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）为都市交通勾勒出一幅变革性的蓝图，但其实际落地受制于高昂的基础设施成本与复杂的运营挑战。为攻克这些难题，我们构建了一个UAM网络模型，该网络利用现有区域性机场，并采用经过优化的异构机队进行运营。我们引入LPSim——一个利用多GPU计算的大型并行模拟框架，能够同时协同优化UAM需求、机队运营及地面交通交互。通过扩展我们的均衡搜索算法，实现对需求的精确预测并确定最高效的机队构成。以旧金山湾区的案例研究为应用场景，结果表明，该UAM模型可为23万次选定出行节省超过20分钟的行程时间。然而，分析也揭示，系统级的成功关键取决于与地面交通的无缝接驳及动态调度能力。