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Shared autonomous vehicles (SAVs) are the next major evolution in urbanmobility. This technology has attracted much interest of car manufacturersaiming at playing a role as transportation network companies (TNCs) in order togain benefits per kilometer and per ride. The majority of future SAVs will mostprobably be electric. It is therefore important to understand how limitedvehicle range and the configuration of charging infrastructure will affect theperformance of shared autonomous electric vehicle (SAEV) services. We aim toexplore the impacts of charging station placement, charging type (rapidcharging, battery swapping) as well as vehicle range onto service efficiencyand customer experience in terms of service availability and response time. Weperform an agent-based simulation of SAEVs across the Rouen Normandiemetropolitan area in France. The simulation process features impact assessmentby considering dynamic demand responsive to the network and traffic. Researchresults suggest that the performance of SAEVs is strongly correlated to thecharging infrastructure. Importantly, faster charging infrastructure andoptimized placement of charging locations in order to minimize distancesbetween demand hubs and charging stations result in a higher performance.Further analysis indicates the importance of dispersing charging stationsacross the service area and how this affects service effectiveness. The resultsalso underline that SAEV battery capacity has to be carefully selected to avoidthe overlaps between demand and charging peak times. Finally, the simulationresults show that by providing battery swapping infrastructure the performanceindicators of SAEV service are significantly improved.

Shared Autonomous Electric Vehicle Service Performance: Assessing the Impact of Charging Infrastructure and Battery Capacity