Urban Autonomous Electric Vehicles Fleet Operation Strategy—From the Perspective of Operators

The complex traffic environment and severe emissions pollution increasingly challenge urban development. The electrified autonomous mobility-on-demand (EAMoD) system is expected to address these issues and promote sustainable urban development. This paper proposes a mixed-integer linear programming (MILP) model designed to optimize the operation of an autonomous electric vehicle (AEV) fleet under the dilemma of passenger orders selection when facilities are limited. This model comprehensively optimizes the received and abandoned passenger orders, rebalancing operation, as well as charging and discharging of AEVs from the perspective of the AEV fleet operator under time-varying travel demands. The effectiveness of the proposed strategy was verified on a 25-node transportation network, and the operation profit of the AEV fleet under the proposed strategy was $44 \%$ higher than the benchmark. Furthermore, the result showed that various factors, such as rebalancing operations, driving speed, fleet size, charging pile size, charging rate, driving range, and electricity usage type, significantly impact the AEV fleet operator’s profits.