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

• 空间智能与智慧城市 • 上一篇

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 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 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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参考文献 39

[1]	ZHAO Pengjun, HU Haoyu. Geographical patterns of traffic congestion in growing megacities: big data analytics from Beijing[J]. Cities, 2019, 92: 164-174.
[2]	LU Juan, LI Bin, LI He, et al. Expansion of city scale, traffic modes, traffic congestion, and air pollution[J]. Cities, 2021, 108: 102974.
[3]	GOYAL R, COHEN A. Advanced air mobility: opportunities and challenges deploying eVTOLs for air ambulance service[J]. Applied Sciences, 2022, 12(3): 1183.
[4]	廖小罕, 黄耀欢, 刘霞. 低空经济时代地理信息科技发展的机遇和挑战[J]. 地球信息科学学报, 2025, 27(1): 1-9.
	LIAO Xiaohan, HUANG Yaohuan, LIU Xia. Opportunities and challenges in developing geographic information science and technology in the era of the low-altitude economy[J]. Journal of Geo-information Science, 2025, 27(1): 1-9.
[5]	MORADI N, WANG Chun, MAFAKHERI F. Urban air mobility for last-mile transportation: a review[J]. Vehicles, 2024, 6(3): 1383-1414.
[6]	WANG Leilei, DENG Xiaoheng, GUI Jinsong, et al. A review of urban air mobility-enabled intelligent transportation systems: mechanisms, applications and challenges[J]. Journal of Systems Architecture, 2023, 141: 102902.
[7]	陈军, 高崟, 郭辰阳, 等. 实景三维赋能低空经济的基本思路与重点任务[J]. 时空信息学报, 2025, 32(1): 1-10.
	CHEN Jun, GAO Yin, GUO Chenyang, et al. Harnessing 3D realistic geospatial landscape model to empower the low-altitude economy: fundamental problems and major tasks[J]. Journal of Spatio-Temporal Information, 2025, 32(1): 1-10.
[8]	BULUSU V, ONAT E B, SENGUPTA R, et al. A traffic demand analysis method for urban air mobility[J]. IEEE Transactions on Intelligent Transportation Systems, 2021, 22(9): 6039-6047.
[9]	ROTHFELD R, FU Mengying, BALAĆ M, et al. Potential urban air mobility travel time savings: an exploratory analysis of Munich, Paris, and San Francisco[J]. Sustainability, 2021, 13(4): 2217.
[10]	CUMMINGS C, MAHMASSANI H. Comparing urban air mobility network airspaces: experiments and insights[J]. Transportation Research Record: Journal of the Transportation Research Board, 2024, 2678(4): 440-454.
[11]	PUKHOVA A, LLORCA C, MORENO A, et al. Flying taxis revived: can urban air mobility reduce road congestion?[J]. Journal of Urban Mobility, 2021, 1: 100002.
[12]	SYED N, RYE M, ADE M, et al. Preliminary considerations for ODM air traffic management based on analysis of commuter passenger demand and travel patterns for the silicon valley region of California[C]//Proceedings of the 17th AIAA Aviation Technology, Integration, and Operations Conference. Reston: AIAA, 2017: 3082.
[13]	SCHWEIGER K, PREIS L. Urban air mobility: systematic review of scientific publications and regulations for vertiport design and operations[J]. Drones, 2022, 6(7): 179.
[14]	BRUNELLI M, DITTA C C, POSTORINO M N. New infrastructures for urban air mobility systems: a systematic review on vertiport location and capacity[J]. Journal of Air Transport Management, 2023, 112: 102460.
[15]	HUANG Yiman, ZHANG Anshu, SU Yuezhu, et al. Comparative analysis of metro passengers' mobility patterns and jobs-housing balance of metropolitan[J]. Journal of Geodesy and Geoinformation Science, 2024, 7(2): 1-17.
[16]	ZHOU Yuqing, FU Xiao, TANG Tianli, et al. Assessing resilience of transit networks: an activity-based space-time accessibility analysis[J]. Sustainable Cities and Society, 2025, 130: 106676.
[17]	LIU Yan, TONG L C, ZHU Xi, et al. Dynamic activity chain pattern estimation under mobility demand changes during COVID-19[J]. Transportation Research Part C: Emerging Technologies, 2021, 131: 103361.
[18]	FU Xiao, ZHANG Yi, DE DIOS ORTÚZAR J, et al. Activity-travel pattern inference based on multi-source big data[J]. Transport Reviews, 2025, 45(1): 26-48.
[19]	DELCEA C, CHIRITA N. Exploring the applications of agent-based modeling in transportation[J]. Applied Sciences, 2023, 13(17): 9815.
[20]	ROTHFELD R, BALAC M, PLOETNER K O, et al. Agent-based simulation of urban air mobility[C]//Proceedings of 2018 Modeling and Simulation Technologies Conference. Atlanta: AIAA, 2018: 3891.
[21]	HAGSPIHL T, KOLISCH R, SCHIFFELS S. Planning an airport shuttle network with air taxis using choice-based optimization[J]. OR Spectrum, 2025: 1-35.
[22]	ROTHFELD R, STRAUBINGER A, FU Mengying, et al. Urban air mobility[M]//Demand for emerging transportation systems. Amsterdam: Elsevier, 2020: 267-284.
[23]	JIANG Yu, LI Zhichao, WANG Yasha, et al. Vertiport location for eVTOL considering multidimensional demand of urban air mobility: an application in Beijing[J]. Transportation Research Part A: Policy and Practice, 2025, 192: 104353.
[24]	JIANG Xuan, CAO Shangqing, MO Baichuan, et al. Simulation-based optimization for vertiport location selection: a surrogate model with machine learning method[J]. Transportation Research Record: Journal of the Transportation Research Board, 2025, 2679(2): 2099-2110.
[25]	PARK H S, JUN C H. A simple and fast algorithm for K-medoids clustering[J]. Expert Systems with Applications, 2009, 36(2): 3336-3341.
[26]	何惠雨, 付晓, 吕启航. 突发事件影响下的城市居民出行活动时空模式研究[J]. 时空信息学报, 2024, 31(2): 259-268.
	HE Huiyu, FU Xiao, LÜ Qihang. Spatiotemporal patterns of urban residents' travel activities under the impact of emergencies[J]. Journal of Spatio-Temporal Information, 2024, 31(2): 259-268.
[27]	PARVIN H, MINAEI-BIDGOLI B, ALINEJAD-ROKNY H, et al. Data weighing mechanisms for clustering ensembles[J]. Computers & Electrical Engineering, 2013, 39(5): 1433-1450.
[28]	屈文虎, 刘振东, 蔡昊琳, 等. 顾及场景连通性的混合式SfM方法[J]. 测绘学报, 2023, 52(6): 966-979. DOI: . doi: 10.11947/j.AGCS.2023.20220448
	QU Wenhu, LIU Zhendong, CAI Haolin, et al. A hybrid SfM method considering scene connectivity[J]. Acta Geodaetica et Cartographica Sinica, 2023, 52(6): 966-979. DOI: . doi: 10.11947/j.AGCS.2023.20220448
[29]	HORN A. The multi-agent transport simulation MATSim[M]. London: Ubiquity Press, 2016.
[30]	KONG Xiaoqiang, ZHANG Yunlong, EISELE W L, et al. Using an interpretable machine learning framework to understand the relationship of mobility and reliability indices on truck drivers' route choices[J]. IEEE Transactions on Intelligent Transportation Systems, 2022, 23(8): 13419-13428.
[31]	HE Feifei, YAN Xuedong, LIU Yang, et al. A traffic congestion assessment method for urban road networks based on speed performance index[J]. Procedia Engineering, 2016, 137: 425-433.
[32]	VASCIK P D, HANSMAN R J. Development of vertiport capacity envelopes and analysis of their sensitivity to topological and operational factors[C]//Proceedings of 2019 AIAA Scitech Forum. San Diego: AIAA, 2019: 0526.
[33]	CHARYPAR D, NAGEL K. Generating complete all-day activity plans with genetic algorithms[J]. Transportation, 2005, 32(4): 369-397.
[34]	SIMONI M D, KOCKELMAN K M, GURUMURTHY K M, et al. Congestion pricing in a world of self-driving vehicles: an analysis of different strategies in alternative future scenarios[J]. Transportation Research Part C: Emerging Technologies, 2019, 98: 167-185.
[35]	ZHANG Bo, ZHAO Meng, HU Xiangpei. Location planning of electric vehicle charging station with users' preferences and waiting time: multi-objective bi-level programming model and HNSGA-II algorithm[J]. International Journal of Production Research, 2023, 61(5): 1394-1423.
[36]	刘志远, 付晓. 基于手机大数据的交通规划方法与应用[M]. 北京: 科学出版社, 2022.
	LIU Zhiyuan, FU Xiao. Traffic planning method and application based on mobile phone big data[M]. Beijing: Science Press, 2022.
[37]	YANG Zhao, ZHANG Ying, BAI Xuelian, et al. Estimation of value of travel time savings using willingness-to-pay method[C]//Proceedings of the 18th COTA International Conference of Transportation Professionals. Beijing: American Society of Civil Engineers, 2018: 2433-2445.
[38]	SCHMID B, MOLLOY J, PEER S, et al. The value of travel time savings and the value of leisure in Zurich: estimation, decomposition and policy implications[J]. Transportation Research Part A: Policy and Practice, 2021, 150: 186-215.
[39]	COPPOLA P, DE FABIIS F, SILVESTRI F. Urban air mobility (UAM): airport shuttles or city-taxis?[J]. Transport Policy, 2024, 150: 24-34.

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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