面向长距离通勤场景的城市垂直起降场布局优化方法

doi:10.11947/j.AGCS.2026.20250434

测绘学报 ›› 2026, Vol. 55 ›› Issue (2): 236-248.doi: 10.11947/j.AGCS.2026.20250434

面向长距离通勤场景的城市垂直起降场布局优化方法

付晓¹^,²^,³(), 朱司蕊¹^,²^,³, 厉旭东⁴, 闾国年¹^,²^,³()

^1.气候系统预测与变化应对全国重点实验室（南京师范大学），江苏　南京　210023
^2.南京师范大学虚拟地理环境教育部重点实验室，江苏　南京　210023
^3.南京师范大学地理科学学院，江苏　南京　210023
^4.丽水市建设技术管理中心，浙江　丽水　323050

收稿日期:2025-10-14 修回日期:2026-01-22 出版日期:2026-03-13 发布日期:2026-03-13
通讯作者: 闾国年 E-mail:fuxiao@njnu.edu.cn;gnlu@njnu.edu.cn
作者简介:付晓（1988—），女，博士，教授，研究方向为交通地理与智能交通。 E-mail：fuxiao@njnu.edu.cn
基金资助:
国家自然科学基金(42471498);江苏省基础研究计划(BK20250140)

An optimization method for the layout of urban vertiports in long-distance commuting scenarios

Xiao FU¹^,²^,³(), Sirui ZHU¹^,²^,³, Xudong LI⁴, Guonian LÜ¹^,²^,³()

^1.State Key Laboratory of Climate System Prediction and Risk Management (Nanjing Normal University), Nanjing 210023, China
^2.Key Laboratory of Virtual Geographic Environment (Ministry of Education of PRC), Nanjing Normal University, Nanjing 210023, China
^3.School of Geography, Nanjing Normal University, Nanjing 210023, China
^4.Lishui Construction Technology Management Center, Lishui 323050, China

Received:2025-10-14 Revised:2026-01-22 Online:2026-03-13 Published:2026-03-13
Contact: Guonian Lü E-mail:fuxiao@njnu.edu.cn;gnlu@njnu.edu.cn
About author:FU Xiao (1988—), female, PhD, professor, majors in transport geography and intelligent transport. E-mail: fuxiao@njnu.edu.cn
Supported by:
The National Natural Science Foundation of China(42471498);Basic Research Program of Jiangsu(BK20250140)

摘要/Abstract

摘要：

城市空中交通为居民出行提供了新兴的交通选择，垂直起降场等关键基础设施的选址与空间布局将直接影响未来城市居民的出行模式与行为特征。聚焦城市居民的长距离通勤场景，本文基于真实的通勤需求数据，分析了垂直起降场的合理布局。本文构建了一个双层规划模型，模拟垂直起降场的选址与居民出行选择之间的互动机制，旨在寻求能够最小化通勤者单程通勤时间、提升高峰期地面通勤关键道路运行效率的选址方案。在上层，将站点选址构建为一个多目标优化模型，以候选站点的组合为决策变量，采用多目标遗传算法求解；在下层，通过多智能体交通仿真模拟典型通勤者的活动-出行链，评估布局方案对通勤效率的综合影响。以南京市长距离通勤场景为案例，试验结果表明，本文方法能有效提升长距离通勤效率，使整体通勤时间缩短约5%。本文为未来城市多模式交通的规划与管理提供了理论依据与支持。

关键词: 城市空中交通, 垂直起降场布局, 双层规划模型, 长距离通勤

Abstract:

Urban air mobility offers new options for residents' travel. The location and spatial layout of key infrastructure such as vertiports directly affect the travel patterns and behavioral characteristics of future urban residents. Focusing on long-distance commuting scenarios of urban residents, this paper analyzes the optimal layout of vertiports based on real commuting demand data. A bi-level programming model is proposed to model the interaction mechanism between the location of vertiports and residents' travel choices, aiming to find the locations that can minimize the one-way commuting time of commuters and improve the operational efficiency of key ground transportation routes during peak hours. At the upper level, the location problem is formulated as a multi-objective optimization model, with the combination of candidate sites as the decision variable, and solved using the multi-objective genetic algorithm; at the lower level, the activities and travel chains of typical commuters are modeled through multi-agent traffic simulation to evaluate the comprehensive impact of the layout schemes on commuting efficiency. Taking the long-distance commuting scenarios in Nanjing as a case study, the experimental results show that the proposed method can effectively improve the efficiency of long-distance commuting, reducing the aggregate commuting time by approximately 5%. This paper provides theoretical basis and support for the planning and management of multi-modal transportation in future cities.

Key words: urban air mobility, layout of vertiports, bi-level programming model, long-distance commuting

中图分类号:

P208

付晓, 朱司蕊, 厉旭东, 闾国年. 面向长距离通勤场景的城市垂直起降场布局优化方法[J]. 测绘学报, 2026, 55(2): 236-248.

Xiao FU, Sirui ZHU, Xudong LI, Guonian LÜ. An optimization method for the layout of urban vertiports in long-distance commuting scenarios[J]. Acta Geodaetica et Cartographica Sinica, 2026, 55(2): 236-248.

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链接本文: http://xb.chinasmp.com/CN/10.11947/j.AGCS.2026.20250434

http://xb.chinasmp.com/CN/Y2026/V55/I2/236

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出行方式	出行的时间边际效用/h	单位距离货币成本/（元/km）
私家车	－6	－2
公共交通	－1.2	－0.2
慢行交通	－3.6	0
UAM	－18	－12

方案	站点数量	站点序号	航线数量	总容量（机位）	目标1：单程通勤时间/s	目标2：关键通勤道路拥堵指数	UAM分担率/（%）
1	27	1，2，3，5，6，8，10，12，13，14，16，17，20，23，24，25，26，27，29，31，32，33，34，35，37，38，40	150	276	2 959.261 3	0.523 1	6.60
2	22	5，8，9，10，12，13，14，17，19，20，24，25，26，27，29，31，32，33，35，37，38，40	94	228	2 966.758 2	0.523 1	5.55
3	25	5，6，8，9，10，12，13，14，16，17，20，21，23，24，25，26，27，31，32，33，34，35，37，38，40	134	260	2 979.004 2	0.524 2	6.25
4	23	5，8，9，10，12，13，14，16，17，20，23，24，25，26，27，29，31，32，33，35，37，38，40	106	240	2 982.306 1	0.525 9	6.03
5	18	3，5，8，9，10，20，22，24，25，27，28，31，32，33，34，35，36，37	63	172	3 070.402 8	0.527 3	3.59

面向长距离通勤场景的城市垂直起降场布局优化方法

An optimization method for the layout of urban vertiports in long-distance commuting scenarios

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