Abstract
This research examines whether transportation network companies (TNCs), such as Uber and Lyft, live up to their stated vision of reducing congestion in major cities. Existing research has produced conflicting results and has been hampered by a lack of data. Using data scraped from the application programming interfaces of two TNCs, combined with observed travel time data, we find that contrary to their vision, TNCs are the biggest contributor to growing traffic congestion in San Francisco. Between 2010 and 2016, weekday vehicle hours of delay increased by 62% compared to 22% in a counterfactual 2016 scenario without TNCs. The findings provide insight into expected changes in major cities as TNCs continue to grow, informing decisions about how to integrate TNCs into the existing transportation system.
Document Type
Article
Publication Date
5-8-2019
Digital Object Identifier (DOI)
https://doi.org/10.1126/sciadv.aau2670
Funding Information
The San Francisco County Transportation Authority funded this study.
Related Content
All data needed to evaluate the conclusions in the paper are present in the paper and/or Supplementary Materials. Additional data and software may be requested from the authors.
Supplementary material for this article is available at http://advances.sciencemag.org/cgi/content/full/5/5/eaau2670/DC1.
Repository Citation
Erhardt, Gregory D.; Roy, Sneha; Cooper, Drew; Sana, Bhargava; Chen, Mei; and Castiglione, Joe, "Do Transportation Network Companies Decrease or Increase Congestion?" (2019). Civil Engineering Faculty Publications. 16.
https://uknowledge.uky.edu/ce_facpub/16
Supplementary Materials
aau2670_Data_S1.zip (136 kB)
Data S1
aau2670_Data_S2.zip (1059 kB)
Data S2
aau2670_Data_S3.zip (928 kB)
Data S3
aau2670_Data_S4.zip (2279 kB)
Data S4
Notes/Citation Information
Published in Science Advances, v. 5, no. 5, eaau2670, p. 1-11.
Copyright © 2019 The Authors, some rights reserved; exclusive licensee American Association for the Advancement of Science. No claim to original U.S. Government Works.
Distributed under a Creative Commons Attribution License 4.0 (CC BY). This is an open-access article distributed under the terms of the Creative Commons Attribution license, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.