The Autonomous Mobility-on-Demand (AMoD) service is emerging as a potential alternative to on-demand urban mobility, but its operational performance relative to traditional street-hailing services and the effectiveness of related operational strategies remain unclear. This study presents a simulation framework integrating a graph theory-based trip-vehicle matching mechanism and uses historical street-hailing operations data to simulate AMoD services in Chengdu, China. The operational performance of these two urban mobility modes is evaluated using three key performance indicators: average passenger waiting time (APWT), average deadheading mileage (ADM), and average deadheading energy consumption (ADEC). We further evaluate the impacts of four operational strategies on simulated AMoD performance: vehicle repositioning, fleet size management, geofencing, and request rejection. Simulation results indicate that, under the same historical trip demand, fleet-size constraints, and road network as the observed street-hailing system, the simulated AMoD service is estimated to have lower values of APWT, ADM, and ADEC by 73.3% to 83.4%, 75.0%, and 74.0%, respectively, reflecting the potential operational gains associated with centralized dispatch in simulation settings. These differences are most pronounced during early-morning low-demand hours and in remote areas such as airports.

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