How Riders Use Shared Autonomous Vehicles

Abstract

Autonomous vehicles (AVs) are expected to play a crucial role in achieving sustainable transportation in the near future, making it essential to acquire insights into the public’s acceptance of them so they can be successfully integrated into the existing infrastructure. This study uses data obtained from riders of RAPID (Rideshare Automation Payment Integration Demonstration), a shared autonomous vehicle (SAV) service in Arlington, Texas, to identify the factors that influence how frequently riders take advantage of SAV services. A structured survey was developed to gather data related to the riders’ general travel behavior, attitudes towards the SAV service, frequency of use, and sociodemographic characteristics. A conceptual framework was developed based on the relationship between the key variables and the frequency of SAV usage. Structural equation modelling was employed to explore the direct relationship between the users’ sociodemographic characteristics, SAV attitudes, and general travel behavior and the impact on frequency of use. The results indicated that race, trip purpose, waiting time, and the availability of a private vehicle significantly influence the use of RAPID. Our model also identifies how users’ perception of the service affects how often it is used. This study provides insight into SAV services and its use in low-density areas that will enable transportation planners and policymakers to develop policies and a transportation infrastructure that will enhance SAV operations universally.