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为系统评估城市轨道交通网络应对不确定性干扰的抵御能力,以石家庄城市轨道交通(规划)网络为研究对象,基于复杂网络理论,采用Space-L方法构建无向无权网络拓扑模型,通过计算度分布、平均路径长度、聚类系数等拓扑参数识别网络特征,设计涵盖节点与连边失效的6种攻击策略,结合网络效率和最大连通子图比例评估城市轨道交通网络在随机攻击与蓄意攻击场景下的网络抗毁性,采用模拟单节点和单连边失效的方式识别关键节点和关键连边。结果表明:通过对比石家庄轨道交通(规划)网络与随机网络的拓扑参数,计算得到小世界网络指数s=1.110 3>1.000 0,且度分布近似服从泊松分布,表明该网络具有小世界网络特性;该网络对基于节点度与节点介数的蓄意攻击较敏感,对基于连边介数的攻击表现出较强韧性;不同节点或连边失效对网络效率影响差异显著,节点和连边的静态拓扑指标(度、介数)与其失效所引起的实际网络性能下降不存在正相关关系。应通过强化关键站点与区间防护、优化网络拓扑结构、建立多模式交通应急联运体系增强石家庄城市轨道交通(规划)网络的抗毁性。
Abstract:To systematically evaluate the ability of urban rail transit networks to withstand uncertain disruptions, this study takes the Shijiazhuang urban rail transit(planned) network as the research object. Based on complex network theory, an undirected and unweighted topology is constructed using the Space-L method. Network characteristics are identified by computing topological parameters such as degree distribution, average path length, and clustering coefficient. Six attack strategies covering node and edge failures are designed, and network resilience under random and targeted attacks is assessed using network efficiency and the proportion of the largest connected component. Critical nodes and edges are identified through sequential single-node and single-edge failure experiments. Results show that, compared with a corresponding random network, the Shijiazhuang(planned) network yields a small-world index of s=1.110 3>1.000 0, and its degree distribution approximately follows a Poisson distribution, indicating small-world properties. The network is sensitive to targeted attacks based on node degree and node betweenness, while it exhibits stronger robustness to attacks based on edge betweenness. The impacts of different node or edge failures on network efficiency vary markedly, and static topological indicators(degree, betweenness) are not in a simple positive correspondence with the actual performance degradation caused by their failures. Resilience should be enhanced by strengthening the protection of critical stations and track sections, optimizing the network topology, and establishing a multimodal emergency intermodal system.
[1] 马飞,蒋金凤,敖誉芸,等.非均衡大客流冲击下城市轨道交通网络抗毁性建模及演化特征[J].清华大学学报(自然科学版),2024,64(10):1717-1733.
[2] 马敏,胡大伟,刘杰,等.基于客流加权的城市轨道交通网络抗毁性分析[J].中国安全科学学报,2022,32(12):141-149.
[3] 赵瑞琳,牟海波,肖丁,等.基于复杂网络理论的城轨线网抗毁性对比分析[J].交通信息与安全,2021,39(3):41-49.
[4] 李僖,马亮,郭进,等.城市轨道交通线网拓扑特性及抗毁性分析[J].铁道标准设计,2025,69(7):39-45.
[5] ZHANG J H,WANG S L,WANG X Y.Comparison analysis on vulnerability of metro networks based on complex network[J].Physica A:Statistical Mechanics and Its Applications,2018,496:72-78.
[6] XU X G,XU C,ZHANG W X.Research on the destruction resistance of giant urban rail transit network from the perspective of vulnerability[J].Sustainability,2022,14(12):7210.
[7] 张晨,梁亦辰,彭朋,等.西安地铁网络节点重要性评估[PP].铁道标准设计.(2024-09-26)[(2025-03-16].https://doi.org/10.13238/j.issn.1004-2954.202404010013.
[8] 高超,蒋世洪,王震,等.基于动态客流的城市轨道交通关键站点识别[J].中国科学:信息科学,2021,51(9):1490-1506.
[9] 霍非舟,曲方新,马亚萍,等.城市地铁有向加权网络级联失效模型[J].中国安全生产科学技术,2024,20(12):143-149.
[10] 路庆昌,崔欣,谢驰,等.城市轨道交通网络关键站点识别方法对比与分析[J].北京交通大学学报,2022,46(3):18-25.
[11] 涂敏,韩雨濛.改进TOPSIS法的武汉城市轨道交通节点重要度评估[J].重庆交通大学学报(自然科学版),2023,42(9):113-121.
[12] MENG Y Y,TIAN X L,LI Z W,et al.Exploring node importance evolution of weighted complex networks in urban rail transit[J].Physica A:Statistical Mechanics and Its Applications,2020,558:124925.
[13] 王亭,张永,周明妮,等.融合网络拓扑结构特征与客流量的城市轨道交通关键节点识别研究[J].交通运输系统工程与信息,2022,22(6):201-211.
[14] 潘恒彦,张文会,胡宝雨,等.城市公交-地铁加权复合网络构建及鲁棒性分析[J].吉林大学学报(工学版),2022,52(11):2582-2591.
[15] 张英贵,陆强,高全,等.基于关键站点识别的区域轨道交通路网抗干扰性研究[J].铁道科学与工程学报,2022,19(7):1845-1853.
[16] 强添纲,赵明明,裴玉龙.城市多模式交通网络的复杂网络特性与鲁棒性研究[J].交通信息与安全,2019,37(1):65-71.
[17] 左忠义,刘泽宇,杨广川.基于引力影响模型的轨道交通网络关键节点识别研究[J].交通运输系统工程与信息,2025,25(1):102-112.
[18] 崔欣,路庆昌,徐鹏程,等.基于重要性贡献矩阵的城市轨道交通关键站点识别[J].铁道科学与工程学报,2022,19(9):2524-2531.
[19] DENG Y L,LI Q M,LU Y,et al.Topology vulnerability analysis and measure of urban metro network:the case of Nanjing[J].Journal of Networks,2013,8(6):1350-1356.
[20] ZHOU J,SHAO Y H.Rational selection of rail transit emergency site using complex network topology and genetic algorithm[J].Scientific Programming,2022,2022(1):6420806.
[21] FENG S M,XIN M W,LV T L,et al.A novel evolving model of urban rail transit networks based on the local-world theory[J].Physica A:Statistical Mechanics and Its Applications,2019,535:122227.
[22] KOPSIDAS A,KEPAPTSOGLOU K.Identification of critical stations in a metro system:a substitute complex network analysis[J].Physica A:Statistical Mechanics and Its Applications,2022,596:127123.
[23] WATTS D J,STROGATZ S H.Collective dynamics of ′small-world′ networks[J].Nature,1998,393(6684):440-442.
[24] 吴俊,谭跃进.复杂网络抗毁性测度研究[J].系统工程学报,2005,20(2):128-131.
[25] YANG Y H,LIU Y X,ZHOU M X,et al.Robustness assessment of urban rail transit based on complex network theory:a case study of the Beijing Subway[J].Safety Science,2015,79:149-162.
[26] 沈犁,向阳,王周全,等.城市公共交通复合系统抗毁性仿真研究[J].运筹与管理,2017,26(9):105-112.
[27] DU Z Y,TANG J J,QI Y,et al.Identifying critical nodes in metro network considering topological potential:a case study in Shenzhen City:China[J].Physica A:Statistical Mechanics and its Applications,2020,539:122926.
基本信息:
中图分类号:U239.5
引用信息:
[1]王中政,顼玉卿.融合复杂网络与攻击模拟的城市轨道交通网络抗毁性分析[J].山东交通学院学报,2026,34(01):14-24.
基金信息:
国家社会科学基金项目(20FJYB030)