City of Arlington: Integration of shared autonomous vehicles (SAVs) into existing transportation services: A focus group study.

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Title: Integration of shared autonomous vehicles (SAVs) into existing transportation services: A focus group study.

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

Published in: Transportation Research Interdisciplinary Perspectives

Publication Date: December 2021

Abstract: Autonomous vehicles (AVs) utilize new technology that promises to enhance transportation equity, accessibility, and safety; however, their acceptance by consumers, as well as their future performance, is reliant upon the planners understanding the public’s perceptions of the current transportation services. Most of the existing studies that evaluate the public’s opinions and perceptions of AV technology were conducted by using stated preference surveys in metropolitan areas with multiple public transit options; little emphasis was given to a qualitative approach that included all of the potential users. This study explores the concerns and preferences of future riders about the integration of a shared self-driving shuttle into an existing ridesharing service in a city with no existing public transit system, multiple population segments, and potential users. Utilizing a qualitative approach, we conducted three focus groups that were comprised of the general public; university faculty, staff, and students; and people with disabilities in Arlington, Texas (n = 24). A content analysis method was implemented to analyze the data and identify the main themes and subthemes related to each discussion. The results revealed that all three groups identified service accessibility, flexibility, and reliability as the factors that most strongly shape the demand for existing transportation services. Additionally, people with disabilities cited the capacity of the existing paratransit service and the lack of access to distant health care facilities as barriers that frequently prevent them from using the service. Participants also expressed their concerns about the proposed shared autonomous vehicle (SAV) service, and while accessibility and safety were the primary concerns, they also worried about the capacity of the service, the trip cost, and provisions for people with disabilities. Participants with disabilities indicated that they would adopt the SAV as long as service planners provide a supportive environment such as access to sidewalks, ramps, and curb cuts in the pick-up and drop-off locations. This study provides insights into transportation strategies that can integrate SAVs into existing on-demand ridesharing services to improve people’s mobility needs. To predict the short-term and long-term adoption of an SAV deployment, it is essential to identify the potential users’ concerns, preferences, and expectations of self-driving technology. The research findings are expected to support transportation planners and policymakers in their quest to recognize and utilize the most effective ways to promote the efficiency of SAV services.