发表机构
North Carolina A&T State University（北卡罗来纳A&T州立大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文提出需求驱动的 UAM 网络设计框架，经大洛杉矶案例验证，更大机队可提升服务表现但无法消除空驶，UAM 更适合长距离或拥堵出行。
AI 中文摘要
本文提出一种用于按需城市空中交通（Urban Air Mobility, UAM）网络设计的需求驱动框架，该框架将 vertiport 选址、机队仿真与门到门出行时间可行性相连接。需求从通勤者和乘客活动数据中估算，转换为空间出行终点，并用 K-means 聚类生成候选 vertiport 位置。候选网络通过范围和最小站点间距约束进行筛选，随后采用离散事件仿真评估，该仿真对多车辆调度、空驶重定位、电池更换及服务规律性进行建模。飞行时间和能耗通过点质量 eVTOL 性能模型计算。在大洛杉矶案例研究中，优选设计从低需求下的 4 个站点和 4 架 eVTOL，扩展至最高测试需求水平下的 16 个站点和 12 架 eVTOL。结果表明，更大的机队可缩短完成时间并提升车辆到达规律性，但无法消除空驶飞行，说明空间需求失衡仍是运营负担。出行时间节约分析进一步显示，UAM 最适用于较长或拥堵严重的出行，在扣除飞行时间后仍有充足的非飞行时间。
英文摘要
This paper presents a demand-driven framework for on-demand Urban Air Mobility (UAM) network design that links vertiport siting, fleet simulation, and door-to-door travel-time feasibility. Demand is estimated from commuter and passenger activity data, converted into spatial trip-end points, and clustered using K-means to generate candidate vertiport locations. Candidate networks are screened using range and minimum station-spacing constraints, then evaluated with a discrete-event simulation that models multi-vehicle dispatch, deadhead relocation, battery swaps, and service regularity. Flight time and energy consumption are computed using a point-mass eVTOL performance model. In a Greater Los Angeles case study, the preferred design expands from four stations and four eVTOLs at low demand to sixteen stations and twelve eVTOLs at the highest tested demand level. Results show that larger fleets improve completion time and vehicle-arrival regularity but do not eliminate deadhead flights, indicating that spatial demand imbalance remains an operational burden. The travel-time savings analysis further suggests that UAM is most defensible for longer or congestion-heavy trips where sufficient non-flight time remains after accounting for flight time.
CommentsAccepted for presentation at the 2026 IEEE International Conference on Systems, Man, and Cybernetics (SMC 2026)
