Envisioning shared autonomous vehicles (SAVs) for 374 small and medium-sized urban areas in the United States: The roles of road network and travel demand

This study envisions a future of operating shared autonomous vehicles (SAVs) to provide mobility services in 374 small and medium-sized urban areas (population < 375,000) in the United States. Specifically, this study 1) generates large-scale synthetic commuting trip data for 374 urban areas, 2) employs an agent-based modeling framework to simulate the operations of SAV fleets serving commuters in these areas, and 3) develops models to explain the variations of fleet performances across study areas. The main objectives of this study are to compare the performances of SAVs serving commuting trips across urban areas and to identify the correlates of SAV fleet performances related to regional road networks and travel demand characteristics. This study generated 30 million synthetic commuting trips for 374 small and medium-sized urban areas based on the Census' latest Longitudinal Employer-Household Dynamics (LEHD) data and the National Household Travel Survey (NHTS) data. Based on the agent-based modeling results, SAV fleet performances across 374 urban areas were analyzed. Key performance measures include the average number of trips per SAV vehicle for fleet efficiency and the percentage of empty vehicle miles traveled (VMT) for extra operating costs. Results showed significant spatial variations in SAV fleet performances across 374 urban areas. The results highlighted urban areas that either outperform or underperform other areas. For example, Great Falls MT, is associated with the highest fleet efficiency, and Fort Walton Beach-Navarre-Wright, FL, has the lowest percentage of empty VMT. Through statistical modeling, this study identified significant correlates of SAV fleet performance, which are significantly related to factors of road network properties and travel demand characteristics. A larger, denser, and more connected network is associated with greater fleet efficiency and reduced operating cost, while a network with a local clustering tendency may lead to decreased fleet efficiency and increased operating cost. Areas with denser trips or longer trips are associated with lower fleet efficiency and decreased operating costs. More modeling results are discussed in the paper, providing insights into how different local road network characteristics and travel patterns influence the operation of SAVs. These results help to identify strategies for tailoring SAV services to the unique needs of different regions. This study complements the literature on shared mobility and automation research currently centered on great metropolitan areas. The research findings are intended to serve as the basis for future discussions regarding opportunities and challenges of deploying emerging mobility services in small and medium-sized areas. The large-scale synthetic commuting data generated in this study are valuable for researchers and agencies to develop advanced travel and mobility models and to further explore the potential of deploying emerging mobility systems in these areas.