The aim of this library is to provide a simple python library for researchers who like to process trajectories. This library is in the early stage and we gradually add features to it.
take a look at TrajLib_Usage_Example.ipynb
we create an example code "create_geolife_features.py" here that generates segment features and points features for Geolife dataset. This is not a full dataset and is filltered by one user. If you need to use the full geolife dataset, please run this code on the full dataset.
If you are using this library in your work, please cite to the following paper. This library is developed during the implementation of the paper.
link: https://link.springer.com/chapter/10.1007/978-3-319-89656-4_24
Etemad, M., Soares Júnior, A., & Matwin, S. (2018). Predicting Transportation Modes of GPS Trajectories using Feature Engineering and Noise Removal. In Advances in Artificial Intelligence: 31st Canadian Conference on Artificial Intelligence, Canadian AI 2018, Toronto, ON, Canada, May 8–11, 2018, Proceedings 31 (pp. 259-264). Springer International Publishing.
@inproceedings{etemad2018predicting,
title={Predicting Transportation Modes of GPS Trajectories using Feature Engineering and Noise Removal},
author={Etemad, Mohammad and Soares J{\'u}nior, Am{\'\i}lcar and Matwin, Stan},
booktitle={Advances in Artificial Intelligence: 31st Canadian Conference on Artificial Intelligence, Canadian AI 2018, Toronto, ON, Canada, May 8--11, 2018, Proceedings 31},
pages={259--264},
year={2018},
organization={Springer}
}
