Optimized parking lot locations for shared autonomous vehicles: A perspective from minimum route distance

Shared autonomous vehicles (SAVs) are revolutionizing urban mobility systems, necessitating significant updates to transportation infrastructure, particularly parking facilities, due to their unique driverless nature and shared operational mode. For SAVs, parking lots not only function as depots for idle vehicles but also serve as origins and destinations of services, thereby impacting their service levels and fleet operational efficiency. Along with the emission reduction initiatives, the strategic planning of parking lot locations for SAVs is a critical issue. In this paper, we propose a two-step optimization framework to locate SAV parking lots. The first step employs a vehicle-trip assignment strategy to explore SAV operations, addressing the high computational costs of simulation methods and the limited problem size of traditional operations research approaches. The second step extends the classical capacitated p-median problem (CPMP) model to a capacitated flow termini median model (CFTMM) for location selection, incorporating dual-node demand to better depict reality. Using Chengdu as a case study, we demonstrate the effectiveness of our framework and gain some actionable insights for urban policymakers and transport network companies. It shows that the CFTMM demonstrates superior performance compared to the two benchmarks by effectively minimizing the empty driving costs to and from parking lots. Moreover, 6,500 SAVs can meet all travel demand, with over 75\% of SAVs operating during the day and gradually finishing their work after 8 PM. The optimized parking lot location distribution highlights the importance of retaining some parking spaces in the city center and balancing the parking demand of urban and suburban areas. The extended capacitated flow termini median model with dynamics (CFTMM-D) yields comparable results, further confirming the effectiveness and efficiency of CFTMM.