LAUSR

Fleet sizing is critical for shared autonomous vehicle(SAV) fleet management to reduce maintenance costs and traffic congestions. As the passenger demands are dynamic, how to make the demand-aware dynamic ridesharing and calculate the fleet size is an important and challenging problem. In this paper, we propose a minimum fleet sizing method called Fleet Sizing for demand-aware Dynamic Ridesharing (FSDR) to accurately determine the fleet size for ridesharing enabled SAV system. Specifically, the travel demands are first predicted by an ensemble method that takes account of temporal correlations between regions. Then a concept called demand utility is proposed to measure the travel demands when planning vehicle paths, and the ride-matching dispatches vehicles to high travel demand regions by maximizing the demand utility along the vehicle path. Based on the ride-matching result, the minimum fleet size is calculated based on a trip graph by the Hopcroft-Karp algorithm. FSDR is evaluated on two real GPS taxi trajectory datasets from Wuhan, China, and San Francisco, USA. The result validates that the proposed demand-aware ridesharing in FSDR can significantly reduce the fleet size compared to the existing ride-matching methods. Moreover, the result shows that by allowing ridesharing, the vehicle fleet size can be reduced by 30%.