Impact of Autonomous Vehicle (AV)-Based on Demand Transportation Services on Traffic Crashes

There has been two recent major advancements in the area of transportation industry which facilitated the mobility across the nation. The first technological advancement has brought Autonomous Vehicles (AVs) into reality, and the other major advancement is the emergence of on-demand ride services which has overcome many of the conventional travel barriers and improved personal mobility options. Although some studies explored the benefits and positive outcomes of both AVs and on-demand services; however, there are some other studies which found different outcomes and do not recommend the technology and service for safety reasons. Therefore, there is a need to study the impact of AV-based on-demand services on crashes as this will be future of the transportation systems in various cities and states. This study aims to evaluate the impact of AV-based on demand transportation services on traffic crashes with different severity levels. This project will analyze the (1) impact of the AV-based on-demand transportation services on frequency (number of) of crashes, (2) impact of the AV-based on-demand transportation services on total number of injuries, and (3) impact of the AV-based on-demand transportation services the number of serious injuries to analyze the crash patterns before and after the deployment of these services. To successfully achieve the objectives of this project, this study will use the data from City of Arlington, Texas Department of Transportation (TxDOT), and Arlington RAPID project. Arlington RAPID project is an implementation case study which has integrated the AVs into on-demand transportation services in Arlington, Texas. This study will use Difference in Difference, Time Series analysis, and Multivariate Regression modeling to evaluate the safety aspects of this integration and develop models for planners and policy-makers to estimate the changes in the number and severity of the crashes in their area while integrating AVs into their on-demand public transportation services. The results will be implemented in three other cities with different congestion and populations. The outcomes of this study will help with the planning for future deployment of AVs and integration of them into on-demand transportation services.

• Supplemental Notes:
  • 22ITSUTA50

Language

• English

Project

• Status: Active
• Funding: $106006
• Contract Numbers:

  69A3551747106

• Sponsor Organizations:

  Office of the Assistant Secretary for Research and Technology

  University Transportation Centers Program
  Department of Transportation
  Washington, DC  United States  20590
• Managing Organizations:

  Transportation Consortium of South-Central States (Tran-SET)

  Louisiana State University
  Baton Rouge, LA  United States  70803
• Project Managers:

  Dhasmana, Heena

• Performing Organizations:

  University of Texas at Arlington

  Box 19308
  Arlington, TX  United States  76019-0308
• Principal Investigators:

  Kermanshachi, Sharareh

• Start Date: 20220401
• Expected Completion Date: 0
• Actual Completion Date: 0
• USDOT Program: University Transportation Centers Program

Subject/Index Terms

• TRT Terms: Autonomous vehicles; Cities; Crash analysis; Crash severity; Demand responsive transportation; Traffic crashes; Traffic safety; Travel behavior
• Subject Areas: Highways; Planning and Forecasting; Public Transportation; Safety and Human Factors; Vehicles and Equipment;

Filing Info

• Accession Number: 01844957
• Record Type: Research project
• Source Agency: Transportation Consortium of South-Central States (Tran-SET)
• Contract Numbers: 69A3551747106
• Files: UTC, RIP
• Created Date: May 9 2022 11:04AM