Urban Air Mobility (UAM) presents a transformative vision for metropolitan transportation, but its practical implementation is hindered by substantial infrastructure costs and operational complexities. We address these challenges by modeling a UAM network that leverages existing regional airports and operates with an optimized, heterogeneous fleet of aircraft. We introduce LPSim, a Large-Scale Parallel Simulation framework that utilizes multi-GPU computing to co-optimize UAM demand, fleet operations, and ground transportation interactions simultaneously. Our equilibrium search algorithm is extended to accurately forecast demand and determine the most efficient fleet composition. Applied to a case study of the San Francisco Bay Area, our results demonstrate that this UAM model can yield over 20 minutes' travel time savings for 230,000 selected trips. However, the analysis also reveals that system-wide success is critically dependent on seamless integration with ground access and dynamic scheduling.

翻译：城市空中交通（UAM）为都市交通带来了变革性愿景，但其实际实施受到高昂基础设施成本和复杂运营模式的制约。为解决这些挑战，我们构建了一个UAM网络模型，该模型利用现有区域机场，并采用经过优化的异构飞机机队进行运营。我们提出了LPSim，一种大规模并行仿真框架，利用多GPU计算同时协同优化UAM需求、机队运营及地面交通交互。我们的均衡搜索算法经过扩展，能够精确预测需求并确定最高效的机队构成。应用于旧金山湾区的案例研究表明，该UAM模型可为23万次选定行程节省超过20分钟的出行时间。然而，分析也表明，系统的整体成功关键依赖于与地面接驳的无缝整合及动态调度。