交通运输系统工程与信息 ›› 2021, Vol. 21 ›› Issue (6): 203-209.DOI: 10.16097/j.cnki.1009-6744.2021.06.023

• 系统工程理论与方法 • 上一篇    下一篇

城市轨道交通系统动态风险模态分析建模

范博松,邵春福*   

  1. 北京交通大学,综合交通运输大数据应用技术交通运输行业重点实验室,北京 100044
  • 收稿日期:2021-06-28 修回日期:2021-07-19 接受日期:2021-07-27 出版日期:2021-12-25 发布日期:2021-12-24
  • 作者简介:范博松(1996- ),男,山西万荣人,博士生。
  • 基金资助:
    国家自然科学基金

Dynamic Risk Analysis and Modeling of Urban Rail Transit System

FAN Bo-song, SHAO Chun-fu*   

  1. Key Laboratory of Transport Industry of Big Data Application Technologies for Comprehensive Transport, Beijing Jiaotong University, Beijing 100044, China
  • Received:2021-06-28 Revised:2021-07-19 Accepted:2021-07-27 Online:2021-12-25 Published:2021-12-24
  • Supported by:
    National Natural Science Foundation of China(71621001)

摘要: 城市轨道交通突发事件给系统运营和居民出行带来不利影响。为了分析导致突发事件的 关键风险因素和突发事件给运营造成的滞后时间长度,本文提出基于复杂网络的有向权重动态 风险模型。为了分析模型的拓扑特性,首先引入动态风险模态的概念,将每一时刻突发事件的风 险因素、滞后时间长度和事件等级有机组合为动态风险模态,以从不同角度整体反映这一时刻城 市轨道交通系统的风险状态。其次,提出相关性-时间敏感系数,分析在一定的滞后时间区间内, 前序风险因素与后序事件等级的显著关系,进而表征不同风险模态的相关性。最后,将动态风险 模态及其演化过程映射为基于复杂网络的动态风险模型,根据复杂网络模型的特性进行节点强 度分析,提取动态风险模态的关键信息,用以指导轨道交通风险管理工作。

关键词: 城市交通, 系统动态风险, 复杂网络, 风险模态, 风险演化

Abstract: Urban rail transit emergency situations bring adverse impacts on system operation and residents' travel. To analyze the key risk factors that lead to emergencies and the lag time caused by emergencies, this paper proposes a directed weighted dynamic risk model based on the complex networks (DWRN). To analyze the topological characteristics of the model, the concept of dynamic risk mode (DRM) is introduced, and the risk factors, lag time length, and result level of emergencies at each moment are combined into the DRM to reflect the overall risk state of urban rail transit system from different perspectives. Then the correlation-time sensitivity coefficient is proposed to analyze the significant relationship between the preorder risk factors and the postorder consequence levels within a certain lag time interval, and the correlation of different risk modes is characterized and analyzed. The DRM and its evolution process are mapped to the DWRN. Considering the characteristics of the complex network model, this paper analyzes the node strength and extracts the key information of the DRM, which provide references for the rail transit risk management.

Key words: urban traffic, system dynamic risk, complex network, risk mode, risk evolution

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