Emperical Study of Economies of Scale in Ridesplitting using open-source ridesharing data
Table of Contents
Ridesplitting -- a type of ride-hailing in which riders share vehicles with other riders -- has become a common travel mode in some major cities. This type of shared ride option is currently provided by transportation network companies (TNCs) such as Uber, Lyft, and Via and has attracted increasing numbers of users, particularly before the COVID-19 pandemic. Previous findings have suggested ridesplitting can lower travel costs and even lessen congestion by reducing the number of vehicles needed to move people. Recent studies have also posited that ridesplitting should experience positive feedback mechanisms in which the quality of the service would improve with the number of users. Specifically, these systems should benefit from economies of scale and increasing returns to scale. This paper demonstrates evidence of their existence using trip data reported by TNCs to the City of Chicago between January and September 2019. Specifically, it shows that increases in the number of riders requesting or authorizing shared trips during a given time period is associated with shorter trip detours, higher rates of riders being matched together, lower costs relative to non-shared trips, and higher willingness for riders to share trips.
This repository consists of the necessary code needed to replicate the figures and tables in our paper Scale Effects in Ridesplitting: A Case Study of the City of Chicago. The paper is available on Transportation Research - Part A. The ridesharing data used in the paper is obtained from Chicago Data portal. The processed data for the year 2019 is available on Harvard Dataverse.
The code to replicate the paper is split in two parts - R and. The detour plots in the paper are made in R and the R code is available in the file Detour_plots.R. The rest of the plots are developed in python and the code is available in analysis.py and code.py. The example.ipynb provides examples to use the code and obtain the plots.
NOTE: Download the necessary data from Harvard Dataverse to be able to run R and python code provided in the repository.
The dependencies to run the code and obtain plots are
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Distributed under the MIT License. See LICENSE.txt for more information.
If you use code or paper in your research please use the following BibTeX entry to cite us:
@article{LIU2023103690,
title = {Scale effects in ridesplitting: A case study of the City of Chicago},
journal = {Transportation Research Part A: Policy and Practice},
volume = {173},
pages = {103690},
year = {2023},
issn = {0965-8564},
doi = {https://doi.org/10.1016/j.tra.2023.103690},
url = {https://www.sciencedirect.com/science/article/pii/S0965856423001106},
author = {Hao Liu and Saipraneeth Devunuri and Lewis Lehe and Vikash V. Gayah},
keywords = {Ridesplitting, Transportation Network Company (TNC), Scale effects, Empirical study, Willingness-to-share}
}Contributions are what make the open source community such an amazing place to learn, inspire, and create. Any contributions you make are greatly appreciated.
If you have a suggestion that would make this better, please fork the repo and create a pull request. You can also simply open an issue with the tag "enhancement". Don't forget to give the project a star! Thanks again!
- Fork the Project
- Create your Feature Branch (
git checkout -b feature/AmazingFeature) - Commit your Changes (
git commit -m 'Add some AmazingFeature') - Push to the Branch (
git push origin feature/AmazingFeature) - Open a Pull Request
Hao Liu - hfl5376@psu.edu | Saipraneeth Devunuri - sd37@illinois.edu
Project Link: https://github.com/UTEL-UIUC/Ridesharing-Scale-Effects