A Vertiport Location Method for Urban Low-Altitude Air-Ground Coordinated Transportation
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摘要: 低空经济快速发展背景下, 以电动垂直起降(electric vertical take-off and landing, eVTOL)飞行器为运载工具的低空综合立体交通成为突破城市交通瓶颈的新路径, 垂直起降端选址是实现低空与地面交通高效协同的关键。现有研究多忽略用户出行意愿的概率化分布, 且未能量化空地交通动态交互过程中空中交通对地面路网流量分配的影响。为此, 提出"非机动接驳+eVTOL"的空地机动并联运营模式, 建立城市空地协同交通网络用户出行意愿概率分布模型, 基于多项Logit离散选择模型刻画用户在纯地面出行与低空出行之间的选择行为；构建"上层选址决策+下层用户均衡分配"的垂直起降端选址双层规划模型, 采用BPR函数计算地面阻抗, 通过"地面阻抗-方式选择概率-需求重分配"的迭代耦合机制求解, 量化空中交通与地面交通的相互作用。算例结果表明：空地协同交通网络存在起降端建设数量的最优值, 本算例中起降端数量$ B=6 $时运营效益达到峰值87200.74元, 最优布局下网络交通阻抗较纯地面网络改善近14%；低空网络通过"截流"关键边流量, 重塑用户出行方式与起讫点(origin-destination, OD)需求分配, 形成"需求转移—流量优化—效率提升"的空地耦合机制。同时发现, 当eVTOL单位票价超过7元后, 用户低空出行意愿显著下降, 空中交通对地面阻抗的缓解作用亦明显减弱。Abstract: Against the backdrop of the rapid development of the low-altitude economy, urban air mobility (UAM), which employs electric vertical take-off and landing (eVTOL) aircraft as the carrier, has emerged as a new pathway to breaking through urban traffic bottlenecks. Vertiport siting is the key to achieving efficient air-ground coordination between low-altitude and ground transportation. Existing studies neglect the probability distribution of users'travel willingness and fail to quantify the impact of air traffic on ground road network flow allocation during the dynamic air-ground interaction. To address this gap, an air-ground parallel operation mode of “non-motorized access-egress + eVTOL” is proposed, and a travel willingness probability model for the urban air-ground coordinated transportation network is developed, based on the multinomial logit (MNL) model, to characterize users' choice behavior between pure ground travel and low-altitude travel. A bilevel programming model for vertiport siting is then established, with the upper layer devoted to location decision-making and the lower layer to user equilibrium assignment; the ground impedance is calculated by the Bureau of Public Roads (BPR) function, and the interaction mechanism between air and ground traffic is quantified through an iterative coupled solution of“ground impedance-mode choice probability-demand reallocation.”Numerical results show that an optimal number of vertiports exists in the air-ground coordinated network: in the case study, the operating benefit reaches its peak of 87 200.74 yuan when the number of vertiports B is 6, and the network traffic impedance is improved by nearly 14% relative to the pure ground network. The resulting low-altitude network reshapes users' travel mode choices and origin-destination (OD) demand allocation by diverting flow from critical links, thereby forming an air-ground coupling mechanism of “demand shift-flow optimization-efficiency improvement.”It is further found that once the unit eVTOL fare exceeds 7 yuan, both users' low-altitude travel willingness and the mitigating effect of air traffic on ground impedance decline significantly.
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图 1 空地协同交通网络示意图
Figure 1. Illustration of the urban low-altitude air-ground transportation network
图 7 外层遗传算法迭代展示
Figure 7. Objective function values during iterations of the outer genetic algorithm
图 8 最后1次内层流量分配算法迭代过程
Figure 8. The iterative process of the last inner flow distribution algorithm
表 1 模型参数设置
Table 1. Value of parameters in the numeric study
参数 值 参数 值 $ q^{r s}/\mathrm{veh} $ [20, 200] $ \beta_{c e} $ -0.0817 $ C_{a}/(\mathrm{veh}/\mathrm{h}) $ 6000 $ \beta_{c g} $ -0.0485 $ \tau_{a}/\mathrm{h} $ 0.17 $ \beta_{\text {te}} $ -0.0284 $ v_{e}/(\mathrm{km}/\mathrm{h}) $ 150 $ \beta_{\mathrm{tg}} $ -0.0387 $ c_{e}/$元 6 $ \tau_{e} $ 0.816 $ c_{g}/$元 3 $ \tau_{g} $ 0 $ t_{u c}/\min $ 15 $ \gamma $ 1 $ \alpha $ 0.15 $ \beta $ 4 $ B $ 4 $ M $ 9999 
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表 2 起降端运营引发的OD需求再分配
Table 2. Impacts on travel demand and roadway volumes at the vertiport
起降端对 受影响的地面出行OD对 地面出行OD对用户出行转移量 对应地面OD对整体通行时间变化/s 1-5 1-4 -10 -3.00 1-13 -40 -2.40 11-5 -38 -3.49 1-19 1-11 -39 -1.22 19-12 -35 -2.24 4-1 -34 -1.51 1-21 21-4 -38 -3.09 21-17 -35 -0.33 13-21 -34 -1.85
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表 3 不同起降端数量下的模型结果变化
Table 3. Model results for different numbers of vertiports
起降端数量$ B $ 选址方案 运营效益/元 节点覆盖比例 地面总阻抗$/\left(\times 10^{6}\right) $ 均航段用户数 航段人均费用/元 初始 1.440085 4 1, 5, 19, 21 48120.42 4/25 1.293532 307 121.77 5 1, 5, 19, 21, 25 82864.01 5/25 1.263435 230 126.71 6 1, 3, 5, 19, 21, 25 87200.74 6/25 1.238596 178 128.63 7 1, 3, 5, 10, 19, 21, 25 71171.95 7/25 1.217285 141 130.21 8 1, 3, 5, 10, 19, 21, 24, 25 41244.81 8/25 1.200780 116 129.23 9 1, 3, 5, 10, 15, 19, 21, 24, 25 -2735.29 9/25 1.193212 94 132.05 10 1, 3, 5, 10, 15, 19, 21, 22, 24, 25 -48 272.57 10/25 1.183900 78 135.09 11 1, 3, 5, 10, 15, 19, 21, 22, 23, 24, 25 -115 526.19 11/25 1.172987 66 136.04 12 1, 2, 3, 5, 10, 15, 19, 21, 22, 23, 24, 25 -209808.11 12/25 1.168471 56 135.12
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表 4 差异化定价下的用户选择与效益对比
Table 4. Comparison of user choices and cost-benefit outcomes under differential pricing
eVTOL单位运价/元 选址方案 eVTOL用户人数 地面总阻抗$/\times 10^{6} $ 5 1, 5, 19, 21 4021 1.245188 6 1, 5, 19, 21 3870 1.293532 7 1, 5, 19, 21 2727 1.339770 8 1, 5, 19, 21 1815 1.373155 9 1, 5, 19, 21 1172 1.396848
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[1] 王芳, 白傑, 杨丽平, 等. 探索飞行汽车通勤新模式的城市空中交通发展分析[J]. 北京理工大学学报, 2023, 43(7): 665-675.WANG F, BAI J, YANG L P, et al. An overview on development of urban air traffic with a new mode of flying car commuting[J]. Transactions of Beijing Institute of Technology, 2023, 43(7): 665-675. (in Chinese) [2] STRAUBINGER A, ROTHFELD R, SHAMIYEH M, et al. An overview of current research and developments in urban air mobility-setting the scene for UAM introduction[J]. Journal of Air Transport Management, 2020, 87: 101852. doi: 10.1016/j.jairtraman.2020.101852 [3] BRUNUELLI M, DITTA 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, 113: 102460. [4] 李诚龙, 屈文秋, 李彦冬, 等. 面向eVTOL航空器的城市空中运输交通管理综述[J]. 交通运输工程学报, 2020, 20(4): 35-54.LI C L, QU W Q, LI Y D, et al. Overview of traffic management of urban air mobility (UAM) with eVTOL aircraft[J]. Journal of Traffic and Transportation Engineering, 2020, 20(4): 35-54. (in Chinese) [5] HUANG C, FANG S, WU H, et al. Low-altitude intelligent transportation: system architecture, infrastructure, and key technologies[J]. Journal of Industrial Information Integration, 2024, 42: 100694. doi: 10.1016/j.jii.2024.100694 [6] SUN X Q, WANDELT S, STUMPF E. Competitiveness of on-demand air taxis regarding door-to-door travel time: a race through Europe[J]. Transportation Research Part E: Logistics and Transportation Review, 2018, 119: 1-13. [7] LONG Q, MA J, JIANG F, et al. Demand analysis in urban air mobility: a literature review[J]. Journal of Air Transport Management, 2023, 118: 102436. [8] BECKER K, TEREKHOV I, GOLLNICK V. A global gravity model for air passenger demand between city pairs and future interurban air mobility markets identification[C]. 2018 Aviation Technology, Integration, and Operations Conference. Atlanta, GA: AIAA, 2018. [9] RAJENDRAN S, SRINIVAS S. Air taxi service for urban mobility: a critical review of recent developments, future challenges, and opportunities[J]. Transportation Research Part E: Logistics and Transportation Review, 2020, 143: 102090. doi: 10.1016/j.tre.2020.102090 [10] WU Z Q, ZHANG Y. Integrated network design and demand forecast for on-demand urban air mobility[J]. Engineering, 2021(7): 473-483. [11] RATH S, CHOW J Y J. Air taxi skyport location problem with single-allocation choice-constrained elastic demand for airport access[J]. Journal of Air Transport Management, 2022, 105: 102294. doi: 10.1016/j.jairtraman.2022.102294 [12] 李伯权, 张明, 李松锐, 等. 航空器性能约束下的空地协同选址优化[J]. 航空计算技术, 2021, 51(1): 27-31.LI B Q, ZHANG M, LI S R, et al. Air-ground cooperative location optimization under constraints of aircraft performance[J]. Aeronautical Computing Technique, 2021, 51(1): 27-31. (in Chinese) [13] ZHANG H, LIU Z, DONG Y, et al. A novel network equilibrium model integrating urban aerial mobility[J]. Transportation Research Part A: Policy and Practice, 2024, 187: 104160. doi: 10.1016/j.tra.2024.104160 [14] ZHANG H, HUO J, CHEN C, et al. A composite transportation network design problem with land-air coordinated operations[J]. Transportation Research Part C: Emerging Technologies, 2025, 171: 104967. doi: 10.1016/j.trc.2024.104967 [15] 张兆磊, 郝威, 李豪, 等. 基于拓展交通均衡的高速路网充电桩优化部署[J]. 中国公路学报, 2024, 37(4): 61-71.ZHANG Z L, HAO W, LI H, et al. Optimized deployment of charging piles based on extended traffic equilibrium in highway[J]. China Journal of Highway and Transport, 2024, 37(4): 61-71. (in Chinese) [16] 陈坚, 李兆康, 周涛, 等. 基于用户均衡的双模式接驳公交一体化布局优化[J]. 交通运输系统工程与信息, 2023(3): 56-65.CHEN J, LI Z K, ZHOU T, et al. Optimizing integrated layout of bi-modal feeder buses based on user equilibrium[J]. Journal of Transportation Systems Engineering and Information Technology, 2023, 23(3): 56-65. (in Chinese) [17] BODDUPALLI S, GARROW L A, GERMAN B J, et al. Mode choice modeling for an electric vertical takeoff and landing (eVTOL) air taxi commuting service[J]. Transportation Research Part A: Policy and Practice, 2024, 181: 104000. doi: 10.1016/j.tra.2024.104000 [18] CHAE M, KIM S H, KIM M, et al. Potential market based policy considerations for urban air mobility[J]. Journal of Air Transport Management, 2024, 119: 102654. doi: 10.1016/j.jairtraman.2024.102654 [19] 姜耀, 赵胜川, 王新月, 等. 基于巢式Logit模型的城际轨道交通枢纽换乘阻抗测度方法[J]. 交通信息与安全, 2025, 43(1): 161-168. doi: 10.3963/j.jssn.1674-4861.2025.01.015JIANG Y, ZHAO S C, WANG X Y, et al. Transfer penalty measurement of intercity rail transit hub based on nest logit model[J]. Journal of Transport Information and Safety, 2025, 43(1): 161-168. (in Chinese) doi: 10.3963/j.jssn.1674-4861.2025.01.015 [20] 庄焱, 董春娇, 米雪玉, 等. 基于随机参数Logit的中小城市居民出行方式选择建模[J]. 吉林大学学报(工学版), 2024, 54(2): 461-468.ZHUANG Y, DONG C J, MI X Y, et al. Travel mode choice in small and media-sized cities based on random parameters logit model[J]. Journal of Jilin University (Engineering and Technology Edition), 2024, 54(2): 461-468. (in Chinese) [21] DU M, ZHOU J, CHEN A, et al. Modeling the capacity of multimodal and intermodal urban transportation networks that incorporate emerging travel modes[J]. Transportation Research Part E: Logistics and Transportation Review, 2022, 168: 102937. doi: 10.1016/j.tre.2022.102937 [22] 李昌兵, 袁浩, 杜茂康. 基于层次遗传算法的非线性双层规划问题求解策略[J]. 系统工程, 2011, 29(4): 109-113.LI C B, YUAN H, DU M K. A solution strategy for nonliear bilevel programming problem based on hierarchical genetic algorithm[J]. Systems Engineering, 2011, 29(4): 109-113. (in Chinese) [23] 李和成. 非线性双层规划问题的遗传算法研究[D]. 西安: 西安电子科技大学, 2009.LI H C. Research on genetic algorithms for nonlinear bilevel programming problems[D]. Xi'an: Xidian University, 2009. (in Chinese) [24] 高建树, 王明强, 宋兆康, 等. 基于遗传算法的机场充电桩布局选址研究[J]. 计算机工程与应用, 2018, 54(23): 210-216.GAO J S, WANG M Q, SONG Z K, et al. Study on site selection of airport charging pile based on genetic algorithm[J]. Computer Engineering and Applications, 2018, 54(23): 210-216. (in Chinese) [25] 魏贺, 刘昊飞, 许丹丹, 等. 双层规划在城市交通领域研究与应用的系统综述[J]. 运筹学学报, 2023, 27(2): 1-26.WEI H, LIU H F, XU D D, et al. A systematic review of researches and applications of bi-level programming in the context of urban transport[J]. Operations Research Transactions, 2023, 27(2): 1-26. (in Chinese) [26] 吴鹏, 颜宝卿. 基于双层规划的自动卡车运输专用网络设计优化[J]. 控制与决策, 2023, 38(9): 2691-2700.WU P, YAN B Q. Optimal design of dedicated network for automated truck transportation based on bi-level programming[J]. Control and Decision, 2023, 38(9): 2691-2700. (in Chinese) [27] 徐猛, 屈云超, 高自友. Frank-Wolfe算法求解交通分配问题: 比较不同流量更新策略和线搜索技术[J]. 交通运输系统工程与信息, 2008, 8(3): 14-22.XU M, QU Y C, GAO Z Y. Implementing Frank-Wolfe algorithm for traffic assignment problem under different flow update strategies and line search technologies[J]. Journal of Transportation Systems Engineering and Information Technology, 2008, 8(3): 14-22. (in Chinese) -
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