面向城市低空物流的分层航路网构建与韧性恢复策略

doi:10.16097/j.cnki.1009-6744.2026.04.005

交通运输系统工程与信息 ›› 2026, Vol. 26 ›› Issue (4): 52-64.DOI: 10.16097/j.cnki.1009-6744.2026.04.005

面向城市低空物流的分层航路网构建与韧性恢复策略

高远，黄慧，孙赫迎*，张文会

东北林业大学，土木与交通学院，哈尔滨 150040

收稿日期:2025-12-24 修回日期:2026-02-15 接受日期:2026-03-02 出版日期:2026-08-25 发布日期:2026-08-21
作者简介:高远（1993— ），男，山东菏泽人，副教授，博士
基金资助:
国家自然科学基金 (52572369)；黑龙江省哲学社会科学研究规划项目 (24GLC014)

Construction and Resilience Recovery of Hierarchical Air-route Network for Urban Low-altitude Logistics

GAO Yuan, HUANG Hui, SUN Heying^*, ZHANG Wenhui

School of Civil Engineering and Transportation, Northeast Forestry University, Harbin 150040, China

Received:2025-12-24 Revised:2026-02-15 Accepted:2026-03-02 Online:2026-08-25 Published:2026-08-21
Supported by:
National Natural Science Foundation of China (52572369); Philosophy and Social Science Foundation of Heilongjiang Province (24GLC014)

摘要/Abstract

摘要： 低空经济作为国家战略性新兴产业，正进入快速发展阶段，然而，低空空域资源碎片化、空域使用效率低下及安全风险较高等问题仍然是亟待解决的挑战。为此，本文提出一种面向城市低空物流的航路网络构建方法，并进一步开展网络韧性恢复研究，实现突发事件下网络功能的快速恢复与高效重构。首先，采用增量优化策略布局节点空间，构建由干线-次干线-支线组成的分层拓扑结构；随后，引入多目标遗传算法，对不同恢复阶段的节点与边进行动态优化排序，获得高效的恢复策略。以天津市和济南市物流驿站的经纬度坐标和机场禁飞区数据为基础，结合公路货运量数据模拟低空物流需求与运行约束条件，构建典型城市低空航路网络。实验结果表明，本文提出的韧性恢复策略可显著提升网络整体效率与流量韧性；帕累托前沿分析进一步验证多目标优化方法在综合恢复性能上的优势。本文提出的新型低空航路网络抗扰动与动态恢复方法体系，可为无人机物流配送、城市空中交通及应急救援等低空经济应用提供有效的技术路径与实践依据。

关键词: 航空运输, 网络韧性, 遗传算法, 低空物流网络, 航路构建, 恢复策略

Abstract: The low-altitude economy in China is rapidly developing but challenges still remain, such as fragmented airspace resources, low utilization efficiency, and high safety risks. To address these issues, this study proposes a method for constructing and restoring urban low-altitude logistics air-route networks, enabling rapid and efficient recovery under disruptive events. An incremental optimization strategy is used to design the spatial layout of nodes and develop a hierarchical topology consisting of trunk, secondary, and branch routes. A multi-objective genetic algorithm is introduced to dynamically prioritize node and edge restoration, yielding an efficient recovery strategy. Using logistics station coordinates and airport no-fly zone data from the cities of Tianjin and Jinan, the study develops a representative urban air- route network with road- freight- based demand and operational constraints. The experimental results indicate that the proposed restoration strategy markedly improves network efficiency and flow resilience, while Pareto frontier analysis confirms the advantage of the multi-objective optimization approach. The framework provides a practical foundation for enhancing Unmanned Aerial Vehicle logistics operations, urban air mobility, and emergency response under the emerging low-altitude economy.

Key words: air transportation, network resilience, genetic algorithm, low-altitude logistics network, air- route construction, recovery strategy

中图分类号:

高远, 黄慧, 孙赫迎, 张文会. 面向城市低空物流的分层航路网构建与韧性恢复策略[J]. 交通运输系统工程与信息, 2026, 26(4): 52-64.

GAO Yuan, HUANG Hui, SUN Heying, ZHANG Wenhui. Construction and Resilience Recovery of Hierarchical Air-route Network for Urban Low-altitude Logistics[J]. Journal of Transportation Systems Engineering and Information Technology, 2026, 26(4): 52-64.

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参考文献

[1] 张洪海, 张连东, 刘皞, 等. 城市低空物流无人机航迹规划模型研究[J]. 交通运输系统工程与信息, 2022, 22 (1): 256-264. [ZHANG H H, ZHANG L D, LIU H, et al. Track planning model for logistics unmanned aerial vehicle in urban low-altitude airspace[J]. Journal of Transportation Systems Engineering and Information Technology, 2022, 22(1): 256-264.]

[2] 陈丹, 徐天枢, 马园园, 等. 复杂城市低空物流无人机任务协同动态分配[J/OL]. 航空学报, (2025-10- 17) [2025-12-19]. https://hkxb.buaa.edu.cn/CN/10.7527/ S1000-6893.2025.32657. [CHEN D, XU T S, MA Y Y, et al. Complex urban low-altitude logistics unmanned aerial vehicle task collaborative dynamic allocation [J/OL]. Acta Aeronautica et Astronautica Sinica, https:// hkxb.buaa.edu.cn/CN/10.7527/S1000-6893.2025.32657.]

[3] 周星宇, 李姝涵, 薛锋. 基于多智能体图神经网络的城市无人机配送路径优化[J]. 交通运输工程与信息学报, 2025, 23(4): 50-61. [ZHOU X Y , LI S H , XUE F, Urban UAV delivery routing optimization using multiagent graph neural networks[J]. Journal of Transportation Engineering and Information, 2025, 23(4): 50-61.]

[4] TANG G, XIAO T, DU P, et al. Improved pso-based two-phase logistics uav path planning under dynamic demand and wind conditions[J]. Drones, 2024, 8(8): 356.

[5] CHAN Y Y, NG K K H, WANG T, et al. Near time-optimal trajectory optimisation for drones in last-mile delivery using spatial reformulation approach[J]. Transportation Research Part C: Emerging Technologies, 2025, 171: 104986.

[6] RAHMAN M, SARKAR N I, LUTUI R. A survey on multi-UAV path planning: Classification, algorithms, open research problems, and future directions[J]. Drones, 2025, 9(4): 263.

[7] 廖小罕, 徐晨晨, 叶虎平, 等. 无人机应用发展关键基础设施与低空公共航路网规划[J]. 中国科学院院刊, 2022, 37(7): 977-988. [LIAO X H, XU C C, YE H P, et al. Critical infrastructures for developing UAVs' applications and low-altitude public air-route network planning[J]. Bulletin of Chinese Academy of Sciences, 2022, 37(7): 977-988.]

[8] 徐晨晨, 叶虎平, 岳焕印, 等. 城镇化区域无人机低空航路网迭代构建的理论体系与技术路径[J]. 地理学报, 2020, 75(5): 917-930. [XU C C, YE H P, YUE H Y, et al. Iterative construction of UAV low-altitude air route network in an urbanized region: Theoretical system and technical roadmap[J]. Acta Geographica Sinica, 2020, 75 (5): 917-930.]

[9] 吕彪, 谢智宇, 康宇翔, 等. 基于动态分流元胞传输模型的城市道路网络韧性评估[J]. 交通运输系统工程与信息, 2022, 22(6): 134-143, 211. [LV B, XIE Z Y, KANG Y X, et al. Resilience assessment on urban road network by dynamic shunt cell transmission model[J]. Journal of Transportation Systems Engineering and Information Technology, 2022, 22(6): 134-143, 211.]

[10] 刘继新, 张新珏, 蒋伶潇, 等. 低空无人机群自组织网络弹性综述[J]. 科学技术与工程, 2023, 23(20): 8504- 8514. [LIU J X, ZHANG X J, JIANG L X, et al. Review of self-organizing UAV swarm network resilience in low altitude airspace[J]. Science Technology and Engineering, 2023, 23(20): 8504-8514.]

[11] 陈丹, 汤程, 谢宇, 等. 面向城市低空物流配送的无人机实时迹双层规划 [J]. 航空学报, 2025, 46(16): 331621. [CHEN D, TANG C, XIE Y, et al. Real time dual layer path planning of unmanned aerial vehicles for urban low altitude logistics distribution[J]. Acta Aeronautica et Astronautica Sinica, 2025, 46(16): 331621.]

[12] LIU T, BAI G, TAO J, et al. A multistate network approach for resilience analysis of UAV swarm considering information exchange capacity[J]. Reliability Engineering & System Safety, 2024, 241: 109606.

[13] ZHU Y, BAI G, XU Z, et al. Disintegrating the information exchange network of UAV swarm based on relative resilience[J]. Reliability Engineering & System Safety, 2025: 111745.

[14] 郭文彤, 李翔宇, 金温妍, 等. 中国主要城市低空适飞空域分析[J]. 交通运输工程与信息学报, 2025, 23(3): 60- 73. [GUO W T, LI X Y, JIN W Y, Analysis of low altitude flyable airspace in major cities in China[J]. Journal of Transportation Engineering and Information, 2025, 23(3): 60-73.]

[15] 王飞, 徐浩凡, 王京硕. 面向多配送中心的空地协同配送路线优化[J/OL]. 华南理工大学学报(自然科学版), (2025-10-24) [2025-12-19]. https://link.cnki.net/urlid/ 44.1251.T.20251023.1502.002. [WANG F, XU H F, WANG J S. Vehicle-drone collaborative distribution route optimization based on the NSGA-II algorithm[J/ OL]. Journal of South China University of Technology (Natural Science Edition), https://link.cnki.net/urlid/ 44.1251. T.20251023.1502.002. ]

面向城市低空物流的分层航路网构建与韧性恢复策略

Construction and Resilience Recovery of Hierarchical Air-route Network for Urban Low-altitude Logistics

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