交通运输系统工程与信息

交通运输系统工程与信息 ›› 2026, Vol. 26 ›› Issue (4): 202-212.DOI: 10.16097/j.cnki.1009-6744.2026.04.017

• 系统工程理论与方法 • 上一篇    下一篇

城市低空通勤电动垂直起降飞行器站点选址与路线规划

宋翠颖*，王佳欣，聂良涛，陈雨农   

  1. 石家庄铁道大学，交通运输学院，石家庄 050043
  • 收稿日期:2026-03-11 修回日期:2026-04-23 接受日期:2026-05-22 出版日期:2026-08-25 发布日期:2026-08-21
  • 作者简介:宋翠颖（1988— ），女，河北雄县人，讲师
  • 基金资助:
    河北省高等学校科学研究项目 (QN2026466)；河北省教育科学规划课题 (2503096)。

Site Selection and Route Planning for Urban Low-altitude Commuter eVTOL Systems

SONG Cuiying*, WANG Jiaxin, NIE Liangtao, CHEN Yunong   

  1. School of Traffic and Transportation, Shijiazhuang Tiedao University, Shijiazhuang 050043, China
  • Received:2026-03-11 Revised:2026-04-23 Accepted:2026-05-22 Online:2026-08-25 Published:2026-08-21
  • Supported by:
    Science and Technology Project of Hebei Education Department (QN2026466); Educational Science Planning Project of Hebei (2503096)

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摘要： 职住空间分离作为现代大都市的典型特征，直接导致长距离通勤现象的普遍化，为突破这一地面交通瓶颈，促进职住空间平衡，采用电动垂直起降飞行器（eVTOL）这一创新交通模式作为解决方案。eVTOL凭借其航速快、灵活性强和不依赖地面道路等优势，有望成为未来城市中途通勤的重要载具。然而，当前，关于城市空中交通（UAM）的研究和实践多数聚焦于无人机领域，面向载人场景的研究尚处于起步阶段，因此，本文基于eVTOL提出系统性的站点选址和路线规划方法。首先，通过提取有效通勤出行样本，结合禁飞区约束条件，以“覆盖最大化和站点数最小化”为目标，利用基于K-Dimensional Tree（KD-Tree）的改进贪心覆盖聚类算法实现eVTOL站点选址；其次，构建以“最小化运营成本”为目标的路线规划模型，综合考虑eVTOL载客量、航程、固定成本和距离成本等约束，并采用“贪心算法生成初始解+改进遗传算法迭代优化”的双阶段混合策略进行求解；最后，通过具体案例验证方法的有效性，输出可行的路线规划方案，并从距离与通勤时间两个维度对比评估其效果。结果表明：与小汽车相比，eVTOL在出行距离上平均节省28.29%， 在时间上平均节省61.58%，在提升通勤效率与空间利用率方面优势明显。

关键词: 城市交通, 路线规划, 改进遗传算法, eVTOL, 站点选址

Abstract: Job-housing spatial separation, as a typical feature of modern metropolises, has led directly to the widespread prevalence of long-distance commuting. To overcome this ground transportation bottleneck and promote job-housing balance, the electric vertical take-off and landing (eVTOL) aircraft provides an innovative solution and a new transport mode. With advantages such as high speed, strong flexibility, and independence from ground road networks, eVTOLs are expected to become a key vehicle for future urban medium-distance commuting. However, current research and practices in urban air mobility (UAM) mostly focus on unmanned aerial vehicles, with studies on manned operations still in their early stages. Therefore, this study proposes a systematic method for site selection and route planning based on eVTOL operations. First, by extracting valid commuting trip samples and incorporating no- fly zone constraints, this study applies an improved greedy coverage clustering algorithm based on K-Dimensional Tree (KD-Tree) to achieve eVTOL site selection, aiming at "maximum coverage with a minimum number of sites". A route planning model was then constructed with the objective of minimizing operational costs and the constraints of passenger capacity, flight range, fixed costs, and distance-based costs. A two-stage hybrid strategy combining a greedy algorithm for initial solution generation and an improved genetic algorithm for iterative optimization is adopted for solving the model. The proposed method is validated through a concrete case study, resulting in a feasible route plan. A comparative evaluation is conducted from the perspectives of travel distance and commuting time. The results indicate that, compared with private cars, the eVTOL save on average 28.29% in travel distance and 61.58% in travel time, demonstrating its advantages in improving commuting efficiency and spatial utilization.

Key words: urban transportation；route planning；improved genetic algorithm；electric vertical take-off and landing (eVTOL), site selection

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