Pedaling Through Divides: Spatial, Social, and Climatic Inequalities in Trento’s Bike-Sharing System

Overview

This repository contains the code and datasets for the final project of the Computational Social Science course (a.y. 2024/2025).
The project investigates intermodal urban mobility in Trento, focusing on the integration between bike-sharing stations, the public transport network, and the role of weather conditions in shaping accessibility and usage patterns.

Research Questions

1. How are bike-sharing stations distributed relative to the local public transport network?
2. Which urban areas appear underserved from an intermodal perspective?
3. How do urban mobility patterns vary with weather?

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Data

Bike-sharing and boundaries

• data/interim/circoscrizioni.geojson – Administrative boundaries of Trento’s circoscrizioni.
• data/raw/stazioni_trento.csv – Locations and attributes of bike-sharing stations.

Weather

• data/raw/trento_era5_daily_2020_2022.json – Daily weather data (temperature and precipitation) for Trento, 2020–2022.

Public transport (GTFS)

The project also includes a full GTFS feed from Trentino Trasporti S.p.A.. Not all files were directly used in the scripts, but they are preserved here for completeness:

• calendar.txt – Weekly operating days and service periods:contentReference[oaicite:0]{index=0}.
• calendar_dates.txt – Exceptions to the regular service calendar:contentReference[oaicite:1]{index=1}.
• feed_info.txt – Publisher and feed metadata:contentReference[oaicite:2]{index=2}.
• routes.txt – Route identifiers, names, and transport types:contentReference[oaicite:3]{index=3}.
• shapes.txt – Geographic shapes of routes (polylines):contentReference[oaicite:4]{index=4}.
• stops.txt – Locations and attributes of PT stops:contentReference[oaicite:5]{index=5}.
• stopslevel.txt – Stop hierarchy information:contentReference[oaicite:6]{index=6}.
• stop_times.txt – Scheduled arrival and departure times for each stop on a trip:contentReference[oaicite:7]{index=7}.
• transfers.txt – Allowed transfers between stops:contentReference[oaicite:8]{index=8}.
• trips.txt – Specific service trips linked to routes and shapes:contentReference[oaicite:9]{index=9}.

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Pipeline

Run the scripts in the following order to reproduce the project:

1. clean_all.py

• Cleans and prepares all raw datasets (bike-sharing,weather, GTFS).
• Standardizes formats (CRS, dates, column names).
• Outputs: processed versions under data/processed/.

2. build_datasets.py

• Integrates cleaned datasets into analysis-ready tables.
• Constructs spatial features (e.g., station-to-circoscrizione assignments, PT proximity).
• Outputs: consolidated datasets for later analysis.

3. analysis_suite.py

• Exploratory and descriptive analysis.
• Produces summary statistics and plots (maps, distributions, temporal graphs).
• Makes GAM estimations (Gaussian) with smoothers for weather.
• Outputs: results/figures/ and results/tables/.

4. population_stations_analysis.py

• Examines the relationship between population distribution and bike-sharing accessibility.
• Computes accessibility per resident by circoscrizione.
• Outputs: CSV tables and choropleth maps.

5. rq2_analysis.py

• Focuses on underserved areas from an intermodal perspective.
• Computes intermodality indicators combining bike-sharing and PT accessibility.
• Flags underserved circoscrizioni.
• Outputs: maps and tables highlighting accessibility gaps.

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Installation & Requirements

To set up the environment:

git clone https://github.com/camillabonomo02/CSS_project.git
cd CSS_project
python -m venv .venv
source .venv/bin/activate   # On Windows: .venv\Scripts\activate
pip install -r requirements.txt

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How to reproduce the project

Run the scripts in sequence:

python scripts/clean_all.py
python scripts/build_datasets.py
python scripts/analysis_suite.py
python scripts/population_stations_analysis.py
python scripts/rq2_analysis.py

Outputs (tables, figures, maps) will be saved in the results/ folder.

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Authors

Camilla Bonomo, Sara Lammouchi, Silvia Bortoluzzi, Diego Conti, Paolo Fabbri