Cities around the world are piloting projects to evaluate the feasibility and benefits of shared autonomous vehicles (SAVs), as their large-scale implementation and integration into public transit systems have the potential to improve individuals' accessibility and transportation equity. To understand the full potential of SAVs and their likely adoption, it is important to identify how the services can be utilized most effectively and what determines the composition of the ridership. This research aims to explore the usage and adoption of SAVs, focusing on a project called RAPID (Rideshare, Automation, and Payment Integration Demonstration) that was launched in Arlington, Texas. We used real-time trip-level ridership data from the SAV platform, conducted a survey of SAV riders, and developed a study based on ordered logistic regression to estimate the determinants of ridership frequency. Data analysis of real-time ridership data revealed that spatial distribution of activities and service accessibility have crucial roles in forming the current users' travel patterns. The findings from the logistic regression demonstrated that those with higher household incomes are less likely to be frequent riders of RAPID, while those who usually walk, bike, or utilize on-demand ridesharing services are likely to use SAVs often. Users with higher levels of safety perception are also more likely to be frequent users of the service. The findings of this study will provide planners with a better understanding of SAV ridership patterns and will guide decision-makers nationwide in establishing and adopting policies that will be appropriate for future SAV implementation projects.
All Science Journal Classification (ASJC) codes
- Civil and Structural Engineering
- Geography, Planning and Development
- Urban Studies