城市轨道交通运行风险演化网络动态仿真研究  

Dynamic simulation of urban rail transit operation risk evolution network

作　　者：许慧 邓宁辉 李杨 李树秀 杜磊 XU Hui;DENG Ning-hui;LI Yang;LI Shu-xiu;DU Lei(School of Economics and Management,Chongqing University of Posts and Telecommunications,Chongqing 400065,China;Chongqing Innovation Center of Industrial Big-Data Co.,Ltd.,National Engineering Laboratory for Industrial Big-data Application Technology,Chongqing 400707,China;Faculty of Infrastructure Engineering,Dalian University of Technology,Dalian 116024,Liaoning,China;Zhongguancun Smart City Co.,Ltd.,Beijing 100080,China;School of Management and Economics,Beijing Institute of Technology,Beijing 100081,China)

机构地区：[1]重庆邮电大学经济管理学院,重庆400065 [2]重庆工业大数据创新中心有限公司,工业大数据应用技术国家工程实验室,重庆400707 [3]大连理工大学建设工程学部,辽宁大连116024 [4]中关村科学城城市大脑股份有限公司,北京100080 [5]北京理工大学管理与经济学院,北京100081

出　　处：《安全与环境学报》2023年第2期372-382,共11页Journal of Safety and Environment

基　　金：重庆市自然科学基金面上项目(cstc2021jcyj-msxmX0951);重庆市教育委员会人文社会科学研究重点项目(21SKGH061);重庆市教委科学技术研究计划项目(KJQN202000631);中国博士后科学基金项目(2021M700617);国家自然科学基金青年科学基金项目(71801026);2020年留创计划创新类项目(cx2020035)。

摘　　要：为探究轨道交通事故中的重要风险类别,从风险演化角度出发,根据172起轨道交通运行事故,梳理出354条风险演化链,总结出35个风险因子,在此基础上基于复杂网络分析方法构建城市轨道交通运行静态风险演化网络。根据网络拓扑特征识别网络中高影响力的风险节点,并利用蓄意攻击失效的原理从单个节点失效与节点连续失效角度进行动态仿真,以探究风险对整个网络的影响程度。结果表明:静态风险演化网络属于复杂网络;列车脱轨、列车停运/延误、列车其他设施故障、暴力恐怖袭击事件、滞留、火灾和人员伤亡在网络中具备较高地位,属于轨道交通运行静态风险网络的中心节点;列车停运/延误、制动故障、列车其他设施故障和火灾节点的失效对轨道交通运行具有突出影响,当制动故障、列车其他设施故障均失效后,即连续失效节点数为2时,风险网络的网络连通度出现拐点。研究确定了影响轨道交通运行的关键风险,分析结果可为城市轨道交通运行安全管理及事故演化控制提供参考依据。To explore the significant risks in the rail transit accidents, 354 risk evolution chains were extracted from the 172 rail transit accidents from the perspective of risk evolution, and 35 risk factors were determined. The complex network analysis was adopted to construct the static risk evolution network of urban rail transit operation by using the UCINET 6. The network satisfies the small-world and scale-free characteristics. The topology characteristics of the static risk evolution network of urban rail transit operation were calculated, including network Density, Betweenness centrality, Degree centrality, etc., to identify high-impact risk nodes in the network. The results show that Train Derailment, Train Suspension/Delay, Faults in other Facilities of the Train, Violent Terrorist Attacks, Retention, Fire, and Casualties have a high influence on the network and are decided as the key nodes of the network. Then, the dynamic simulation analysis of the rail transit risk evolution network was carried out by using the failure principle of a deliberate attack. The simulation of the risk failure was to delete the node and its relationship with other risks so that the node has no connection in the network. The risks in the core position of the network and having a great influence on the network were defined as the key risks. Based on the Core-edge analysis, dynamic simulation was carried out from the perspective of single node failure and continuous node failure to explore the impact degree of the risk. The results show that the Train Suspension/Delay, Brake Failure, Faults in other Facilities of the Train, and Fire is of great significance to the network. When both Faults in other Facilities of the Train and Brake Failure fail at the same time, that is, the number of continuous failure nodes is 2, the Network Connectivity of the risk network appeared inflection point. This study determined the key risks affecting the operation of rail transit, and the analysis results can provide a reference for the daily operation

关 键 词：安全工程 轨道交通 动态仿真 复杂网络 

分 类 号：X951[环境科学与工程—安全科学]