Do ridesharing transportation services alleviate traffic crashes? A time series analysis

Abstract

Objectives

On-demand ridesharing services are suggested to provide several benefits, such as improving accessibility and mobility, reducing drive-alone trips and greenhouse gas emissions. However, the impacts of these services on traffic crashes are not completely clear. This paper investigates the availability of Via- an on-demand ridesharing service in Arlington, TX, to identify the effects of this service on traffic crashes. We hypothesize that the launch of Via would result in more shared rides, fewer drive-alone trips and fewer traffic crashes.

Methods

We implement an Interrupted Time Series Analysis (ITSA) approach to study the impact of Via service availability on traffic crashes using weekly counts of all traffic crashes, the number of injuries, and serious injuries that occurred in Arlington from 2014 to 2021.

Results

The results show a statistically significant reduction in the weekly number of total crashes and total injuries but do not show any significant impact on the number of serious injuries. Shared Autonomous Vehicles have the potential to reduce traffic crashes caused by driver's fault.

Conclusions

This study reveals the potential impacts ridesharing services can have on traffic crashes and injuries in a mid-sized city. The results of this study can help decision and policymakers to understand the full potential of ridesharing services that can contribute to making relevant decisions toward creating sustainable and safer transportation systems in cities.

Keywords:

• On-demand ridesharing
• vehicle crashes
• shared autonomous vehicles
• time series analysis
• traffic safety

Data availability statement

All data and models that support the findings of this study are available from the corresponding author upon reasonable request.

Disclosure statement

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Notes

1 American Community Survey (ACS), 2019

2 ACS, 2019

3 City of Arlington

4 https://www.caliper.com/maptitude/mappingsoftware.htm

Additional information

Funding

The work presented herein is a part of the Arlington RAPID (Rideshare, Automation, and Payment Integration Demonstration) project, which is supported by the Federal Transit Administration (FTA) Integrated Mobility Innovation (IMI) Program funded by the United States Department of Transportation and City of Arlington. The RAPID project is a collaboration among different partners, including the City of Arlington, Via, May Mobility, and the University of Texas at Arlington.