Shared autonomous electric vehicles: A simulation study on operational efficiency and service dynamics

Shared autonomous electric vehicles (SAEVs) represent a transformative shift in urban mobility, promising enhanced flexibility, cost efficiency, and environmental sustainability. This study investigates the impact of integrating SAEVs into taxi-hailing platforms, focusing on operational performance, revenue generation, and environmental outcomes. Using large-scale real-world trip datasets from New York City, Chicago, San Francisco, and the District of Columbia, we develop a simulation framework that models SAEV fleet operations under realistic urban constraints, including charging infrastructure capacity, fleet size limitations, and stochastic demand fluctuations. The simulation evaluates key performance indicators such as CO 2 emissions, fleet utilization, completed trip rate, and net revenue, and compares SAEVs with traditional 15-hour driver-based taxis as well as 24-hour ride-hailing platform. The results confirm the expected advantages of SAEVs, demonstrating superior performance across key economic and environmental metrics regardless of fleet size. On average, net revenue increases by more than 250% relative to traditional services, while CO 2 emissions are substantially reduced due to electrification and optimized fleet allocation. Compared to continuous 24-hour ride-hailing platform, SAEVs achieve higher revenue and significantly lower emissions, although a moderate decline in operational efficiency is observed under constrained fleet conditions. Importantly, this efficiency gap can be mitigated through appropriate fleet scaling and infrastructure planning.