City of Arlington: Exploring willingness to use shared autonomous vehicles

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Title: Exploring willingness to use shared autonomous vehicles

Authors: Ronik Ketankumar Patel (University of Texas at Arlington), Roya Etminani-Ghasrodashti (University of Texas at Arlington), Sharareh Kermanshachi (University of Texas at Arlington), Jay Michael Rosenberger (University of Texas at Arlington), Ann Foss (City of Arlington)

Published in: International Journal of Transportation Science and Technology

Publication Date: September 2023

Abstract: Although multiple studies have modeled and predicted the potential effects of shared autonomous vehicles (SAVs), the research on the adoption of SAVs by riders with actual ridership experience is still limited. In addition, the increasing tendency towards operating SAV technology requires understanding its efficiency while integrating it into the existing transportation network infrastructure. This study aims to identify the factors affecting the user’s willingness to ride the SAVs based on the data collected from a comprehensive survey distributed among users and non-users of a self-driving pilot project called RAPID (Rideshare, Automation, and Payment Integration Demonstration) in Arlington, Texas. Using structural equation modeling (SEM), we identify the effects from vehicle ownership, RAPID usage, existing modes of transportation, RAPID service attributes (comfort and safety), and sociodemographic variables on individuals’ willingness to use SAVs in the future. Results indicate that most riders of the RAPID service are young Asian individuals and students from low-income households with limited or no access to a private vehicle. Furthermore, SEM results show that RAPID usage directly impacts willingness to use SAVs, implying that people start developing trust for the technology with an increase in the frequency of using the service. Our model suggests that sociodemographic attributes of the SAV riders indirectly influence the willingness to use SAVs through the mediators, including RAPID usage, existing modes of transportation, and vehicle ownership. This study provides crucial insights about individual travel behavior after integrating SAVs into existing transportation infrastructure to assist policymakers and transportation planners in developing AV-related policies.