0,9,0,0,0,0,11,0,47,0,0,PORTLAND,PORTLAND,0,0.0,0.0,0.0,,282,0,,,,0,7,28,66,1.5256697308337923,0.0,0.309 +513,38,0,0,0,0,0,0,0,0,0,0,0,51,0,0,75,0,0,PORTLAND,PORTLAND,0,0.0,0.0,0.0,,281,0,,,,0,7,17,43,1.5363536698075546,0.0,0.309 +514,38,0,6,0,0,0,2,0,0,19,2,0,0,0,0,15,0,0,PORTLAND,PORTLAND,0,0.0,0.0,0.0,,282,0,,,,0,7,212,582,1.5343593467627232,0.048,0.357 +515,38,0,0,0,0,0,0,0,0,44,0,0,0,8,0,55,0,0,PORTLAND,PORTLAND,0,0.0,0.0,0.0,,273,0,,,,0,7,66,154,1.5666430985703108,0.0,0.405 +516,38,0,0,0,0,1,15,0,0,0,0,0,0,0,0,23,0,0,PORTLAND,PORTLAND,0,0.0,0.0,0.0,,274,0,,,,0,7,6,14,1.5649616375669213,0.048,0.415 +517,38,0,0,31,0,0,15,0,3,0,0,0,0,0,0,31,0,0,PORTLAND,PORTLAND,0,0.0,0.0,0.0,,268,0,,,,0,7,13,34,1.5508218159665177,0.049,0.454 +518,38,0,0,0,0,0,0,0,0,0,0,0,0,0,2,2,0,0,PORTLAND,PORTLAND,0,0.0,0.0,0.0,,266,0,,,,0,7,10,25,1.5527762407609424,0.0,0.463 +519,38,0,23,0,0,0,45,0,0,0,0,0,0,0,0,53,0,0,PORTLAND,PORTLAND,0,0.0,0.0,0.0,,263,0,,,,0,7,122,323,1.5519561602938083,0.098,0.503 +520,38,0,0,0,25,0,0,0,0,0,0,0,16,0,0,35,0,0,PORTLAND,PORTLAND,0,0.0,0.0,0.0,,260,0,,,,0,7,23,56,1.5402068528020665,0.097,0.512 +521,38,0,0,0,0,2,9,0,0,0,0,0,0,0,0,11,0,0,PORTLAND,PORTLAND,0,0.0,0.0,0.0,,257,0,,,,0,7,28,68,1.547619640566623,0.098,0.551 +522,39,0,28,0,0,0,6,0,0,0,0,0,0,0,0,38,0,0,PORTLAND,PORTLAND,0,0.0,0.0,0.0,,285,0,,,,0,7,15,39,1.518515676261614,0.0,0.15 +523,39,0,69,0,0,0,0,0,0,0,0,0,0,0,0,58,0,0,PORTLAND,PORTLAND,0,2.0,0.0,0.0,,274,0,,,,0,7,4,10,1.3129848711541747,0.0,0.088 +524,39,0,0,0,0,0,2,0,9,0,0,0,0,1,0,20,0,0,PORTLAND,PORTLAND,0,2.0,0.0,0.0,,275,0,,,,0,7,7,18,1.175071664211552,0.0,0.123 +525,39,0,0,0,0,0,0,0,19,0,0,0,0,0,0,18,0,0,PORTLAND,PORTLAND,0,0.0,0.0,0.0,,284,0,,,,0,7,30,79,1.55197574983357,0.0,0.172 +526,39,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,PORTLAND,PORTLAND,0,0.0,0.0,0.0,,278,0,,,,0,7,102,249,1.55770759508908,0.05,0.22 +527,39,0,20,0,0,0,0,0,52,0,0,0,0,0,0,102,0,0,PORTLAND,PORTLAND,0,2.0,0.0,0.0,,269,0,,,,0,7,35,89,1.5428278532156063,0.048,0.171 +528,39,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,PORTLAND,PORTLAND,0,2.0,9.99499999999999,8.13599999999999,,272,0,,,,0,7,53,116,4.137702740774821,0.048,0.171 +529,39,0,29,0,0,4,9,0,4,5,0,0,0,0,0,74,0,0,PORTLAND,PORTLAND,0,2.0,12.787,10.409,,258,0,,,,0,7,38,85,1.5516822190037616,0.0,0.172 +530,39,0,0,0,0,20,0,0,0,0,0,0,0,0,0,31,0,0,PORTLAND,PORTLAND,0,0.0,10.33,8.409,,278,2,,,,0,7,29,76,3.98184491815962,0.0,0.357 +531,39,0,0,0,0,0,0,0,0,0,0,2,0,33,0,38,0,0,PORTLAND,PORTLAND,0,2.0,0.0,0.0,,269,0,,,,0,7,254,553,1.5520325483072104,0.0,0.22 +532,39,0,0,0,0,0,2,0,46,0,0,0,0,0,0,89,0,0,PORTLAND,PORTLAND,0,2.0,0.0,0.0,,260,0,,,,0,7,113,280,1.5527782100980283,0.0,0.269 +533,40,0,28,0,0,0,0,0,0,23,0,0,0,0,0,56,0,0,PORTLAND,PORTLAND,0,2.0,0.0,0.0,,223,0,,,,0,7,94,242,3.107761211997538,0.048,0.294 +534,40,0,116,0,0,0,1,0,0,0,0,0,0,0,0,42,0,0,PORTLAND,PORTLAND,0,2.0,0.0,0.0,,213,0,,,,0,7,155,383,2.091631183524393,0.0,0.342 +535,40,0,0,0,0,0,6,0,0,148,0,0,0,0,0,234,0,0,PORTLAND,PORTLAND,0,2.0,0.0,0.0,,245,0,,,,0,7,25,51,1.7223794467541025,0.0,0.205 +536,40,0,66,0,0,0,36,0,17,0,0,0,3,0,0,79,0,0,PORTLAND,PORTLAND,0,2.0,0.0,0.0,,233,0,,,,0,7,35,83,3.820695387377384,0.049,0.308 +537,40,0,0,0,0,0,10,0,0,0,0,0,0,0,0,8,0,0,PORTLAND,PORTLAND,0,2.0,0.0,0.0,,223,0,,,,0,7,9,15,3.696232830268968,0.099,0.353 +538,40,0,25,0,0,0,2,0,13,3,0,0,0,0,0,53,0,0,PORTLAND,PORTLAND,0,2.0,0.0,0.0,,239,0,,,,0,7,61,150,3.1045067540046145,0.049,0.322 +539,40,0,0,0,0,2,4,0,1,0,19,0,0,3,0,25,0,0,PORTLAND,PORTLAND,0,2.0,0.0,0.0,,225,0,,,,0,7,7,12,3.103662199161324,0.098,0.446 +540,41,0,21,0,0,0,9,0,0,0,0,0,1,0,0,76,0,0,PORTLAND,PORTLAND,0,2.0,0.0,0.0,,240,0,,,,0,7,6,10,3.101871952021773,0.0,0.406 +541,41,0,90,0,0,0,2,0,0,144,0,0,0,0,0,253,0,0,PORTLAND,PORTLAND,0,2.0,0.0,0.0,,222,0,,,,0,7,24,56,3.1046989764349577,0.098,0.495 +542,41,0,18,0,0,4,1,0,1,0,0,16,0,26,0,102,268,69,PORTLAND,PORTLAND,0,2.0,0.0,0.0,,239,2,,,,0,7,13,29,3.104936964278215,0.0,0.454 +543,41,0,19,0,0,0,2,0,0,0,38,0,0,0,0,82,0,0,PORTLAND,PORTLAND,0,2.0,0.0,0.0,,221,0,,,,0,7,15,31,3.1025170917545664,0.097,0.592 +544,42,0,0,0,0,0,0,0,0,0,0,0,0,1,0,1,0,0,PORTLAND,PORTLAND,0,0.0,0.0,0.0,,144,0,,,,0,7,414,1070,3.10254320159644,0.231,1.167 +545,42,0,0,0,0,0,3,0,0,4,0,0,5,0,0,20,0,0,PORTLAND,PORTLAND,0,0.0,0.0,0.0,,152,0,,,,0,7,49,140,5.286686595288254,0.097,1.033 +546,42,0,0,0,0,0,1,0,0,0,0,0,0,0,0,1,0,0,PORTLAND,PORTLAND,0,2.0,0.0,0.0,,160,0,,,,0,7,50,143,3.0359850370061783,0.1,1.108 +547,42,0,0,0,0,49,0,0,7,3,0,0,76,0,0,88,0,0,PORTLAND,PORTLAND,0,2.0,0.0,0.0,,166,0,,,,0,7,75,91,3.0447013556746154,0.1,1.059 +548,42,0,21,0,0,0,4,0,1,0,0,0,0,0,0,22,0,0,PORTLAND,PORTLAND,0,2.0,0.0,0.0,,173,0,,,,0,7,12,31,3.104972937432486,0.023,0.94 +549,42,0,0,0,0,0,4,0,0,0,0,0,0,0,0,6,0,0,PORTLAND,PORTLAND,0,2.0,0.0,0.0,,173,0,,,,0,7,9,25,3.102310215074031,0.194,0.865 +550,42,0,44,0,0,0,8,0,0,28,0,0,0,0,0,63,0,0,PORTLAND,PORTLAND,0,2.0,12.0,9.769,,181,0,,,,0,7,91,161,2.685009225175862,0.145,0.816 +551,42,0,51,0,0,0,5,0,0,37,0,0,0,2,0,69,0,0,PORTLAND,PORTLAND,0,2.0,0.0,0.0,,174,0,,,,0,7,27,76,3.101721471376913,0.097,0.865 +552,42,0,0,0,0,21,0,0,0,0,0,0,0,0,1,35,0,0,PORTLAND,PORTLAND,0,2.0,0.0,0.0,,170,0,,,,0,7,102,221,3.1045516498221257,0.0,0.838 +553,42,0,34,0,0,1,0,0,0,41,0,0,0,0,0,97,0,0,PORTLAND,PORTLAND,0,2.0,0.0,0.0,,172,0,,,,0,7,45,133,3.122335047833616,0.145,0.915 +554,42,0,0,0,0,157,0,0,23,0,0,0,0,0,0,185,0,0,PORTLAND,PORTLAND,0,2.0,7.0,5.698,,166,0,,,,0,7,7,19,3.104513458392752,0.048,0.888 +555,42,0,0,0,0,0,2,0,0,0,0,0,0,0,0,2,0,0,PORTLAND,PORTLAND,0,0.0,0.0,0.0,,161,0,,,,0,7,0,0,3.0731941216621665,0.083,1.019 +556,42,0,2,5,0,0,3,0,7,2,0,0,0,0,0,35,0,0,PORTLAND,PORTLAND,0,2.0,0.0,0.0,,173,0,,,,0,7,9,16,2.441693147270324,0.097,0.963 +557,42,0,0,1,0,85,20,0,1,0,0,0,0,0,0,134,0,0,PORTLAND,PORTLAND,0,0.0,0.0,0.0,,161,0,,,,0,7,33,79,3.1044791783689245,0.048,0.984 +558,42,0,0,0,0,37,0,0,0,0,0,0,0,0,0,30,0,0,PORTLAND,PORTLAND,0,2.0,0.0,0.0,,165,0,,,,0,7,23,45,2.9080939659735923,0.049,0.985 +559,42,0,0,0,0,19,2,0,0,0,0,1,0,0,0,18,0,0,PORTLAND,PORTLAND,0,2.0,0.0,0.0,,168,0,,,,0,7,7,12,3.0580463298965306,0.049,0.985 +560,42,0,0,0,0,0,0,0,0,0,0,0,3,0,0,9,0,0,PORTLAND,PORTLAND,0,0.0,0.0,0.0,,158,0,,,,0,7,46,93,3.0425514624139938,0.097,1.033 +561,43,0,0,0,0,2693,0,0,0,0,0,0,0,0,0,3009,0,0,PORTLAND,PORTLAND,0,2.0,0.0,0.0,,187,0,,,,0,7,19,43,3.027462647963945,999999.0,999999.0 +562,43,0,26,0,0,0,2,0,0,13,0,22,0,0,0,46,0,0,PORTLAND,PORTLAND,0,2.0,0.0,0.0,,210,0,,,,0,7,9,25,6.206734275042561,0.049,0.739 +563,43,0,0,0,0,39,1,0,3,0,0,0,0,0,0,39,0,0,PORTLAND,PORTLAND,0,2.0,0.0,0.0,,201,1,,,,0,7,19,52,3.0975897099556238,0.0,0.788 +564,43,0,14,0,0,2,23,0,0,0,0,8,1,0,0,97,0,0,PORTLAND,PORTLAND,0,2.0,0.0,0.0,,230,0,,,,0,7,14,36,3.101724448255376,0.049,0.649 +565,43,0,0,0,0,23,30,0,1,4,0,0,0,3,0,40,0,0,PORTLAND,PORTLAND,0,2.0,0.0,0.0,,236,0,,,,0,7,37,99,3.108901737641503,0.0,0.551 +566,43,0,30,0,0,9,7,0,0,0,0,2,0,0,0,54,0,0,PORTLAND,PORTLAND,0,2.0,0.0,0.0,,219,0,,,,0,7,12,25,3.1060752385961514,0.097,0.592 +567,43,0,8,0,0,0,17,0,15,0,0,0,0,0,0,57,0,0,PORTLAND,PORTLAND,0,2.0,0.0,0.0,,236,0,,,,0,7,16,37,3.103756352761719,0.0,0.551 +568,43,0,20,0,0,0,1,0,0,0,0,0,2,0,0,21,0,0,PORTLAND,PORTLAND,0,2.0,0.0,0.0,,217,0,,,,0,7,120,283,3.101160303436441,0.099,0.641 +569,43,0,5,0,0,1,2,0,0,17,0,0,4,2,0,43,0,0,PORTLAND,PORTLAND,0,2.0,0.0,0.0,,195,0,,,,0,7,237,450,3.104592411509344,0.049,0.72 +570,43,0,0,0,0,20,11,0,1,0,0,0,0,0,0,45,0,0,PORTLAND,PORTLAND,0,2.0,0.0,0.0,,235,0,,,,0,7,10,21,3.102443995850772,0.049,0.6 +571,43,0,60,0,0,0,1,0,0,5,0,0,0,0,0,80,0,0,PORTLAND,PORTLAND,0,2.0,0.0,0.0,,212,0,,,,0,7,16,35,3.1051572490396997,0.098,0.69 +572,43,0,123,0,0,0,2,0,0,31,0,0,5,0,0,96,0,0,PORTLAND,PORTLAND,0,2.0,0.0,0.0,,188,0,,,,0,7,10,17,3.119937108393489,0.0,0.769 +573,43,0,18,0,0,104,17,0,3,27,0,0,0,0,0,149,0,0,PORTLAND,PORTLAND,0,2.0,0.0,0.0,,186,0,,,,0,7,77,149,3.0890473522857698,0.049,0.818 +574,44,0,0,0,0,36,8,0,10,39,11,0,0,0,0,84,0,0,PORTLAND,PORTLAND,0,2.0,0.0,0.0,,180,0,,,,0,7,351,747,3.103988145569737,0.0,0.515 +575,44,0,6,0,0,0,14,0,29,38,0,0,0,0,0,43,0,0,PORTLAND,PORTLAND,0,2.0,0.0,0.0,,184,0,,,,0,7,45,94,1.966958088649476,0.0,0.532 +576,44,0,0,0,0,17,13,0,0,0,0,0,2,0,0,49,0,0,PORTLAND,PORTLAND,0,2.0,0.0,0.0,,198,0,,,,0,7,65,110,1.417196117888496,0.0,0.472 +577,44,0,35,0,0,0,3,0,0,0,0,0,2,0,0,63,0,0,PORTLAND,PORTLAND,0,2.0,0.0,0.0,,207,0,,,,0,7,15,30,2.922990044654619,0.053,0.395 +578,44,0,0,0,0,292,24,0,4,18,0,0,0,0,0,347,0,0,PORTLAND,PORTLAND,0,2.0,0.0,0.0,,208,0,,,,0,7,23,55,3.306129030766645,0.148,0.496 +579,44,0,0,0,0,61,5,0,0,5,0,0,0,0,0,56,0,0,PORTLAND,PORTLAND,0,2.0,0.0,0.0,,179,0,,,,0,7,244,400,3.1039824544912995,0.0,0.559 +580,44,0,7,0,0,0,0,0,0,55,0,0,0,0,0,25,0,0,PORTLAND,PORTLAND,0,2.0,0.0,0.0,,202,0,,,,0,7,192,431,2.5149343113752862,0.048,0.574 +581,44,0,0,2,0,0,1,2,0,0,0,0,0,0,0,4,0,0,PORTLAND,PORTLAND,0,2.0,0.0,0.0,,173,0,,,,0,7,3,5,3.10338697127522,0.097,0.768 +582,44,0,5,0,0,16,6,0,0,0,0,0,0,0,0,39,0,0,PORTLAND,PORTLAND,0,2.0,0.0,0.0,,179,0,,,,0,7,25,61,2.6256644751994003,0.0,0.619 +583,44,0,27,0,0,0,31,0,9,25,0,1,6,0,0,123,0,0,PORTLAND,PORTLAND,0,2.0,0.0,0.0,,204,0,,,,0,7,27,55,3.3409057996927096,0.0,0.623 +584,44,0,28,0,0,3,11,0,0,12,0,0,0,0,0,43,0,0,PORTLAND,PORTLAND,0,2.0,12.0,9.769,,184,0,,,,0,7,17,38,3.103864879279261,0.097,0.72 +585,44,0,22,0,0,7,9,0,0,27,0,2,0,0,0,52,0,0,PORTLAND,PORTLAND,0,2.0,0.0,0.0,,198,0,,,,0,7,9,16,3.104725521895033,0.0,0.671 +586,44,0,0,2,0,32,1,0,43,81,0,0,0,0,0,193,0,0,PORTLAND,PORTLAND,0,2.0,12.0,6.818,,182,0,,,,0,7,32,78,3.1039031618950035,0.097,0.768 +587,45,0,0,0,0,138,1,0,0,4,0,0,0,0,0,200,0,0,PORTLAND,PORTLAND,0,2.0,0.0,0.0,,200,0,,,,0,7,14,30,3.1046873313440466,0.049,0.837 +588,45,0,36,0,0,6,3,0,0,6,0,0,0,0,0,38,0,0,PORTLAND,PORTLAND,0,2.0,0.0,0.0,,198,0,,,,0,7,0,0,3.103864524979969,0.0,0.886 +589,45,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,PORTLAND,PORTLAND,0,2.0,0.0,0.0,,193,0,,,,0,7,18,39,3.1028097454746613,0.048,0.934 +590,45,0,19,0,0,24,21,0,0,0,0,0,202,0,0,284,0,0,PORTLAND,PORTLAND,0,2.0,0.0,0.0,,191,0,,,,0,7,13,36,3.105383459480652,0.097,0.983 +591,45,0,38,0,0,0,18,0,1,130,4,0,2,22,0,197,0,0,PORTLAND,PORTLAND,0,2.0,0.0,0.0,,182,0,,,,0,7,21,55,3.1041613946789117,0.0,1.081 +592,45,0,135,0,0,2,255,0,7,4,22,0,2,7,102,299,0,0,PORTLAND,PORTLAND,0,2.0,0.0,0.0,,174,0,,,,0,7,54,167,3.1042395844374475,0.099,1.18 +593,46,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,PORTLAND,PORTLAND,0,2.0,0.0,0.0,,180,0,,,,0,7,19,56,6.233702079758423,0.0,0.956 +594,46,0,11,0,0,104,0,0,0,25,0,0,0,0,0,62,0,0,PORTLAND,PORTLAND,0,2.0,0.0,0.0,,180,0,,,,0,7,22,65,3.104927580349875,0.0,1.011 +595,46,0,64,0,0,7,28,0,1,1,0,2,0,0,0,89,0,0,PORTLAND,PORTLAND,0,2.0,0.0,0.0,,175,0,,,,0,7,16,48,3.107147023525972,0.049,1.06 +596,46,0,19,0,0,1,0,0,0,1,0,0,0,6,0,29,0,0,PORTLAND,PORTLAND,0,2.0,0.0,0.0,,162,0,,,,0,7,10,27,3.08621674808886,0.149,1.158 +597,46,0,53,0,0,0,40,0,0,2,0,6,0,50,0,193,0,0,PORTLAND,PORTLAND,0,2.0,0.0,0.0,,171,0,,,,0,7,15,42,3.102178953699258,0.049,1.109 +598,46,0,0,0,0,14,3,0,7,0,0,5,0,12,0,46,0,0,PORTLAND,PORTLAND,0,2.0,0.0,0.0,,162,0,,,,0,7,11,29,3.1196356358873265,0.147,1.206 +599,46,0,73,3,0,6,12,0,0,13,5,0,1,0,0,90,0,0,PORTLAND,PORTLAND,0,2.0,0.0,0.0,,169,0,,,,0,7,15,44,3.104283325620988,0.0,1.158 +600,46,0,7,17,0,4,5,0,3,0,0,15,0,0,0,35,0,0,PORTLAND,PORTLAND,0,2.0,0.0,0.0,,160,0,,,,0,7,16,44,3.104218784309296,0.097,999999.0 +601,46,0,26,0,0,0,65,0,3,0,4,0,0,0,0,171,0,0,PORTLAND,PORTLAND,0,2.0,0.0,0.0,,166,0,,,,0,7,18,49,3.0996847215559544,0.049,1.207 +602,46,0,36,0,50,13,4,0,13,4,0,0,8,0,0,60,0,0,PORTLAND,PORTLAND,0,2.0,0.0,0.0,,155,0,,,,0,7,18,50,3.102492709692477,0.0,999999.0 +603,47,0,0,0,0,9,2442,0,33,0,0,0,0,0,0,2403,0,0,PORTLAND,PORTLAND,0,2.0,0.0,0.0,,227,0,,,,0,7,0,0,3.104607668950161,0.0,0.66 +604,47,0,9,0,0,3,9,0,0,2,1,0,0,0,2,29,0,0,PORTLAND,PORTLAND,0,2.0,0.0,0.0,,225,0,,,,0,7,0,0,3.099469484533392,0.0,0.698 +605,47,0,0,0,0,0,22,0,0,0,0,0,0,0,0,30,0,0,PORTLAND,PORTLAND,0,2.0,0.0,0.0,,217,0,,,,0,7,0,0,3.110079827913023,0.0,0.757 +606,47,0,0,0,0,0,10,0,0,0,0,0,0,0,0,7,0,0,PORTLAND,PORTLAND,0,2.0,0.0,0.0,,217,0,,,,0,7,0,0,3.1044344061480205,0.049,0.747 +607,47,0,0,0,0,86,0,0,0,0,0,0,0,0,0,76,0,0,PORTLAND,PORTLAND,0,2.0,0.0,0.0,,210,0,,,,0,7,0,0,3.1044355560179078,0.0,0.757 +608,47,0,45,0,0,0,0,0,2,0,0,0,0,0,0,22,0,0,PORTLAND,PORTLAND,0,2.0,0.0,0.0,,208,0,,,,0,7,10,24,3.104392293150846,0.0,0.796 +609,47,0,2,0,0,5,13,0,1,1,0,0,1,0,0,25,0,0,PORTLAND,PORTLAND,0,2.0,0.0,0.0,,197,0,,,,0,7,10,25,3.1018928964382284,0.096,0.853 +610,47,0,11,0,0,63,38,0,0,16,0,1,0,0,0,178,0,0,PORTLAND,PORTLAND,0,2.0,0.0,0.0,,198,0,,,,0,7,13,32,3.111474634854898,0.0,0.845 +611,48,0,33,36,0,5,141,0,0,9,15,244,151,0,164,935,0,0,PORTLAND,PORTLAND,0,0.0,0.0,0.0,,9,0,,,,0,7,21,59,3.1229743761036253,0.016,999999.0 +612,48,0,3,118,0,0,7,0,20,30,0,137,22,0,58,575,0,0,PORTLAND,PORTLAND,0,0.0,0.0,0.0,,57,0,,,,0,7,26,63,240.8833347590012,0.47,999999.0 +613,48,0,0,36,25,0,126,0,0,3,0,599,120,0,153,900,0,0,PORTLAND,PORTLAND,0,0.0,0.0,0.0,,2,0,,,,0,7,15,39,40.68055565355332,0.0,999999.0 +614,48,0,0,21,0,0,8,0,0,0,0,0,54,0,37,147,0,0,PORTLAND,PORTLAND,0,0.0,0.0,0.0,,9,0,,,,0,7,46,143,827.1022145853689,999999.0,999999.0 +615,48,0,0,0,0,0,1,0,0,0,0,0,55,0,29,56,0,0,PORTLAND,PORTLAND,0,0.0,0.0,0.0,,11,0,,,,0,7,18,60,98.93849382408702,0.034,999999.0 +616,49,0,0,0,0,0,25,0,0,9,0,0,65,0,28,270,0,0,PORTLAND,PORTLAND,0,0.0,0.0,0.0,,51,0,,,,0,7,11,22,52.65004324916018,0.134,999999.0 +617,49,0,0,0,0,0,43,0,0,0,0,4,41,0,13,120,0,0,PORTLAND,PORTLAND,0,0.0,0.0,0.0,,43,0,,,,0,7,152,365,23.340454273468527,0.384,999999.0 +618,49,0,0,1,0,0,3,0,0,5,0,38,12,0,0,51,0,0,PORTLAND,PORTLAND,0,0.0,0.0,0.0,,63,0,,,,0,7,36,82,13.285918978244602,0.134,999999.0 +619,49,0,0,3,0,0,5,10,3,8,0,39,7,0,0,73,0,0,PORTLAND,PORTLAND,0,0.0,0.0,0.0,,69,0,,,,0,7,17,47,8.319863894491379,0.0,999999.0 +620,49,0,0,20,0,0,12,0,0,6,0,73,12,0,0,178,0,0,PORTLAND,PORTLAND,0,0.0,0.0,0.0,,80,0,,,,0,7,25,59,8.270076451787446,0.098,999999.0 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b/resident/model_data/metro/data_cropped/transitSkims__PM.omx new file mode 100644 index 0000000..bb83b1b Binary files /dev/null and b/resident/model_data/metro/data_cropped/transitSkims__PM.omx differ diff --git a/resident/model_data/metro/data_cropped/walkSkim.omx b/resident/model_data/metro/data_cropped/walkSkim.omx new file mode 100644 index 0000000..691b4e9 Binary files /dev/null and b/resident/model_data/metro/data_cropped/walkSkim.omx differ diff --git a/resident/preprocessor.py b/resident/preprocessor.py new file mode 100644 index 0000000..a8c4315 --- /dev/null +++ b/resident/preprocessor.py @@ -0,0 +1,465 @@ +""" +Preprocessor for ActivitySim input files. +Adds derived columns to land_use, households, and persons tables. +""" + +import pandas as pd +import geopandas as gpd +import sys +import time +import yaml +from pathlib import Path +from dataclasses import dataclass, field + + +@dataclass +class PreprocessorSettings: + """Settings for the preprocessor.""" + data_dir: Path = field(default_factory=lambda: Path("data")) + output_dir: Path = None # None means overwrite input files + households_file: str = "households.csv" + persons_file: str = "persons.csv" + land_use_file: str = "land_use.csv" + maz_shp_file: str = None # Path to MAZ shapefile for calculating acres + maz_stop_walk_file: str = None # Path to MAZ stop walk distances file + + def __post_init__(self): + # Convert strings to Path objects + if isinstance(self.data_dir, str): + self.data_dir = Path(self.data_dir) + if isinstance(self.output_dir, str): + self.output_dir = Path(self.output_dir) + + @classmethod + def from_yaml(cls, yaml_path: Path) -> "PreprocessorSettings": + """Load settings from a YAML file.""" + with open(yaml_path, "r") as f: + config = yaml.safe_load(f) or {} + return cls(**config) + + +def load_data(settings: PreprocessorSettings) -> tuple[pd.DataFrame, pd.DataFrame, pd.DataFrame]: + """Load the three input CSV files.""" + households = pd.read_csv(settings.data_dir / settings.households_file) + persons = pd.read_csv(settings.data_dir / settings.persons_file) + land_use = pd.read_csv(settings.data_dir / settings.land_use_file) + return households, persons, land_use + + +def fix_duplicate_household_ids( + households: pd.DataFrame, + persons: pd.DataFrame +) -> tuple[pd.DataFrame, pd.DataFrame]: + """ + Check if household_id is unique. If not, create new unique IDs. + + Uses MAZ to match persons to the correct duplicated household. + Original household_id is saved to non_unique_household_id. + + Parameters + ---------- + households : pd.DataFrame + Households table + persons : pd.DataFrame + Persons table + + Returns + ------- + tuple[pd.DataFrame, pd.DataFrame] + Updated (households, persons) DataFrames + """ + # Check for duplicates + duplicate_mask = households.duplicated(subset=["household_id"], keep=False) + num_duplicates = duplicate_mask.sum() + + if num_duplicates == 0: + print("All household_id values are unique") + return households, persons + + print( + f"Found {num_duplicates} rows with duplicate household_id values:\n\t \ + {households[duplicate_mask].sort_values('household_id')}" + ) + + # Save original household_id + households["non_unique_household_id"] = households["household_id"] + persons["non_unique_household_id"] = persons["household_id"] + + # Create new unique IDs for each duplicate row + # Start new IDs from max existing ID + 1 + max_id = households["household_id"].max() + + # Assign a unique new ID to EACH row that has a duplicate household_id + # (not just unique combinations, since same household_id + MAZ can appear multiple times) + num_duplicates_to_fix = duplicate_mask.sum() + households.loc[duplicate_mask, "household_id"] = range( + max_id + 1, max_id + 1 + num_duplicates_to_fix + ) + + # Update persons table + # Join persons with households to get MAZ, then map to new household_id + persons_with_maz = persons.merge( + households[["household_id", "non_unique_household_id", "MAZ"]].drop_duplicates(), + left_on="non_unique_household_id", + right_on="non_unique_household_id", + how="left", + suffixes=("_old", "") + ) + + # For persons that matched, use the new household_id + if "household_id_old" in persons_with_maz.columns: + persons_with_maz["household_id"] = persons_with_maz["household_id"].fillna( + persons_with_maz["household_id_old"] + ) + persons_with_maz = persons_with_maz.drop(columns=["household_id_old"]) + + # Drop the MAZ column we added from the merge (keep original if exists) + if "MAZ" in persons.columns: + persons_with_maz = persons_with_maz.drop(columns=["MAZ"]) + + persons = persons_with_maz + + # Verify the fix + remaining_duplicates = households.duplicated(subset=["household_id"], keep=False).sum() + if remaining_duplicates > 0: + print(f"Warning: {remaining_duplicates} duplicate household_id values remain") + else: + print(f"Successfully created unique household_id values") + print(f"Original IDs saved to 'non_unique_household_id' column") + + return households, persons + + +def add_tothhs(land_use: pd.DataFrame, households: pd.DataFrame) -> pd.DataFrame: + """Add TOTHHS (total households per zone) to land_use if not present.""" + if "TOTHHS" not in land_use.columns: + tothhs = ( + households.groupby("MAZ") + .size() + .reindex(land_use.index) + .fillna(0) + .astype(int) + ) + land_use["TOTHHS"] = tothhs.values + print("Added TOTHHS to land_use") + else: + print("TOTHHS already exists in land_use") + return land_use + + +def add_totpop(land_use: pd.DataFrame, persons: pd.DataFrame, households: pd.DataFrame) -> pd.DataFrame: + """Add TOTPOP (total population per zone) to land_use if not present.""" + if "TOTPOP" not in land_use.columns: + # Join persons to households to get MAZ + if "MAZ" not in persons.columns: + persons_with_zone = persons.merge( + households[["household_id", "MAZ"]], + on="household_id", + how="left" + ) + else: + persons_with_zone = persons + totpop = ( + persons_with_zone.groupby("MAZ") + .size() + .reindex(land_use.index) + .fillna(0) + .astype(int) + ) + land_use["TOTPOP"] = totpop.values + print("Added TOTPOP to land_use") + else: + print("TOTPOP already exists in land_use") + return land_use + + +def add_acres(land_use: pd.DataFrame, maz_shp_file: str = None) -> pd.DataFrame: + """Add ACRES to land_use if not present, calculating from shapefile. + + Parameters + ---------- + land_use : pd.DataFrame + Land use table with MAZ index + maz_shp_file : str, optional + Path to MAZ shapefile. Required if ACRES field is missing. + + Returns + ------- + pd.DataFrame + Updated land_use with ACRES field + """ + if "ACRES" in land_use.columns: + print("ACRES already exists in land_use") + return land_use + + if maz_shp_file is None: + print("Warning: ACRES field not found and no maz_shp_file provided, skipping") + return land_use + + print(f"Reading shapefile: {maz_shp_file}") + gdf = gpd.read_file(maz_shp_file) + + # Identify the MAZ ID column in the shapefile + # Common variations: MAZ, MAZ_ID, maz, ID, etc. + maz_col = None + for col in gdf.columns: + if col.upper() in ['MAZ', 'MAZ_ID', 'ID', 'MAZ_NO']: + maz_col = col + break + + if maz_col is None: + print(f"Warning: Could not identify MAZ ID column in shapefile") + print(f"Available columns: {list(gdf.columns)}") + return land_use + + print(f"Using '{maz_col}' as MAZ identifier") + + # Calculate area in acres + # Convert to appropriate CRS if needed (shapefile should be in projected coordinates) + if gdf.crs is None: + print("Warning: Shapefile has no CRS defined, assuming units are in feet") + sq_units_per_acre = 43560 # square feet per acre + elif gdf.crs.is_geographic: + print(f"Warning: Shapefile is in geographic CRS ({gdf.crs}), reprojecting to UTM for accurate area calculation") + # Estimate UTM zone from centroid + gdf = gdf.to_crs(gdf.estimate_utm_crs()) + sq_units_per_acre = 4046.86 # square meters per acre + else: + # Check the linear units of the projected CRS + axis_info = gdf.crs.axis_info + if axis_info and len(axis_info) > 0: + unit_name = axis_info[0].unit_name.lower() + if 'foot' in unit_name or 'feet' in unit_name or 'ft' in unit_name: + sq_units_per_acre = 43560 # square feet per acre + print(f"CRS units: feet") + elif 'metre' in unit_name or 'meter' in unit_name or 'm' in unit_name: + sq_units_per_acre = 4046.86 # square meters per acre + print(f"CRS units: meters") + else: + raise RuntimeError("Warning: Could not determine CRS units from shapefile for ACRES calculation") + else: + raise RuntimeError("Warning: Could not determine CRS units from shapefile for ACRES calculation") + + # Calculate area in native units, then convert to acres + gdf['ACRES'] = gdf.geometry.area / sq_units_per_acre + + # Create a mapping from MAZ to ACRES + acres_map = gdf.set_index(maz_col)['ACRES'].to_dict() + + # Add ACRES to land_use based on MAZ index + land_use['ACRES'] = land_use.index.map(acres_map) + + # Check for missing values + missing_count = land_use['ACRES'].isna().sum() + if missing_count > 0: + print(f"Warning: {missing_count} MAZ zones have no matching geometry in shapefile") + # Fill missing with 0 or leave as NaN? + land_use['ACRES'] = land_use['ACRES'].fillna(0) + + # Reasonableness checks + min_acres = land_use['ACRES'].min() + max_acres = land_use['ACRES'].max() + mean_acres = land_use['ACRES'].mean() + median_acres = land_use['ACRES'].median() + + print(f"Added ACRES to land_use (calculated from shapefile)") + print(f" Min: {min_acres:.2f}, Max: {max_acres:.2f}, Mean: {mean_acres:.2f}, Median: {median_acres:.2f}") + + # Sanity checks for typical urban MAZ sizes + assert median_acres >= 0.1, f"Median ACRES ({median_acres:.4f}) seems too small. Check CRS units." + assert median_acres <= 1000, f"Median ACRES ({median_acres:.2f}) seems too large. Check CRS units." + # assert max_acres <= 50000, f"Max ACRES ({max_acres:.2f}) is very large. Some zones may be rural or external." + assert min_acres >= 0, f"Negative ACRES detected ({min_acres:.4f}). Check for invalid geometries." + + return land_use + + +def merge_maz_stop_walk(land_use: pd.DataFrame, maz_stop_walk_file: str = None) -> pd.DataFrame: + """Merge MAZ to stop walk distances to land_use. + + Parameters + ---------- + land_use : pd.DataFrame + Land use table with MAZ column + maz_stop_walk_file : str, optional + Path to MAZ stop walk distances CSV file. + + Returns + ------- + pd.DataFrame + Updated land_use with walk distance columns + """ + if maz_stop_walk_file is None: + print("No maz_stop_walk_file provided, skipping") + return land_use + + print(f"Reading MAZ stop walk file: {maz_stop_walk_file}") + maz_stop_walk = pd.read_csv(maz_stop_walk_file) + + # Check which columns will be added + new_cols = [col for col in maz_stop_walk.columns if col != 'maz' and col not in land_use.columns] + existing_cols = [col for col in maz_stop_walk.columns if col != 'maz' and col in land_use.columns] + + if existing_cols: + print(f" Columns already in land_use (skipping): {existing_cols}") + + if not new_cols: + print(" All columns already exist in land_use, skipping merge") + return land_use + + # Merge on MAZ + # Determine the MAZ column name in land_use (could be 'MAZ' or 'maz') + if 'MAZ' in land_use.columns: + land_use_maz_col = 'MAZ' + elif 'maz' in land_use.columns: + land_use_maz_col = 'maz' + else: + print("Warning: Could not find MAZ column in land_use") + return land_use + + # Merge only the new columns plus the join key + cols_to_merge = ['maz'] + new_cols + land_use = land_use.merge( + maz_stop_walk[cols_to_merge], + left_on=land_use_maz_col, + right_on='maz', + how='left' + ) + + # Drop the duplicate maz column from the merge if it was added + if 'maz' in land_use.columns and land_use_maz_col == 'MAZ': + land_use = land_use.drop(columns=['maz']) + + # Check for missing values + for col in new_cols: + missing_count = land_use[col].isna().sum() + if missing_count > 0: + print(f" Warning: {missing_count} MAZ zones have no {col} value, filling with 0") + land_use[col] = land_use[col].fillna(0) + + print(f" Added columns to land_use: {new_cols}") + + return land_use + + +def dataframes_equal(df1: pd.DataFrame, df2: pd.DataFrame) -> bool: + """Check if two DataFrames are equal.""" + if df1.shape != df2.shape: + return False + if list(df1.columns) != list(df2.columns): + return False + return df1.equals(df2) + + +def write_output( + settings: PreprocessorSettings, + households: pd.DataFrame, + persons: pd.DataFrame, + land_use: pd.DataFrame, + original_households: pd.DataFrame, + original_persons: pd.DataFrame, + original_land_use: pd.DataFrame, +) -> None: + """ + Write output files to the output directory. + + If overwriting input files (output_dir is None), only write files that changed. + """ + output_dir = settings.output_dir if settings.output_dir else settings.data_dir + output_dir.mkdir(parents=True, exist_ok=True) + + overwriting = settings.output_dir is None + + # Write households + households_path = output_dir / settings.households_file + if overwriting and dataframes_equal(households, original_households): + print(f"No changes to households, skipping write") + else: + households.to_csv(households_path, index=False) + print(f"Saved households to {households_path}") + + # Write persons + persons_path = output_dir / settings.persons_file + if overwriting and dataframes_equal(persons, original_persons): + print(f"No changes to persons, skipping write") + else: + persons.to_csv(persons_path, index=False) + print(f"Saved persons to {persons_path}") + + # Write land_use + land_use_path = output_dir / settings.land_use_file + if overwriting and dataframes_equal(land_use, original_land_use): + print(f"No changes to land_use, skipping write") + else: + land_use.to_csv(land_use_path, index=False) + print(f"Saved land_use to {land_use_path}") + + +def preprocess(settings: PreprocessorSettings) -> tuple[pd.DataFrame, pd.DataFrame, pd.DataFrame]: + """ + Main preprocessing function. + + Parameters + ---------- + settings : PreprocessorSettings + Configuration settings for the preprocessor + + Returns + ------- + tuple[pd.DataFrame, pd.DataFrame, pd.DataFrame] + Updated (households, persons, land_use) DataFrames + """ + # Load data + households, persons, land_use = load_data(settings) + + # Keep copies of originals for change detection + original_households = households.copy() + original_persons = persons.copy() + original_land_use = land_use.copy() + + # Fix duplicate household IDs + households, persons = fix_duplicate_household_ids(households, persons) + + # Add TOTHHS and TOTPOP + land_use = add_tothhs(land_use, households) + land_use = add_totpop(land_use, persons, households) + + # Add ACRES if needed + land_use = add_acres(land_use, settings.maz_shp_file) + + # Merge MAZ stop walk distances if provided + land_use = merge_maz_stop_walk(land_use, settings.maz_stop_walk_file) + + # Write output files + write_output( + settings, + households, + persons, + land_use, + original_households, + original_persons, + original_land_use, + ) + + return households, persons, land_use + + +if __name__ == "__main__": + start_time = time.time() + + # Load settings from YAML if path provided, otherwise use defaults + if len(sys.argv) > 1: + yaml_path = Path(sys.argv[1]) + if not yaml_path.exists(): + print(f"Error: YAML file not found: {yaml_path}") + sys.exit(1) + settings = PreprocessorSettings.from_yaml(yaml_path) + print(f"Loaded settings from {yaml_path}") + else: + settings = PreprocessorSettings() + print("Using default settings") + + preprocess(settings) + + elapsed_time = time.time() - start_time + print(f"\nPreprocessor completed in {elapsed_time:.2f} seconds") \ No newline at end of file diff --git a/resident/preprocessor_settings.yaml b/resident/preprocessor_settings.yaml new file mode 100644 index 0000000..a0b584c --- /dev/null +++ b/resident/preprocessor_settings.yaml @@ -0,0 +1,10 @@ +data_dir: C:\projects\odot_joint_estimation\pnr\tasks\run_dir_new_zones\input\SyntheticPopulation +# output_dir: null # null means overwrite input files +output_dir: C:\projects\odot_joint_estimation\pnr\tasks\run_dir_new_zones\input +households_file: households.csv +persons_file: persons.csv +land_use_file: land_use.csv + +maz_shp_file: C:\projects\odot_joint_estimation\metro_data_prep\MAZs_22333_2025-11-20\MAZs_22333_2025-11-20.shp + +maz_stop_walk_file: C:\projects\odot_joint_estimation\pnr\tasks\run_dir_new_zones\input\maz_stop_walk.csv \ No newline at end of file diff --git a/resident/simulation.py b/resident/simulation.py new file mode 100644 index 0000000..5d31553 --- /dev/null +++ b/resident/simulation.py @@ -0,0 +1,24 @@ +# ActivitySim +# See full license in LICENSE.txt. + +import sys +import argparse +import os +import pandas as pd +import warnings + +from activitysim.cli.run import add_run_args, run + +import extensions + +if __name__ == "__main__": + + warnings.simplefilter(action="ignore", category=pd.errors.PerformanceWarning) + + parser = argparse.ArgumentParser() + add_run_args(parser) + args = parser.parse_args() + + os.environ["MKL_NUM_THREADS"] = "1" + + sys.exit(run(args)) diff --git a/resident/test/test_cropped_dataset.py b/resident/test/test_cropped_dataset.py new file mode 100644 index 0000000..7c63c5b --- /dev/null +++ b/resident/test/test_cropped_dataset.py @@ -0,0 +1,47 @@ +import subprocess +import os +import sys + +def test_cropped_example(): + """ + Run the cropped dataset example. + Test passes if the model runs to completion, fails if it crashes. + """ + + # Get the path to simulation.py relative to this test file + test_dir = os.path.dirname(__file__) + resident_dir = os.path.dirname(test_dir) + file_path = os.path.join(resident_dir, "simulation.py") + + run_args = [ + "-c", os.path.join(resident_dir, "configs"), + "-d", os.path.join(resident_dir, "model_data/metro/data_cropped"), + "-o", os.path.join(resident_dir, "outputs/test") + ] + + # Run the simulation - subprocess.run with check=True will raise + # CalledProcessError if the process returns a non-zero exit code + try: + result = subprocess.run( + [sys.executable, file_path] + run_args, + check=True, + capture_output=True, + text=True + ) + print(result.stdout) + if result.stderr: + print(result.stderr, file=sys.stderr) + except subprocess.CalledProcessError as e: + # Print full output on failure so CI logs show what went wrong + print("=== STDOUT ===") + print(e.stdout) + print("=== STDERR ===") + print(e.stderr, file=sys.stderr) + raise # Re-raise to fail the test + + return True + + +if __name__ == "__main__": + test_cropped_example() + print("Test passed: Model ran to completion.") \ No newline at end of file diff --git a/skimming_and_assignment/maz_maz_stop_skims/.gitignore b/skimming_and_assignment/maz_maz_stop_skims/.gitignore new file mode 100644 index 0000000..f4f7aec --- /dev/null +++ b/skimming_and_assignment/maz_maz_stop_skims/.gitignore @@ -0,0 +1,4 @@ +# Folders +input/ +output/ +preprocessing/ \ No newline at end of file diff --git a/skimming_and_assignment/maz_maz_stop_skims/2zoneSkim.py b/skimming_and_assignment/maz_maz_stop_skims/2zoneSkim.py new file mode 100644 index 0000000..13b3e72 --- /dev/null +++ b/skimming_and_assignment/maz_maz_stop_skims/2zoneSkim.py @@ -0,0 +1,607 @@ +import pandas as pd +import pandana as pdna +import numpy as np +import yaml +import os +import geopandas as gpd +from datetime import datetime +from typing import Tuple, Dict, List +from dataclasses import dataclass +import logging + +@dataclass +class SkimParameters: + """Configuration parameters for skim generation read from YAML file""" + max_maz_maz_walk_dist_feet: int + max_maz_maz_bike_dist_feet: int + max_maz_local_bus_stop_walk_dist_feet: int + max_maz_premium_transit_stop_walk_dist_feet: int + walk_speed_mph: float + drive_speed_mph: float + + @classmethod + def from_yaml(cls, yaml_data: dict) -> 'SkimParameters': + """Create parameters from YAML configuration""" + mmms = yaml_data['mmms'] + return cls( + max_maz_maz_walk_dist_feet=int(mmms['max_maz_maz_walk_dist_feet']), + max_maz_maz_bike_dist_feet=int(mmms['max_maz_maz_bike_dist_feet']), + max_maz_local_bus_stop_walk_dist_feet=int(mmms['max_maz_local_bus_stop_walk_dist_feet']), + max_maz_premium_transit_stop_walk_dist_feet=int(mmms['max_maz_premium_transit_stop_walk_dist_feet']), + walk_speed_mph=float(mmms['walk_speed_mph']), + drive_speed_mph=float(mmms['drive_speed_mph']) + ) + +class NetworkBuilder: + """ + Handles network construction, and node assignment, and transit stop processing for transportation networks. + + This class processes raw node and link data to create a network and processes transit stops. It handles coordinate projections, network topology, + and centroid connections. + """ + + def __init__(self, nodes: gpd.GeoDataFrame, links: gpd.GeoDataFrame, + stops: pd.DataFrame, routes: pd.DataFrame, config: dict): + """ + Initialize the NetworkBuilder with nodes, links, and configuration. + + Args: + nodes (gpd.GeoDataFrame): GeoDataFrame containing node geometries and attributes. + Must include columns for node ID and coordinates. + links (gpd.GeoDataFrame): GeoDataFrame containing link geometries and attributes. + Must include columns for from/to nodes and length. + config (dict): Configuration dictionary containing: + - mmms (dict): Network construction parameters + - mmms_link_ref_id (str): Column name for link start node + - mmms_link_nref_id (str): Column name for link end node + - mmms_link_len (str): Column name for link length + + Note: + The network is built immediately upon initialization using _build_network(). + """ + self.nodes = nodes + self.links = links + self.stops = stops + self.routes = routes + self.config = config + self.network = self._build_network(nodes, links, config) + + @classmethod + def from_files(cls, model_inputs: str, config: dict) -> 'NetworkBuilder': + """ + Create a NetworkBuilder instance by reading nodes and links from shapefiles. + + This factory method handles reading and processing the raw input files to create + a properly configured NetworkBuilder instance. + + Args: + model_inputs (str): Path to directory containing input shapefiles + config (dict): Configuration dictionary containing: + - mmms (dict): File and processing parameters + - shapefile_node_name (str): Filename for nodes shapefile + - shapefile_name (str): Filename for links shapefile + + Returns: + NetworkBuilder: A fully initialized NetworkBuilder instance with processed + nodes and links. + """ + # Read and process nodes + nodes = gpd.read_file(os.path.join(model_inputs, config['mmms']['shapefile_node_name'])) + nodes = cls._process_nodes(nodes, config) + + # Read and process links + links = gpd.read_file(os.path.join(model_inputs, config['mmms']['shapefile_name'])) + links = cls._process_links(links, config) + + # Read and process stops and routes + stops = pd.read_csv(os.path.join(model_inputs, config['stop_attributes']['file'])) + routes = pd.read_csv(os.path.join(model_inputs, config['route_attributes']['file'])) + cols = [config['route_attributes']['rid_field'], + config['route_attributes']['mode']] + routes = routes.filter(cols) + stops = stops.merge(routes, + left_on=config['stop_attributes']['rid_field'], + right_on=config['route_attributes']['rid_field']) + network = cls._build_network(nodes, links, config) + stops = cls.process_transit_stops(stops, network, nodes, config) + + return cls(nodes, links, stops, routes, config) + + @staticmethod + def _process_nodes(nodes: gpd.GeoDataFrame, config: dict) -> gpd.GeoDataFrame: + """ + Process raw nodes GeoDataFrame by projecting and adding coordinates. + + Performs the following operations: + 1. Projects the geometries to project projected crs + 2. Sets NodeLev_ID as the index + 3. Extracts X and Y coordinates from the geometry + + Args: + nodes (gpd.GeoDataFrame): Raw nodes GeoDataFrame with geometry column + + Returns: + gpd.GeoDataFrame: Processed nodes with: + - Projected coordinates + - NodeLev_ID as index + - X and Y columns containing coordinates + """ + crs = config['settings']['epsg'] + nodes = nodes.to_crs(crs).set_index(config['stop_attributes']['id_field']) + nodes['X'] = nodes.geometry.x + nodes['Y'] = nodes.geometry.y + return nodes + + @staticmethod + def _process_links(links: gpd.GeoDataFrame, config: dict) -> gpd.GeoDataFrame: + """ + Process raw links GeoDataFrame. + + Args: + links: Raw links GeoDataFrame + + Returns: + Processed links GeoDataFrame + """ + return links.to_crs(config['settings']['epsg']) + + @classmethod + def _build_network(cls, nodes: gpd.GeoDataFrame, links: gpd.GeoDataFrame, config: dict) -> pdna.Network: + """Build pandana network from nodes and links""" + mmms = config['mmms'] + # nodes.index = nodes.index.astype('int32') + # links.index = links.index.astype('int32') + # links[mmms['mmms_link_ref_id']] = links[mmms['mmms_link_ref_id']].astype('int32') + # links[mmms['mmms_link_nref_id']] = links[mmms['mmms_link_nref_id']].astype('int32') + + # links[mmms['mmms_link_ref_id']] = links[mmms['mmms_link_ref_id']].astype('float64') + # links[mmms['mmms_link_nref_id']] = links[mmms['mmms_link_nref_id']].astype('float64') + + # print(links[mmms['mmms_link_ref_id']].dtype) + # print(links[mmms['mmms_link_nref_id']].dtype) + # print(links[mmms['mmms_link_len']].dtype) + # print("Before: ", links.index.dtype) + # links.index = links.index.astype('int32') + # print("After: ", links.index.dtype) + # print("NaN's:", links.isna().sum()) + + # links[mmms['mmms_link_len']] = links[mmms['mmms_link_len']].astype('int32') + + return pdna.Network( + nodes.X, + nodes.Y, + links[mmms['mmms_link_ref_id']], + links[mmms['mmms_link_nref_id']], + links[[mmms['mmms_link_len']]] / 5280.0, + twoway=mmms['two_way_network'] + ) + + @classmethod + def get_closest_net_node_to_MGRA(cls, nodes: gpd.GeoDataFrame, links: gpd.GeoDataFrame, + config: dict) -> pd.DataFrame: + """ + Gets closest network nodes to MAZ centroids. This is used to assign network nodes to MAZ centroids. This assigned nodes is then used to calculate skims. + + Args: + nodes: Processed nodes GeoDataFrame + links: Processed links GeoDataFrame + config: Configuration dictionary + + Returns: + DataFrame containing centroid information + """ + # Get closest network nodes for MAZ centroids, e.g. 'centroid connector' start and end nodes + maz_closest_network_node_id = cls._get_closest_network_nodes(nodes, links, config) + + # Create centroids DataFrame + centroids = cls._create_centroids_df(nodes, config) + + # Merge with closest network nodes. This will add the end node of the connector as the associated network node for the MGRA + return cls._merge_centroids_with_connector_end_nodes(centroids, maz_closest_network_node_id, + nodes, config) + + @staticmethod + def _get_closest_network_nodes(nodes: gpd.GeoDataFrame, + links: gpd.GeoDataFrame, + config: dict) -> pd.DataFrame: + """Get closest network nodes for MAZs""" + mmms = config['mmms'] + maz_id = mmms['mmms_node_maz_id'] + + # Return the 'centroid connector' start and end nodes + return links[links[mmms["mmms_link_ref_id"]].isin( + list(nodes[nodes[maz_id]!=0].index) + )][[mmms["mmms_link_ref_id"], mmms["mmms_link_nref_id"]]] + + @staticmethod + def _create_centroids_df(nodes: gpd.GeoDataFrame, config: dict) -> pd.DataFrame: + """Create initial centroids DataFrame""" + maz_nodes = nodes[nodes[config['mmms']['mmms_node_maz_id']]!=0] + return pd.DataFrame({ + 'X': maz_nodes.X, + 'Y': maz_nodes.Y, + 'MAZ': maz_nodes.MAZ, + 'MAZ_centroid_id': maz_nodes.index + }) + + @staticmethod + def _merge_centroids_with_connector_end_nodes(centroids: pd.DataFrame, + closest_nodes: pd.DataFrame, + nodes: gpd.GeoDataFrame, + config: dict) -> pd.DataFrame: + """Merge centroids with their closest network nodes at the nd of the connector""" + mmms = config['mmms'] + + centroids = pd.merge( + centroids, + closest_nodes, + left_on='MAZ_centroid_id', + right_on=mmms["mmms_link_ref_id"], + how='left' + ) + + centroids = centroids.rename(columns={mmms["mmms_link_nref_id"]: 'network_node_id'}) + + # Add network node coordinates + centroids["network_node_x"] = nodes["X"].loc[centroids["network_node_id"]].tolist() + centroids["network_node_y"] = nodes["Y"].loc[centroids["network_node_id"]].tolist() + + return centroids + + @staticmethod + def _process_stop_geometry(stops: pd.DataFrame, config: dict) -> gpd.GeoDataFrame: + """ + Convert stops to GeoDataFrame and project coordinates. + + Args: + stops (pd.DataFrame): Stops DataFrame with Longitude/Latitude + + Returns: + gpd.GeoDataFrame: Projected stops with updated coordinates + """ + crs = config['settings']['epsg'] + pd.set_option('display.float_format', lambda x: '%.9f' % x) + + gpd_stops = gpd.GeoDataFrame( + stops, + geometry=gpd.points_from_xy(stops.Longitude, stops.Latitude, crs='epsg:4326') + ) + gpd_stops = gpd_stops.to_crs(crs) + + gpd_stops['Longitude'] = gpd_stops['geometry'].x + gpd_stops['Latitude'] = gpd_stops['geometry'].y + + return gpd_stops + + @staticmethod + def _assign_network_nodes_to_stops( + stops: pd.DataFrame, + gpd_stops: gpd.GeoDataFrame, + net: pdna.Network, + nodes: pd.DataFrame + ) -> pd.DataFrame: + """ + Assign network nodes to stops. + + Args: + stops (pd.DataFrame): Stops DataFrame + gpd_stops (gpd.GeoDataFrame): Projected stops + net (pdna.Network): Network object + nodes (pd.DataFrame): Network nodes + + Returns: + pd.DataFrame: Stops with assigned network nodes and coordinates + """ + stops["network_node_id"] = net.get_node_ids(gpd_stops['Longitude'], gpd_stops['Latitude']) + stops["network_node_x"] = nodes["X"].loc[stops["network_node_id"]].tolist() + stops["network_node_y"] = nodes["Y"].loc[stops["network_node_id"]].tolist() + return stops + + @staticmethod + def _assign_transit_modes(stops: pd.DataFrame, config: dict) -> pd.DataFrame: + # FIXME: Add mode dict to configs + """ + Assign simplified transit modes to stops. + + Modes: + - L: Local + - E: Premium + - N: None (should not occur) + + Args: + stops (pd.DataFrame): Stops DataFrame with Mode column + + Returns: + pd.DataFrame: Stops with assigned simplified modes + """ + stops['Mode'] = np.where( + stops['Mode'].isin(config['modes']['local_modes']), 'L', + np.where(stops['Mode'].isin(config['modes']['prm_modes']), 'E', 'N') + ) + return stops + + @classmethod + def process_transit_stops(cls, stops: pd.DataFrame, network: pdna.Network, + nodes: pd.DataFrame, config: dict) -> pd.DataFrame: + """Process transit stops using provided network.""" + gpd_stops = cls._process_stop_geometry(stops, config) + stops = cls._assign_network_nodes_to_stops(stops, gpd_stops, network, nodes) + stops = cls._assign_transit_modes(stops, config) + return stops + +class SkimGenerator: + """Main class for generating walk, bike, and stop skims""" + + def __init__(self, network_builder: NetworkBuilder, params: SkimParameters, output_path: str): + self.network_builder = network_builder + self.params = params + self.output_path = output_path + self.net_centroids = self._get_closest_net_node(network_builder.nodes, network_builder.links, network_builder.config) + self.stops = self.network_builder.stops + + def _get_closest_net_node(self, nodes: gpd.GeoDataFrame, links: gpd.GeoDataFrame, config: dict) -> pd.DataFrame: + """Get centroids DataFrame""" + return self.network_builder.get_closest_net_node_to_MGRA(nodes, links, config) + + def generate_maz_maz_walk_skim(self) -> pd.DataFrame: + """Generate MAZ to MAZ walk skims""" + maz_pairs = self._create_maz_pairs(self.net_centroids) + walk_skim = self._get_walk_distances(maz_pairs, self.params.max_maz_maz_walk_dist_feet) + # Add intrazonal distances + walk_skim = self._add_intrazonal_distances(walk_skim) + walk_skim = self._convert_columns_to_type(walk_skim, {'OMAZ': 'uint16', 'DMAZ': 'uint16', 'i': 'uint16', 'j': 'uint16'}) + return walk_skim + + def generate_maz_maz_bike_skim(self) -> pd.DataFrame: + """Generate MAZ to MAZ bike skims""" + maz_pairs = self._create_maz_pairs(self.net_centroids) + bike_skim = self._get_bike_distances(maz_pairs, self.params.max_maz_maz_bike_dist_feet) + bike_skim = self._convert_columns_to_type(bike_skim, {'OMAZ': 'uint16', 'DMAZ': 'uint16'}) + return bike_skim + + def generate_maz_stop_walk_skim(self) -> pd.DataFrame: + """Generate MAZ to stop walk skims""" + maz_stop_pairs, maz_stop_output = self._create_maz_stop_pairs(self.net_centroids, self.stops) + stop_skim = self._get_stop_distances(maz_stop_pairs) + stop_skim = self._process_stop_skims_by_mode(stop_skim, maz_stop_output) + + return stop_skim.sort_values('maz') + + def _create_maz_pairs(self, centroids: pd.DataFrame) -> pd.DataFrame: + """Create all possible MAZ to MAZ pairs with their network nodes""" + o_m = np.repeat(centroids['MAZ'].tolist(), len(centroids)) + d_m = np.tile(centroids['MAZ'].tolist(), len(centroids)) + + pairs = pd.DataFrame({ + "OMAZ": o_m, + "DMAZ": d_m, + "OMAZ_NODE": np.repeat(centroids['network_node_id'].tolist(), len(centroids)), + "DMAZ_NODE": np.tile(centroids['network_node_id'].tolist(), len(centroids)), + "OMAZ_NODE_X": np.repeat(centroids['network_node_x'].tolist(), len(centroids)), + "OMAZ_NODE_Y": np.repeat(centroids['network_node_y'].tolist(), len(centroids)), + "DMAZ_NODE_X": np.tile(centroids['network_node_x'].tolist(), len(centroids)), + "DMAZ_NODE_Y": np.tile(centroids['network_node_y'].tolist(), len(centroids)) + }) + + pairs["DISTWALK"] = pairs.eval("(((OMAZ_NODE_X-DMAZ_NODE_X)**2 + (OMAZ_NODE_Y-DMAZ_NODE_Y)**2)**0.5) / 5280.0") + return pairs[pairs["OMAZ"] != pairs["DMAZ"]] + + def _create_maz_stop_pairs(self, centroids: pd.DataFrame, stops: pd.DataFrame, ) -> Tuple[pd.DataFrame, pd.DataFrame]: + """ + Build a table of MAZ to transit stop connections with initial distances. + + Creates a cross-join between MAZs and stops, calculating the straight-line + distance between each MAZ-stop pair. Distances are converted to miles. + + + """ + # Create cross product of MAZs and stops + o_m = np.repeat(centroids['MAZ'].tolist(), len(stops)) + o_m_nn = np.repeat(centroids['network_node_id'].tolist(), len(stops)) + d_t = np.tile(stops['NO'].tolist(), len(centroids)) + d_t_nn = np.tile(stops['network_node_id'].tolist(), len(centroids)) + o_m_x = np.repeat(centroids['network_node_x'].tolist(), len(stops)) + o_m_y = np.repeat(centroids['network_node_y'].tolist(), len(stops)) + d_t_x = np.tile(stops['network_node_x'].tolist(), len(centroids)) + d_t_y = np.tile(stops['network_node_y'].tolist(), len(centroids)) + mode = np.tile(stops['Mode'].tolist(), len(centroids)) + + # Create DataFrame + pairs = pd.DataFrame({ + "MAZ": o_m, + "stop": d_t, + "OMAZ_NODE": o_m_nn, + "DSTOP_NODE": d_t_nn, + "OMAZ_NODE_X": o_m_x, + "OMAZ_NODE_Y": o_m_y, + "DSTOP_NODE_X": d_t_x, + "DSTOP_NODE_Y": d_t_y, + "MODE": mode + }) + + # Calculate distances in miles + pairs["DISTANCE"] = pairs.eval( + "(((OMAZ_NODE_X-DSTOP_NODE_X)**2 + (OMAZ_NODE_Y-DSTOP_NODE_Y)**2)**0.5) / 5280.0" + ) + + #create stop distances output file + maz_stop_output = pd.DataFrame(centroids['MAZ']) + maz_stop_output = maz_stop_output.rename(columns = {'MAZ': 'maz'}) + maz_stop_output['maz'] = maz_stop_output['maz'].astype('int') + maz_stop_output.sort_values(by=['maz'], inplace=True) + + return pairs, maz_stop_output + + def _get_walk_distances(self, pairs: pd.DataFrame, max_dist: float) -> pd.DataFrame: + """Process walking distances between MAZ pairs + This finction also adds i, j ,and actual columns that are required by Java code for TNC routing""" + filtered = pairs[pairs["DISTWALK"] <= max_dist / 5280.0].copy().reset_index(drop=True) + filtered["DISTWALK"] = self.network_builder.network.shortest_path_lengths( + filtered["OMAZ_NODE"], filtered["DMAZ_NODE"]) + result = filtered[filtered["DISTWALK"] <= max_dist / 5280.0] + + # Add missing MAZs + result = self._add_missing_mazs(self.net_centroids, result, pairs, 'DISTWALK') + + # Add required fields for TNC routing + result[['i', 'j']] = result[['OMAZ', 'DMAZ']] + result['actual'] = result['DISTWALK'] / self.params.walk_speed_mph * 60.0 + + return result + + def _get_bike_distances(self, pairs: pd.DataFrame, max_dist: float) -> pd.DataFrame: + """Process bike distances between MAZ pairs""" + filtered = pairs[pairs["DISTWALK"] <= max_dist / 5280.0].copy() + filtered["DISTBIKE"] = self.network_builder.network.shortest_path_lengths( + filtered["OMAZ_NODE"], filtered["DMAZ_NODE"]) + result = filtered[filtered["DISTBIKE"] <= max_dist / 5280.0] + + # Add missing MAZs + result = self._add_missing_mazs(self.net_centroids, result, pairs, 'DISTBIKE') + + return result + + def _get_stop_distances(self, pairs: pd.DataFrame) -> pd.DataFrame: + """Process stop distances between MAZ pairs""" + filtered = pairs[(pairs["DISTANCE"] <= self.params.max_maz_local_bus_stop_walk_dist_feet / 5280.0) & (pairs['MODE'] == 'L') | + (pairs["DISTANCE"] <= self.params.max_maz_premium_transit_stop_walk_dist_feet / 5280.0) & (pairs['MODE'] == 'E')] + filtered.reset_index(drop=True, inplace=True) + filtered["DISTWALK"] = self.network_builder.network.shortest_path_lengths( + filtered["OMAZ_NODE"], filtered["DSTOP_NODE"]) + + result = filtered[(filtered["DISTWALK"] <= self.params.max_maz_local_bus_stop_walk_dist_feet / 5280.0) & (filtered['MODE'] == 'L') | + (filtered["DISTWALK"] <= self.params.max_maz_premium_transit_stop_walk_dist_feet / 5280.0) & (filtered['MODE'] == 'E')] + + return result + + def _process_stop_skims_by_mode(self, stop_skims: pd.DataFrame, maz_stop_output: pd.DataFrame) -> pd.DataFrame: + """ + Process stop skims by transit mode and merge with output DataFrame. + + Args: + stop_skims (pd.DataFrame): DataFrame containing stop skim data with columns: + - MODE: Transit mode ('L' or 'E') + - MAZ: MAZ ID + - DISTWALK: Walk distance + maz_stop_output (pd.DataFrame): Base DataFrame to merge results into + + Returns: + pd.DataFrame: Processed DataFrame with walk distances by mode: + - maz: MAZ ID + - walk_dist_local_bus: Walk distance to nearest local bus stop + - walk_dist_premium_transit: Walk distance to nearest premium transit stop + """ + modes = {"L": "local_bus", "E": "premium_transit"} + + for mode, mode_descr in modes.items(): + stop_skims_by_mode = ( + stop_skims[stop_skims.MODE == mode] + .groupby("MAZ")["DISTWALK"] + .min() + .reset_index() + ) + + stop_skims_by_mode = stop_skims_by_mode.rename( + { + "MAZ": "maz", + "DISTWALK": f"walk_dist_{mode_descr}", + }, + axis="columns", + ) + + maz_stop_output = maz_stop_output.merge( + stop_skims_by_mode, + on="maz", + how="outer" + ) + maz_stop_output[f"walk_dist_{mode_descr}"].fillna(999999, inplace=True) + + return maz_stop_output + + def _add_intrazonal_distances(self, skim: pd.DataFrame) -> pd.DataFrame: + """Add intrazonal distances based on 3 nearest neighbors""" + skim = skim.sort_values(['OMAZ', 'DISTWALK']) + skim.set_index(['OMAZ', 'DMAZ'], inplace=True) + unique_omaz = skim.index.get_level_values(0).unique() + # find the average of the closest 3 zones + means = skim.loc[(unique_omaz, slice(None)), 'DISTWALK'].groupby(level=0).head(3).groupby(level=0).mean() + intra_skims = pd.DataFrame({ + 'OMAZ': unique_omaz, + 'DMAZ': unique_omaz, + 'DISTWALK': means.values/2, + 'i': unique_omaz, + 'j': unique_omaz, + 'actual': (means.values/self.params.walk_speed_mph * 60.0) / 2 + }).set_index(['OMAZ', 'DMAZ']) + + return pd.concat([skim, intra_skims], axis=0).reset_index() + + def _add_missing_mazs(self, centroids: pd.DataFrame, skim_table: pd.DataFrame, + cost_table: pd.DataFrame, dist_col: str = 'DISTWALK') -> pd.DataFrame: + """Add missing MAZs to skim table since some MAZs will not be within distance of a stop or each other. + This will make sure we will have skims for all MAZs in the region.""" + missing_maz = centroids[~centroids['MAZ'].isin(skim_table['OMAZ'])][['MAZ']] + missing_maz = missing_maz.rename(columns={'MAZ': 'OMAZ'}) + + filtered_cost = cost_table[cost_table['OMAZ'] != cost_table['DMAZ']] + if dist_col != 'DISTWALK': + filtered_cost = filtered_cost.rename(columns={'DISTWALK': dist_col}) + sorted_cost = filtered_cost.sort_values(dist_col) + grouped_cost = sorted_cost.groupby('OMAZ').agg({ + 'DMAZ': 'first', + dist_col: 'first' + }).reset_index() + + missing_maz = missing_maz.merge(grouped_cost, on='OMAZ', how='left') + skim_table = pd.concat([skim_table[["OMAZ", "DMAZ", dist_col]], missing_maz]).sort_values(["OMAZ", "DMAZ"]) + + return skim_table + + def _convert_columns_to_type(self, df: pd.DataFrame, columns: Dict[str, str]) -> pd.DataFrame: + """ + Convert specified columns in a DataFrame to a given data type. + + Args: + df (pd.DataFrame): The DataFrame to convert. + columns (Dict[str, str]): A dictionary where keys are column names and values are the target data types. + + Returns: + pd.DataFrame: The DataFrame with converted columns. + """ + return df.astype(columns) + +def main(path: str): + """Main execution function""" + # Load configuration + with open(os.path.join(path, "2zoneSkim_params.yaml"), 'r') as f: + config = yaml.safe_load(f) + + params = SkimParameters.from_yaml(config) + model_inputs = os.path.join(path, "input") + output_path = os.path.join(path, "output") + + # Create network builder using class method + network_builder = NetworkBuilder.from_files(model_inputs, config) + + # Initialize skim generator + skim_generator = SkimGenerator(network_builder, params, output_path) + + # Generate and save skims + if config['settings']['run_maz_maz_walk']: + print(f"{datetime.now().strftime('%H:%M:%S')} Generating MAZ-MAZ walk skims...") + walk_skim = skim_generator.generate_maz_maz_walk_skim() + walk_skim.to_csv(os.path.join(output_path, config['settings']['maz_maz_walk_output']), index=False) + + if config['settings']['run_maz_maz_bike']: + print(f"{datetime.now().strftime('%H:%M:%S')} Generating MAZ-MAZ bike skims...") + bike_skim = skim_generator.generate_maz_maz_bike_skim() + bike_skim.to_csv(os.path.join(output_path, config['settings']['maz_maz_bike_output']), index=False) + + if config['settings']['run_maz_stop_walk']: + print(f"{datetime.now().strftime('%H:%M:%S')} Generating MAZ-stop walk skims...") + stop_skim = skim_generator.generate_maz_stop_walk_skim() + stop_skim.to_csv(os.path.join(output_path, config['settings']['maz_stop_walk_output']), index=False) + +if __name__ == "__main__": + import sys + main(sys.argv[1]) \ No newline at end of file diff --git a/skimming_and_assignment/maz_maz_stop_skims/2zoneSkim_params.yaml b/skimming_and_assignment/maz_maz_stop_skims/2zoneSkim_params.yaml new file mode 100644 index 0000000..876ef77 --- /dev/null +++ b/skimming_and_assignment/maz_maz_stop_skims/2zoneSkim_params.yaml @@ -0,0 +1,65 @@ +# Recommend to use SANDAG's python environment: +# https://github.com/SANDAG/ABM/blob/main/src/asim/scripts/environment.yml + +# Preprocessing settings +preprocessing: + input_dir: data + links_file: "Network_link.shp" + nodes_file: "Network_node.shp" + maz_file: "MAZs_22333_2025-11-20.shp" + stops: "Stops.csv" + routes: "LineRoutes.csv" + network_epsg: 2913 + output_dir: input_test + +# Project settings +settings: + run_maz_maz_walk: True + run_maz_maz_bike: False + run_maz_stop_walk: False + maz_maz_walk_output: "maz_maz_walk.csv" + maz_maz_bike_output: "maz_maz_bike.csv" + maz_stop_walk_output: "maz_stop_walk.csv" + input_dir: input + output_dir: output + epsg: 2838 # projected crs of project + +# Stop attributes CSV, expectd in input_dir +# One stop (row) per transit route +stop_attributes: + file: "stops.csv" + id_field: "NO" + x_field: "Latitude" # epsg:4326 + y_field: "Longitude" # epsg:4326 + rid_field: "Route_ID" + +# Route attributes CSV, expected in input_dir +route_attributes: + file: "routes.csv" + rid_field: "Route_ID" + mode: "Mode" + +# Modes +modes: + prm_modes: ['a', 'l', 'r'] # premium + local_modes: ['b', 'e'] + +# Network and nodes shp, expected in input_dir +# Network must include connector links +# Nodes must include connector centroids (MAZ centroids) as nodes +# Node id must be consistent across network and node files +mmms: + shapefile_name: "links.shp" + shapefile_node_name: "nodes.shp" + two_way_network: False # True if links are bidirectional, False if one link per direction + mmms_link_ref_id: "FROMNODENO" + mmms_link_nref_id: "TONODENO" + mmms_link_id: "NO" # not used + mmms_link_len: "length" # feet + mmms_node_maz_id: "MAZ" + max_maz_maz_walk_dist_feet: 15840 # int + max_maz_maz_bike_dist_feet: 26400 # int + max_maz_local_bus_stop_walk_dist_feet: 3960 # int + max_maz_premium_transit_stop_walk_dist_feet: 6600 # int + walk_speed_mph: 3.0 # float + drive_speed_mph: 25.0 # float \ No newline at end of file diff --git a/skimming_and_assignment/maz_maz_stop_skims/2zoneSkim_preprocessor.py b/skimming_and_assignment/maz_maz_stop_skims/2zoneSkim_preprocessor.py new file mode 100644 index 0000000..d7480e3 --- /dev/null +++ b/skimming_and_assignment/maz_maz_stop_skims/2zoneSkim_preprocessor.py @@ -0,0 +1,236 @@ +# This script prepares inputs for 2zoneSkim.py using Visum outputs: +# - Links: [TSYSSET, TYPNO] +# - Nodes: [NO] +# - MAZs: (provided by user) +# - Transit stops: transit stops with list of routes; [STOP_ID, Lines] +# - Routes/Lines: [ROUTE_ID, SYSMODE] + +# And generates the following outputs: +# - MAZ centroids: [MAZs_NO] +# - Connectors: from MAZ centorid to nearest node on walk network +# - Nodes: network nodes + MAZ centroids (consistent node numbering); [MAZ, NO] +# - Links: links + connectors (consistent node numbering; [FROMNODENO, TONODENO] +# - Routes: one route per row; [Route_ID, Mode] +# - Stops: one stop per row; [NO, Latitute, Longitute, Route_ID] + +import yaml +import os +import sys +import geopandas as gpd +import numpy as np +import pandas as pd +from shapely.geometry import LineString + +class ConfigLoader(): + def __init__(self, config_file): + self.config_file = config_file + self.config = self.load_config() + + def load_config(self): + with open(self.config_file, 'r') as file: + return yaml.load(file, Loader = yaml.FullLoader) + +class DataLoader(): + def __init__(self, config): + self.config = config.config + self.links = None + self.mazs = None + self.nodes = None + self.epsg = None + self.routes = None + self.stops = None + self.maz_centroids = None + self.load_data() + + def load_data(self): + os.chdir(self.config['preprocessing']['input_dir']) + self.links = gpd.read_file(self.config['preprocessing']['links_file']) + self.nodes = gpd.read_file(self.config['preprocessing']['nodes_file']).rename(columns = {'NO':'NODE_NO'}) + self.mazs = gpd.read_file(self.config['preprocessing']['maz_file']).rename(columns = {'MAZ_NO':'MAZ'}) + self.epsg = self.config['preprocessing']['network_epsg'] + self.routes = pd.read_csv(self.config['preprocessing']['routes']) + self.stops = pd.read_csv(self.config['preprocessing']['stops']) + self.maz_centroids = self._get_maz_centroids() + + def _get_maz_centroids(self): + centroids = self.mazs[['MAZ', 'geometry']].copy() + centroids['centroid_geom'] = centroids['geometry'].centroid + centroids = centroids[['MAZ', 'centroid_geom']].rename(columns={'centroid_geom':'geometry'}) + return centroids + +def create_centroid_connectors(inputs): + """ + Create connector links that go from the MAZ centroid to the nearest node on the walk network + """ + # Load data + links = inputs.links[['NO', 'FROMNODENO', 'TONODENO', 'TYPENO', 'TSYSSET', 'geometry']] + maz_centroids = inputs.maz_centroids + nodes = inputs.nodes[['geometry', 'NODE_NO']] + + # Extract walk network + links['TSYSSET'] = np.where( + (links['TYPENO'] == '0') & (links['TSYSSET'].isna()), + "wlk", + links['TSYSSET'] + ) + walk_network_links = links[ + (links['TSYSSET'].str.contains("wlk", na=False) & + (~links['TYPENO'].isin(['1', '4', '10', '19', '20', '70', '71', '79'])))] + + walk_network_links['TYPENO'].value_counts().sort_index() + + # Remove nodes that are not in the walk network + walk_network_nodes = nodes[nodes['NODE_NO'].isin(walk_network_links['FROMNODENO']) | nodes['NODE_NO'].isin(walk_network_links['TONODENO'])] + + # Check if centroids and nodes have the same CRS + if maz_centroids.crs != walk_network_nodes.crs: + maz_centroids = maz_centroids.to_crs(walk_network_nodes.crs) + + # Find nearest walk node for each centroid + centroids_to_nearest_node = gpd.sjoin_nearest( + maz_centroids, walk_network_nodes, how='left', distance_col='distance' + ) + + # Create connector polyline from centroid to nearest node + centroids_to_nearest_node = centroids_to_nearest_node.merge( + nodes[['NODE_NO', 'geometry']], + on='NODE_NO', + how='left', + suffixes=('_left', '_right') + ) + + centroids_to_nearest_node['connector'] = centroids_to_nearest_node.apply( + lambda row: LineString([row['geometry_right'], row['geometry_left']]), axis=1 + ) + + # Rename columns + connectors = centroids_to_nearest_node[['MAZ', 'NODE_NO', 'connector']].rename( + columns={'connector': 'geometry'} + ) + connectors = gpd.GeoDataFrame(connectors, geometry='geometry', crs=walk_network_nodes.crs) + + # Create new node id's for MAZ centroids + no_range = np.arange(nodes['NODE_NO'].max() + 1, nodes['NODE_NO'].max() + 1 + len(maz_centroids)) + connectors = connectors.sort_values(by = 'MAZ') + connectors['MAZ_NO'] = no_range + + return connectors, walk_network_nodes, walk_network_links + +def prepare_nodes(inputs, walk_network_nodes): + """ + Prepare nodes file by adding MAZ centroids as nodes and adjusting numbering + """ + # Load data + maz_centroids = inputs.maz_centroids + walk_network_nodes = walk_network_nodes.rename(columns = {'NODE_NO': 'NO'}) + + # Make sure both are the same CRS + if maz_centroids.crs != walk_network_nodes.crs: + maz_centroids = maz_centroids.to_crs(walk_network_nodes.crs) + + # Add MAZ column + walk_network_nodes['MAZ'] = 0 + + # Add node numbering consistent with links/connectors + no_range = np.arange(walk_network_nodes['NO'].max() + 1, walk_network_nodes['NO'].max() + 1 + len(maz_centroids)) + maz_centroids = maz_centroids.sort_values(by = 'MAZ') + maz_centroids['NO'] = no_range + + # Join walk nodes with MAZ centroids + nodes_merged = pd.concat([walk_network_nodes[[ 'geometry', 'NO', 'MAZ']], maz_centroids[['geometry', 'NO', 'MAZ']]], ignore_index=True) + + return nodes_merged + +def prepare_links(connectors, walk_network_links, walk_network_nodes): + """ + Add connector links to network links + """ + # Network is bi-directionaly (one link per direction) + # Add FROMNODENO and TONODENO to connectors + connectors['FROMNODENO'] = connectors['MAZ_NO'] + connectors['TONODENO'] = connectors['NODE_NO'] + + # Duplicate connectors to create other direction link + connectors_2 = connectors.copy() + connectors_2['FROMNODENO'] = connectors_2['NODE_NO'] + connectors_2['TONODENO'] = connectors_2['MAZ_NO'] + + # Make sure they are the same crs + if connectors.crs != walk_network_links.crs: + connectors = connectors.to_crs(walk_network_links) + + # Merge connectors with links + keep_cols = ['FROMNODENO', 'TONODENO', 'geometry'] + links_merged = pd.concat([ + walk_network_links[keep_cols], + connectors[keep_cols], + connectors_2[keep_cols]], + ignore_index = True, + ) + + assert (links_merged['FROMNODENO'].isin(walk_network_nodes['NO']).all()) and \ + (links_merged['TONODENO'].isin(walk_network_nodes['NO']).all()), "not all nodes in links are in nodes list" + + # Add length + links_merged['length'] = links_merged['geometry'].length + + return links_merged + +def prepare_transit_routes_and_stops(inputs): + """ + Rename Routes cols and + """ + routes = inputs.routes + stops = inputs.stops + + # Prepare routes + routes.rename(columns={'TSysCode':'Mode', + 'LineName':'Route_ID'}, inplace=True) + + # Create dictionary for mapping + routes_mode_dict = dict(zip(routes['Route_ID'], routes['Mode'])) + + # Prepare stops + # Convert coordinates to epsg 4326 + stops_gdf = gpd.GeoDataFrame( + stops, + geometry = gpd.points_from_xy(stops['X-Coordinate'], stops['Y-Coordinate']), + crs = inputs.epsg + ) + stops_gdf = stops_gdf.to_crs(epsg=4326) + stops_gdf['Latitude'] = stops_gdf.geometry.y + stops_gdf['Longitude'] = stops_gdf.geometry.x + + stops_gdf.rename(columns = { + 'StopID':'NO'}, inplace=True) + + # Explode mode - need route per row + stops_gdf['Route_ID'] = stops['Lines'].apply(lambda x: [i for i in x.split(',')]) + stops_gdf = stops_gdf.explode('Route_ID') + + # Format + keep_cols = ['NO', 'Route_ID','Latitude', 'Longitude'] + + return routes, stops_gdf[keep_cols] + +def main(config_file): + config = ConfigLoader(config_file) + inputs = DataLoader(config) + + # Process data + connectors, walk_network_nodes, walk_network_links = create_centroid_connectors(inputs) + nodes = prepare_nodes(inputs, walk_network_nodes) + links = prepare_links(connectors, walk_network_links, nodes) + routes, stops = prepare_transit_routes_and_stops(inputs) + + # Export + output_dir = config.config['preprocessing']['output_dir'] + os.chdir(os.path.dirname(os.path.abspath(__file__))) + os.makedirs(output_dir, exist_ok=True) + nodes.to_file(os.path.join(output_dir, 'nodes.shp')) + links.to_file(os.path.join(output_dir, 'links.shp')) + routes.to_csv(os.path.join(output_dir, 'routes.csv')) + stops.to_csv(os.path.join(output_dir, 'stops.csv')) + +if __name__ == "__main__": + main(sys.argv[1]) \ No newline at end of file diff --git a/skimming_and_assignment/visum/.gitattributes b/skimming_and_assignment/visum/.gitattributes new file mode 100644 index 0000000..dd16bb2 --- /dev/null +++ b/skimming_and_assignment/visum/.gitattributes @@ -0,0 +1,3 @@ +# Auto detect text files and perform LF normalization +* text=auto +*.ver filter=lfs diff=lfs merge=lfs -text diff --git a/skimming_and_assignment/visum/.gitignore b/skimming_and_assignment/visum/.gitignore new file mode 100644 index 0000000..3865438 --- /dev/null +++ b/skimming_and_assignment/visum/.gitignore @@ -0,0 +1,2 @@ +*__pycache__* +*.ver diff --git a/skimming_and_assignment/visum/config/CapacitySettingBasedOnEMME.xml b/skimming_and_assignment/visum/config/CapacitySettingBasedOnEMME.xml new file mode 100644 index 0000000..9b9c736 --- /dev/null +++ b/skimming_and_assignment/visum/config/CapacitySettingBasedOnEMME.xml @@ -0,0 +1,871 @@ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + 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b/skimming_and_assignment/visum/filters/asn_linkApproach.fil new file mode 100644 index 0000000..bae2c95 --- /dev/null +++ b/skimming_and_assignment/visum/filters/asn_linkApproach.fil @@ -0,0 +1,34 @@ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + diff --git a/skimming_and_assignment/visum/filters/asn_linkCBD.fil b/skimming_and_assignment/visum/filters/asn_linkCBD.fil new file mode 100644 index 0000000..d54f004 --- /dev/null +++ b/skimming_and_assignment/visum/filters/asn_linkCBD.fil @@ -0,0 +1,17 @@ + + + + + + + + + + + + + + + + + diff --git a/skimming_and_assignment/visum/filters/asn_linkFreeway.fil b/skimming_and_assignment/visum/filters/asn_linkFreeway.fil new file mode 100644 index 0000000..4a55adb --- /dev/null +++ b/skimming_and_assignment/visum/filters/asn_linkFreeway.fil @@ -0,0 +1,20 @@ + + + + + + + + + + + + + + + + + + + + diff --git a/skimming_and_assignment/visum/filters/asn_linkHOV.fil b/skimming_and_assignment/visum/filters/asn_linkHOV.fil new file mode 100644 index 0000000..840bb69 --- /dev/null +++ b/skimming_and_assignment/visum/filters/asn_linkHOV.fil @@ -0,0 +1,14 @@ + + + + + + + + + + + + + + diff --git a/skimming_and_assignment/visum/filters/asn_linkNonFreeway.fil b/skimming_and_assignment/visum/filters/asn_linkNonFreeway.fil new file mode 100644 index 0000000..5339b79 --- /dev/null +++ b/skimming_and_assignment/visum/filters/asn_linkNonFreeway.fil @@ -0,0 +1,20 @@ + + + + + + + + + + + + + + + + + + + + diff --git a/skimming_and_assignment/visum/filters/asn_linkRamp.fil b/skimming_and_assignment/visum/filters/asn_linkRamp.fil new file mode 100644 index 0000000..e9323c4 --- /dev/null +++ b/skimming_and_assignment/visum/filters/asn_linkRamp.fil @@ -0,0 +1,14 @@ + + + + + + + + + + + + + + diff --git a/skimming_and_assignment/visum/filters/asn_nodeMetNetIntersection.fil b/skimming_and_assignment/visum/filters/asn_nodeMetNetIntersection.fil new file mode 100644 index 0000000..5e1e169 --- /dev/null +++ b/skimming_and_assignment/visum/filters/asn_nodeMetNetIntersection.fil @@ -0,0 +1,28 @@ + + + + + + + + + + + + + + + + + + + + + + + + + + + + diff --git a/skimming_and_assignment/visum/filters/asn_turnHOV.fil b/skimming_and_assignment/visum/filters/asn_turnHOV.fil new file mode 100644 index 0000000..ea16b28 --- /dev/null +++ b/skimming_and_assignment/visum/filters/asn_turnHOV.fil @@ -0,0 +1,22 @@ + + + + + + + + + + + + + + + + + + + + + + diff --git a/skimming_and_assignment/visum/filters/linkMetNet.fil b/skimming_and_assignment/visum/filters/linkMetNet.fil new file mode 100644 index 0000000..31c8fa4 --- /dev/null +++ b/skimming_and_assignment/visum/filters/linkMetNet.fil @@ -0,0 +1,14 @@ + + + + + + + + + + + + + + diff --git a/skimming_and_assignment/visum/filters/linkMetered.fil b/skimming_and_assignment/visum/filters/linkMetered.fil new file mode 100644 index 0000000..3e1caba --- /dev/null +++ b/skimming_and_assignment/visum/filters/linkMetered.fil @@ -0,0 +1,31 @@ + + + + + + + + + + diff --git a/skimming_and_assignment/visum/filters/linkRamps_AM.fil b/skimming_and_assignment/visum/filters/linkRamps_AM.fil new file mode 100644 index 0000000..130aaff --- /dev/null +++ b/skimming_and_assignment/visum/filters/linkRamps_AM.fil @@ -0,0 +1,17 @@ + + + + + + + + diff --git a/skimming_and_assignment/visum/filters/linkRamps_MD.fil b/skimming_and_assignment/visum/filters/linkRamps_MD.fil new file mode 100644 index 0000000..9f47e4f --- /dev/null +++ b/skimming_and_assignment/visum/filters/linkRamps_MD.fil @@ -0,0 +1,17 @@ + + + + + + + + diff --git a/skimming_and_assignment/visum/filters/linkRamps_PM.fil b/skimming_and_assignment/visum/filters/linkRamps_PM.fil new file mode 100644 index 0000000..259e687 --- /dev/null +++ b/skimming_and_assignment/visum/filters/linkRamps_PM.fil @@ -0,0 +1,17 @@ + + + + + + + + diff --git a/skimming_and_assignment/visum/filters/linkTruckPath1.fil b/skimming_and_assignment/visum/filters/linkTruckPath1.fil new file mode 100644 index 0000000..83ce163 --- /dev/null +++ b/skimming_and_assignment/visum/filters/linkTruckPath1.fil @@ -0,0 +1,17 @@ + + + + + + + + diff --git a/skimming_and_assignment/visum/filters/linkTruckPath2.fil b/skimming_and_assignment/visum/filters/linkTruckPath2.fil new file mode 100644 index 0000000..1a46bd9 --- /dev/null +++ b/skimming_and_assignment/visum/filters/linkTruckPath2.fil @@ -0,0 +1,17 @@ + + + + + + + + diff --git a/skimming_and_assignment/visum/filters/linkTruckPath4.fil b/skimming_and_assignment/visum/filters/linkTruckPath4.fil new file mode 100644 index 0000000..6288d76 --- /dev/null +++ b/skimming_and_assignment/visum/filters/linkTruckPath4.fil @@ -0,0 +1,17 @@ + + + + + + + + diff --git a/skimming_and_assignment/visum/outputs/assignment/feedback/.gitignore b/skimming_and_assignment/visum/outputs/assignment/feedback/.gitignore new file mode 100644 index 0000000..86d0cb2 --- /dev/null +++ b/skimming_and_assignment/visum/outputs/assignment/feedback/.gitignore @@ -0,0 +1,4 @@ +# Ignore everything in this directory +* +# Except this file +!.gitignore \ No newline at end of file diff --git a/skimming_and_assignment/visum/outputs/assignment/final_assign/.gitignore b/skimming_and_assignment/visum/outputs/assignment/final_assign/.gitignore new file mode 100644 index 0000000..86d0cb2 --- /dev/null +++ b/skimming_and_assignment/visum/outputs/assignment/final_assign/.gitignore @@ -0,0 +1,4 @@ +# Ignore everything in this directory +* +# Except this file +!.gitignore \ No newline at end of file diff --git a/skimming_and_assignment/visum/outputs/reports/.gitignore b/skimming_and_assignment/visum/outputs/reports/.gitignore new file mode 100644 index 0000000..86d0cb2 --- /dev/null +++ b/skimming_and_assignment/visum/outputs/reports/.gitignore @@ -0,0 +1,4 @@ +# Ignore everything in this directory +* +# Except this file +!.gitignore \ No newline at end of file diff --git a/skimming_and_assignment/visum/outputs/skims/.gitignore b/skimming_and_assignment/visum/outputs/skims/.gitignore new file mode 100644 index 0000000..86d0cb2 --- /dev/null +++ b/skimming_and_assignment/visum/outputs/skims/.gitignore @@ -0,0 +1,4 @@ +# Ignore everything in this directory +* +# Except this file +!.gitignore \ No newline at end of file diff --git a/skimming_and_assignment/visum/outputs/trips/logs/.gitignore b/skimming_and_assignment/visum/outputs/trips/logs/.gitignore new file mode 100644 index 0000000..86d0cb2 --- /dev/null +++ b/skimming_and_assignment/visum/outputs/trips/logs/.gitignore @@ -0,0 +1,4 @@ +# Ignore everything in this directory +* +# Except this file +!.gitignore \ No newline at end of file diff --git a/skimming_and_assignment/visum/outputs/trips/model/peak/.gitignore b/skimming_and_assignment/visum/outputs/trips/model/peak/.gitignore new file mode 100644 index 0000000..86d0cb2 --- /dev/null +++ b/skimming_and_assignment/visum/outputs/trips/model/peak/.gitignore @@ -0,0 +1,4 @@ +# Ignore everything in this directory +* +# Except this file +!.gitignore \ No newline at end of file diff --git a/skimming_and_assignment/visum/outputs/trips/model/reports/.gitignore b/skimming_and_assignment/visum/outputs/trips/model/reports/.gitignore new file mode 100644 index 0000000..86d0cb2 --- /dev/null +++ b/skimming_and_assignment/visum/outputs/trips/model/reports/.gitignore @@ -0,0 +1,4 @@ +# Ignore everything in this directory +* +# Except this file +!.gitignore \ No newline at end of file diff --git a/skimming_and_assignment/visum/outputs/trips/model_hbo/.gitignore b/skimming_and_assignment/visum/outputs/trips/model_hbo/.gitignore new file mode 100644 index 0000000..86d0cb2 --- /dev/null +++ b/skimming_and_assignment/visum/outputs/trips/model_hbo/.gitignore @@ -0,0 +1,4 @@ +# Ignore everything in this directory +* +# Except this file +!.gitignore \ No newline at end of file diff --git a/skimming_and_assignment/visum/outputs/trips/model_hbr/.gitignore b/skimming_and_assignment/visum/outputs/trips/model_hbr/.gitignore new file mode 100644 index 0000000..86d0cb2 --- /dev/null +++ b/skimming_and_assignment/visum/outputs/trips/model_hbr/.gitignore @@ -0,0 +1,4 @@ +# Ignore everything in this directory +* +# Except this file +!.gitignore \ No newline at end of file diff --git a/skimming_and_assignment/visum/outputs/trips/model_hbs/.gitignore b/skimming_and_assignment/visum/outputs/trips/model_hbs/.gitignore new file mode 100644 index 0000000..86d0cb2 --- /dev/null +++ b/skimming_and_assignment/visum/outputs/trips/model_hbs/.gitignore @@ -0,0 +1,4 @@ +# Ignore everything in this directory +* +# Except this file +!.gitignore \ No newline at end of file diff --git a/skimming_and_assignment/visum/outputs/trips/model_hbw/.gitignore b/skimming_and_assignment/visum/outputs/trips/model_hbw/.gitignore new file mode 100644 index 0000000..86d0cb2 --- /dev/null +++ b/skimming_and_assignment/visum/outputs/trips/model_hbw/.gitignore @@ -0,0 +1,4 @@ +# Ignore everything in this directory +* +# Except this file +!.gitignore \ No newline at end of file diff --git a/skimming_and_assignment/visum/outputs/trips/model_nh/.gitignore b/skimming_and_assignment/visum/outputs/trips/model_nh/.gitignore new file mode 100644 index 0000000..86d0cb2 --- /dev/null +++ b/skimming_and_assignment/visum/outputs/trips/model_nh/.gitignore @@ -0,0 +1,4 @@ +# Ignore everything in this directory +* +# Except this file +!.gitignore \ No newline at end of file diff --git a/skimming_and_assignment/visum/outputs/trips/model_sc/.gitignore b/skimming_and_assignment/visum/outputs/trips/model_sc/.gitignore new file mode 100644 index 0000000..86d0cb2 --- /dev/null +++ b/skimming_and_assignment/visum/outputs/trips/model_sc/.gitignore @@ -0,0 +1,4 @@ +# Ignore everything in this directory +* +# Except this file +!.gitignore \ No newline at end of file diff --git a/skimming_and_assignment/visum/scripts/AssignmentMetrics_Export.py b/skimming_and_assignment/visum/scripts/AssignmentMetrics_Export.py new file mode 100644 index 0000000..9b43438 --- /dev/null +++ b/skimming_and_assignment/visum/scripts/AssignmentMetrics_Export.py @@ -0,0 +1,837 @@ +# SRTC: Full script to handle Assignment Summary, Model Reporting, and Results export. Uses same timestamped folder for all outputs +# Adapted to LCOG, only outputs Assignment Summary + +""" +created 5/13/2025 + +@author: luke.gordon + +""" + +# Libraries +import VisumPy.helpers +import VisumPy.excel +import pandas as pd +import numpy as np +import csv +from datetime import datetime +import math +import os.path + + +# Pull timestamp for folder name from Visum network attribute +date = Visum.Net.AttValue("output_date") + +# Read user inputs from Visum +proj_dir = Visum.GetPath(2) + +# Formatting functions +# Formatting for columns +# Create formatting function for large numbers (no decimals and thousand commas) +def format_commas(column): + return column.apply(lambda x: '{:,.0f}'.format(x) if pd.notna(x) else None) +# Create formatting function for percentages (2 decimals and percent symbol, also multiplies by 100) +def format_percent(column): + return column.apply(lambda x: '{:.2%}'.format(x) if pd.notna(x) else None) +# Create formatting function for small numbers (2 decimals) +def format_twoplaces(column): + return column.apply(lambda x: '{:.2f}'.format(x) if pd.notna(x) else None) +# Create formatting function for small numbers (1 decimal) +def format_oneplace(column): + return column.apply(lambda x: '{:.1f}'.format(x) if pd.notna(x) else None) +# Create formatting function for small numbers (0 decimals) +def format_zeroplaces(column): + return column.apply(lambda x: '{:.0f}'.format(x) if pd.notna(x) else None) + + +# Formatting for single cells +# Create formatting function for large numbers (no decimals and thousand commas) +def format_commas_cell(cell_value): + return '{:,.0f}'.format(cell_value) if pd.notna(cell_value) else None +# Create formatting function for percentages (2 decimals and percent symbol, also multiplies by 100) +def format_percent_cell(cell_value): + return '{:.2%}'.format(cell_value) if pd.notna(cell_value) else None +# Create formatting function for small numbers (2 decimals) +def format_twoplaces_cell(cell_value): + return '{:.2f}'.format(cell_value) if pd.notna(cell_value) else None +# Create formatting function for small numbers (0 decimals) +def format_zeroplaces_cell(cell_value): + return '{:.0f}'.format(cell_value) if pd.notna(cell_value) else None + + +# Round to nearest even number function. Used in Volume corridor reporting +def round_to_nearest_even(number): + rounded = round(number) + return rounded + (rounded % 2 == 1) + +# Create Percent Error function +def pct_error(count , flow): + error = ((sum(flow)/len(flow)) - (sum(count)/len(count))) / (sum(count)/len(count)) + + return error + +# Create Percent RMSE function +def pct_rmse(count , sqerror): + rmse = math.sqrt(sum(sqerror)/len(sqerror))/(sum(count)/len(count)) + + return rmse + +# Create VMT function (needs to pull data from all links, not just ones with counts) +def vmt(flow , length): + vmt = sum(flow*length) + + return vmt + + +# Create VHT function (needs to pull data from all links and periods, not just ones with counts) + # Need to have logic to handle daily vs. a single period +def vht_dly(am_flow, am_time, pm_flow, pm_time, op_flow, op_time): + vht = sum(am_flow*(am_time/3600)) + sum(pm_flow*(pm_time/3600)) + sum(op_flow*(op_time/3600)) + return vht +def vht_per(flow, time): + vht = sum(flow*time) + return vht + + + +# Assignment summary function +def assignment_summary(auto_count, sut_count, mut_count, all_count, auto_flow, sut_flow, mut_flow, all_flow): #, cong_auto_time, cong_trk_time, period): + # DAILY + # Percent Error and Percent RMSE + # Import ID fields and fields with Counts and Flows + # Link ID fields + NO = VisumPy.helpers.GetMulti(Visum.Net.Links,"No", activeOnly = True) + FCLASS = VisumPy.helpers.GetMulti(Visum.Net.Links,"TYPENO", activeOnly = True) + LENGTH = VisumPy.helpers.GetMulti(Visum.Net.Links,"Length", activeOnly = True) + SCRNLINE = VisumPy.helpers.GetMulti(Visum.Net.Links,r"CONCATENATE:SCREENLINES\CODE", activeOnly = True) + + # Counts + Auto_Count = VisumPy.helpers.GetMulti(Visum.Net.Links,auto_count, activeOnly = True) + SUT_Count = VisumPy.helpers.GetMulti(Visum.Net.Links,sut_count, activeOnly = True) + MUT_Count = VisumPy.helpers.GetMulti(Visum.Net.Links,mut_count, activeOnly = True) + Tot_Count = VisumPy.helpers.GetMulti(Visum.Net.Links,all_count, activeOnly = True) + AADT = VisumPy.helpers.GetMulti(Visum.Net.Links,'AADT', activeOnly = True) + # Link Daily Flows + Auto_Flow = VisumPy.helpers.GetMulti(Visum.Net.Links,auto_flow, activeOnly = True) + SUT_Flow = VisumPy.helpers.GetMulti(Visum.Net.Links,sut_flow, activeOnly = True) + MUT_Flow = VisumPy.helpers.GetMulti(Visum.Net.Links,mut_flow, activeOnly = True) + Tot_Flow = VisumPy.helpers.GetMulti(Visum.Net.Links,all_flow, activeOnly = True) + + # Screenline Names for Report + SL_NAME = VisumPy.helpers.GetMulti(Visum.Net.Screenlines,"Name", activeOnly = True) + SL_NAME = list(dict.fromkeys(SL_NAME)) + + # Make Visum list with link data + summary_list = [NO, FCLASS, SCRNLINE, LENGTH, Auto_Flow, SUT_Flow, MUT_Flow, Tot_Flow, Auto_Count, SUT_Count, MUT_Count, Tot_Count, AADT] + + # Put Visum link list into dataframe + df = pd.DataFrame(np.column_stack(summary_list), columns = ['NO', 'FCLASS', 'SCRNLINE', 'LENGTH', + 'Auto_Flow', 'SUT_Flow', 'MUT_Flow', 'Tot_Flow', + 'Auto_Count', 'SUT_Count', 'MUT_Count', 'Tot_Count','AADT']) + + # Break out SCRNLINE field to separate by commas into individual columns + df[['SCRNLINE']] = df[['SCRNLINE']].astype(str) + df = pd.concat([df,df['SCRNLINE'].str.split(',', expand = True)], axis = 1) + # Change Screenline field names + if 1 not in df: + df[1] = None + df = df.rename(columns = {0:'SCRNLINE1',1:'SCRNLINE2'}) + # Replace null values with 0 in the screenline fields + df['SCRNLINE1'] = df['SCRNLINE1'].replace('',np.nan).fillna(0) + df['SCRNLINE2'] = df['SCRNLINE2'].replace('',np.nan).fillna(0) + + # Define custom_sum function to maintain null values when aggregating Counts and Flows by LinkNO + def custom_sum(series): + # If all values are null, return null; otherwise, return the sum of the values + return series.sum() if series.notna().any() else None + ## GROUP EACH DATAFRAME BY 'NO' COLUMN TO COMBINE COUNTS ON EACH LINK INTO BOTH DIRECTIONS + df = df.groupby('NO').agg( + FCLASS =('FCLASS', 'max'), + LENGTH =('LENGTH', 'max'), + SCRNLINE1 =('SCRNLINE1', 'first'), + SCRNLINE2 =('SCRNLINE2', 'first'), + Auto_Flow =('Auto_Flow', custom_sum), + SUT_Flow =('SUT_Flow', custom_sum), + MUT_Flow =('MUT_Flow', custom_sum), + Tot_Flow =('Tot_Flow', custom_sum), + Auto_Count =('Auto_Count', custom_sum), + SUT_Count =('SUT_Count', custom_sum), + MUT_Count =('MUT_Count', custom_sum), + Tot_Count =('Tot_Count', custom_sum), + AADT =('AADT', custom_sum)).reset_index() + + # Drop AADT from df and join in AADT from df_aadt (to have 2-way AADT by link) + #df = df.drop('AADT', axis=1) + #df = pd.merge(df, df_aadt, on='NO', how='left') + + # Convert FCLASS to integer + df[['FCLASS']] = df[['FCLASS']].astype(int) + + # Convert SCRNLINE1 and SCRNLINE2 to Integer + df[['SCRNLINE1','SCRNLINE2']] = df[['SCRNLINE1','SCRNLINE2']].astype(float) + df[['SCRNLINE1','SCRNLINE2']] = df[['SCRNLINE1','SCRNLINE2']].astype(int) + + + # Build results dictionary to use as results dataframe to save summary stats for group stats + results = {"Segment":['Auto', + 'Auto: AADT <5k','Auto: AADT 5-10k','Auto: AADT 10-15k','Auto: AADT 15-20k','Auto: AADT 20-30k','Auto: AADT 30-40k','Auto: AADT 40-50k', + 'Auto: LinkType 1', 'Auto: LinkType 2', 'Auto: LinkType 3', 'Auto: LinkType 4', 'Auto: LinkType 5', 'Auto: LinkType 6', 'Auto: LinkType 7', + 'Auto: LinkType 8', 'Auto: LinkType 9', 'Auto: LinkType 10', 'Auto: LinkType 11', 'Auto: LinkType 12', 'Auto: LinkType 30', 'Auto: LinkType 32', + 'Auto: SL '+SL_NAME[0] ,'Auto: SL '+SL_NAME[1] ,'Auto: SL '+SL_NAME[2] ,'Auto: SL '+SL_NAME[3] ,'Auto: SL '+SL_NAME[4] ,'Auto: SL '+SL_NAME[5] ,'Auto: SL '+SL_NAME[6] , + 'Auto: SL '+SL_NAME[7] ,'Auto: SL '+SL_NAME[8] ,'Auto: SL '+SL_NAME[9] ,'Auto: SL '+SL_NAME[10],'Auto: SL '+SL_NAME[11],'Auto: SL '+SL_NAME[12], + 'SUT', + 'SUT: AADT <5k','SUT: AADT 5-10k','SUT: AADT 10-15k','SUT: AADT 15-20k','SUT: AADT 20-30k','SUT: AADT 30-40k','SUT: AADT 40-50k', + 'SUT: LinkType 1', 'SUT: LinkType 2', 'SUT: LinkType 3', 'SUT: LinkType 4', 'SUT: LinkType 5', 'SUT: LinkType 6', 'SUT: LinkType 7', + 'SUT: LinkType 8', 'SUT: LinkType 9', 'SUT: LinkType 10', 'SUT: LinkType 11', 'SUT: LinkType 12', 'SUT: LinkType 30', 'SUT: LinkType 32', + 'SUT: SL '+SL_NAME[0] ,'SUT: SL '+SL_NAME[1] ,'SUT: SL '+SL_NAME[2] ,'SUT: SL '+SL_NAME[3] ,'SUT: SL '+SL_NAME[4] ,'SUT: SL '+SL_NAME[5] ,'SUT: SL '+SL_NAME[6] , + 'SUT: SL '+SL_NAME[7] ,'SUT: SL '+SL_NAME[8] ,'SUT: SL '+SL_NAME[9] ,'SUT: SL '+SL_NAME[10],'SUT: SL '+SL_NAME[11],'SUT: SL '+SL_NAME[12], + 'MUT', + 'MUT: AADT <5k','MUT: AADT 5-10k','MUT: AADT 10-15k','MUT: AADT 15-20k','MUT: AADT 20-30k','MUT: AADT 30-40k','MUT: AADT 40-50k', + 'MUT: LinkType 1', 'MUT: LinkType 2', 'MUT: LinkType 3', 'MUT: LinkType 4', 'MUT: LinkType 5', 'MUT: LinkType 6', 'MUT: LinkType 7', + 'MUT: LinkType 8', 'MUT: LinkType 9', 'MUT: LinkType 10', 'MUT: LinkType 11', 'MUT: LinkType 12', 'MUT: LinkType 30', 'MUT: LinkType 32', + 'MUT: SL '+SL_NAME[0] ,'MUT: SL '+SL_NAME[1] ,'MUT: SL '+SL_NAME[2] ,'MUT: SL '+SL_NAME[3] ,'MUT: SL '+SL_NAME[4] ,'MUT: SL '+SL_NAME[5] ,'MUT: SL '+SL_NAME[6] , + 'MUT: SL '+SL_NAME[7],'MUT: SL '+SL_NAME[8] ,'MUT: SL '+SL_NAME[9] ,'MUT: SL '+SL_NAME[10],'MUT: SL '+SL_NAME[11],'MUT: SL '+SL_NAME[12], + 'All Modes', + 'All Modes: AADT <5k','All Modes: AADT 5-10k','All Modes: AADT 10-15k','All Modes: AADT 15-20k','All Modes: AADT 20-30k','All Modes: AADT 30-40k','All Modes: AADT 40-50k', + 'All Modes: LinkType 1', 'All Modes: LinkType 2', 'All Modes: LinkType 3', 'All Modes: LinkType 4', 'All Modes: LinkType 5', 'All Modes: LinkType 6', 'All Modes: LinkType 7', + 'All Modes: LinkType 8', 'All Modes: LinkType 9', 'All Modes: LinkType 10', 'All Modes: LinkType 11', 'All Modes: LinkType 12', 'All Modes: LinkType 30', 'All Modes: LinkType 32', + 'All Modes: SL '+SL_NAME[0] ,'All Modes: SL '+SL_NAME[1] ,'All Modes: SL '+SL_NAME[2] ,'All Modes: SL '+SL_NAME[3] ,'All Modes: SL '+SL_NAME[4] ,'All Modes: SL '+SL_NAME[5] , + 'All Modes: SL '+SL_NAME[6] ,'All Modes: SL '+SL_NAME[7] ,'All Modes: SL '+SL_NAME[8] ,'All Modes: SL '+SL_NAME[9] ,'All Modes: SL '+SL_NAME[10],'All Modes: SL '+SL_NAME[11], + 'All Modes: SL '+SL_NAME[12] + ]} + + + + # Plug results dictionary into results_df dataframe + results_df = pd.DataFrame(data = results) + + # Add stats columns + results_df['Percent Error'] = None + results_df['Percent RMSE'] = None + results_df['Total VMT'] = None + results_df['Total VHT'] = None + results_df['Number of Observations'] = None + results_df['Sum of Counts'] = None + results_df['Mean of Counts'] = None + results_df['Median of Counts'] = None + results_df['Count VMT, Links with Counts'] = None + results_df['Modeled VMT, Links with Counts'] = None + + + # For links with counts only, used for Pct. Error and Pct. RMSE + # Filter out links where count is null or 0 and by each condition + # All Links with Auto Counts + count_df = df[df['Auto_Count'].notna()] + + # By AADT Volume + under_5k_df = count_df[(count_df['AADT'] < 5000)] + btwn_5_10k_df = count_df[(count_df['AADT'] >= 5000) & (count_df['AADT'] < 10000)] + btwn_10_15k_df = count_df[(count_df['AADT'] >= 10000) & (count_df['AADT'] < 15000)] + btwn_15_20k_df = count_df[(count_df['AADT'] >= 15000) & (count_df['AADT'] < 20000)] + btwn_20_30k_df = count_df[(count_df['AADT'] >= 20000) & (count_df['AADT'] < 30000)] + btwn_30_40k_df = count_df[(count_df['AADT'] >= 30000) & (count_df['AADT'] < 40000)] + btwn_40_50k_df = count_df[(count_df['AADT'] >= 40000) & (count_df['AADT'] < 50000)] + #over_50k_df = count_df[(count_df['AADT'] >= 50000)] + # By Functional Class + fc1_df = count_df[(count_df['FCLASS'] == 1)] + fc2_df = count_df[(count_df['FCLASS'] == 2)] + fc3_df = count_df[(count_df['FCLASS'] == 3)] + fc4_df = count_df[(count_df['FCLASS'] == 4)] + fc5_df = count_df[(count_df['FCLASS'] == 5)] + fc6_df = count_df[(count_df['FCLASS'] == 6)] + fc7_df = count_df[(count_df['FCLASS'] == 7)] + fc8_df = count_df[(count_df['FCLASS'] == 8)] + fc9_df = count_df[(count_df['FCLASS'] == 9)] + fc10_df = count_df[(count_df['FCLASS'] == 10)] + fc11_df = count_df[(count_df['FCLASS'] == 11)] + fc12_df = count_df[(count_df['FCLASS'] == 12)] + fc30_df = count_df[(count_df['FCLASS'] == 30)] + fc32_df = count_df[(count_df['FCLASS'] == 32)] + + # By Screenline + sl_1_df = count_df[(count_df['SCRNLINE1'] == 1) | (count_df['SCRNLINE2'] == 1)] + sl_2_df = count_df[(count_df['SCRNLINE1'] == 2) | (count_df['SCRNLINE2'] == 2)] + sl_3_df = count_df[(count_df['SCRNLINE1'] == 3) | (count_df['SCRNLINE2'] == 3)] + sl_4_df = count_df[(count_df['SCRNLINE1'] == 4) | (count_df['SCRNLINE2'] == 4)] + sl_5_df = count_df[(count_df['SCRNLINE1'] == 5) | (count_df['SCRNLINE2'] == 5)] + sl_6_df = count_df[(count_df['SCRNLINE1'] == 6) | (count_df['SCRNLINE2'] == 6)] + sl_7_df = count_df[(count_df['SCRNLINE1'] == 7) | (count_df['SCRNLINE2'] == 7)] + sl_8_df = count_df[(count_df['SCRNLINE1'] == 8) | (count_df['SCRNLINE2'] == 8)] + sl_9_df = count_df[(count_df['SCRNLINE1'] == 9) | (count_df['SCRNLINE2'] == 9)] + sl_10_df = count_df[(count_df['SCRNLINE1'] == 10) | (count_df['SCRNLINE2'] == 10)] + sl_11_df = count_df[(count_df['SCRNLINE1'] == 11) | (count_df['SCRNLINE2'] == 11)] + sl_12_df = count_df[(count_df['SCRNLINE1'] == 12) | (count_df['SCRNLINE2'] == 12)] + sl_13_df = count_df[(count_df['SCRNLINE1'] == 13) | (count_df['SCRNLINE2'] == 13)] + #sl_14_df = count_df[(count_df['SCRNLINE1'] == 14) | (count_df['SCRNLINE2'] == 14)] + #sl_15_df = count_df[(count_df['SCRNLINE1'] == 15) | (count_df['SCRNLINE2'] == 15)] + #sl_16_df = count_df[(count_df['SCRNLINE1'] == 16) | (count_df['SCRNLINE2'] == 16)] + #sl_17_df = count_df[(count_df['SCRNLINE1'] == 17) | (count_df['SCRNLINE2'] == 17)] + #sl_18_df = count_df[(count_df['SCRNLINE1'] == 18) | (count_df['SCRNLINE2'] == 18)] + #sl_19_df = count_df[(count_df['SCRNLINE1'] == 19) | (count_df['SCRNLINE2'] == 19)] + #sl_20_df = count_df[(count_df['SCRNLINE1'] == 20) | (count_df['SCRNLINE2'] == 20)] + #sl_21_df = count_df[(count_df['SCRNLINE1'] == 21) | (count_df['SCRNLINE2'] == 21)] + #sl_22_df = count_df[(count_df['SCRNLINE1'] == 22) | (count_df['SCRNLINE2'] == 22)] + #sl_23_df = count_df[(count_df['SCRNLINE1'] == 23) | (count_df['SCRNLINE2'] == 23)] + #sl_24_df = count_df[(count_df['SCRNLINE1'] == 24) | (count_df['SCRNLINE2'] == 24)] + #sl_25_df = count_df[(count_df['SCRNLINE1'] == 25) | (count_df['SCRNLINE2'] == 25)] + #sl_26_df = count_df[(count_df['SCRNLINE1'] == 26) | (count_df['SCRNLINE2'] == 26)] + + # Build list of dataframes to loop thru + auto_df_list = [count_df,#internal_df,external_df, + under_5k_df,btwn_5_10k_df,btwn_10_15k_df,btwn_15_20k_df,btwn_20_30k_df,btwn_30_40k_df,btwn_40_50k_df, + fc1_df,fc2_df,fc3_df,fc4_df,fc5_df,fc6_df,fc7_df,fc8_df,fc9_df,fc10_df,fc11_df,fc12_df,fc30_df,fc32_df, + sl_1_df,sl_2_df,sl_3_df,sl_4_df,sl_5_df,sl_6_df,sl_7_df,sl_8_df,sl_9_df,sl_10_df,sl_11_df,sl_12_df,sl_13_df] + + + + # All Links with SUT Counts + count_df = df[df['SUT_Count'].notna()] + # By AADT Volume + under_5k_df = count_df[(count_df['AADT'] < 5000)] + btwn_5_10k_df = count_df[(count_df['AADT'] >= 5000) & (count_df['AADT'] < 10000)] + btwn_10_15k_df = count_df[(count_df['AADT'] >= 10000) & (count_df['AADT'] < 15000)] + btwn_15_20k_df = count_df[(count_df['AADT'] >= 15000) & (count_df['AADT'] < 20000)] + btwn_20_30k_df = count_df[(count_df['AADT'] >= 20000) & (count_df['AADT'] < 30000)] + btwn_30_40k_df = count_df[(count_df['AADT'] >= 30000) & (count_df['AADT'] < 40000)] + btwn_40_50k_df = count_df[(count_df['AADT'] >= 40000) & (count_df['AADT'] < 50000)] + #over_50k_df = count_df[(count_df['AADT'] >= 50000)] + # By Functional Class + fc1_df = count_df[(count_df['FCLASS'] == 1)] + fc2_df = count_df[(count_df['FCLASS'] == 2)] + fc3_df = count_df[(count_df['FCLASS'] == 3)] + fc4_df = count_df[(count_df['FCLASS'] == 4)] + fc5_df = count_df[(count_df['FCLASS'] == 5)] + fc6_df = count_df[(count_df['FCLASS'] == 6)] + fc7_df = count_df[(count_df['FCLASS'] == 7)] + fc8_df = count_df[(count_df['FCLASS'] == 8)] + fc9_df = count_df[(count_df['FCLASS'] == 9)] + fc10_df = count_df[(count_df['FCLASS'] == 10)] + fc11_df = count_df[(count_df['FCLASS'] == 11)] + fc12_df = count_df[(count_df['FCLASS'] == 12)] + fc30_df = count_df[(count_df['FCLASS'] == 30)] + fc32_df = count_df[(count_df['FCLASS'] == 32)] + # By Screenline + sl_1_df = count_df[(count_df['SCRNLINE1'] == 1) | (count_df['SCRNLINE2'] == 1)] + sl_2_df = count_df[(count_df['SCRNLINE1'] == 2) | (count_df['SCRNLINE2'] == 2)] + sl_3_df = count_df[(count_df['SCRNLINE1'] == 3) | (count_df['SCRNLINE2'] == 3)] + sl_4_df = count_df[(count_df['SCRNLINE1'] == 4) | (count_df['SCRNLINE2'] == 4)] + sl_5_df = count_df[(count_df['SCRNLINE1'] == 5) | (count_df['SCRNLINE2'] == 5)] + sl_6_df = count_df[(count_df['SCRNLINE1'] == 6) | (count_df['SCRNLINE2'] == 6)] + sl_7_df = count_df[(count_df['SCRNLINE1'] == 7) | (count_df['SCRNLINE2'] == 7)] + sl_8_df = count_df[(count_df['SCRNLINE1'] == 8) | (count_df['SCRNLINE2'] == 8)] + sl_9_df = count_df[(count_df['SCRNLINE1'] == 9) | (count_df['SCRNLINE2'] == 9)] + sl_10_df = count_df[(count_df['SCRNLINE1'] == 10) | (count_df['SCRNLINE2'] == 10)] + sl_11_df = count_df[(count_df['SCRNLINE1'] == 11) | (count_df['SCRNLINE2'] == 11)] + sl_12_df = count_df[(count_df['SCRNLINE1'] == 12) | (count_df['SCRNLINE2'] == 12)] + sl_13_df = count_df[(count_df['SCRNLINE1'] == 13) | (count_df['SCRNLINE2'] == 13)] + #sl_14_df = count_df[(count_df['SCRNLINE1'] == 14) | (count_df['SCRNLINE2'] == 14)] + #sl_15_df = count_df[(count_df['SCRNLINE1'] == 15) | (count_df['SCRNLINE2'] == 15)] + #sl_16_df = count_df[(count_df['SCRNLINE1'] == 16) | (count_df['SCRNLINE2'] == 16)] + #sl_17_df = count_df[(count_df['SCRNLINE1'] == 17) | (count_df['SCRNLINE2'] == 17)] + #sl_18_df = count_df[(count_df['SCRNLINE1'] == 18) | (count_df['SCRNLINE2'] == 18)] + #sl_19_df = count_df[(count_df['SCRNLINE1'] == 19) | (count_df['SCRNLINE2'] == 19)] + #sl_20_df = count_df[(count_df['SCRNLINE1'] == 20) | (count_df['SCRNLINE2'] == 20)] + #sl_21_df = count_df[(count_df['SCRNLINE1'] == 21) | (count_df['SCRNLINE2'] == 21)] + #sl_22_df = count_df[(count_df['SCRNLINE1'] == 22) | (count_df['SCRNLINE2'] == 22)] + #sl_23_df = count_df[(count_df['SCRNLINE1'] == 23) | (count_df['SCRNLINE2'] == 23)] + #sl_24_df = count_df[(count_df['SCRNLINE1'] == 24) | (count_df['SCRNLINE2'] == 24)] + #sl_25_df = count_df[(count_df['SCRNLINE1'] == 25) | (count_df['SCRNLINE2'] == 25)] + #sl_26_df = count_df[(count_df['SCRNLINE1'] == 26) | (count_df['SCRNLINE2'] == 26)] + + # Build list of dataframes to loop thru + sut_df_list = [count_df,#internal_df,external_df, + under_5k_df,btwn_5_10k_df,btwn_10_15k_df,btwn_15_20k_df,btwn_20_30k_df,btwn_30_40k_df,btwn_40_50k_df, + fc1_df,fc2_df,fc3_df,fc4_df,fc5_df,fc6_df,fc7_df,fc8_df,fc9_df,fc10_df,fc11_df,fc12_df,fc30_df,fc32_df, + sl_1_df,sl_2_df,sl_3_df,sl_4_df,sl_5_df,sl_6_df,sl_7_df,sl_8_df,sl_9_df,sl_10_df,sl_11_df,sl_12_df,sl_13_df] + + + # All Links with MUT Counts + count_df = df[df['MUT_Count'].notna()] + # By AADT Volume + under_5k_df = count_df[(count_df['AADT'] < 5000)] + btwn_5_10k_df = count_df[(count_df['AADT'] >= 5000) & (count_df['AADT'] < 10000)] + btwn_10_15k_df = count_df[(count_df['AADT'] >= 10000) & (count_df['AADT'] < 15000)] + btwn_15_20k_df = count_df[(count_df['AADT'] >= 15000) & (count_df['AADT'] < 20000)] + btwn_20_30k_df = count_df[(count_df['AADT'] >= 20000) & (count_df['AADT'] < 30000)] + btwn_30_40k_df = count_df[(count_df['AADT'] >= 30000) & (count_df['AADT'] < 40000)] + btwn_40_50k_df = count_df[(count_df['AADT'] >= 40000) & (count_df['AADT'] < 50000)] + #over_50k_df = count_df[(count_df['AADT'] >= 50000)] + # By Functional Class + fc1_df = count_df[(count_df['FCLASS'] == 1)] + fc2_df = count_df[(count_df['FCLASS'] == 2)] + fc3_df = count_df[(count_df['FCLASS'] == 3)] + fc4_df = count_df[(count_df['FCLASS'] == 4)] + fc5_df = count_df[(count_df['FCLASS'] == 5)] + fc6_df = count_df[(count_df['FCLASS'] == 6)] + fc7_df = count_df[(count_df['FCLASS'] == 7)] + fc8_df = count_df[(count_df['FCLASS'] == 8)] + fc9_df = count_df[(count_df['FCLASS'] == 9)] + fc10_df = count_df[(count_df['FCLASS'] == 10)] + fc11_df = count_df[(count_df['FCLASS'] == 11)] + fc12_df = count_df[(count_df['FCLASS'] == 12)] + fc30_df = count_df[(count_df['FCLASS'] == 30)] + fc32_df = count_df[(count_df['FCLASS'] == 32)] + # By Screenline + sl_1_df = count_df[(count_df['SCRNLINE1'] == 1) | (count_df['SCRNLINE2'] == 1)] + sl_2_df = count_df[(count_df['SCRNLINE1'] == 2) | (count_df['SCRNLINE2'] == 2)] + sl_3_df = count_df[(count_df['SCRNLINE1'] == 3) | (count_df['SCRNLINE2'] == 3)] + sl_4_df = count_df[(count_df['SCRNLINE1'] == 4) | (count_df['SCRNLINE2'] == 4)] + sl_5_df = count_df[(count_df['SCRNLINE1'] == 5) | (count_df['SCRNLINE2'] == 5)] + sl_6_df = count_df[(count_df['SCRNLINE1'] == 6) | (count_df['SCRNLINE2'] == 6)] + sl_7_df = count_df[(count_df['SCRNLINE1'] == 7) | (count_df['SCRNLINE2'] == 7)] + sl_8_df = count_df[(count_df['SCRNLINE1'] == 8) | (count_df['SCRNLINE2'] == 8)] + sl_9_df = count_df[(count_df['SCRNLINE1'] == 9) | (count_df['SCRNLINE2'] == 9)] + sl_10_df = count_df[(count_df['SCRNLINE1'] == 10) | (count_df['SCRNLINE2'] == 10)] + sl_11_df = count_df[(count_df['SCRNLINE1'] == 11) | (count_df['SCRNLINE2'] == 11)] + sl_12_df = count_df[(count_df['SCRNLINE1'] == 12) | (count_df['SCRNLINE2'] == 12)] + sl_13_df = count_df[(count_df['SCRNLINE1'] == 13) | (count_df['SCRNLINE2'] == 13)] + #sl_14_df = count_df[(count_df['SCRNLINE1'] == 14) | (count_df['SCRNLINE2'] == 14)] + #sl_15_df = count_df[(count_df['SCRNLINE1'] == 15) | (count_df['SCRNLINE2'] == 15)] + #sl_16_df = count_df[(count_df['SCRNLINE1'] == 16) | (count_df['SCRNLINE2'] == 16)] + #sl_17_df = count_df[(count_df['SCRNLINE1'] == 17) | (count_df['SCRNLINE2'] == 17)] + #sl_18_df = count_df[(count_df['SCRNLINE1'] == 18) | (count_df['SCRNLINE2'] == 18)] + #sl_19_df = count_df[(count_df['SCRNLINE1'] == 19) | (count_df['SCRNLINE2'] == 19)] + #sl_20_df = count_df[(count_df['SCRNLINE1'] == 20) | (count_df['SCRNLINE2'] == 20)] + #sl_21_df = count_df[(count_df['SCRNLINE1'] == 21) | (count_df['SCRNLINE2'] == 21)] + #sl_22_df = count_df[(count_df['SCRNLINE1'] == 22) | (count_df['SCRNLINE2'] == 22)] + #sl_23_df = count_df[(count_df['SCRNLINE1'] == 23) | (count_df['SCRNLINE2'] == 23)] + #sl_24_df = count_df[(count_df['SCRNLINE1'] == 24) | (count_df['SCRNLINE2'] == 24)] + #sl_25_df = count_df[(count_df['SCRNLINE1'] == 25) | (count_df['SCRNLINE2'] == 25)] + #sl_26_df = count_df[(count_df['SCRNLINE1'] == 26) | (count_df['SCRNLINE2'] == 26)] + + + # Build list of dataframes to loop thru + mut_df_list = [count_df,#internal_df,external_df, + under_5k_df,btwn_5_10k_df,btwn_10_15k_df,btwn_15_20k_df,btwn_20_30k_df,btwn_30_40k_df,btwn_40_50k_df, + fc1_df,fc2_df,fc3_df,fc4_df,fc5_df,fc6_df,fc7_df,fc8_df,fc9_df,fc10_df,fc11_df,fc12_df,fc30_df,fc32_df, + sl_1_df,sl_2_df,sl_3_df,sl_4_df,sl_5_df,sl_6_df,sl_7_df,sl_8_df,sl_9_df,sl_10_df,sl_11_df,sl_12_df,sl_13_df] + + # All Links with All Modes Counts + count_df = df[df['Tot_Count'].notna()] + # By AADT Volume + under_5k_df = count_df[(count_df['AADT'] < 5000)] + btwn_5_10k_df = count_df[(count_df['AADT'] >= 5000) & (count_df['AADT'] < 10000)] + btwn_10_15k_df = count_df[(count_df['AADT'] >= 10000) & (count_df['AADT'] < 15000)] + btwn_15_20k_df = count_df[(count_df['AADT'] >= 15000) & (count_df['AADT'] < 20000)] + btwn_20_30k_df = count_df[(count_df['AADT'] >= 20000) & (count_df['AADT'] < 30000)] + btwn_30_40k_df = count_df[(count_df['AADT'] >= 30000) & (count_df['AADT'] < 40000)] + btwn_40_50k_df = count_df[(count_df['AADT'] >= 40000) & (count_df['AADT'] < 50000)] + # By Functional Class + fc1_df = count_df[(count_df['FCLASS'] == 1)] + fc2_df = count_df[(count_df['FCLASS'] == 2)] + fc3_df = count_df[(count_df['FCLASS'] == 3)] + fc4_df = count_df[(count_df['FCLASS'] == 4)] + fc5_df = count_df[(count_df['FCLASS'] == 5)] + fc6_df = count_df[(count_df['FCLASS'] == 6)] + fc7_df = count_df[(count_df['FCLASS'] == 7)] + fc8_df = count_df[(count_df['FCLASS'] == 8)] + fc9_df = count_df[(count_df['FCLASS'] == 9)] + fc10_df = count_df[(count_df['FCLASS'] == 10)] + fc11_df = count_df[(count_df['FCLASS'] == 11)] + fc12_df = count_df[(count_df['FCLASS'] == 12)] + fc30_df = count_df[(count_df['FCLASS'] == 30)] + fc32_df = count_df[(count_df['FCLASS'] == 32)] + # By Screenline + sl_1_df = count_df[(count_df['SCRNLINE1'] == 1) | (count_df['SCRNLINE2'] == 1)] + sl_2_df = count_df[(count_df['SCRNLINE1'] == 2) | (count_df['SCRNLINE2'] == 2)] + sl_3_df = count_df[(count_df['SCRNLINE1'] == 3) | (count_df['SCRNLINE2'] == 3)] + sl_4_df = count_df[(count_df['SCRNLINE1'] == 4) | (count_df['SCRNLINE2'] == 4)] + sl_5_df = count_df[(count_df['SCRNLINE1'] == 5) | (count_df['SCRNLINE2'] == 5)] + sl_6_df = count_df[(count_df['SCRNLINE1'] == 6) | (count_df['SCRNLINE2'] == 6)] + sl_7_df = count_df[(count_df['SCRNLINE1'] == 7) | (count_df['SCRNLINE2'] == 7)] + sl_8_df = count_df[(count_df['SCRNLINE1'] == 8) | (count_df['SCRNLINE2'] == 8)] + sl_9_df = count_df[(count_df['SCRNLINE1'] == 9) | (count_df['SCRNLINE2'] == 9)] + sl_10_df = count_df[(count_df['SCRNLINE1'] == 10) | (count_df['SCRNLINE2'] == 10)] + sl_11_df = count_df[(count_df['SCRNLINE1'] == 11) | (count_df['SCRNLINE2'] == 11)] + sl_12_df = count_df[(count_df['SCRNLINE1'] == 12) | (count_df['SCRNLINE2'] == 12)] + sl_13_df = count_df[(count_df['SCRNLINE1'] == 13) | (count_df['SCRNLINE2'] == 13)] + #sl_14_df = count_df[(count_df['SCRNLINE1'] == 14) | (count_df['SCRNLINE2'] == 14)] + #sl_15_df = count_df[(count_df['SCRNLINE1'] == 15) | (count_df['SCRNLINE2'] == 15)] + #sl_16_df = count_df[(count_df['SCRNLINE1'] == 16) | (count_df['SCRNLINE2'] == 16)] + #sl_17_df = count_df[(count_df['SCRNLINE1'] == 17) | (count_df['SCRNLINE2'] == 17)] + #sl_18_df = count_df[(count_df['SCRNLINE1'] == 18) | (count_df['SCRNLINE2'] == 18)] + #sl_19_df = count_df[(count_df['SCRNLINE1'] == 19) | (count_df['SCRNLINE2'] == 19)] + #sl_20_df = count_df[(count_df['SCRNLINE1'] == 20) | (count_df['SCRNLINE2'] == 20)] + #sl_21_df = count_df[(count_df['SCRNLINE1'] == 21) | (count_df['SCRNLINE2'] == 21)] + #sl_22_df = count_df[(count_df['SCRNLINE1'] == 22) | (count_df['SCRNLINE2'] == 22)] + #sl_23_df = count_df[(count_df['SCRNLINE1'] == 23) | (count_df['SCRNLINE2'] == 23)] + #sl_24_df = count_df[(count_df['SCRNLINE1'] == 24) | (count_df['SCRNLINE2'] == 24)] + #sl_25_df = count_df[(count_df['SCRNLINE1'] == 25) | (count_df['SCRNLINE2'] == 25)] + #sl_26_df = count_df[(count_df['SCRNLINE1'] == 26) | (count_df['SCRNLINE2'] == 26)] + + + # Build list of dataframes to loop thru + allmodes_df_list = [count_df,#internal_df,external_df, + under_5k_df,btwn_5_10k_df,btwn_10_15k_df,btwn_15_20k_df,btwn_20_30k_df,btwn_30_40k_df,btwn_40_50k_df, + fc1_df,fc2_df,fc3_df,fc4_df,fc5_df,fc6_df,fc7_df,fc8_df,fc9_df,fc10_df,fc11_df,fc12_df,fc30_df,fc32_df, + sl_1_df,sl_2_df,sl_3_df,sl_4_df,sl_5_df,sl_6_df,sl_7_df,sl_8_df,sl_9_df,sl_10_df,sl_11_df,sl_12_df,sl_13_df] + + + # Add squared error column to each df + # Auto + for i in auto_df_list: + i['Auto_SqError'] = (i.Auto_Flow - i.Auto_Count)**2 + # SUT + for i in sut_df_list: + i['SUT_SqError'] = (i.SUT_Flow - i.SUT_Count)**2 + # MUT + for i in mut_df_list: + i['MUT_SqError'] = (i.MUT_Flow - i.MUT_Count)**2 + # All Modes + for i in allmodes_df_list: + i['Tot_SqError'] = (i.Tot_Flow - i.Tot_Count)**2 + + # Create attributes for 'y' in the next section + auto_loc = 0 + sut_loc = auto_loc + len(sut_df_list) + mut_loc = sut_loc + len(mut_df_list) + allmodes_loc = mut_loc + len(allmodes_df_list) + + + # Calculate Auto pct error and pct rmse from each dataframe and save in results dataframe + y = auto_loc + for i in auto_df_list: + if len(i) == 0: + results_df.at[y,"Number of Observations"] = 0 + y = y + 1 + #continue + else: + results_df.at[y,"Percent Error"] = pct_error(i.Auto_Count,i.Auto_Flow) + results_df.at[y,"Percent RMSE"] = pct_rmse(i.Auto_Count,i.Auto_SqError) + results_df.at[y,"Number of Observations"] = len(i) + results_df.at[y,"Sum of Counts"] = np.sum(i.Auto_Count) + results_df.at[y,"Mean of Counts"] = np.mean(i.Auto_Count) + results_df.at[y,"Median of Counts"] = np.median(i.Auto_Count) + results_df.at[y,"Count VMT, Links with Counts"] = vmt(i.Auto_Count,i.LENGTH) + results_df.at[y,'Modeled VMT, Links with Counts'] = vmt(i.Auto_Flow,i.LENGTH) + y = y + 1 + + # Calculate SUT pct error and pct rmse from each dataframe and save in results dataframe + y = sut_loc + for i in sut_df_list: + if len(i) == 0: + results_df.at[y,"Number of Observations"] = 0 + y = y + 1 + #continue + else: + results_df.at[y,"Percent Error"] = pct_error(i.SUT_Count,i.SUT_Flow) + results_df.at[y,"Percent RMSE"] = pct_rmse(i.SUT_Count, i.SUT_SqError) + results_df.at[y,"Number of Observations"] = len(i) + results_df.at[y,"Sum of Counts"] = np.sum(i.SUT_Count) + results_df.at[y,"Mean of Counts"] = np.mean(i.SUT_Count) + results_df.at[y,"Median of Counts"] = np.median(i.SUT_Count) + results_df.at[y,"Count VMT, Links with Counts"] = vmt(i.SUT_Count,i.LENGTH) + results_df.at[y,'Modeled VMT, Links with Counts'] = vmt(i.SUT_Flow,i.LENGTH) + y = y + 1 + + # Calculate MUT pct error and pct rmse from each dataframe and save in results dataframe + y = mut_loc + for i in mut_df_list: + if len(i) == 0: + results_df.at[y,"Number of Observations"] = 0 + y = y + 1 + #continue + else: + results_df.at[y,"Percent Error"] = pct_error(i.MUT_Count,i.MUT_Flow) + results_df.at[y,"Percent RMSE"] = pct_rmse(i.MUT_Count, i.MUT_SqError) + results_df.at[y,"Number of Observations"] = len(i) + results_df.at[y,"Sum of Counts"] = np.sum(i.MUT_Count) + results_df.at[y,"Mean of Counts"] = np.mean(i.MUT_Count) + results_df.at[y,"Median of Counts"] = np.median(i.MUT_Count) + results_df.at[y,"Count VMT, Links with Counts"] = vmt(i.MUT_Count,i.LENGTH) + results_df.at[y,'Modeled VMT, Links with Counts'] = vmt(i.MUT_Flow,i.LENGTH) + y = y + 1 + + # Calculate All Modes pct error and pct rmse from each dataframe and save in results dataframe + y = allmodes_loc + for i in allmodes_df_list: + if len(i) == 0: + results_df.at[y,"Number of Observations"] = 0 + y = y + 1 + #continue + else: + results_df.at[y,"Percent Error"] = pct_error(i.Tot_Count,i.Tot_Flow) + results_df.at[y,"Percent RMSE"] = pct_rmse(i.Tot_Count,i.Tot_SqError) + results_df.at[y,"Number of Observations"] = len(i) + results_df.at[y,"Sum of Counts"] = np.sum(i.Tot_Count) + results_df.at[y,"Mean of Counts"] = np.mean(i.Tot_Count) + results_df.at[y,"Median of Counts"] = np.median(i.Tot_Count) + results_df.at[y,"Count VMT, Links with Counts"] = vmt(i.Tot_Count,i.LENGTH) + results_df.at[y,'Modeled VMT, Links with Counts'] = vmt(i.Tot_Flow,i.LENGTH) + y = y + 1 + + + # Total VMT and Total VHT + + # Import ID fields and fields with Counts and Flows + # Link ID fields + NO = VisumPy.helpers.GetMulti(Visum.Net.Links,"No", activeOnly = True) + FCLASS = VisumPy.helpers.GetMulti(Visum.Net.Links,"TYPENO", activeOnly = True) + LENGTH = VisumPy.helpers.GetMulti(Visum.Net.Links,"Length", activeOnly = True) + SCRNLINE = VisumPy.helpers.GetMulti(Visum.Net.Links,r"CONCATENATE:SCREENLINES\CODE", activeOnly = True) + # Pull CONGTIME Auto by period, Length, and Flows by Period for Total VMT/Total VHT Calculations + CONGTIME_AM_C = VisumPy.helpers.GetMulti(Visum.Net.Links,"AMTCUR_A", activeOnly = True) + CONGTIME_PM_C = VisumPy.helpers.GetMulti(Visum.Net.Links,"PMTCUR_A", activeOnly = True) + CONGTIME_OP_C = VisumPy.helpers.GetMulti(Visum.Net.Links,"OPTCUR_A", activeOnly = True) + # Pull CONGTIME SUT by period, Length, and Flows by Period for Total VMT/Total VHT Calculations + CONGTIME_AM_S = VisumPy.helpers.GetMulti(Visum.Net.Links,"AMTCUR_MED", activeOnly = True) + CONGTIME_PM_S = VisumPy.helpers.GetMulti(Visum.Net.Links,"PMTCUR_MED", activeOnly = True) + CONGTIME_OP_S = VisumPy.helpers.GetMulti(Visum.Net.Links,"OPTCUR_MED", activeOnly = True) + # Pull CONGTIME SUT by period, Length, and Flows by Period for Total VMT/Total VHT Calculations + CONGTIME_AM_M = VisumPy.helpers.GetMulti(Visum.Net.Links,"AMTCUR_HVY", activeOnly = True) + CONGTIME_PM_M = VisumPy.helpers.GetMulti(Visum.Net.Links,"PMTCUR_HVY", activeOnly = True) + CONGTIME_OP_M = VisumPy.helpers.GetMulti(Visum.Net.Links,"OPTCUR_HVY", activeOnly = True) + + # Link Flows by Period + # AM + sov_flow = VisumPy.helpers.GetMulti(Visum.Net.Links,"AMSVOL", activeOnly = True) + hov_flow = VisumPy.helpers.GetMulti(Visum.Net.Links,"AMHVOL", activeOnly = True) + + AM_Auto_Flow = np.add(sov_flow,hov_flow) + AM_SUT_Flow = VisumPy.helpers.GetMulti(Visum.Net.Links,"AMMTVOL", activeOnly = True) + AM_MUT_Flow = VisumPy.helpers.GetMulti(Visum.Net.Links,"AMHTVOL", activeOnly = True) + AM_Tot_Flow = VisumPy.helpers.GetMulti(Visum.Net.Links,"AMVOLPCU_N", activeOnly = True) + # OP + sov_flow = VisumPy.helpers.GetMulti(Visum.Net.Links,"PMSVOL", activeOnly = True) + hov_flow = VisumPy.helpers.GetMulti(Visum.Net.Links,"PMHVOL", activeOnly = True) + + PM_Auto_Flow = np.add(sov_flow,hov_flow) + PM_SUT_Flow = VisumPy.helpers.GetMulti(Visum.Net.Links,"PMMTVOL", activeOnly = True) + PM_MUT_Flow = VisumPy.helpers.GetMulti(Visum.Net.Links,"PMHTVOL", activeOnly = True) + PM_Tot_Flow = VisumPy.helpers.GetMulti(Visum.Net.Links,"PMVOLPCU_N", activeOnly = True) + # OP + sov_flow = VisumPy.helpers.GetMulti(Visum.Net.Links,"OPSVOL", activeOnly = True) + hov_flow = VisumPy.helpers.GetMulti(Visum.Net.Links,"OPHVOL", activeOnly = True) + + OP_Auto_Flow = np.add(sov_flow,hov_flow) + OP_SUT_Flow = VisumPy.helpers.GetMulti(Visum.Net.Links,"OPMTVOL", activeOnly = True) + OP_MUT_Flow = VisumPy.helpers.GetMulti(Visum.Net.Links,"OPHTVOL", activeOnly = True) + OP_Tot_Flow = VisumPy.helpers.GetMulti(Visum.Net.Links,"OPVOLPCU_N", activeOnly = True) + + # Period for functions + Auto_Flow = VisumPy.helpers.GetMulti(Visum.Net.Links,auto_flow, activeOnly = True) + SUT_Flow = VisumPy.helpers.GetMulti(Visum.Net.Links,sut_flow, activeOnly = True) + MUT_Flow = VisumPy.helpers.GetMulti(Visum.Net.Links,mut_flow, activeOnly = True) + Tot_Flow = VisumPy.helpers.GetMulti(Visum.Net.Links,all_flow, activeOnly = True) + + + # Make Visum list with link data + summary_list = [NO, FCLASS, LENGTH, SCRNLINE, + CONGTIME_AM_C, CONGTIME_PM_C, CONGTIME_OP_C, + CONGTIME_AM_S, CONGTIME_PM_S, CONGTIME_OP_S, + CONGTIME_AM_M, CONGTIME_PM_M, CONGTIME_OP_M, + AM_Auto_Flow, AM_SUT_Flow, AM_MUT_Flow, AM_Tot_Flow, + PM_Auto_Flow, PM_SUT_Flow, PM_MUT_Flow, PM_Tot_Flow, + OP_Auto_Flow, OP_SUT_Flow, OP_MUT_Flow, OP_Tot_Flow, + Auto_Flow, SUT_Flow, MUT_Flow, Tot_Flow] + + # Put Visum link list into dataframe + df_all = pd.DataFrame(np.column_stack(summary_list), columns = ['NO', 'FCLASS', 'LENGTH', 'SCRNLINE', + 'CONGTIME_AM_C', 'CONGTIME_PM_C', 'CONGTIME_OP_C', + 'CONGTIME_AM_S', 'CONGTIME_PM_S', 'CONGTIME_OP_S', + 'CONGTIME_AM_M', 'CONGTIME_PM_M', 'CONGTIME_OP_M', + 'AM_Auto_Flow', 'AM_SUT_Flow', 'AM_MUT_Flow', 'AM_Tot_Flow', + 'PM_Auto_Flow', 'PM_SUT_Flow', 'PM_MUT_Flow', 'PM_Tot_Flow', + 'OP_Auto_Flow', 'OP_SUT_Flow', 'OP_MUT_Flow', 'OP_Tot_Flow', + 'Auto_Flow', 'SUT_Flow', 'MUT_Flow', 'Tot_Flow']) + + + # Break out SCRNLINE field to separate by commas into individual columns + df_all[['SCRNLINE']] = df_all[['SCRNLINE']].astype(str) + df_all = pd.concat([df_all,df_all['SCRNLINE'].str.split(',', expand = True)], axis = 1) + # Change Screenline field names + if 1 not in df_all: + df_all[1] = 0 + df_all = df_all.rename(columns = {0:'SCRNLINE1',1:'SCRNLINE2'}) + + ## Break out SCRNLINE field to separate by commas into individual columns + #df_all = pd.concat([df_all,df_all['SCRNLINE'].str.split(',', expand = True)], axis = 1) + ## Change Screenline field names + #df_all = df_all.rename(columns = {0:'SCRNLINE1',1:'SCRNLINE2'}) + + # Replace null values with 0 in the screenline fields + df_all['SCRNLINE1'] = df_all['SCRNLINE1'].replace('',np.nan).fillna(0) + df_all['SCRNLINE2'] = df_all['SCRNLINE2'].replace('',np.nan).fillna(0) + + # Convert all flow and time fields to float to make multiplication and other operations run smoothly. Read in as strings + df_all[['NO', 'FCLASS', 'LENGTH', 'SCRNLINE1','SCRNLINE2', + 'CONGTIME_AM_C', 'CONGTIME_PM_C', 'CONGTIME_OP_C', + 'CONGTIME_AM_S', 'CONGTIME_PM_S', 'CONGTIME_OP_S', + 'CONGTIME_AM_M', 'CONGTIME_PM_M', 'CONGTIME_OP_M', + 'AM_Auto_Flow', 'AM_SUT_Flow', 'AM_MUT_Flow', 'AM_Tot_Flow', + 'PM_Auto_Flow', 'PM_SUT_Flow', 'PM_MUT_Flow', 'PM_Tot_Flow', + 'OP_Auto_Flow', 'OP_SUT_Flow', 'OP_MUT_Flow', 'OP_Tot_Flow', + 'Auto_Flow', 'SUT_Flow', 'MUT_Flow', 'Tot_Flow']] = df_all[['NO', 'FCLASS', 'LENGTH', 'SCRNLINE1','SCRNLINE2', + 'CONGTIME_AM_C', 'CONGTIME_PM_C', 'CONGTIME_OP_C', + 'CONGTIME_AM_S', 'CONGTIME_PM_S', 'CONGTIME_OP_S', + 'CONGTIME_AM_M', 'CONGTIME_PM_M', 'CONGTIME_OP_M', + 'AM_Auto_Flow', 'AM_SUT_Flow', 'AM_MUT_Flow', 'AM_Tot_Flow', + 'PM_Auto_Flow', 'PM_SUT_Flow', 'PM_MUT_Flow', 'PM_Tot_Flow', + 'OP_Auto_Flow', 'OP_SUT_Flow', 'OP_MUT_Flow', 'OP_Tot_Flow', + 'Auto_Flow', 'SUT_Flow', 'MUT_Flow', 'Tot_Flow']].astype(float) + + # Convert ID fields to integer + df_all[['NO','FCLASS','SCRNLINE1','SCRNLINE2']] = df_all[['NO','FCLASS','SCRNLINE1','SCRNLINE2']].astype(int) + + + # For links with counts only, used for Pct. Error and Pct. RMSE + # Filter out links where count is null and by each condition + # All Links with Counts + count_df_all = df_all + # By AADT Volume + under_5k_df_all = df_all[df_all['NO'].isin(under_5k_df['NO'])] + btwn_5_10k_df_all = df_all[df_all['NO'].isin(btwn_5_10k_df['NO'])] + btwn_10_15k_df_all = df_all[df_all['NO'].isin(btwn_10_15k_df['NO'])] + btwn_15_20k_df_all = df_all[df_all['NO'].isin(btwn_15_20k_df['NO'])] + btwn_20_30k_df_all = df_all[df_all['NO'].isin(btwn_20_30k_df['NO'])] + btwn_30_40k_df_all = df_all[df_all['NO'].isin(btwn_30_40k_df['NO'])] + btwn_40_50k_df_all = df_all[df_all['NO'].isin(btwn_40_50k_df['NO'])] + # By Functional Class + fc1_df_all = df_all[(df_all['FCLASS'] == 1)] + fc2_df_all = df_all[(df_all['FCLASS'] == 2)] + fc3_df_all = df_all[(df_all['FCLASS'] == 3)] + fc4_df_all = df_all[(df_all['FCLASS'] == 4)] + fc5_df_all = df_all[(df_all['FCLASS'] == 5)] + fc6_df_all = df_all[(df_all['FCLASS'] == 6)] + fc7_df_all = df_all[(df_all['FCLASS'] == 7)] + fc8_df_all = df_all[(df_all['FCLASS'] == 8)] + fc9_df_all = df_all[(df_all['FCLASS'] == 9)] + fc10_df_all = df_all[(df_all['FCLASS'] == 10)] + fc11_df_all = df_all[(df_all['FCLASS'] == 11)] + fc12_df_all = df_all[(df_all['FCLASS'] == 12)] + fc30_df_all = df_all[(df_all['FCLASS'] == 30)] + fc32_df_all = df_all[(df_all['FCLASS'] == 32)] + # By Screenline + sl_1_df_all = df_all[(df_all['SCRNLINE1'] == 1) | (df_all['SCRNLINE2'] == 1)] + sl_2_df_all = df_all[(df_all['SCRNLINE1'] == 2) | (df_all['SCRNLINE2'] == 2)] + sl_3_df_all = df_all[(df_all['SCRNLINE1'] == 3) | (df_all['SCRNLINE2'] == 3)] + sl_4_df_all = df_all[(df_all['SCRNLINE1'] == 4) | (df_all['SCRNLINE2'] == 4)] + sl_5_df_all = df_all[(df_all['SCRNLINE1'] == 5) | (df_all['SCRNLINE2'] == 5)] + sl_6_df_all = df_all[(df_all['SCRNLINE1'] == 6) | (df_all['SCRNLINE2'] == 6)] + sl_7_df_all = df_all[(df_all['SCRNLINE1'] == 7) | (df_all['SCRNLINE2'] == 7)] + sl_8_df_all = df_all[(df_all['SCRNLINE1'] == 8) | (df_all['SCRNLINE2'] == 8)] + sl_9_df_all = df_all[(df_all['SCRNLINE1'] == 9) | (df_all['SCRNLINE2'] == 9)] + sl_10_df_all = df_all[(df_all['SCRNLINE1'] == 10) | (df_all['SCRNLINE2'] == 10)] + sl_11_df_all = df_all[(df_all['SCRNLINE1'] == 11) | (df_all['SCRNLINE2'] == 11)] + sl_12_df_all = df_all[(df_all['SCRNLINE1'] == 12) | (df_all['SCRNLINE2'] == 12)] + sl_13_df_all = df_all[(df_all['SCRNLINE1'] == 13) | (df_all['SCRNLINE2'] == 13)] + #sl_14_df_all = df_all[(df_all['SCRNLINE1'] == 14) | (df_all['SCRNLINE2'] == 14)] + #sl_15_df_all = df_all[(df_all['SCRNLINE1'] == 15) | (df_all['SCRNLINE2'] == 15)] + #sl_16_df_all = df_all[(df_all['SCRNLINE1'] == 16) | (df_all['SCRNLINE2'] == 16)] + #sl_17_df_all = df_all[(df_all['SCRNLINE1'] == 17) | (df_all['SCRNLINE2'] == 17)] + #sl_18_df_all = df_all[(df_all['SCRNLINE1'] == 18) | (df_all['SCRNLINE2'] == 18)] + #sl_19_df_all = df_all[(df_all['SCRNLINE1'] == 19) | (df_all['SCRNLINE2'] == 19)] + #sl_20_df_all = df_all[(df_all['SCRNLINE1'] == 20) | (df_all['SCRNLINE2'] == 20)] + #sl_21_df_all = df_all[(df_all['SCRNLINE1'] == 21) | (df_all['SCRNLINE2'] == 21)] + #sl_22_df_all = df_all[(df_all['SCRNLINE1'] == 22) | (df_all['SCRNLINE2'] == 22)] + #sl_23_df_all = df_all[(df_all['SCRNLINE1'] == 23) | (df_all['SCRNLINE2'] == 23)] + #sl_24_df_all = df_all[(df_all['SCRNLINE1'] == 24) | (df_all['SCRNLINE2'] == 24)] + #sl_25_df_all = df_all[(df_all['SCRNLINE1'] == 25) | (df_all['SCRNLINE2'] == 25)] + #sl_26_df_all = df_all[(df_all['SCRNLINE1'] == 26) | (df_all['SCRNLINE2'] == 26)] + + + # Build list of dataframes to loop thru + df_list_all = [count_df_all,#internal_df_all,external_df_all, + under_5k_df_all,btwn_5_10k_df_all,btwn_10_15k_df_all,btwn_15_20k_df_all,btwn_20_30k_df_all,btwn_30_40k_df_all,btwn_40_50k_df_all, + fc1_df_all,fc2_df_all,fc3_df_all,fc4_df_all,fc5_df_all,fc6_df_all,fc7_df_all,fc8_df_all,fc9_df_all,fc10_df_all,fc11_df_all,fc12_df_all, + fc30_df_all,fc32_df_all, + sl_1_df_all,sl_2_df_all,sl_3_df_all,sl_4_df_all,sl_5_df_all,sl_6_df_all,sl_7_df_all,sl_8_df_all,sl_9_df_all,sl_10_df_all,sl_11_df_all, + sl_12_df_all,sl_13_df_all] + + + # Calculate Auto Total VMT and Total VHT from each dataframe and save in results dataframe + y = auto_loc + for i in df_list_all: + if len(i) == 0: #sum(i.Auto_Flow) == 0.0: + y = y + 1 + continue + else: + results_df.at[y,"Total VMT"] = vmt(i.Auto_Flow,i.LENGTH) + results_df.at[y,"Total VHT"] = vht_dly(i.AM_Auto_Flow,i.CONGTIME_AM_C,i.PM_Auto_Flow,i.CONGTIME_PM_C,i.OP_Auto_Flow,i.CONGTIME_OP_C) + y = y + 1 + + # Calculate SUT Total VMT and Total VHT from each dataframe and save in results dataframe + y = sut_loc + for i in df_list_all: + if len(i) == 0: #sum(i.Auto_Flow) == 0.0: + y = y + 1 + continue + else: + results_df.at[y,"Total VMT"] = vmt(i.SUT_Flow,i.LENGTH) + results_df.at[y,"Total VHT"] = vht_dly(i.AM_SUT_Flow,i.CONGTIME_AM_S,i.PM_SUT_Flow,i.CONGTIME_PM_S,i.OP_SUT_Flow,i.CONGTIME_OP_S) + y = y + 1 + + # Calculate MUT Total VMT and Total VHT from each dataframe and save in results dataframe + y = mut_loc + for i in df_list_all: + if len(i) == 0: #sum(i.Auto_Flow) == 0.0: + y = y + 1 + continue + else: + results_df.at[y,"Total VMT"] = vmt(i.MUT_Flow,i.LENGTH) + results_df.at[y,"Total VHT"] = vht_dly(i.AM_MUT_Flow,i.CONGTIME_AM_M,i.PM_MUT_Flow,i.CONGTIME_PM_M,i.OP_MUT_Flow,i.CONGTIME_OP_M) + y = y + 1 + + # Calculate All Modes Total VMT and Total VHT from each dataframe and save in results dataframe + y = allmodes_loc + a = auto_loc + s = sut_loc + m = mut_loc + for i in df_list_all: + if len(i) == 0: #sum(i.Tot_Flow) == 0.0: + y = y + 1 + continue + else: + results_df.at[y,"Total VMT"] = results_df.at[a,"Total VMT"] + results_df.at[s,"Total VMT"] + results_df.at[m,"Total VMT"] # vmt(i.Tot_Flow,i.LENGTH) + results_df.at[y,"Total VHT"] = results_df.at[a,"Total VHT"] + results_df.at[s,"Total VHT"] + results_df.at[m,"Total VHT"] # vht(i.AM_Tot_Flow,i.CONGTIME_AM,i.MD_Tot_Flow,i.CONGTIME_MD,i.PM_Tot_Flow,i.CONGTIME_PM,i.NI_Tot_Flow,i.CONGTIME_NT) + y = y + 1 + a = a + 1 + s = s + 1 + m = m + 1 + + + # Apply the formatting function to specific columns + results_df['Percent Error'] = format_percent(results_df['Percent Error']) + results_df['Percent RMSE'] = format_percent(results_df['Percent RMSE']) + results_df['Total VMT'] = format_commas(results_df['Total VMT']) + results_df['Total VHT'] = format_commas(results_df['Total VHT']) + results_df['Number of Observations'] = format_commas(results_df['Number of Observations']) + results_df['Sum of Counts'] = format_commas(results_df['Sum of Counts']) + results_df['Mean of Counts'] = format_commas(results_df['Mean of Counts']) + results_df['Median of Counts'] = format_commas(results_df['Median of Counts']) + results_df['Count VMT, Links with Counts'] = format_commas(results_df['Count VMT, Links with Counts']) + results_df['Modeled VMT, Links with Counts'] = format_commas(results_df['Modeled VMT, Links with Counts']) + + + # Save Daily Summary file to new timestamped folder + results_df.to_csv(proj_dir+"outputs/reports/ModelRun_"+date+"/Assignment Results/AssignmentSummary.csv") + +# Transit Unlinked Trips Summary Table +def transit_report(): + + # Pull Lines table attributes + Name = VisumPy.helpers.GetMulti(Visum.Net.Lines,"Name", activeOnly = True) + Description = VisumPy.helpers.GetMulti(Visum.Net.Lines,"Emme_Description", activeOnly = True) + AM_Trips = VisumPy.helpers.GetMulti(Visum.Net.Lines,"AM_UL_TRIPS", activeOnly = True) + PM_Trips = VisumPy.helpers.GetMulti(Visum.Net.Lines,"PM_UL_TRIPS", activeOnly = True) + OP_Trips = VisumPy.helpers.GetMulti(Visum.Net.Lines,"OP_UL_TRIPS", activeOnly = True) + DLY_Trips = VisumPy.helpers.GetMulti(Visum.Net.Lines,"DLY_UL_TRIPS", activeOnly = True) + + # Make Visum list with link data + summary_list = [Name, Description, AM_Trips, PM_Trips, OP_Trips, DLY_Trips] + + # Put Visum link list into dataframe + df = pd.DataFrame(np.column_stack(summary_list), columns = ['Name', 'Description', 'AM_Trips', 'PM_Trips', 'OP_Trips', 'DLY_Trips']) + + # Save Transit results to folder + df.to_csv(proj_dir+"outputs/reports/ModelRun_"+date+"/Assignment Results/TransitSummary.csv") + + + +# Daily +assignment_summary('AUTO_COUNT_DLY', 'SUT_COUNT_DLY', 'MUT_COUNT_DLY', 'TOT_COUNT_DLY', 'AUTO_VOL_DLY', 'SUT_VOL_DLY', 'MUT_VOL_DLY', 'TOT_VOL_DLY') # 'DAILY_LCV_VOL', 'AM3_CTIME_C', 'AM3_CTIME_T', 'Daily') # Daily VHT Calculated from full day, so AM3_CTIME here is just a placeholder for the function + +# Transit +transit_report() + + diff --git a/skimming_and_assignment/visum/scripts/CostSkim_Setup.py b/skimming_and_assignment/visum/scripts/CostSkim_Setup.py new file mode 100644 index 0000000..0958dd1 --- /dev/null +++ b/skimming_and_assignment/visum/scripts/CostSkim_Setup.py @@ -0,0 +1,158 @@ +# Setting up for PrT cost skimming +# 6/24/2025 - Luke Gordon (RSG) +# Adapted from code from Chetan Joshi (PTV) + +import tables +import numpy as np +import pandas as pd +import os +import VisumPy.helpers as h +import openmatrix as omx + + +def costskim_setup(period, mode, vot): + + # Links + # Pull AddVals for skimming + addval1 = h.GetMulti(Visum.Net.Links,r"ADDVAL1" , activeOnly = True) # TEMPORARY, NEED TO REPLACE WITH ACTUAL TRAVEL TIME BY PERIOD USING VDF + addval2 = h.GetMulti(Visum.Net.Links,r"ADDVAL2" , activeOnly = True) + # Pull Toll Fields + EA_SOV_TOLL = h.GetMulti(Visum.Net.Links,r"EA_SOV_TOLL", activeOnly = True) + EA_SR2_TOLL = h.GetMulti(Visum.Net.Links,r"EA_SR2_TOLL", activeOnly = True) + EA_SR3_TOLL = h.GetMulti(Visum.Net.Links,r"EA_SR3_TOLL", activeOnly = True) + EA_MT_TOLL = h.GetMulti(Visum.Net.Links,r"EA_MT_TOLL" , activeOnly = True) + EA_HT_TOLL = h.GetMulti(Visum.Net.Links,r"EA_HT_TOLL" , activeOnly = True) + AM_SOV_TOLL = h.GetMulti(Visum.Net.Links,r"AM_SOV_TOLL", activeOnly = True) + AM_SR2_TOLL = h.GetMulti(Visum.Net.Links,r"AM_SR2_TOLL", activeOnly = True) + AM_SR3_TOLL = h.GetMulti(Visum.Net.Links,r"AM_SR3_TOLL", activeOnly = True) + AM_MT_TOLL = h.GetMulti(Visum.Net.Links,r"AM_MT_TOLL" , activeOnly = True) + AM_HT_TOLL = h.GetMulti(Visum.Net.Links,r"AM_HT_TOLL" , activeOnly = True) + MD_SOV_TOLL = h.GetMulti(Visum.Net.Links,r"MD_SOV_TOLL", activeOnly = True) + MD_SR2_TOLL = h.GetMulti(Visum.Net.Links,r"MD_SR2_TOLL", activeOnly = True) + MD_SR3_TOLL = h.GetMulti(Visum.Net.Links,r"MD_SR3_TOLL", activeOnly = True) + MD_MT_TOLL = h.GetMulti(Visum.Net.Links,r"MD_MT_TOLL" , activeOnly = True) + MD_HT_TOLL = h.GetMulti(Visum.Net.Links,r"MD_HT_TOLL" , activeOnly = True) + PM_SOV_TOLL = h.GetMulti(Visum.Net.Links,r"PM_SOV_TOLL", activeOnly = True) + PM_SR2_TOLL = h.GetMulti(Visum.Net.Links,r"PM_SR2_TOLL", activeOnly = True) + PM_SR3_TOLL = h.GetMulti(Visum.Net.Links,r"PM_SR3_TOLL", activeOnly = True) + PM_MT_TOLL = h.GetMulti(Visum.Net.Links,r"PM_MT_TOLL" , activeOnly = True) + PM_HT_TOLL = h.GetMulti(Visum.Net.Links,r"PM_HT_TOLL" , activeOnly = True) + EV_SOV_TOLL = h.GetMulti(Visum.Net.Links,r"EV_SOV_TOLL", activeOnly = True) + EV_SR2_TOLL = h.GetMulti(Visum.Net.Links,r"EV_SR2_TOLL", activeOnly = True) + EV_SR3_TOLL = h.GetMulti(Visum.Net.Links,r"EV_SR3_TOLL", activeOnly = True) + EV_MT_TOLL = h.GetMulti(Visum.Net.Links,r"EV_MT_TOLL" , activeOnly = True) + EV_HT_TOLL = h.GetMulti(Visum.Net.Links,r"EV_HT_TOLL" , activeOnly = True) + + + # Make Visum list with link data + att_list = [addval1,addval2, + EA_SOV_TOLL,EA_SR2_TOLL,EA_SR3_TOLL,EA_MT_TOLL,EA_HT_TOLL, + AM_SOV_TOLL,AM_SR2_TOLL,AM_SR3_TOLL,AM_MT_TOLL,AM_HT_TOLL, + MD_SOV_TOLL,MD_SR2_TOLL,MD_SR3_TOLL,MD_MT_TOLL,MD_HT_TOLL, + PM_SOV_TOLL,PM_SR2_TOLL,PM_SR3_TOLL,PM_MT_TOLL,PM_HT_TOLL, + EV_SOV_TOLL,EV_SR2_TOLL,EV_SR3_TOLL,EV_MT_TOLL,EV_HT_TOLL] + + # Put Visum link list into dataframe + df = pd.DataFrame(np.column_stack(att_list), columns = ['addval1','addval2', + 'EA_SOV_TOLL','EA_SR2_TOLL','EA_SR3_TOLL','EA_MT_TOLL','EA_HT_TOLL', + 'AM_SOV_TOLL','AM_SR2_TOLL','AM_SR3_TOLL','AM_MT_TOLL','AM_HT_TOLL', + 'MD_SOV_TOLL','MD_SR2_TOLL','MD_SR3_TOLL','MD_MT_TOLL','MD_HT_TOLL', + 'PM_SOV_TOLL','PM_SR2_TOLL','PM_SR3_TOLL','PM_MT_TOLL','PM_HT_TOLL', + 'EV_SOV_TOLL','EV_SR2_TOLL','EV_SR3_TOLL','EV_MT_TOLL','EV_HT_TOLL']) + + # TEMPORARY, NEED TO REPLACE WITH ACTUAL TRAVEL TIME BY PERIOD USING VDF AND VOLUME + df['time'] = df['addval1'] + + # Mode VOTs + sov_low = 3.39 + sov_med = 7.49 + sov_high = 20.51 + sr2_low = 5.11 + sr2_med = 10.92 + sr2_high = 27.30 + sr3_low = 7.76 + sr3_med = 16.82 + sr3_high = 44.04 + + # Set AddVal2 to Cost in Time + # SOV, Low VOT + if mode == "SOV" and vot == "Low": + df['addval2'] = df['time'] + (df[period+'_'+mode+'_TOLL'] / sov_low) * 3600 # Gen Cost (Time in Seconds) + # SR2, Low VOT + elif mode == "SR2" and vot == "Low": + df['addval2'] = df['time'] + (df[period+'_'+mode+'_TOLL'] / sr2_low) * 3600 # Gen Cost (Time in Seconds) + # SR3, Low VOT + elif mode == "SR3" and vot == "Low": + df['addval2'] = df['time'] + (df[period+'_'+mode+'_TOLL'] / sr3_low) * 3600 # Gen Cost (Time in Seconds) + # SOV, Medium VOT + elif mode == "SOV" and vot == "Medium": + df['addval2'] = df['time'] + (df[period+'_'+mode+'_TOLL'] / sov_med) * 3600 # Gen Cost (Time in Seconds) + # SR2, Medium VOT + elif mode == "SR2" and vot == "Medium": + df['addval2'] = df['time'] + (df[period+'_'+mode+'_TOLL'] / sr2_med) * 3600 # Gen Cost (Time in Seconds) + # SR3, Medium VOT + elif mode == "SR3" and vot == "Medium": + df['addval2'] = df['time'] + (df[period+'_'+mode+'_TOLL'] / sr3_med) * 3600 # Gen Cost (Time in Seconds) + # SOV, High VOT + elif mode == "SOV" and vot == "High": + df['addval2'] = df['time'] + (df[period+'_'+mode+'_TOLL'] / sov_high) * 3600 # Gen Cost (Time in Seconds) + # SR2, High VOT + elif mode == "SR2" and vot == "High": + df['addval2'] = df['time'] + (df[period+'_'+mode+'_TOLL'] / sr2_high) * 3600 # Gen Cost (Time in Seconds) + # SR3, High VOT + elif mode == "SR3" and vot == "High": + df['addval2'] = df['time'] + (df[period+'_'+mode+'_TOLL'] / sr3_high) * 3600 # Gen Cost (Time in Seconds) + + + # Set tolls by period and mode + df['toll'] = df[period+'_'+mode+'_TOLL'] # Money in Dollars + + # Set fields back in Visum + # Gen Cost + h.SetMulti(Visum.Net.Links ,r"ADDVAL2", df['addval2']) + # Toll + if mode == "SOV": + h.SetMulti(Visum.Net.Links ,r"TOLL_PRTSYS(S)", df['toll']) + elif mode == "SR2": + h.SetMulti(Visum.Net.Links ,r"TOLL_PRTSYS(SR2)", df['toll']) + elif mode == "SR3": + h.SetMulti(Visum.Net.Links ,r"TOLL_PRTSYS(SR3)", df['toll']) + + + + + + # REPEAT FOR CONNECTORS + # Pull AddVals for skimming + addval1 = h.GetMulti(Visum.Net.Connectors,r"ADDVAL1" , activeOnly = True) # TEMPORARY, NEED TO REPLACE WITH ACTUAL TRAVEL TIME BY PERIOD USING VDF + + # Make Visum list with link data + att_list = [addval1] + + # Put Visum link list into dataframe + df = pd.DataFrame(np.column_stack(att_list), columns = ['addval1']) + + # TEMPORARY, NEED TO REPLACE WITH ACTUAL TRAVEL TIME BY PERIOD USING VDF AND VOLUME + df['time'] = df['addval1'] + + # Set fields back in Visum + h.SetMulti(Visum.Net.Connectors ,r"ADDVAL1", df['time']) + h.SetMulti(Visum.Net.Connectors ,r"ADDVAL2", df['time']) + + + +# Read user inputs from Visum +proj_dir = Visum.GetPath(2) + +# Pull "Code" field from procedure sequence containing Code, DSegCode, and filename +procedure_code = Visum.Procedures.OperationExecutor.GetCurrentOperation().AttValue("CODE") # Example: outputs a string like -> '[["mfamsov","PuT","AM2_SOV.omx"],["mfmdMpe","PuT","MD1_MPE.omx"]]' +procedure_codes = eval(procedure_code) # Example: outputs a list of lists like -> [["mfamsov","PuT","AM2_SOV.omx"],["mfmdMpe","PuT","MD1_MPE.omx"]] + +# Loop thru each matrix set in the "Code" field and export +#for x in range(len(procedure_codes)): +per = procedure_codes[0] +m = procedure_codes[1] +tval = procedure_codes[2] + +costskim_setup(per, m, tval) + diff --git a/skimming_and_assignment/visum/scripts/Export_Feedback_OMXs.py b/skimming_and_assignment/visum/scripts/Export_Feedback_OMXs.py new file mode 100644 index 0000000..a4e95a8 --- /dev/null +++ b/skimming_and_assignment/visum/scripts/Export_Feedback_OMXs.py @@ -0,0 +1,42 @@ +# Export OMX files from LCOG model based on "Code" field in Procedure Sequence +# 4/29/2025 - Luke Gordon (RSG) +# Adapted from code from Chetan Joshi (PTV) + +import tables +import numpy as np +import pandas as pd +import os +import VisumPy.helpers as h +import openmatrix as omx + + + +def omx_export(mtx_code, mtx_dseg, omx_fn): #mtx_num, mtx_code): + omx_file = omx.open_file(omx_fn, 'w') + # Pull matrix out but close .omx file if there is an error + try: + mx = h.GetMatrixRaw(Visum, {"CODE": mtx_code , "DSegCode": mtx_dseg}) + except Exception as e: + print(f"Error getting matrix for CODE={mtx_code}, DSegCode={mtx_dseg}: {e}") + omx_file.close() + core_name = mtx_code + omx_file[core_name] = mx + omx_file.close() + + +# Read user inputs from Visum +proj_dir = Visum.GetPath(2) + +# Pull "Code" field from procedure sequence containing Code, DSegCode, and filename +procedure_code = Visum.Procedures.OperationExecutor.GetCurrentOperation().AttValue("CODE") # Example: outputs a string like -> '[["mfamsov","PuT","AM2_SOV.omx"],["mfmdMpe","PuT","MD1_MPE.omx"]]' +procedure_codes = eval(procedure_code) # Example: outputs a list of lists like -> [["mfamsov","PuT","AM2_SOV.omx"],["mfmdMpe","PuT","MD1_MPE.omx"]] + +# Loop thru each matrix set in the "Code" field and export +for x in range(len(procedure_codes)): + code = procedure_codes[x][0] + dsegcode = procedure_codes[x][1] + filename = procedure_codes[x][2] + + + omx_export(code, dsegcode, proj_dir + "outputs\\assignment\\feedback\\" + filename) + diff --git a/skimming_and_assignment/visum/scripts/Export_FinalAssign_OMXs.py b/skimming_and_assignment/visum/scripts/Export_FinalAssign_OMXs.py new file mode 100644 index 0000000..90b4448 --- /dev/null +++ b/skimming_and_assignment/visum/scripts/Export_FinalAssign_OMXs.py @@ -0,0 +1,42 @@ +# Export OMX files from LCOG model based on "Code" field in Procedure Sequence +# 4/29/2025 - Luke Gordon (RSG) +# Adapted from code from Chetan Joshi (PTV) + +import tables +import numpy as np +import pandas as pd +import os +import VisumPy.helpers as h +import openmatrix as omx + + + +def omx_export(mtx_code, mtx_dseg, omx_fn): #mtx_num, mtx_code): + omx_file = omx.open_file(omx_fn, 'w') + # Pull matrix out but close .omx file if there is an error + try: + mx = h.GetMatrixRaw(Visum, {"CODE": mtx_code , "DSegCode": mtx_dseg}) + except Exception as e: + print(f"Error getting matrix for CODE={mtx_code}, DSegCode={mtx_dseg}: {e}") + omx_file.close() + core_name = mtx_code + omx_file[core_name] = mx + omx_file.close() + + +# Read user inputs from Visum +proj_dir = Visum.GetPath(2) + +# Pull "Code" field from procedure sequence containing Code, DSegCode, and filename +procedure_code = Visum.Procedures.OperationExecutor.GetCurrentOperation().AttValue("CODE") # Example: outputs a string like -> '[["mfamsov","PuT","AM2_SOV.omx"],["mfmdMpe","PuT","MD1_MPE.omx"]]' +procedure_codes = eval(procedure_code) # Example: outputs a list of lists like -> [["mfamsov","PuT","AM2_SOV.omx"],["mfmdMpe","PuT","MD1_MPE.omx"]] + +# Loop thru each matrix set in the "Code" field and export +for x in range(len(procedure_codes)): + code = procedure_codes[x][0] + dsegcode = procedure_codes[x][1] + filename = procedure_codes[x][2] + + + omx_export(code, dsegcode, proj_dir + "outputs\\assignment\\final_assign\\" + filename) + diff --git a/skimming_and_assignment/visum/scripts/Export_Skim_OMXs.py b/skimming_and_assignment/visum/scripts/Export_Skim_OMXs.py new file mode 100644 index 0000000..dfcb335 --- /dev/null +++ b/skimming_and_assignment/visum/scripts/Export_Skim_OMXs.py @@ -0,0 +1,66 @@ +# Export OMX files from LCOG model based on "Code" field in Procedure Sequence +# 4/29/2025 - Luke Gordon (RSG) +# Adapted from code from Chetan Joshi (PTV) + +import tables +import numpy as np +import pandas as pd +import os +import VisumPy.helpers as h +import openmatrix as omx + + + +def omx_export(mtx_code, corename, mtx_dseg, omx_fn): + + omx_file = omx.open_file(omx_fn, 'a') + # Pull matrix out but close .omx file if there is an error + try: + mx = h.GetMatrixRaw(Visum, {"CODE": mtx_code , "DSegCode": mtx_dseg}) + except Exception as e: + print(f"Error getting matrix for CODE={mtx_code}, DSegCode={mtx_dseg}: {e}") + omx_file.close() + + core_name = corename + # Delete the core if it already exists + if core_name in omx_file: + del omx_file[core_name] + + + omx_file[core_name] = mx.astype(np.float32) + + # Create mapping to TAZ numbers + zones = np.array(h.GetMulti(Visum.Net.Zones,r"NO", activeOnly = True)) + taz_equivs = np.arange(1, len(zones) + 1) # 1-number of zones inclusive + + try: + omx_file.create_mapping('taz', taz_equivs) + omx_file.close() + except Exception as e: + omx_file.close() + + + +# Read user inputs from Visum +proj_dir = Visum.GetPath(2) + +# Pull "Code" field from procedure sequence containing Code, DSegCode, and filename +procedure_code = Visum.Procedures.OperationExecutor.GetCurrentOperation().AttValue("CODE") # Example: outputs a string like -> '[["mfamsov","PuT","AM2_SOV.omx"],["mfmdMpe","PuT","MD1_MPE.omx"]]' +procedure_codes = eval(procedure_code) # Example: outputs a list of lists like -> [["mfamsov","PuT","AM2_SOV.omx"],["mfmdMpe","PuT","MD1_MPE.omx"]] + +# Delete omx file if it exists +filename = procedure_codes[0][3] +if os.path.exists(filename): + os.remove(filename) + + +# Loop thru each matrix set in the "Code" field and export +for x in range(len(procedure_codes)): + code = procedure_codes[x][0] + core = procedure_codes[x][1] + dsegcode = procedure_codes[x][2] + filename = procedure_codes[x][3] + + + omx_export(code, core, dsegcode, proj_dir + "outputs\\skims\\" + filename) + diff --git a/skimming_and_assignment/visum/scripts/Import_Feedback_OMXs.py b/skimming_and_assignment/visum/scripts/Import_Feedback_OMXs.py new file mode 100644 index 0000000..dc32e6f --- /dev/null +++ b/skimming_and_assignment/visum/scripts/Import_Feedback_OMXs.py @@ -0,0 +1,41 @@ +# Export OMX files from LCOG model based on "Code" field in Procedure Sequence +# 4/30/2025 - Luke Gordon (RSG) +# Adapted from code from Chetan Joshi (PTV) + +import openmatrix as omx +import os +import shutil +import numpy as np +import pandas as pd +import VisumPy.matrices as vmx +import VisumPy.helpers as h + + +def omx_import(mtx_code, mtx_dseg, omx_fn): + omx_file = omx.open_file(omx_fn, 'r') + mx_name = omx_file.list_matrices()[0] # 1 matrix per .omx file + mx_array = omx_file[mx_name][:] + # Read matrix but close .omx file if there is an error + try: + h.SetMatrixRaw(Visum, {"CODE": mtx_code , "DSegCode": mtx_dseg}, mx_array) + except Exception as e: + print(f"Error getting matrix for CODE={mtx_code}, DSegCode={mtx_dseg}: {e}") + omx_file.close() + omx_file.close() + + +# Read user inputs from Visum +proj_dir = Visum.GetPath(2) + +# Pull "Code" field from procedure sequence containing Code, DSegCode, and filename +procedure_code = Visum.Procedures.OperationExecutor.GetCurrentOperation().AttValue("CODE") # Example: outputs a string like -> '[["mfamsov","PuT","AM2_SOV.omx"],["mfmdMpe","PuT","MD1_MPE.omx"]]' +procedure_codes = eval(procedure_code) # Example: outputs a list of lists like -> [["mfamsov","PuT","AM2_SOV.omx"],["mfmdMpe","PuT","MD1_MPE.omx"]] + +# Loop thru each matrix set in the "Code" field and export +for x in range(len(procedure_codes)): + code = procedure_codes[x][0] + dsegcode = procedure_codes[x][1] + filename = procedure_codes[x][2] + + + omx_import(code, dsegcode, proj_dir + "outputs\\assignment\\feedback\\" + filename) diff --git a/skimming_and_assignment/visum/scripts/Import_FinalAssign_OMXs.py b/skimming_and_assignment/visum/scripts/Import_FinalAssign_OMXs.py new file mode 100644 index 0000000..b3b845a --- /dev/null +++ b/skimming_and_assignment/visum/scripts/Import_FinalAssign_OMXs.py @@ -0,0 +1,41 @@ +# Export OMX files from LCOG model based on "Code" field in Procedure Sequence +# 4/30/2025 - Luke Gordon (RSG) +# Adapted from code from Chetan Joshi (PTV) + +import openmatrix as omx +import os +import shutil +import numpy as np +import pandas as pd +import VisumPy.matrices as vmx +import VisumPy.helpers as h + + +def omx_import(mtx_code, mtx_dseg, omx_fn): + omx_file = omx.open_file(omx_fn, 'r') + mx_name = omx_file.list_matrices()[0] # 1 matrix per .omx file + mx_array = omx_file[mx_name][:] + # Read matrix but close .omx file if there is an error + try: + h.SetMatrixRaw(Visum, {"CODE": mtx_code , "DSegCode": mtx_dseg}, mx_array) + except Exception as e: + print(f"Error getting matrix for CODE={mtx_code}, DSegCode={mtx_dseg}: {e}") + omx_file.close() + omx_file.close() + + +# Read user inputs from Visum +proj_dir = Visum.GetPath(2) + +# Pull "Code" field from procedure sequence containing Code, DSegCode, and filename +procedure_code = Visum.Procedures.OperationExecutor.GetCurrentOperation().AttValue("CODE") # Example: outputs a string like -> '[["mfamsov","PuT","AM2_SOV.omx"],["mfmdMpe","PuT","MD1_MPE.omx"]]' +procedure_codes = eval(procedure_code) # Example: outputs a list of lists like -> [["mfamsov","PuT","AM2_SOV.omx"],["mfmdMpe","PuT","MD1_MPE.omx"]] + +# Loop thru each matrix set in the "Code" field and export +for x in range(len(procedure_codes)): + code = procedure_codes[x][0] + dsegcode = procedure_codes[x][1] + filename = procedure_codes[x][2] + + + omx_import(code, dsegcode, proj_dir + "outputs\\assignment\\final_assign\\" + filename) diff --git a/skimming_and_assignment/visum/scripts/Import_WarmStart_OMXs.py b/skimming_and_assignment/visum/scripts/Import_WarmStart_OMXs.py new file mode 100644 index 0000000..55edd0c --- /dev/null +++ b/skimming_and_assignment/visum/scripts/Import_WarmStart_OMXs.py @@ -0,0 +1,41 @@ +# Export OMX files from LCOG model based on "Code" field in Procedure Sequence +# 4/30/2025 - Luke Gordon (RSG) +# Adapted from code from Chetan Joshi (PTV) + +import openmatrix as omx +import os +import shutil +import numpy as np +import pandas as pd +import VisumPy.matrices as vmx +import VisumPy.helpers as h + + +def omx_import(mtx_code, mtx_dseg, omx_fn): + omx_file = omx.open_file(omx_fn, 'r') + mx_name = omx_file.list_matrices()[0] # 1 matrix per .omx file + mx_array = omx_file[mx_name][:] + # Read matrix but close .omx file if there is an error + try: + h.SetMatrixRaw(Visum, {"CODE": mtx_code , "DSegCode": mtx_dseg}, mx_array) + except Exception as e: + print(f"Error getting matrix for CODE={mtx_code}, DSegCode={mtx_dseg}: {e}") + omx_file.close() + omx_file.close() + + +# Read user inputs from Visum +proj_dir = Visum.GetPath(2) + +# Pull "Code" field from procedure sequence containing Code, DSegCode, and filename +procedure_code = Visum.Procedures.OperationExecutor.GetCurrentOperation().AttValue("CODE") # Example: outputs a string like -> '[["mfamsov","PuT","AM2_SOV.omx"],["mfmdMpe","PuT","MD1_MPE.omx"]]' +procedure_codes = eval(procedure_code) # Example: outputs a list of lists like -> [["mfamsov","PuT","AM2_SOV.omx"],["mfmdMpe","PuT","MD1_MPE.omx"]] + +# Loop thru each matrix set in the "Code" field and export +for x in range(len(procedure_codes)): + code = procedure_codes[x][0] + dsegcode = procedure_codes[x][1] + filename = procedure_codes[x][2] + + + omx_import(code, dsegcode, proj_dir + "warmstart_omxs\\" + filename) diff --git a/skimming_and_assignment/visum/scripts/Initialize_Output_Bins.py b/skimming_and_assignment/visum/scripts/Initialize_Output_Bins.py new file mode 100644 index 0000000..6f0611c --- /dev/null +++ b/skimming_and_assignment/visum/scripts/Initialize_Output_Bins.py @@ -0,0 +1,37 @@ +# Set output_date field in Visum network attributes and create folders for reports + +""" +created 5/14/2025 + +@author: luke.gordon + +""" + +# Libraries +import VisumPy.helpers +import VisumPy.excel +import pandas as pd +import numpy as np +from datetime import datetime +import os.path + + + +# Calculate timestamp for folder name and save in Network attribute in Visum +date = datetime.now().strftime("(%Y-%m-%d)-%H_%M_%S") +Visum.Net.SetAttValue("output_date", date) + +# Read user inputs from Visum +proj_dir = Visum.GetPath(2) + +# Create timestamped folder for all results +os.mkdir(proj_dir+"outputs/reports/ModelRun_"+date) + +# Create folder for storing this runs assignment quality results +os.mkdir(proj_dir+"outputs/reports/ModelRun_"+date+"/PrT Assignment Quality Reports") + +# Create folder for storing this runs assignment results (Pct. Error, RMSE, etc.) +os.mkdir(proj_dir+"outputs/reports/ModelRun_"+date+"/Assignment Results") + +# Create folder for PuT assignment stats +os.mkdir(proj_dir+"outputs/reports/ModelRun_"+date+"/PuT Assignment Stats") \ No newline at end of file diff --git a/skimming_and_assignment/visum/scripts/KNRConnectors_Setup.py b/skimming_and_assignment/visum/scripts/KNRConnectors_Setup.py new file mode 100644 index 0000000..04a7458 --- /dev/null +++ b/skimming_and_assignment/visum/scripts/KNRConnectors_Setup.py @@ -0,0 +1,214 @@ + +#script to insert KNR connectors +# Chetan Joshi, PTV Portland OR 6/25/2025 + +import numpy as np +import scipy.spatial +import json +import csv +import os +import VisumPy.helpers as h +import pandas as pd +PRIO = 20480 + + +_TABLE = "KnRConstraints" +SCALEFACTOR = 5280 + +CONSPEED = 30 #mph +# FN = os.path.join(Visum.GetPath(2), "taz_connectors.json") +FN = os.path.join(Visum.GetPath(2), "connectors_knr.net") + +def vision_header(): + vision_head = [["$VISION"], + ["$VERSION:VERSNR", "FILETYPE", "LANGUAGE", "UNIT"], + [15,"Net","ENG","MI"]] + return vision_head + + +def create_knr_connectors(): + + # Check for type 10 connectors (knr connectors), if they exist, don't add any more connectors + checktype = np.array(h.GetMulti(Visum.Net.Connectors,r"TYPENO", activeOnly = True)) + if 10 in checktype: + Visum.Log(PRIO, 'KNR connectors already built!') + # Save original TSysSet in TSYS_HOLDING for later use + #tsysorig = h.GetMulti(Visum.Net.Connectors,r"TSYSSET", activeOnly = True) + #h.SetMulti(Visum.Net.Connectors, r"TSYS_HOLDING", tsysorig) + + return + else: + Visum.Log(PRIO, 'Building KNR Connectors') + # Save original TSysSet in TSYS_HOLDING for later use + #tsysorig = h.GetMulti(Visum.Net.Connectors,r"TSYSSET", activeOnly = True) + #h.SetMulti(Visum.Net.Connectors, r"TSYS_HOLDING", tsysorig) + + + + Visum.Log(PRIO, 'generate search list...') + tsys_constr = dict([tsys, [maxdist, maxstops]] for tsys, maxdist, maxstops in Visum.Net.TableDefinitions.ItemByKey(_TABLE).TableEntries.GetMultipleAttributes(["TSYS", "MAXDIST", "MAXSTOPS"])) + + cutoff = 5*SCALEFACTOR + tazs = Visum.Net.Zones.GetMultiAttValues("NO", False) + stp_no = np.array(Visum.Net.StopAreas.GetMultiAttValues("NO", False), dtype='int')[:, 1] + stop_tsys = dict([[sno, [nodeno, tsys]] for sno, nodeno, tsys in Visum.Net.StopAreas.GetMultipleAttributes(["NO", "NODENO", "DISTINCT:STOPPOINTS\\TSYSSET"])]) + taz_xy = np.array(Visum.Net.Zones.GetMultipleAttributes(["XCOORD", "YCOORD"], False)) + stp_xy = np.array(Visum.Net.StopAreas.GetMultipleAttributes(["XCOORD", "YCOORD"], False)) + + Visum.Log(PRIO, 'calculate nearest stop in catchment for each taz...') + + distance = scipy.spatial.distance.cdist(taz_xy, stp_xy, 'euclidean') + result = dict() + connector_table = [["$CONNECTOR:ZONENO","NODENO","DIRECTION","TYPENO","TSYSSET"]] + for ix, taz in tazs: + connector_nodes = set() + catchment_stops = stp_no.compress(distance[ix-1, :] <= cutoff) + catchment_distance = distance[ix-1, :].compress(distance[ix-1, :] <= cutoff) + + for tsys in tsys_constr: + max_dist, max_stops = tsys_constr[tsys] + max_dist = max_dist*SCALEFACTOR + stops_added = 0 + for dist, stopno in sorted(zip(catchment_distance, catchment_stops)): + snode, stsys = stop_tsys[stopno] + stsys = stsys.split(",") + if dist < max_dist and tsys in stsys: + connector_nodes.add(snode) + stops_added+=1 + if stops_added >= max_stops: + break + + result[taz] = list(connector_nodes) + + # THIS IS HOW CONNECTORS ARE ADDED USING COM - BUT THIS IS TOO SLOW: SO USE NET FILE BATCHING INSTEAD. + # for node in connector_nodes: + # if Visum.Net.Connectors.ExistsByKey(node, taz): + # Visum.Log(PRIO, "{} -> {} | already exists!".format(taz, node)) + # Visum.Net.Connectors.SourceItemByKey(taz, node).SetAttValue("TypeNo", 7) + # else: + # connector = Visum.Net.AddConnector(taz, node) + # connector.SetAttValue("TypeNo", 10) + + # GENERATE NET FILE BATCH TABLE FOR CONNECTORS. + # need to preserve original connectors esp: walk to destination(?) + for node in connector_nodes: + # --- ["$CONNECTOR:ZONENO","NODENO","DIRECTION","TYPENO"] + if Visum.Net.Connectors.ExistsByKey(node, taz): + # Visum.Log(PRIO, "{} -> {} | already exists!".format(taz, node)) + connector_table.append([taz, node, 'O', 7, 'i']) # origin/access connector + dtsys = Visum.Net.Connectors.DestItemByKey(node, taz).AttValue("TSYSSET") + connector_table.append([taz, node, 'D', 7, dtsys]) # destination/egress connector + else: + connector_table.append([taz, node, 'O', 10, 'i']) # origin/access connector + connector_table.append([taz, node, 'D', 10, '']) # destination/egress connector + + Visum.Log(PRIO, 'write results to file...') + # with open(FN, 'w') as out_file: + # json.dump(result, out_file, indent=2) + + with open(FN, 'w', newline='') as csvfile: + writer = csv.writer(csvfile, delimiter =';') + writer.writerows(vision_header()) + writer.writerows(connector_table) + + Visum.Log(PRIO, 'read knr connectors...') + + loadnetctrl = Visum.IO.CreateAddNetReadController() # read additionally controller + loadnetctrl.SetWhatToDo("CONNECTOR", 5) # conflict handling - overwrite attributes + Visum.IO.LoadNet(FN, ReadAdditive=True, RouteSearch=None, AddNetRead=loadnetctrl, NormalizePolygons=False, + MergeSameCoordPolygonPoints=False, DecimalsForMergeSameCoordPolygonPoints=-1) + + # Set Length for new connectors based on preexisting connector lengths (which come from Emme originally) + length = h.GetMulti(Visum.Net.Connectors,r"LENGTH" , activeOnly = True) + zoneno = h.GetMulti(Visum.Net.Connectors,r"ZONENO" , activeOnly = True) + typeno = h.GetMulti(Visum.Net.Connectors,r"TYPENO" , activeOnly = True) + att_list = [length,zoneno,typeno] + df = pd.DataFrame(np.column_stack(att_list), columns = ['length','zoneno','typeno']) + df_filtered = df[df['typeno'] < 10] # All preexisting connectors will be TypeNO = 0 or 7 + df_unique = df_filtered.groupby('zoneno').agg(length_max=('length', 'max')).reset_index() + df = pd.merge(df, df_unique, on='zoneno', how='left') + h.SetMulti(Visum.Net.Connectors,r"LENGTH" ,df['length_max']) + + ## Set Length for new connectors for direction not already set (using Visum calculated length) + #length = h.GetMulti(Visum.Net.Connectors,r"LENGTH" , activeOnly = True) + #zoneno = h.GetMulti(Visum.Net.Connectors,r"ZONENO" , activeOnly = True) + #nodeno = h.GetMulti(Visum.Net.Connectors,r"NODENO" , activeOnly = True) + #att_list = [length,zoneno,nodeno] + #df = pd.DataFrame(np.column_stack(att_list), columns = ['length','zoneno','nodeno']) + #df[['zoneno','nodeno']] = df[['zoneno','nodeno']].astype(str) + #df['concat'] = df['zoneno'] + df['nodeno'] + #df_unique = df.groupby('concat').agg(length_max=('length', 'max')).reset_index() + #df = pd.merge(df, df_unique, on='concat', how='left') + #h.SetMulti(Visum.Net.Connectors,r"LENGTH" ,df['length_max']) + + + + Visum.Log(PRIO, 'done!') + + +def set_connector_properties(knrdirection): + + # Save TSysSet in TSys_Holding if the original values are present in TSysSet + checktype = np.array(h.GetMulti(Visum.Net.Connectors,r"TSYSSET", activeOnly = True)) + if 'i' in checktype: + Visum.Log(PRIO, 'TSYS_HOLDING Overwriting Skipped') + else: + Visum.Log(PRIO, 'TSYS_HOLDING Overwritten') + tsys = h.GetMulti(Visum.Net.Connectors,r"TSYSSET", activeOnly = True) + h.SetMulti(Visum.Net.Connectors,r"TSYS_HOLDING" ,tsys) + + # Pull attributes + connector_type_dir = Visum.Net.Connectors.GetMultipleAttributes(["TypeNo", "Direction", "Length", "TSYS_HOLDING"]) + connector_tsys = [] + connector_time = [] + # we assume here that KNR is not open on any connector to start + for typeno, direction, distance, tsys_hold in connector_type_dir: + if knrdirection == "KTW": + if direction == 1: # Origin, leaving a zone + if typeno in [7, 10]: + connector_tsys.append("i") + connector_time.append([3600*distance/CONSPEED, 999999]) + else: + connector_tsys.append("") + connector_time.append([999999, 999999]) + + elif direction == 2: # Destination, entering a zone + if typeno == 10: + # a knr drive only connector- so no walk on this. + connector_tsys.append("") + connector_time.append([999999, 999999]) + else: + # could be a walk destination connector, + # we assume here that KNR is not open on any connector to start so keep Tsys the way it was (else: could also set to 'w') + connector_tsys.append(tsys_hold) + connector_time.append([999999, 3600*distance/2.5]) + elif knrdirection == "WTK": + if direction == 2: # Destination, entering a zone + if typeno in [7, 10]: + connector_tsys.append("i") + connector_time.append([3600*distance/CONSPEED, 999999]) + else: + connector_tsys.append("") + connector_time.append([999999, 999999]) + elif direction == 1: # Origin, leaving a zone + if typeno == 10: + # a knr drive only connector- so no walk on this. + connector_tsys.append("") + connector_time.append([999999, 999999]) + else: + # could be a walk destination connector, + # we assume here that KNR is not open on any connector to start so keep Tsys the way it was (else: could also set to 'w') + connector_tsys.append(tsys_hold) + connector_time.append([999999, 3600*distance/2.5]) + elif knrdirection == "WTW": + connector_tsys.append(tsys_hold) + connector_time.append([999999, 3600*distance/2.5]) + + h.SetMulti(Visum.Net.Connectors, "TSysSet", connector_tsys) + Visum.Net.Connectors.SetMultipleAttributes(["T0_TSYS(I)", "T0_TSYS(W)"], connector_time) + + + +code = Visum.Procedures.OperationExecutor.GetCurrentOperation().AttValue("CODE") +create_knr_connectors() +set_connector_properties(code) diff --git a/skimming_and_assignment/visum/scripts/OP_SetTransitRunTimes.py b/skimming_and_assignment/visum/scripts/OP_SetTransitRunTimes.py new file mode 100644 index 0000000..4b6f5c5 --- /dev/null +++ b/skimming_and_assignment/visum/scripts/OP_SetTransitRunTimes.py @@ -0,0 +1,76 @@ +# replicate Emme TTF in Visum +# chetan joshi, ptv portland or 2/11/2025 +# @luke.gordon, adapted further for LCOG model 2/11/2025 + + # a ft1 =timau * 1.15 + # a ft2 =timau * 1.2 + # a ft3 =timau + # a ft4 =timau * 1.09 + # a ft5 =60 * length / us1 + # a ft6 =60 * length / us1 + 60 * length / 180 + # a ft11 =(timau * 1.15) + # a ft12 =(timau * 1.3) + # a ft13 =(timau) + # a ft14 =(timau * 1.09) + # a ft15 =(60 * length / us1) + # a ft16 =(60 * length / us1 + 60 * length / 180) + # a ft21 =timau * 1.05 + # a ft22 =timau * 1.03 + +default_speed = 30 #mph -> default speed of transit + +def calc_ttf(ft, timau, length, us1): + if us1 <= 0: + us1 = default_speed + + if length: + transit_time = 3600*length / default_speed + + if timau < 999: + if ft == 1: + transit_time = timau * 1.15 + elif ft == 2: + transit_time = timau * 1.20 + elif ft == 3: + transit_time = timau + elif ft == 4: + transit_time = timau * 1.09 + elif ft == 5: + transit_time = 60* (60 * length / us1) + elif ft == 6: + transit_time = 60* (60 * length / us1 + 60 * length / 180) + elif ft ==11: + transit_time = timau * 1.15 + elif ft ==12: + transit_time = timau * 1.30 + elif ft ==13: + transit_time = timau + elif ft ==14: + transit_time =timau * 1.09 + elif ft == 15: + transit_time = 60* (60 * length / us1) + elif ft == 16: + transit_time = 60* (60 * length / us1 + 60 * length / 180) + elif ft == 21: + transit_time = timau * 1.05 + elif ft == 22: + transit_time = timau * 1.03 + else: + transit_time = 1 + + return transit_time + +def update_transit_time(): + tpitems = Visum.Net.TimeProfileItems.GetMultipleAttributes(["LINEROUTEITEM\\EMME_TTFINDEX", "SUM:USEDLINEROUTEITEMS\\OUTLINK\\AddVal2","SUM:USEDLINEROUTEITEMS\\POSTLENGTH", + "LINEROUTEITEM\\EMME_DATA1"]) + result = [] + for ft, timau, length, us1 in tpitems: + haul_time = calc_ttf(ft, timau, length, us1) + result.append([haul_time, ]) + + Visum.Net.TimeProfileItems.SetMultipleAttributes(["AddVal"], result) + + +update_transit_time() + + diff --git a/skimming_and_assignment/visum/scripts/PK_SetTransitRunTimes.py b/skimming_and_assignment/visum/scripts/PK_SetTransitRunTimes.py new file mode 100644 index 0000000..9eb0479 --- /dev/null +++ b/skimming_and_assignment/visum/scripts/PK_SetTransitRunTimes.py @@ -0,0 +1,76 @@ +# replicate Emme TTF in Visum +# chetan joshi, ptv portland or 2/11/2025 +# @luke.gordon, adapted further for LCOG model 2/11/2025 + + # a ft1 =timau * 1.15 + # a ft2 =timau * 1.2 + # a ft3 =timau + # a ft4 =timau * 1.09 + # a ft5 =60 * length / us1 + # a ft6 =60 * length / us1 + 60 * length / 180 + # a ft11 =(timau * 1.15) + # a ft12 =(timau * 1.3) + # a ft13 =(timau) + # a ft14 =(timau * 1.09) + # a ft15 =(60 * length / us1) + # a ft16 =(60 * length / us1 + 60 * length / 180) + # a ft21 =timau * 1.05 + # a ft22 =timau * 1.03 + +default_speed = 30 #mph -> default speed of transit + +def calc_ttf(ft, timau, length, us1): + if us1 <= 0: + us1 = default_speed + + if length: + transit_time = 3600*length / default_speed + + if timau < 999: + if ft == 1: + transit_time = timau * 1.15 + elif ft == 2: + transit_time = timau * 1.20 + elif ft == 3: + transit_time = timau + elif ft == 4: + transit_time = timau * 1.09 + elif ft == 5: + transit_time = 60* (60 * length / us1) + elif ft == 6: + transit_time = 60* (60 * length / us1 + 60 * length / 180) + elif ft ==11: + transit_time = timau * 1.15 + elif ft ==12: + transit_time = timau * 1.30 + elif ft ==13: + transit_time = timau + elif ft ==14: + transit_time =timau * 1.09 + elif ft == 15: + transit_time = 60* (60 * length / us1) + elif ft == 16: + transit_time = 60* (60 * length / us1 + 60 * length / 180) + elif ft == 21: + transit_time = timau * 1.05 + elif ft == 22: + transit_time = timau * 1.03 + else: + transit_time = 1 + + return transit_time + +def update_transit_time(): + tpitems = Visum.Net.TimeProfileItems.GetMultipleAttributes(["LINEROUTEITEM\\EMME_TTFINDEX", "SUM:USEDLINEROUTEITEMS\\OUTLINK\\AddVal1","SUM:USEDLINEROUTEITEMS\\POSTLENGTH", + "LINEROUTEITEM\\EMME_DATA1"]) + result = [] + for ft, timau, length, us1 in tpitems: + haul_time = calc_ttf(ft, timau, length, us1) + result.append([haul_time, ]) + + Visum.Net.TimeProfileItems.SetMultipleAttributes(["AddVal"], result) + + +update_transit_time() + + diff --git a/skimming_and_assignment/visum/scripts/PrTAssignmentQualityData_Export.py b/skimming_and_assignment/visum/scripts/PrTAssignmentQualityData_Export.py new file mode 100644 index 0000000..f859866 --- /dev/null +++ b/skimming_and_assignment/visum/scripts/PrTAssignmentQualityData_Export.py @@ -0,0 +1,59 @@ +# LCOG: Pull PrT Assignment Quality Report from Visum and save as csv file + +""" +created 5/14/2025 + +@author: luke.gordon + +""" + +# Libraries +import VisumPy.helpers +import VisumPy.excel +import pandas as pd +import numpy as np +import csv +#from datetime import datetime +import math +import os.path + + +# Pull timestamp for folder name and iteration number from Visum network attribute +date = Visum.Net.AttValue("output_date") +iter = Visum.Net.AttValue("iter") +iter = str(int(iter)) + +# Read user inputs from Visum +proj_dir = Visum.GetPath(2) + +# Pull "Code" field from procedure sequence to name files +code = Visum.Procedures.OperationExecutor.GetCurrentOperation().AttValue("CODE") + +# Pull PrT assignment quality report and format into a table for export +list = Visum.Workbench.Lists.CreatePrTAssQualityList + +list.AddKeyColumns() + +list.AddColumn(Attribut="MEANABSVOLDIFFTOTAL") +list.AddColumn(Attribut="MEANRELVOLDIFFTOTAL") +list.AddColumn(Attribut="ASSIGNEDDEMAND") +list.AddColumn(Attribut="VEHMITRAVPRT") +list.AddColumn(Attribut="VEHHOURTRAVT0") +list.AddColumn(Attribut="VEHHOURTRAVTCUR") +list.AddColumn(Attribut="VEHHOURIMP") +list.AddColumn(Attribut="TOTALEXCESSCOST") +list.AddColumn(Attribut="AVGEXCESSCOST") +list.AddColumn(Attribut="GAP") + +df = list.SaveToArray() + + +df = pd.DataFrame(df) + + +df = df.rename(columns={0 : 'Demand segment set code' , 1 : 'Iteration' , 2 : 'Mean absolute volume difference total' , 3 : 'Mean relative volume difference total' , + 4 : 'Assigned demand' , 5 : 'Vehicle miles traveled PrT' , 6 : 'Vehicle hours traveled t0' , 7 : 'Vehicle hours traveled tCur' , + 8 : 'Vehicle hour impedance' , 9 : 'Total excess cost' , 10: 'Mean excess cost', 11: 'Gap'}) + +df.to_csv(proj_dir+"outputs/reports/ModelRun_"+date+"/PrT Assignment Quality Reports/"+code+"_Iter"+iter+".csv") + diff --git a/skimming_and_assignment/visum/scripts/PrTSkim_PostProcessing.py b/skimming_and_assignment/visum/scripts/PrTSkim_PostProcessing.py new file mode 100644 index 0000000..2253a59 --- /dev/null +++ b/skimming_and_assignment/visum/scripts/PrTSkim_PostProcessing.py @@ -0,0 +1,64 @@ +# Post-processing PrT skims from Visum based on "Code" field in Procedure Sequence +# 6/17/2025 - Luke Gordon (RSG) +# Adapted from code from Chetan Joshi (PTV) + +import tables +import numpy as np +import pandas as pd +import os +import VisumPy.helpers as h +import openmatrix as omx + + +def prtskim_postprocessing(mtx_dseg): + + # Pull Intrazonal attributes out to set to diagonals + intrtime = np.array(h.GetMulti(Visum.Net.Zones,r"intrtime" , activeOnly = True)) + intrdist = np.array(h.GetMulti(Visum.Net.Zones,r"intrdist" , activeOnly = True)) + intrdist_wlk = np.array(h.GetMulti(Visum.Net.Zones,r"intrdist_wlk", activeOnly = True)) + + # Pull, Process, and Set matrices as needed by DSegCode + if mtx_dseg == 'wlk': # Run Walk distance processing + # Pull + dis_wlk = h.GetMatrixRaw(Visum, {"CODE": "DIS" , "DSegCode": mtx_dseg}) # Walk Distance + # Process + np.fill_diagonal(dis_wlk, intrdist_wlk) + # Set + h.SetMatrixRaw(Visum, {"CODE": "DIS" , "DSegCode": mtx_dseg}, dis_wlk) + + else: # Run Auto distance, time, and tolls processing + # Pull + dis = h.GetMatrixRaw(Visum, {"CODE": "DIS" , "DSegCode": mtx_dseg}) # Distance + time = h.GetMatrixRaw(Visum, {"CODE": "AD1" , "DSegCode": mtx_dseg}) # Time + toll = h.GetMatrixRaw(Visum, {"CODE": "TOL" , "DSegCode": mtx_dseg}) # Tolls + # Process + np.fill_diagonal(dis, intrdist) # Distance + np.fill_diagonal(time, intrtime) # Time + time = np.where(time == 9999.00, 9999.00, time / 60) # Seconds to Minutes + np.fill_diagonal(toll, 0) # Tolls + # Set + h.SetMatrixRaw(Visum, {"CODE": "DIS" , "DSegCode": mtx_dseg}, dis) + h.SetMatrixRaw(Visum, {"CODE": "AD1" , "DSegCode": mtx_dseg}, time) + h.SetMatrixRaw(Visum, {"CODE": "TOL" , "DSegCode": mtx_dseg}, toll) + + + + + +# Read user inputs from Visum +proj_dir = Visum.GetPath(2) + +dseg = Visum.Procedures.OperationExecutor.GetCurrentOperation().AttValue("CODE") # Example: outputs a string like 'AM' from AM in the code box +prtskim_postprocessing(dseg) + + +## Pull "Code" field from procedure sequence containing Code, DSegCode, and filename +#procedure_code = Visum.Procedures.OperationExecutor.GetCurrentOperation().AttValue("CODE") # Example: outputs a string like -> '[["mfamsov","PuT","AM2_SOV.omx"],["mfmdMpe","PuT","MD1_MPE.omx"]]' +#procedure_codes = eval(procedure_code) # Example: outputs a list of lists like -> [["mfamsov","PuT","AM2_SOV.omx"],["mfmdMpe","PuT","MD1_MPE.omx"]] +# +## Loop thru each matrix set in the "Code" field and export +#for x in range(len(procedure_codes)): +# dsegcode = procedure_codes[x][0] +# +# prtskim_postprocessing(dsegcode) + diff --git a/skimming_and_assignment/visum/scripts/PuTAssignmentStats_Export.py b/skimming_and_assignment/visum/scripts/PuTAssignmentStats_Export.py new file mode 100644 index 0000000..1b985f6 --- /dev/null +++ b/skimming_and_assignment/visum/scripts/PuTAssignmentStats_Export.py @@ -0,0 +1,66 @@ +# LCOG: Pull PrT Assignment Quality Report from Visum and save as csv file + +""" +created 5/21/2025 + +@author: edna.aguilar + +""" + +# Libraries +import VisumPy.helpers +import VisumPy.excel +import pandas as pd +import numpy as np +import csv +#from datetime import datetime +import math +import os.path + + +# Pull timestamp for folder name and iteration number from Visum network attribute +date = Visum.Net.AttValue("output_date") +iter = Visum.Net.AttValue("iter") +iter = str(int(iter)) + +# Read user inputs from Visum +proj_dir = Visum.GetPath(2) + +# Pull "Code" field from procedure sequence to name files +code = Visum.Procedures.OperationExecutor.GetCurrentOperation().AttValue("CODE") + +# Pull PrT assignment quality report and format into a table for export +list = Visum.Workbench.Lists.CreatePuTStatList + +list.AddKeyColumns() + +attributes = [ + "MeanJourneyTimePut", "MeanRideTimePuT", "MeanInVehTimePuT", + "MeanTransferWaitTimePut", "MeanOriginWaitTimePuT", "MeanWalkTimePuT", + "MeanPuTAuxTimePuT", "MeanSharingTravelTimePuT", "MeanAccessTimePuT", + "MeanEgressTimePuT", "MeanJourneyDistPuT", "MeanRideDistPuT", + "MeanNumTransfersPuT", + "TotalJourneyTimePuT", "TotalRideTimePuT", "TotalInVehTimePuT", + "TotalTransferWaitTimePuT", "TotalOriginWaitTimePuT", "TotalWalkTimePuT", + "TotalPuTAuxTimePut", "TotalSharingTravelTimePuT", "TotalAccessTimePuT", + "TotalEgressTimePuT", "TotalJourneyDistPuT", "TotalRideDistPuT", + "TotalNumTransfersPuT", + "PTripsUnlinkedPuT", "PTripsLinkedTot", "PTripsLinked0", "PTripsLinked1", + "PTripsLinked2", "PTripsLinkedGt2", "PTripsLinkedWRide", + "PTripsLinkedWoRide", "PTripsLinkedWoCon" +] + +for attr in attributes: + list.AddColumn(Attribut=attr) + +df = pd.DataFrame(list.SaveToArray()) + +# Convert to long format +data_only = df.iloc[0] +data_only.index = attributes +df_long = data_only.reset_index() +df_long.columns = ['Attribute', 'Value'] + +# Save +df_long.to_csv(proj_dir + "outputs/reports/ModelRun_" + date + "/PuT Assignment Stats/PuTAssignment_" + code + "_Iter" + iter + ".csv") + diff --git a/skimming_and_assignment/visum/scripts/PuTSkim_PostProcessing.py b/skimming_and_assignment/visum/scripts/PuTSkim_PostProcessing.py new file mode 100644 index 0000000..62549d0 --- /dev/null +++ b/skimming_and_assignment/visum/scripts/PuTSkim_PostProcessing.py @@ -0,0 +1,130 @@ +# Post-processing PuT skims from Visum based on "Code" field in Procedure Sequence +# 6/16/2025 - Luke Gordon (RSG) +# Adapted from code from Chetan Joshi (PTV) + +import tables +import numpy as np +import pandas as pd +import os +import VisumPy.helpers as h +import openmatrix as omx + + +# Create function to build skim matrices that weren't built during the skimming procedure itself if they don't yet exist +def creatematrices(code,mtx_dseg): + try: + mx = h.GetMatrixRaw(Visum, {"CODE": code , "DSegCode": mtx_dseg}) + except Exception as e: + mx = Visum.Net.AddMatrix(No=-1,ObjectTypeRef=2,MatrixType=4) + mx.SetAttValue("CODE",code) + mx.SetAttValue("DSegCode",mtx_dseg) + + +def putskim_postprocessing(mtx_dseg,knr_flag): + + # Build matrices if they don't yet exist + creatematrices("NBR" ,mtx_dseg) + creatematrices("IVTT",mtx_dseg) + creatematrices("STC" ,mtx_dseg) + #creatematrices("OVT" ,mtx_dseg) + creatematrices("VTC" ,mtx_dseg) + + # Pull matrices out of Visum as numpy arrays + wkt = h.GetMatrixRaw(Visum, {"CODE": "WKT" , "DSegCode": mtx_dseg}) # Walk time + act = h.GetMatrixRaw(Visum, {"CODE": "ACT" , "DSegCode": mtx_dseg}) # Access time + egt = h.GetMatrixRaw(Visum, {"CODE": "EGT" , "DSegCode": mtx_dseg}) # Egress time + ntr = h.GetMatrixRaw(Visum, {"CODE": "NTR" , "DSegCode": mtx_dseg}) # Number of transfers + nbr = h.GetMatrixRaw(Visum, {"CODE": "NBR" , "DSegCode": mtx_dseg}) # Number of boardings + wowt = h.GetMatrixRaw(Visum, {"CODE": "WOWT" , "DSegCode": mtx_dseg}) # Weighted origin wait time + wtwt = h.GetMatrixRaw(Visum, {"CODE": "WTWT" , "DSegCode": mtx_dseg}) # Weighted transfer wait time + ivtt = h.GetMatrixRaw(Visum, {"CODE": "IVTT" , "DSegCode": mtx_dseg}) # In-vehicle travel time + ivtt_a = h.GetMatrixRaw(Visum, {"CODE": "IVTT(a)" , "DSegCode": mtx_dseg}) # In-vehicle travel time (BRT) + ivtt_b = h.GetMatrixRaw(Visum, {"CODE": "IVTT(b)" , "DSegCode": mtx_dseg}) # In-vehicle travel time (Bus) + ivtt_e = h.GetMatrixRaw(Visum, {"CODE": "IVTT(e)" , "DSegCode": mtx_dseg}) # In-vehicle travel time (Streetcar) + ivtt_l = h.GetMatrixRaw(Visum, {"CODE": "IVTT(l)" , "DSegCode": mtx_dseg}) # In-vehicle travel time (LRT) + ivtt_r = h.GetMatrixRaw(Visum, {"CODE": "IVTT(r)" , "DSegCode": mtx_dseg}) # In-vehicle travel time (WES) + pla = h.GetMatrixRaw(Visum, {"CODE": "PLA" , "DSegCode": mtx_dseg}) # Stop type constant (raw sum) + stc = h.GetMatrixRaw(Visum, {"CODE": "STC" , "DSegCode": mtx_dseg}) # Stop type constant (final average) + #ovt = h.GetMatrixRaw(Visum, {"CODE": "OVT" , "DSegCode": mtx_dseg}) # Out of vehicle time + vtc = h.GetMatrixRaw(Visum, {"CODE": "VTC" , "DSegCode": mtx_dseg}) # Vehicle type constant + + # Process matrices + # Walk time + if knr_flag == 'wtw': + wkt = np.minimum(wkt + act + egt , 9999.00) + wkt = np.where((wkt < 0.01) | (wkt > 20.0) , 9999.00, wkt) + np.fill_diagonal(wkt, 9999.00) + elif knr_flag == 'ktw': + wkt = np.minimum(wkt + egt , 9999.00) + wkt = np.where((wkt < 0.01) | (wkt > 20.0) , 9999.00, wkt) + np.fill_diagonal(wkt, 9999.00) + elif knr_flag == 'wtk': + wkt = np.minimum(wkt + act , 9999.00) + wkt = np.where((wkt < 0.01) | (wkt > 20.0) , 9999.00, wkt) + np.fill_diagonal(wkt, 9999.00) + + + # Weighted origin wait time + wowt = np.where(wkt == 9999.00, 9999.00, wowt) + # Perceived In-vehicle time by Mode + if mtx_dseg == 'amPuT' or mtx_dseg == 'pmPuT': # Peak + ivtt_a = np.where(ivtt_a == 9999.00, 9999.00, ivtt_a * 0.95) + ivtt_l = np.where(ivtt_l == 9999.00, 9999.00, ivtt_l * 0.88) + ivtt_r = np.where(ivtt_r == 9999.00, 9999.00, ivtt_r * 0.88) + else: # Off-Peak + ivtt_a = np.where(ivtt_a == 9999.00, 9999.00, ivtt_a * 0.95) + ivtt_l = np.where(ivtt_l == 9999.00, 9999.00, ivtt_l * 0.86) + ivtt_r = np.where(ivtt_r == 9999.00, 9999.00, ivtt_r * 0.86) + # Number of boardings + nbr = np.where(ntr == 9999.00, 9999.00, ntr + 1) + # Stop type constant + stc = np.where((pla == 9999.00) | (nbr == 9999.00) , 9999.00, pla / nbr) + stc = np.where(stc == 9999.00, 0.00, stc) + # In-Vehicle time + ivtt = np.minimum(ivtt_a + ivtt_b + ivtt_e + ivtt_l + ivtt_r, 9999.00) + np.fill_diagonal(ivtt, 9999.00) + # Out of vehicle time + #ovt = np.minimum(wowt + wtwt + wkt, 9999.00) + # Vehicle type constant + if mtx_dseg == 'amPuT' or mtx_dseg == 'pmPuT': # Peak + vtc = np.where((ivtt == 9999.00) | (ivtt == 0.00), 9999.00, + ((0.0557 * ivtt_a) / ivtt) + ((0.000 * ivtt_e) / ivtt) + ((0.1858 * ivtt_l) / ivtt) + ((0.1858 * ivtt_r) / ivtt)) + else: # Off-Peak + vtc = np.where((ivtt == 9999.00) | (ivtt == 0.00), 9999.00, + ((0.0432 * ivtt_a) / ivtt) + ((0.0984 * ivtt_e) / ivtt) + ((0.1442 * ivtt_l) / ivtt) + ((0.1442 * ivtt_r) / ivtt)) + + # Set matrices in Visum + h.SetMatrixRaw(Visum, {"CODE": "WKT" , "DSegCode": mtx_dseg}, wkt ) # Walk time + h.SetMatrixRaw(Visum, {"CODE": "ACT" , "DSegCode": mtx_dseg}, act ) # Access time + h.SetMatrixRaw(Visum, {"CODE": "EGT" , "DSegCode": mtx_dseg}, egt ) # Egress time + h.SetMatrixRaw(Visum, {"CODE": "NTR" , "DSegCode": mtx_dseg}, ntr ) # Number of transfers + h.SetMatrixRaw(Visum, {"CODE": "NBR" , "DSegCode": mtx_dseg}, nbr ) # Number of boardings + h.SetMatrixRaw(Visum, {"CODE": "WOWT" , "DSegCode": mtx_dseg}, wowt ) # Weighted origin wait time + h.SetMatrixRaw(Visum, {"CODE": "WTWT" , "DSegCode": mtx_dseg}, wtwt ) # Weighted transfer wait time + h.SetMatrixRaw(Visum, {"CODE": "IVTT" , "DSegCode": mtx_dseg}, ivtt ) # In-vehicle travel time + h.SetMatrixRaw(Visum, {"CODE": "IVTT(a)" , "DSegCode": mtx_dseg}, ivtt_a) # In-vehicle travel time (BRT) + h.SetMatrixRaw(Visum, {"CODE": "IVTT(b)" , "DSegCode": mtx_dseg}, ivtt_b) # In-vehicle travel time (Bus) + h.SetMatrixRaw(Visum, {"CODE": "IVTT(e)" , "DSegCode": mtx_dseg}, ivtt_e) # In-vehicle travel time (Streetcar) + h.SetMatrixRaw(Visum, {"CODE": "IVTT(l)" , "DSegCode": mtx_dseg}, ivtt_l) # In-vehicle travel time (LRT) + h.SetMatrixRaw(Visum, {"CODE": "IVTT(r)" , "DSegCode": mtx_dseg}, ivtt_r) # In-vehicle travel time (WES) + h.SetMatrixRaw(Visum, {"CODE": "PLA" , "DSegCode": mtx_dseg}, pla ) # Stop type constant (raw sum) + h.SetMatrixRaw(Visum, {"CODE": "STC" , "DSegCode": mtx_dseg}, stc ) # Stop type constant (final average) + #h.SetMatrixRaw(Visum, {"CODE": "OVT" , "DSegCode": mtx_dseg}, ovt ) # Out of vehicle time + h.SetMatrixRaw(Visum, {"CODE": "VTC" , "DSegCode": mtx_dseg}, vtc ) # Vehicle type constant + + + +# Read user inputs from Visum +proj_dir = Visum.GetPath(2) + +# Pull "Code" field from procedure sequence containing Code, DSegCode, and filename +procedure_code = Visum.Procedures.OperationExecutor.GetCurrentOperation().AttValue("CODE") # Example: outputs a string like -> '[["mfamsov","PuT","AM2_SOV.omx"],["mfmdMpe","PuT","MD1_MPE.omx"]]' +procedure_codes = eval(procedure_code) # Example: outputs a list of lists like -> [["mfamsov","PuT","AM2_SOV.omx"],["mfmdMpe","PuT","MD1_MPE.omx"]] + +# Loop thru each matrix set in the "Code" field and export +for x in range(len(procedure_codes)): + dsegcode = procedure_codes[x][0] + knr_flag = procedure_codes[x][1] + +putskim_postprocessing(dsegcode,knr_flag) + diff --git a/skimming_and_assignment/visum/scripts/PuT_Skimming_Setup.py b/skimming_and_assignment/visum/scripts/PuT_Skimming_Setup.py new file mode 100644 index 0000000..8c46c08 --- /dev/null +++ b/skimming_and_assignment/visum/scripts/PuT_Skimming_Setup.py @@ -0,0 +1,396 @@ +# Prep for Skimming for Oregon Metro +# 6/12/2025 - Luke Gordon (RSG) +# Adapted from code from Chetan Joshi (PTV) + +import tables +import numpy as np +import pandas as pd +import os +import VisumPy.helpers as h +import openmatrix as omx + + +def skim_setup(period): + + def ismode(): # isbrt, isscr, islrt, & iswes + + # Pull attributes + sa_isbrt = h.GetMulti(Visum.Net.StopAreas,r"isbrt", activeOnly = True) + sa_isscr = h.GetMulti(Visum.Net.StopAreas,r"isscr", activeOnly = True) + sa_islrt = h.GetMulti(Visum.Net.StopAreas,r"islrt", activeOnly = True) + sa_iswes = h.GetMulti(Visum.Net.StopAreas,r"iswes", activeOnly = True) + sp_lrtsyscode = h.GetMulti(Visum.Net.StopAreas,r"FIRST:STOPPOINTS\DISTINCT:LINEROUTES\TSYSCODE", activeOnly = True) + + # Make Visum list with link data + att_list = [sa_isbrt,sa_isscr,sa_islrt,sa_iswes,sp_lrtsyscode] + + # Put Visum link list into dataframe + df = pd.DataFrame(np.column_stack(att_list), columns = ['sa_isbrt','sa_isscr','sa_islrt','sa_iswes','sp_lrtsyscode']) + + # Break out 'DISTINCT:LINEROUTES\TSYSCODE' field to separate by commas into individual columns + df[['sp_lrtsyscode']] = df[['sp_lrtsyscode']].astype(str) + df = pd.concat([df,df['sp_lrtsyscode'].str.split(',', expand = True)], axis = 1) + # Change Screenline field names + if 1 not in df: + df[1] = None + if 2 not in df: + df[2] = None + if 3 not in df: + df[3] = None + if 4 not in df: + df[4] = None + df = df.rename(columns = {0:'Mode1',1:'Mode2',2:'Mode3',3:'Mode4',4:'Mode5'}) + + # Calculate fields + df['isbrt'] = df.apply(lambda row: 1 if row['Mode1'] == 'a' or row['Mode2'] == 'a' or row['Mode3'] == 'a' or row['Mode4'] == 'a' or row['Mode5'] == 'a' else 0, axis=1) + df['isscr'] = df.apply(lambda row: 1 if row['Mode1'] == 'e' or row['Mode2'] == 'e' or row['Mode3'] == 'e' or row['Mode4'] == 'e' or row['Mode5'] == 'e' else 0, axis=1) + df['islrt'] = df.apply(lambda row: 1 if row['Mode1'] == 'l' or row['Mode2'] == 'l' or row['Mode3'] == 'l' or row['Mode4'] == 'l' or row['Mode5'] == 'l' else 0, axis=1) + df['iswes'] = df.apply(lambda row: 1 if row['Mode1'] == 'r' or row['Mode2'] == 'r' or row['Mode3'] == 'r' or row['Mode4'] == 'r' or row['Mode5'] == 'r' else 0, axis=1) + + # Set fields back in Visum + h.SetMulti(Visum.Net.StopAreas ,r"isbrt", df['isbrt']) + h.SetMulti(Visum.Net.StopAreas ,r"isscr", df['isscr']) + h.SetMulti(Visum.Net.StopAreas ,r"islrt", df['islrt']) + h.SetMulti(Visum.Net.StopAreas ,r"iswes", df['iswes']) + + def headway(period): # Headway and Headway_Halved + + # Pull attributes + tp_emmeheadway = h.GetMulti(Visum.Net.TimeProfiles,r"Emme_Headway", activeOnly = True) + tp_headwayhalved = h.GetMulti(Visum.Net.TimeProfiles,r"Headway_Halved", activeOnly = True) + + if period == 'AM' or period == 'PM': + tp_periodheadway = h.GetMulti(Visum.Net.TimeProfiles,r"LINEROUTE\EMME_DATA1", activeOnly = True) + else: + tp_periodheadway = h.GetMulti(Visum.Net.TimeProfiles,r"LINEROUTE\EMME_DATA2", activeOnly = True) + + # Make Visum list with link data + att_list = [tp_emmeheadway,tp_periodheadway,tp_headwayhalved] + + # Put Visum link list into dataframe + df = pd.DataFrame(np.column_stack(att_list), columns = ['tp_emmeheadway','tp_periodheadway','tp_headwayhalved']) + + # Calculate fields + df['tp_emmeheadway'] = df['tp_periodheadway'] * 60 + df['tp_headwayhalved'] = df['tp_emmeheadway'] / 2 + + # Set fields back in Visum + h.SetMulti(Visum.Net.TimeProfiles ,r"Emme_Headway", df['tp_emmeheadway']) + h.SetMulti(Visum.Net.TimeProfiles ,r"Headway_Halved", df['tp_headwayhalved']) + + + def op_bushr(): # op_bushr on lineroutes and stopareas + + # Line Routes + # Pull attributes + lr_opbushr = h.GetMulti(Visum.Net.LineRoutes,r"op_bushr", activeOnly = True) + lr_opheadway = h.GetMulti(Visum.Net.LineRoutes,r"EMME_DATA2", activeOnly = True) + + # Make Visum list with link data + att_list = [lr_opbushr,lr_opheadway] + + # Put Visum link list into dataframe + df = pd.DataFrame(np.column_stack(att_list), columns = ['lr_opbushr','lr_opheadway']) + + # Calculate fields + df['lr_opbushr'] = df.apply(lambda row: 1 / (row['lr_opheadway'] / 60) if row['lr_opheadway'] != 0 else 0,axis=1) + + # Set field back in Visum + h.SetMulti(Visum.Net.LineRoutes ,r"op_bushr", df['lr_opbushr']) + + + # Stop Areas + # Pull attributes + sa_lropbushr = h.GetMulti(Visum.Net.StopAreas,r"FIRST:STOPPOINTS\SUM:LINEROUTES\OP_BUSHR", activeOnly = True) + + # Set field back in Visum + h.SetMulti(Visum.Net.StopAreas ,r"op_bushr", sa_lropbushr) + + + def stoptype(period): # Stop type & stop type constant + + # Pull attributes + # Fields to set + sa_sttyp = h.GetMulti(Visum.Net.StopAreas,r"sttyp" , activeOnly = True) + sa_stcon = h.GetMulti(Visum.Net.StopAreas,r"stcon" , activeOnly = True) + # Condition Fields + sa_istc = h.GetMulti(Visum.Net.StopAreas,r"istc" , activeOnly = True) + sa_istm = h.GetMulti(Visum.Net.StopAreas,r"istm" , activeOnly = True) + sa_isbrt = h.GetMulti(Visum.Net.StopAreas,r"isbrt" , activeOnly = True) + sa_isscr = h.GetMulti(Visum.Net.StopAreas,r"isscr" , activeOnly = True) + sa_islrt = h.GetMulti(Visum.Net.StopAreas,r"islrt" , activeOnly = True) + sa_iswes = h.GetMulti(Visum.Net.StopAreas,r"iswes" , activeOnly = True) + sa_opbushr = h.GetMulti(Visum.Net.StopAreas,r"op_bushr", activeOnly = True) + + # Make Visum list with link data + att_list = [sa_sttyp,sa_stcon,sa_istc,sa_istm ,sa_isbrt,sa_isscr,sa_islrt,sa_iswes,sa_opbushr] + + # Put Visum link list into dataframe + df = pd.DataFrame(np.column_stack(att_list), columns = ['sa_sttyp','sa_stcon','sa_istc','sa_istm','sa_isbrt','sa_isscr','sa_islrt','sa_iswes','sa_opbushr']) + + # Convert condition fields & stop type constant to float + df[['sa_stcon','sa_istc','sa_istm','sa_isbrt','sa_isscr','sa_islrt','sa_iswes','sa_opbushr']] = df[[ + 'sa_stcon','sa_istc','sa_istm','sa_isbrt','sa_isscr','sa_islrt','sa_iswes','sa_opbushr']].astype(float) + + # Calculate Stop Type field + for x in range(len(df)): + if ((df.at[x,'sa_istc'] == 1) | (df.at[x,'sa_istm'] == 1) | (df.at[x,'sa_islrt'] == 1) | (df.at[x,'sa_iswes'] == 1)): + df.at[x,'sa_sttyp'] = 'A,B,C' # Is transit center, is transit mall, is LRT stop, is WES stop + elif ((df.at[x,'sa_isbrt'] == 1) | (df.at[x,'sa_isscr'] == 1) | (df.at[x,'sa_opbushr'] >= 4)): + df.at[x,'sa_sttyp'] = 'D' # Is BRT stop, is streetcar stop, has >= 4 transit vehicles per hour + else: + df.at[x,'sa_sttyp'] = 'E' # All stops with infrequent local bus service + + + # Calculate Stop Type Constant field + for y in range(len(df)): + if period == 'AM' or period == 'PM': # Peak + if df.at[y,'sa_sttyp'] == 'A,B,C': + df.at[y,'sa_stcon'] = 0.1582 + elif df.at[y,'sa_sttyp'] == 'D': + df.at[y,'sa_stcon'] = 0.0531 + elif df.at[y,'sa_sttyp'] == 'E': + df.at[y,'sa_stcon'] = 0.0000 + else: # Off-Peak + if df.at[y,'sa_sttyp'] == 'A,B,C': + df.at[y,'sa_stcon'] = 0.1075 + elif df.at[y,'sa_sttyp'] == 'D': + df.at[y,'sa_stcon'] = 0.0756 + elif df.at[y,'sa_sttyp'] == 'E': + df.at[y,'sa_stcon'] = 0.0000 + + # Set fields back in Visum + h.SetMulti(Visum.Net.StopAreas ,r"sttyp", df['sa_sttyp']) + h.SetMulti(Visum.Net.StopAreas ,r"stcon", df['sa_stcon']) + + + def waittime(period): # Wait time perception factor + + # Pull attributes + sa_sttyp = h.GetMulti(Visum.Net.StopAreas,r"sttyp" , activeOnly = True) + sa_wtpf = h.GetMulti(Visum.Net.StopAreas,r"wtpf" , activeOnly = True) + + # Make Visum list with link data + att_list = [sa_sttyp,sa_wtpf] + + # Put Visum link list into dataframe + df = pd.DataFrame(np.column_stack(att_list), columns = ['sa_sttyp','sa_wtpf']) + + # Calculate field + for y in range(len(df)): + if period == 'AM' or period == 'PM': # Peak + if df.at[y,'sa_sttyp'] == 'A,B,C': + df.at[y,'sa_wtpf'] = 0.88 + elif df.at[y,'sa_sttyp'] == 'D': + df.at[y,'sa_wtpf'] = 0.93 + elif df.at[y,'sa_sttyp'] == 'E': + df.at[y,'sa_wtpf'] = 1.00 + else: # Off-Peak + if df.at[y,'sa_sttyp'] == 'A,B,C': + df.at[y,'sa_wtpf'] = 0.86 + elif df.at[y,'sa_sttyp'] == 'D': + df.at[y,'sa_wtpf'] = 0.94 + elif df.at[y,'sa_sttyp'] == 'E': + df.at[y,'sa_wtpf'] = 1.00 + + # Set fields back in Visum + h.SetMulti(Visum.Net.StopAreas ,r"wtpf", df['sa_wtpf']) + + + def invehicleperceptionfactor(period): # In-vehicle perception factors + + # Pull attributes + tp_tsyscode = h.GetMulti(Visum.Net.TimeProfiles,r"TSYSCODE", activeOnly = True) + tp_ivpf = h.GetMulti(Visum.Net.TimeProfiles,r"ivpf" , activeOnly = True) + + # Make Visum list with link data + att_list = [tp_tsyscode,tp_ivpf] + + # Put Visum link list into dataframe + df = pd.DataFrame(np.column_stack(att_list), columns = ['tp_tsyscode','tp_ivpf']) + + # Calculate field + for y in range(len(df)): + if period == 'AM' or period == 'PM': # Peak + if df.at[y,'tp_tsyscode'] == 'a': + df.at[y,'tp_ivpf'] = 0.95 + elif ((df.at[y,'tp_tsyscode'] == 'l') or (df.at[y,'tp_tsyscode'] == 'r')): + df.at[y,'tp_ivpf'] = 0.88 + else: + df.at[y,'tp_ivpf'] = 1.00 + else: # Off-Peak + if df.at[y,'tp_tsyscode'] == 'a': + df.at[y,'tp_ivpf'] = 0.95 + elif ((df.at[y,'tp_tsyscode'] == 'l') or (df.at[y,'tp_tsyscode'] == 'r')): + df.at[y,'tp_ivpf'] = 0.86 + else: + df.at[y,'tp_ivpf'] = 1.00 + + # Set fields back in Visum + h.SetMulti(Visum.Net.TimeProfiles ,r"ivpf", df['tp_ivpf']) + + + def boardingpenalty(period): # Boarding penalty (seconds) + + # Pull attributes + tp_tsyscode = h.GetMulti(Visum.Net.TimeProfiles,r"TSYSCODE", activeOnly = True) + tp_brdpen = h.GetMulti(Visum.Net.TimeProfiles,r"brdpen" , activeOnly = True) + + # Make Visum list with link data + att_list = [tp_tsyscode,tp_brdpen] + + # Put Visum link list into dataframe + df = pd.DataFrame(np.column_stack(att_list), columns = ['tp_tsyscode','tp_brdpen']) + + # Calculate field + for y in range(len(df)): + if period == 'AM' or period == 'PM': # Peak + if ((df.at[y,'tp_tsyscode'] == 'l') or (df.at[y,'tp_tsyscode'] == 'r')): + df.at[y,'tp_brdpen'] = 000.0 + elif ((df.at[y,'tp_tsyscode'] == 'a') or (df.at[y,'tp_tsyscode'] == 'e')): + df.at[y,'tp_brdpen'] = 372.0 + else: + df.at[y,'tp_brdpen'] = 439.8 + else: # Off-Peak + if ((df.at[y,'tp_tsyscode'] == 'l') or (df.at[y,'tp_tsyscode'] == 'r')): + df.at[y,'tp_brdpen'] = 000.0 + elif ((df.at[y,'tp_tsyscode'] == 'a') or (df.at[y,'tp_tsyscode'] == 'e')): + df.at[y,'tp_brdpen'] = 166.8 + else: + df.at[y,'tp_brdpen'] = 540.6 + + # Add global boarding penalty + for y in range(len(df)): + df.at[y,'tp_brdpen'] = df.at[y,'tp_brdpen'] + 231 + + # Set fields back in Visum + h.SetMulti(Visum.Net.TimeProfiles ,r"brdpen", df['tp_brdpen']) + + + def dwelltime(): + + # Pull attributes + tpi_tsyscode = h.GetMulti(Visum.Net.TimeProfileItems,r"TIMEPROFILE\TSYSCODE" , activeOnly = True) + tpi_emmedwt = h.GetMulti(Visum.Net.TimeProfileItems,r"LINEROUTEITEM\EMME_DWT", activeOnly = True) + tpi_prelength = h.GetMulti(Visum.Net.TimeProfileItems,r"PreLength" , activeOnly = True) + tpi_dwelltime = h.GetMulti(Visum.Net.TimeProfileItems,r"DWELL_TIME" , activeOnly = True) + + + # Make Visum list with link data + att_list = [tpi_tsyscode,tpi_emmedwt,tpi_prelength,tpi_dwelltime] + + # Put Visum link list into dataframe + df = pd.DataFrame(np.column_stack(att_list), columns = ['tpi_tsyscode','tpi_emmedwt','tpi_prelength','tpi_dwelltime']) + + # Convert fields to float + df[['tpi_emmedwt','tpi_prelength','tpi_dwelltime']] = df[['tpi_emmedwt','tpi_prelength','tpi_dwelltime']].astype(float) + + # Calculate field + for y in range(len(df)): + if df.at[y,'tpi_tsyscode'] == 'b': # Local Bus dwell time based on prelength and Emme_DWT + df.at[y,'tpi_dwelltime'] = df.at[y,'tpi_emmedwt'] * 60 * df.at[y,'tpi_prelength'] + else: # Non-Local Bus dwell time based on Emme_DWT only + df.at[y,'tpi_dwelltime'] = df.at[y,'tpi_emmedwt'] * 60 + + # Set fields back in Visum + h.SetMulti(Visum.Net.TimeProfileItems ,r"DWELL_TIME", df['tpi_dwelltime']) + + + def calc_ttf(ft, timau, length, us1, default_speed): # INPUT TO RUNTIME FUNCTION + if us1 <= 0: + us1 = default_speed + + if length: + transit_time = 3600*length / default_speed + + if timau < 999: + if ft == 1: + transit_time = timau * 1.15 + elif ft == 2: + transit_time = timau * 1.20 + elif ft == 3: + transit_time = timau + elif ft == 4: + transit_time = timau * 1.09 + elif ft == 5: + transit_time = 60* (60 * length / us1) + elif ft == 6: + transit_time = 60* (60 * length / us1 + 60 * length / 180) + elif ft ==11: + transit_time = timau * 1.15 + elif ft ==12: + transit_time = timau * 1.30 + elif ft ==13: + transit_time = timau + elif ft ==14: + transit_time =timau * 1.09 + elif ft == 15: + transit_time = 60* (60 * length / us1) + elif ft == 16: + transit_time = 60* (60 * length / us1 + 60 * length / 180) + elif ft == 21: + transit_time = timau * 1.05 + elif ft == 22: + transit_time = timau * 1.03 + else: + transit_time = 1 + + return transit_time + + def runtime(period): + tpitems = Visum.Net.TimeProfileItems.GetMultipleAttributes(["LINEROUTEITEM\\EMME_TTFINDEX", "SUM:USEDLINEROUTEITEMS\\OUTLINK\\"+period+"_TTC","SUM:USEDLINEROUTEITEMS\\POSTLENGTH", + "LINEROUTEITEM\\EMME_DATA1"]) + + #else: + # tpitems = Visum.Net.TimeProfileItems.GetMultipleAttributes(["LINEROUTEITEM\\EMME_TTFINDEX", "SUM:USEDLINEROUTEITEMS\\OUTLINK\\AddVal3","SUM:USEDLINEROUTEITEMS\\POSTLENGTH", + # "LINEROUTEITEM\\EMME_DATA1"]) + result = [] + + default_speed = 30 + for ft, timau, length, us1 in tpitems: + haul_time = calc_ttf(ft, timau, length, us1, default_speed) + result.append([haul_time, ]) + + Visum.Net.TimeProfileItems.SetMultipleAttributes(["AddVal"], result) + + def combinerundwelltimes(): + + # Pull attributes + tpi_addval = h.GetMulti(Visum.Net.TimeProfileItems,r"ADDVAL" , activeOnly = True) + tpi_dwelltime = h.GetMulti(Visum.Net.TimeProfileItems,r"DWELL_TIME" , activeOnly = True) + + # Make Visum list with link data + att_list = [tpi_addval,tpi_dwelltime] + + # Put Visum link list into dataframe + df = pd.DataFrame(np.column_stack(att_list), columns = ['tpi_addval','tpi_dwelltime']) + + # Convert fields to float + df[['tpi_addval','tpi_dwelltime']] = df[['tpi_addval','tpi_dwelltime']].astype(float) + + # Calculate field + df['tpi_addval'] = df['tpi_addval'] + df['tpi_dwelltime'] + + # Set fields back in Visum + h.SetMulti(Visum.Net.TimeProfileItems ,r"ADDVAL", df['tpi_addval']) + + + + # RUN FUNCTIONS + # Setting Skim Attributes + ismode() + headway(period) + op_bushr() + stoptype(period) + waittime(period) + invehicleperceptionfactor(period) + boardingpenalty(period) + # Setting Dwell and Run times + dwelltime() + runtime(period) + combinerundwelltimes() + + +per = Visum.Procedures.OperationExecutor.GetCurrentOperation().AttValue("CODE") # Example: outputs a string like 'AM' from AM in the code box +skim_setup(per) + diff --git a/skimming_and_assignment/visum/scripts/Skimming_Setup.py b/skimming_and_assignment/visum/scripts/Skimming_Setup.py new file mode 100644 index 0000000..54a1923 --- /dev/null +++ b/skimming_and_assignment/visum/scripts/Skimming_Setup.py @@ -0,0 +1,396 @@ +# Prep for Skimming for Oregon Metro +# 6/12/2025 - Luke Gordon (RSG) +# Adapted from code from Chetan Joshi (PTV) + +import tables +import numpy as np +import pandas as pd +import os +import VisumPy.helpers as h +import openmatrix as omx + + +def put_skim_setup(period): + + def ismode(): # isbrt, isscr, islrt, & iswes + + # Pull attributes + sa_isbrt = h.GetMulti(Visum.Net.StopAreas,r"isbrt", activeOnly = True) + sa_isscr = h.GetMulti(Visum.Net.StopAreas,r"isscr", activeOnly = True) + sa_islrt = h.GetMulti(Visum.Net.StopAreas,r"islrt", activeOnly = True) + sa_iswes = h.GetMulti(Visum.Net.StopAreas,r"iswes", activeOnly = True) + sp_lrtsyscode = h.GetMulti(Visum.Net.StopAreas,r"FIRST:STOPPOINTS\DISTINCT:LINEROUTES\TSYSCODE", activeOnly = True) + + # Make Visum list with link data + att_list = [sa_isbrt,sa_isscr,sa_islrt,sa_iswes,sp_lrtsyscode] + + # Put Visum link list into dataframe + df = pd.DataFrame(np.column_stack(att_list), columns = ['sa_isbrt','sa_isscr','sa_islrt','sa_iswes','sp_lrtsyscode']) + + # Break out 'DISTINCT:LINEROUTES\TSYSCODE' field to separate by commas into individual columns + df[['sp_lrtsyscode']] = df[['sp_lrtsyscode']].astype(str) + df = pd.concat([df,df['sp_lrtsyscode'].str.split(',', expand = True)], axis = 1) + # Change Screenline field names + if 1 not in df: + df[1] = None + if 2 not in df: + df[2] = None + if 3 not in df: + df[3] = None + if 4 not in df: + df[4] = None + df = df.rename(columns = {0:'Mode1',1:'Mode2',2:'Mode3',3:'Mode4',4:'Mode5'}) + + # Calculate fields + df['isbrt'] = df.apply(lambda row: 1 if row['Mode1'] == 'a' or row['Mode2'] == 'a' or row['Mode3'] == 'a' or row['Mode4'] == 'a' or row['Mode5'] == 'a' else 0, axis=1) + df['isscr'] = df.apply(lambda row: 1 if row['Mode1'] == 'e' or row['Mode2'] == 'e' or row['Mode3'] == 'e' or row['Mode4'] == 'e' or row['Mode5'] == 'e' else 0, axis=1) + df['islrt'] = df.apply(lambda row: 1 if row['Mode1'] == 'l' or row['Mode2'] == 'l' or row['Mode3'] == 'l' or row['Mode4'] == 'l' or row['Mode5'] == 'l' else 0, axis=1) + df['iswes'] = df.apply(lambda row: 1 if row['Mode1'] == 'r' or row['Mode2'] == 'r' or row['Mode3'] == 'r' or row['Mode4'] == 'r' or row['Mode5'] == 'r' else 0, axis=1) + + # Set fields back in Visum + h.SetMulti(Visum.Net.StopAreas ,r"isbrt", df['isbrt']) + h.SetMulti(Visum.Net.StopAreas ,r"isscr", df['isscr']) + h.SetMulti(Visum.Net.StopAreas ,r"islrt", df['islrt']) + h.SetMulti(Visum.Net.StopAreas ,r"iswes", df['iswes']) + + def headway(period): # Headway and Headway_Halved + + # Pull attributes + tp_emmeheadway = h.GetMulti(Visum.Net.TimeProfiles,r"Emme_Headway", activeOnly = True) + tp_headwayhalved = h.GetMulti(Visum.Net.TimeProfiles,r"Headway_Halved", activeOnly = True) + + if period == 'AM' or period == 'PM': + tp_periodheadway = h.GetMulti(Visum.Net.TimeProfiles,r"LINEROUTE\EMME_DATA1", activeOnly = True) + else: + tp_periodheadway = h.GetMulti(Visum.Net.TimeProfiles,r"LINEROUTE\EMME_DATA2", activeOnly = True) + + # Make Visum list with link data + att_list = [tp_emmeheadway,tp_periodheadway,tp_headwayhalved] + + # Put Visum link list into dataframe + df = pd.DataFrame(np.column_stack(att_list), columns = ['tp_emmeheadway','tp_periodheadway','tp_headwayhalved']) + + # Calculate fields + df['tp_emmeheadway'] = df['tp_periodheadway'] * 60 + df['tp_headwayhalved'] = df['tp_emmeheadway'] / 2 + + # Set fields back in Visum + h.SetMulti(Visum.Net.TimeProfiles ,r"Emme_Headway", df['tp_emmeheadway']) + h.SetMulti(Visum.Net.TimeProfiles ,r"Headway_Halved", df['tp_headwayhalved']) + + + def op_bushr(): # op_bushr on lineroutes and stopareas + + # Line Routes + # Pull attributes + lr_opbushr = h.GetMulti(Visum.Net.LineRoutes,r"op_bushr", activeOnly = True) + lr_opheadway = h.GetMulti(Visum.Net.LineRoutes,r"EMME_DATA2", activeOnly = True) + + # Make Visum list with link data + att_list = [lr_opbushr,lr_opheadway] + + # Put Visum link list into dataframe + df = pd.DataFrame(np.column_stack(att_list), columns = ['lr_opbushr','lr_opheadway']) + + # Calculate fields + df['lr_opbushr'] = df.apply(lambda row: 1 / (row['lr_opheadway'] / 60) if row['lr_opheadway'] != 0 else 0,axis=1) + + # Set field back in Visum + h.SetMulti(Visum.Net.LineRoutes ,r"op_bushr", df['lr_opbushr']) + + + # Stop Areas + # Pull attributes + sa_lropbushr = h.GetMulti(Visum.Net.StopAreas,r"FIRST:STOPPOINTS\SUM:LINEROUTES\OP_BUSHR", activeOnly = True) + + # Set field back in Visum + h.SetMulti(Visum.Net.StopAreas ,r"op_bushr", sa_lropbushr) + + + def stoptype(period): # Stop type & stop type constant + + # Pull attributes + # Fields to set + sa_sttyp = h.GetMulti(Visum.Net.StopAreas,r"sttyp" , activeOnly = True) + sa_stcon = h.GetMulti(Visum.Net.StopAreas,r"stcon" , activeOnly = True) + # Condition Fields + sa_istc = h.GetMulti(Visum.Net.StopAreas,r"istc" , activeOnly = True) + sa_istm = h.GetMulti(Visum.Net.StopAreas,r"istm" , activeOnly = True) + sa_isbrt = h.GetMulti(Visum.Net.StopAreas,r"isbrt" , activeOnly = True) + sa_isscr = h.GetMulti(Visum.Net.StopAreas,r"isscr" , activeOnly = True) + sa_islrt = h.GetMulti(Visum.Net.StopAreas,r"islrt" , activeOnly = True) + sa_iswes = h.GetMulti(Visum.Net.StopAreas,r"iswes" , activeOnly = True) + sa_opbushr = h.GetMulti(Visum.Net.StopAreas,r"op_bushr", activeOnly = True) + + # Make Visum list with link data + att_list = [sa_sttyp,sa_stcon,sa_istc,sa_istm ,sa_isbrt,sa_isscr,sa_islrt,sa_iswes,sa_opbushr] + + # Put Visum link list into dataframe + df = pd.DataFrame(np.column_stack(att_list), columns = ['sa_sttyp','sa_stcon','sa_istc','sa_istm','sa_isbrt','sa_isscr','sa_islrt','sa_iswes','sa_opbushr']) + + # Convert condition fields & stop type constant to float + df[['sa_stcon','sa_istc','sa_istm','sa_isbrt','sa_isscr','sa_islrt','sa_iswes','sa_opbushr']] = df[[ + 'sa_stcon','sa_istc','sa_istm','sa_isbrt','sa_isscr','sa_islrt','sa_iswes','sa_opbushr']].astype(float) + + # Calculate Stop Type field + for x in range(len(df)): + if ((df.at[x,'sa_istc'] == 1) | (df.at[x,'sa_istm'] == 1) | (df.at[x,'sa_islrt'] == 1) | (df.at[x,'sa_iswes'] == 1)): + df.at[x,'sa_sttyp'] = 'A,B,C' # Is transit center, is transit mall, is LRT stop, is WES stop + elif ((df.at[x,'sa_isbrt'] == 1) | (df.at[x,'sa_isscr'] == 1) | (df.at[x,'sa_opbushr'] >= 4)): + df.at[x,'sa_sttyp'] = 'D' # Is BRT stop, is streetcar stop, has >= 4 transit vehicles per hour + else: + df.at[x,'sa_sttyp'] = 'E' # All stops with infrequent local bus service + + + # Calculate Stop Type Constant field + for y in range(len(df)): + if period == 'AM' or period == 'PM': # Peak + if df.at[y,'sa_sttyp'] == 'A,B,C': + df.at[y,'sa_stcon'] = 0.1582 + elif df.at[y,'sa_sttyp'] == 'D': + df.at[y,'sa_stcon'] = 0.0531 + elif df.at[y,'sa_sttyp'] == 'E': + df.at[y,'sa_stcon'] = 0.0000 + else: # Off-Peak + if df.at[y,'sa_sttyp'] == 'A,B,C': + df.at[y,'sa_stcon'] = 0.1075 + elif df.at[y,'sa_sttyp'] == 'D': + df.at[y,'sa_stcon'] = 0.0756 + elif df.at[y,'sa_sttyp'] == 'E': + df.at[y,'sa_stcon'] = 0.0000 + + # Set fields back in Visum + h.SetMulti(Visum.Net.StopAreas ,r"sttyp", df['sa_sttyp']) + h.SetMulti(Visum.Net.StopAreas ,r"stcon", df['sa_stcon']) + + + def waittime(period): # Wait time perception factor + + # Pull attributes + sa_sttyp = h.GetMulti(Visum.Net.StopAreas,r"sttyp" , activeOnly = True) + sa_wtpf = h.GetMulti(Visum.Net.StopAreas,r"wtpf" , activeOnly = True) + + # Make Visum list with link data + att_list = [sa_sttyp,sa_wtpf] + + # Put Visum link list into dataframe + df = pd.DataFrame(np.column_stack(att_list), columns = ['sa_sttyp','sa_wtpf']) + + # Calculate field + for y in range(len(df)): + if period == 'AM' or period == 'PM': # Peak + if df.at[y,'sa_sttyp'] == 'A,B,C': + df.at[y,'sa_wtpf'] = 0.88 + elif df.at[y,'sa_sttyp'] == 'D': + df.at[y,'sa_wtpf'] = 0.93 + elif df.at[y,'sa_sttyp'] == 'E': + df.at[y,'sa_wtpf'] = 1.00 + else: # Off-Peak + if df.at[y,'sa_sttyp'] == 'A,B,C': + df.at[y,'sa_wtpf'] = 0.86 + elif df.at[y,'sa_sttyp'] == 'D': + df.at[y,'sa_wtpf'] = 0.94 + elif df.at[y,'sa_sttyp'] == 'E': + df.at[y,'sa_wtpf'] = 1.00 + + # Set fields back in Visum + h.SetMulti(Visum.Net.StopAreas ,r"wtpf", df['sa_wtpf']) + + + def invehicleperceptionfactor(period): # In-vehicle perception factors + + # Pull attributes + tp_tsyscode = h.GetMulti(Visum.Net.TimeProfiles,r"TSYSCODE", activeOnly = True) + tp_ivpf = h.GetMulti(Visum.Net.TimeProfiles,r"ivpf" , activeOnly = True) + + # Make Visum list with link data + att_list = [tp_tsyscode,tp_ivpf] + + # Put Visum link list into dataframe + df = pd.DataFrame(np.column_stack(att_list), columns = ['tp_tsyscode','tp_ivpf']) + + # Calculate field + for y in range(len(df)): + if period == 'AM' or period == 'PM': # Peak + if df.at[y,'tp_tsyscode'] == 'a': + df.at[y,'tp_ivpf'] = 0.95 + elif ((df.at[y,'tp_tsyscode'] == 'l') or (df.at[y,'tp_tsyscode'] == 'r')): + df.at[y,'tp_ivpf'] = 0.88 + else: + df.at[y,'tp_ivpf'] = 1.00 + else: # Off-Peak + if df.at[y,'tp_tsyscode'] == 'a': + df.at[y,'tp_ivpf'] = 0.95 + elif ((df.at[y,'tp_tsyscode'] == 'l') or (df.at[y,'tp_tsyscode'] == 'r')): + df.at[y,'tp_ivpf'] = 0.86 + else: + df.at[y,'tp_ivpf'] = 1.00 + + # Set fields back in Visum + h.SetMulti(Visum.Net.TimeProfiles ,r"ivpf", df['tp_ivpf']) + + + def boardingpenalty(period): # Boarding penalty (seconds) + + # Pull attributes + tp_tsyscode = h.GetMulti(Visum.Net.TimeProfiles,r"TSYSCODE", activeOnly = True) + tp_brdpen = h.GetMulti(Visum.Net.TimeProfiles,r"brdpen" , activeOnly = True) + + # Make Visum list with link data + att_list = [tp_tsyscode,tp_brdpen] + + # Put Visum link list into dataframe + df = pd.DataFrame(np.column_stack(att_list), columns = ['tp_tsyscode','tp_brdpen']) + + # Calculate field + for y in range(len(df)): + if period == 'AM' or period == 'PM': # Peak + if ((df.at[y,'tp_tsyscode'] == 'l') or (df.at[y,'tp_tsyscode'] == 'r')): + df.at[y,'tp_brdpen'] = 000.0 + elif ((df.at[y,'tp_tsyscode'] == 'a') or (df.at[y,'tp_tsyscode'] == 'e')): + df.at[y,'tp_brdpen'] = 372.0 + else: + df.at[y,'tp_brdpen'] = 439.8 + else: # Off-Peak + if ((df.at[y,'tp_tsyscode'] == 'l') or (df.at[y,'tp_tsyscode'] == 'r')): + df.at[y,'tp_brdpen'] = 000.0 + elif ((df.at[y,'tp_tsyscode'] == 'a') or (df.at[y,'tp_tsyscode'] == 'e')): + df.at[y,'tp_brdpen'] = 166.8 + else: + df.at[y,'tp_brdpen'] = 540.6 + + # Add global boarding penalty + for y in range(len(df)): + df.at[y,'tp_brdpen'] = df.at[y,'tp_brdpen'] + 231 + + # Set fields back in Visum + h.SetMulti(Visum.Net.TimeProfiles ,r"brdpen", df['tp_brdpen']) + + + def dwelltime(): + + # Pull attributes + tpi_tsyscode = h.GetMulti(Visum.Net.TimeProfileItems,r"TIMEPROFILE\TSYSCODE" , activeOnly = True) + tpi_emmedwt = h.GetMulti(Visum.Net.TimeProfileItems,r"LINEROUTEITEM\EMME_DWT", activeOnly = True) + tpi_prelength = h.GetMulti(Visum.Net.TimeProfileItems,r"PreLength" , activeOnly = True) + tpi_dwelltime = h.GetMulti(Visum.Net.TimeProfileItems,r"DWELL_TIME" , activeOnly = True) + + + # Make Visum list with link data + att_list = [tpi_tsyscode,tpi_emmedwt,tpi_prelength,tpi_dwelltime] + + # Put Visum link list into dataframe + df = pd.DataFrame(np.column_stack(att_list), columns = ['tpi_tsyscode','tpi_emmedwt','tpi_prelength','tpi_dwelltime']) + + # Convert fields to float + df[['tpi_emmedwt','tpi_prelength','tpi_dwelltime']] = df[['tpi_emmedwt','tpi_prelength','tpi_dwelltime']].astype(float) + + # Calculate field + for y in range(len(df)): + if df.at[y,'tpi_tsyscode'] == 'b': # Local Bus dwell time based on prelength and Emme_DWT + df.at[y,'tpi_dwelltime'] = df.at[y,'tpi_emmedwt'] * 60 * df.at[y,'tpi_prelength'] + else: # Non-Local Bus dwell time based on Emme_DWT only + df.at[y,'tpi_dwelltime'] = df.at[y,'tpi_emmedwt'] * 60 + + # Set fields back in Visum + h.SetMulti(Visum.Net.TimeProfileItems ,r"DWELL_TIME", df['tpi_dwelltime']) + + + def calc_ttf(ft, timau, length, us1, default_speed): # INPUT TO RUNTIME FUNCTION + if us1 <= 0: + us1 = default_speed + + if length: + transit_time = 3600*length / default_speed + + if timau < 999: + if ft == 1: + transit_time = timau * 1.15 + elif ft == 2: + transit_time = timau * 1.20 + elif ft == 3: + transit_time = timau + elif ft == 4: + transit_time = timau * 1.09 + elif ft == 5: + transit_time = 60* (60 * length / us1) + elif ft == 6: + transit_time = 60* (60 * length / us1 + 60 * length / 180) + elif ft ==11: + transit_time = timau * 1.15 + elif ft ==12: + transit_time = timau * 1.30 + elif ft ==13: + transit_time = timau + elif ft ==14: + transit_time =timau * 1.09 + elif ft == 15: + transit_time = 60* (60 * length / us1) + elif ft == 16: + transit_time = 60* (60 * length / us1 + 60 * length / 180) + elif ft == 21: + transit_time = timau * 1.05 + elif ft == 22: + transit_time = timau * 1.03 + else: + transit_time = 1 + + return transit_time + + def runtime(period): + if period == 'AM' or period == 'PM': # Peak + tpitems = Visum.Net.TimeProfileItems.GetMultipleAttributes(["LINEROUTEITEM\\EMME_TTFINDEX", "SUM:USEDLINEROUTEITEMS\\OUTLINK\\AddVal1","SUM:USEDLINEROUTEITEMS\\POSTLENGTH", + "LINEROUTEITEM\\EMME_DATA1"]) + else: # Off-Peak + tpitems = Visum.Net.TimeProfileItems.GetMultipleAttributes(["LINEROUTEITEM\\EMME_TTFINDEX", "SUM:USEDLINEROUTEITEMS\\OUTLINK\\AddVal2","SUM:USEDLINEROUTEITEMS\\POSTLENGTH", + "LINEROUTEITEM\\EMME_DATA1"]) + result = [] + + default_speed = 30 + for ft, timau, length, us1 in tpitems: + haul_time = calc_ttf(ft, timau, length, us1, default_speed) + result.append([haul_time, ]) + + Visum.Net.TimeProfileItems.SetMultipleAttributes(["AddVal"], result) + + def combinerundwelltimes(): + + # Pull attributes + tpi_addval = h.GetMulti(Visum.Net.TimeProfileItems,r"ADDVAL" , activeOnly = True) + tpi_dwelltime = h.GetMulti(Visum.Net.TimeProfileItems,r"DWELL_TIME" , activeOnly = True) + + # Make Visum list with link data + att_list = [tpi_addval,tpi_dwelltime] + + # Put Visum link list into dataframe + df = pd.DataFrame(np.column_stack(att_list), columns = ['tpi_addval','tpi_dwelltime']) + + # Convert fields to float + df[['tpi_addval','tpi_dwelltime']] = df[['tpi_addval','tpi_dwelltime']].astype(float) + + # Calculate field + df['tpi_addval'] = df['tpi_addval'] + df['tpi_dwelltime'] + + # Set fields back in Visum + h.SetMulti(Visum.Net.TimeProfileItems ,r"ADDVAL", df['tpi_addval']) + + + + # RUN FUNCTIONS + # Setting Skim Attributes + ismode() + headway(period) + op_bushr() + stoptype(period) + waittime(period) + invehicleperceptionfactor(period) + boardingpenalty(period) + # Setting Dwell and Run times + dwelltime() + runtime(period) + combinerundwelltimes() + + +procedure_code = Visum.Procedures.OperationExecutor.GetCurrentOperation().AttValue("CODE") # Example: outputs a string like 'AM' from AM in the code box +put_skim_setup(procedure_code) +