Autonomous vehicles are to revolutionize the way urban mobility demands are served, and they are most likely to be powered by electricity. To accurately quantify the benefits of replacing existing mobility services with autonomous electric vehicles, electric autonomous mobility-on-demand (EAMoD) systems need to be evaluated and designed with the congestion effects they may cause taken into account. In this work, the congestion effects are depicted through a discrete event simulation model with state-dependent link travel speed and limited link space that allow endogenous congestion to emerge, spread, and dissipate across the entire road network. Three mathematical models are integrated into the simulation model to optimally match vehicles with waiting requests, relocate empty vehicles to potential high-demand areas, and assign low charge vehicles to charging stations based on the workloads of the charging stations. Based on the simulation model, we apply Bayesian optimization to jointly design the fleet size and charging facility configuration considering the endogenous congestion incurred by the daily operation of autonomous electric vehicles. Contraction hierarchies are adopted to route vehicles to perform assigned tasks in real-time. The proposed solution method is tested on Manhattan below 60th street, which corresponds to the potential congestion pricing zone in New York City. Experiment results show that the proposed simulation-based optimization approach is computationally tractable, and can find a satisfactory solution to the fleet sizing and charging infrastructure design problem within a tight computation budget. Excluding congestion effects at system design stage would lead to up to 14% passenger loss due to long assignment waiting time. Although the charging facility cost accounts for only 1.8% of the total cost, the number of chargers can indirectly affect the percentage of passengers that can be served through its efficiency in recharging vehicles. A charging facility configuration aligned with the fleet size can help to improve service quality and vehicle utilization rate.

自动驾驶汽车将彻底改变满足城市交通需求的方式，它们很可能由电力驱动。为了准确量化用自动驾驶电动汽车取代现有移动服务的好处，需要评估和设计电动自动驾驶按需移动 (EAMoD) 系统，并考虑它们可能造成的拥堵影响。在这项工作中，拥堵效应通过离散事件仿真模型进行描述，该模型具有状态相关的链路行驶速度和有限的链路空间，允许内生性拥堵在整个道路网络中出现、传播和消散。三个数学模型被集成到仿真模型中，以优化车辆与等待请求的匹配，将空车重新定位到潜在的高需求区域，根据充电站的工作量分配低电量车辆到充电站。基于仿真模型，我们应用贝叶斯优化联合设计车队规模和充电设施配置，同时考虑到自动驾驶电动汽车日常运营产生的内生性拥堵。采用收缩层次结构来安排车辆实时执行分配的任务。所提出的解决方案方法在曼哈顿第 60 街下方进行了测试，该街对应于纽约市的潜在拥堵收费区。实验结果表明，所提出的基于仿真的优化方法在计算上易于处理，并且可以在紧张的计算预算内找到车队规模和充电基础设施设计问题的令人满意的解决方案。在系统设计阶段排除拥堵效应将导致由于任务等待时间长而导致高达 14% 的乘客流失。虽然充电设施成本仅占总成本的1.8%，但充电桩的数量可以通过其为车辆充电的效率间接影响可服务乘客的百分比。与车队规模相匹配的充电设施配置有助于提高服务质量和车辆利用率。