不确定扰动下的区域道路交通网络韧性演化及提升方法研究

doi:10.16097/j.cnki.1009-6744.2026.02.005

交通运输系统工程与信息 ›› 2026, Vol. 26 ›› Issue (2): 47-59.DOI: 10.16097/j.cnki.1009-6744.2026.02.005

不确定扰动下的区域道路交通网络韧性演化及提升方法研究

王笑蓉，李引珍* ，孙颖杰，县勇，李雯，巨玉祥

兰州交通大学，交通运输学院，兰州730070

收稿日期:2025-10-23 修回日期:2026-01-27 接受日期:2026-02-05 出版日期:2026-04-25 发布日期:2026-04-20
作者简介:王笑蓉（1987— ），女，甘肃兰州人，讲师，博士生。
基金资助:
中国工程院战略研究与咨询重大项目 (2024-DFZD-34-02)；国家自然科学基金 (52502390)。

Resilience Evolution and Improvement Methods of Regional Road Traffic Networks Under Uncertain Disturbances

WANG Xiaorong，LI Yinzhen^*，SUN Yingjie，XIAN Yong，LI Wen，JU Yuxiang

College of Traffic and Transportation, Lanzhou Jiaotong University, Lanzhou 730070, China

Received:2025-10-23 Revised:2026-01-27 Accepted:2026-02-05 Online:2026-04-25 Published:2026-04-20
Supported by:
Major Project on Strategic Research and Consulting of the Chinese Academy of Engineering (2024-DFZD-34-02)；National Natural Science Foundation of China (52502390)。

摘要/Abstract

摘要： 本文研究区域道路交通网络在不确定扰动情形下的韧性演化规律与提升方法问题。由于区域道路交通流量具有时变性，在考虑网络拓扑结构的同时，将通行能力实时利用率作为影响网络韧性的重要因素，构建区域道路交通加权网络模型，并以准备度、抗干扰度、适应度与可恢复度4个动态韧性指标作为度量网络韧性的优化指标，在此基础上，构建基于不确定概率性风险的多目标双层规划模型；最后，设计基于动态仿真与并行计算的改进非支配排序遗传算法（Non-dominated Sorting Genetic AlgorithmII,NSGA-II）进行求解。以甘肃省区域骨干道路网络为例，仿真区域性协同失效的灾难情景，分析投资成本与交通网络韧性的演化规律：提升网络韧性的扩容投资方案呈现阶段式负斜率凹函数形态，投资边际具有递减规律，应依据预算规模分阶段地实施差异化投资决策，并通过具体提升方案验证此规律。进一步，分析基础恢复速率的灵敏度可知，在不同等级扰动强度和持续扰动时间下，扩容投资与恢复能力呈非线性耦合特征，并提出针对性决策建议：低恢复速率网络应优先投资应急管理，基础恢复速率0.10~0.15区间是投资回报率最高阶段，可将管理手段与工程建设共同投入，达到效益最大化；高恢复速率网络可通过道路补强优化提升韧性，该演化规律及决策建议对应急资源调配与设施投资的协同优化有借鉴意义。

关键词: 交通工程, 网络韧性, 多目标双层规划, 区域道路交通网络, 不确定扰动, 改进NSGA-II算法

Abstract: This paper investigates the resilience evolution law and improvement methods of regional road traffic networks under uncertain disturbance conditions. Given the time-varying characteristics of regional road traffic flow, this paper uses the real-time utilization rate of traffic capacity as an important factor to reflect the effects on network resilience and considers the network topological structure in the analysis. A weighted network model for regional road traffic is constructed. Four dynamic resilience indicators—preparedness, disturbance resistance, adaptability and recoverability—are adopted as the optimization metrics for measuring network resilience. On this basis, a multi-objective bi-level programming model based on uncertain probabilistic risks is developed, and an improved Non-dominated Sorting Genetic Algorithm II (NSGA-II) algorithm integrated with dynamic simulation and parallel computing is designed to solve the model. The regional backbone road network of Gansu Province is used for the case study. The disaster scenarios of regional coordinated failure are simulated to analyze the evolution laws of investment costs and traffic network resilience. The capacity expansion investment schemes for improving network resilience present a staged negative-slope concave function pattern with a diminishing marginal return on investment, and differentiated investment decisions should be implemented in phases according to the budget scale, which is verified by specific improvement schemes. Further sensitivity analysis of the basic recovery rate reveals that capacity expansion investment and recovery capacity exhibit a nonlinear coupling characteristic under different disturbance intensity levels and sustained disturbance durations. The targeted decision-making suggestions are proposed: for networks with a low recovery rate, priority should be given to investment in emergency management. The range of 0.10 to 0.15 for the basic recovery rate represents the stage with the highest return on investment, where a combination of management measures and engineering construction investment can be adopted to achieve maximum benefits. For networks with a high recovery rate, resilience can be improved through road reinforcement and optimization. The revealed evolution laws and proposed decision-making suggestions provide a reference for the collaborative optimization of emergency resource allocation and facility investment.

Key words: traffic engineering, network resilience, multi-objective bi-level programming, regional road network, uncertain disturbance, improved NSGA-II algorithm

中图分类号:

U491.12

王笑蓉, 李引珍, 孙颖杰, 县勇, 李雯, 巨玉祥. 不确定扰动下的区域道路交通网络韧性演化及提升方法研究[J]. 交通运输系统工程与信息, 2026, 26(2): 47-59.

WANG Xiaorong, LI Yinzhen, SUN Yingjie, XIAN Yong, LI Wen, JU Yuxiang. Resilience Evolution and Improvement Methods of Regional Road Traffic Networks Under Uncertain Disturbances[J]. Journal of Transportation Systems Engineering and Information Technology, 2026, 26(2): 47-59.

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链接本文: http://www.tseit.org.cn/CN/10.16097/j.cnki.1009-6744.2026.02.005

http://www.tseit.org.cn/CN/Y2026/V26/I2/47

参考文献

[1]AZOLIN LG, SILVA ANRD, PINTO N. Incorporating public transport in a methodology for assessing resilience in urban mobility[J]. Transportation Research Part D: Transport and Environment, 2020, 85: 102386.

[2] 徐鹏程,路庆昌,李静,等.韧性视角下高速公路网络拥堵特性分析[J]. 中国公路学报,2024, 37(10): 196 208. [XU P C, LU Q C, LI J, et al. Analysis of congestion characteristics of expressway network from the perspective of resilience[J]. China Journal of Highway and Transport, 2024, 37(10): 196-208.]

[3] 吕彪,高自强,刘一骝.道路交通系统韧性及路段重要度评估[J]. 交通运输系统工程与信息, 2020, 20(2): 114-121. [LV B, GAO Z Q, LIU Y L. Evaluation of road transportation system resilience and link importance[J]. Journal of Transportation Systems Engineering and Information Technology, 2020, 20(2): 114-121.]

[4]ZHANG L, WEN H Y, LU J, et al. Exploring cascading reliability of multi-modal public transit network based on complex networks[J]. Reliability Engineering & System Safety, 2022, 221: 1-27.

[5]赵雪亭,胡立伟,周君.城市交通拥塞风险场级联失效及韧性评估研究[J].交通运输系统工程与信息, 2025, 25(2): 146-156. [ZHAO X T, HU L W, ZHOU J. Research on cascade failure and resilience assessment of urban traffic congestion risk field[J]. Journal of Transportation Systems Engineering and Information Technology, 2025, 25(2): 146-156.]

[6]YADAV N, CHATTERJEE S, GANGULY A R. Resilience of urban transport network-of-networks under intense flood hazards exacerbated by targeted attacks[J]. Scientific Reports, 2020, 10(1): 10350.

[7]徐鹏程,路庆昌,李静,等.连续暴雨灾害下道路网络时变韧性建模与分析[J]. 武汉大学学报(工学版), 2024, 57(11): 1610-1618. [XU P C, LU Q C, LI J, et al. Research on modeling and analysis of time-varied resilience of road network under continuous rainstorm disasters[J]. Engineering Journal of Wuhan University, 2024, 57(11): 1610-1618.]

[8]FANG C, CHU Y, FU H, et al. On the resilience assessment of complementary transportation networks under natural hazards[J]. Transportation Research Part D: Transport and Environment, 2022, 109: 103331.

[9] 张洁斐,任刚,马景峰,等.基于韧性评估的地铁网络修复时序决策方法[J]. 交通运输系统工程与信息, 2020, 20(4): 14-20. [ZHANG J F, REN G, MA J F, et al. Decision-making method of repair sequence for metro network based on resilience evaluation[J]. Journal of Transportation Systems Engineering and Information Technology, 2020, 20(4): 14-20.]

[10] 司志远, 陈章利,贾玲,等.极端降水事件下城市道路网络级联失效研究[J]. 防灾减灾工程学报,2024,44 (4): 792-799. [SI Z Y, CHEN Z L, JIA L, et al. Study on cascading failures of urban road networks under extreme precipitation events[J]. Journal of Disaster Prevention and Mitigation Engineering, 2024, 44(4): 792-799.]

[11] XU X, CHEN A, XU G, et al. Enhancing network resilience by adding redundancy to road networks[J]. Transportation Research Part E: Logistics and Transportation Review, 2021, 154: 102448.

[12] 胡文君,周溪召.基于成对组合Logit的多用户多模式随机用户均衡模型[J].系统工程理论与实践,2013,33 (5): 1318-1326. [HU W J, ZHOU X Z. A multi-user and multi-modal stochastic user equilibrium model based on pairwise combination Logit[J]. Systems Engineering Theory & Practice, 2013, 33(5): 1318-1326.]

[13] SHEFFI Y. Urban transportation networks: Equilibrium analysis and design[M]. Upper Saddle River, NJ: Prentice-Hall, 1984.

[14] 赵娜, 柴焰明,尹春林,等.基于最大连通子图相对效能的相依网络鲁棒性分析[J]. 电子科技大学学报, 2021, 50(4): 627-633. [ZHAO N, CHAI Y M, YIN C L, et al. Robustness analysis of interdependent networks based on relative efficiency of maximum connected subgraphs[J]. Journal of University of Electronic Science and Technology of China, 2021, 50(4): 627-633.]

[15] MELKOTE S, DASKIN M S. An integrated model of facility location and transportation network design[J]. Transportation Research, 2001, 35(6): 515-538.

[16] 蔡震, 梁满贵.基于遗传算法的道路车辆自组织网络源路由机制[J].交通运输系统工程与信息,2019, 19(4): 43-49. [CAI Z, LIANG M G. Source routing mechanism of vehicular AD HOC networks based on genetic algorithm[J]. Journal of Transportation Systems Engineering and Information Technology, 2019, 19(4): 43-49.]

[17] 杜彦良,李引珍,等.甘肃省综合立体交通协调发展研究[R]. 兰州: 中国工程院战略研究与咨询重大项目, 2025. [DU Y L, LI Y Z, et al. Research on the coordinated development of comprehensive three dimensional transportation in Gansu province[R]. Lanzhou: Strategic Research and Consulting Major Project of Chinese Academy of Engineering, 2025.]

不确定扰动下的区域道路交通网络韧性演化及提升方法研究

Resilience Evolution and Improvement Methods of Regional Road Traffic Networks Under Uncertain Disturbances

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