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

doi:10.11947/j.AGCS.2026.20250434

Abstract

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

CLC Number:

P208

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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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