管理评论

管理评论 ›› 2020, Vol. 32 ›› Issue (7): 217-225.

• 中国系统管理学专辑 • 上一篇    下一篇

城市交通系统安全运营状态风险评估——以北京市轨道交通为例

刘福泽1,2, 李娟3, 范博松3, 王珏4   

  1. 1. 中国科学院大学经济与管理学院, 北京 100190;
    2. 北京市交通委员会, 北京 100073;
    3. 北京交通大学交通运输学院, 北京 100044;
    4. 中国科学院数学与系统科学研究院预测科学研究中心, 北京 100190
  • 收稿日期:2019-08-05 出版日期:2020-07-28 发布日期:2020-08-08
  • 通讯作者: 王珏(通讯作者),中国科学院数学与系统科学研究院预测科学研究中心研究员,博士生导师
  • 作者简介:刘福泽,中国科学院大学经济与管理学院博士研究生,北京市交通委员会安全监督与应急处处长;李娟,北京交通大学交通运输学院副教授,博士生导师,博士;范博松,北京交通大学交通运输学院博士研究生。
  • 基金资助:
    国家自然科学基金面上项目（71771208）；中央高校基本科研业务经费专项资金项目（2019JBM036）。

Risk Analysis for Urban Transit——An Empirical Study on the Beijing Rail Transit System

Liu Fuze1,2, Li Juan3, Fan Bosong3, Wang Jue4   

  1. 1. School of Economics and Management, University of Chinese Academy of Sciences, Beijing 100190;
    2. Beijing Municipal Commission of Transport, Beijing 100073;
    3. School of Traffic and Transportation, Beijing Jiaotong University, Beijing 100044;
    4. Center for Forecasting Science, Academy of Mathematics and Systems Science, Chinese Academy of Sciences, Beijing 100190
  • Received:2019-08-05 Online:2020-07-28 Published:2020-08-08

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摘要： 轨道交通是提供城市公共客运服务的运输系统，它的风险管理是一项艰巨复杂的系统工程。为了保障城市轨道交通系统运营安全，提升管理部门应对突发事件能力，需从技术应用综合性和管理决策科学性等方面，对轨道交通运营风险状态进行评价并制定相应管控措施。本文以TEI@I方法论为基础，建立轨道交通延误时长预测和风险评估的贝叶斯网络模型。首先，分析可能导致轨道交通延误的风险源及风险事件；其次，采用对数正态、Weibull和Gamma分布统计模型对延误时长的预测结果进行验证。在此基础上，构建轨道交通延误时长的预测模型，利用统计分析方法计算风险事件发生的可能性，研究轨道交通系统的风险状态。以北京市轨道交通系统为例，进行实证研究，通过分析风险源可能诱发的典型风险事件，对北京市轨道交通系统安全运营状态进行风险评估。实证结果表明，在所有风险事件类型中，行车事故发生可能性最大，建议相关部门引起高度重视。

关键词: 轨道交通, 风险管理, 系统工程, 贝叶斯网络, TEI@I方法论

Abstract: Rail transit is a transportation system that provides urban public passenger services. Its risk management is an arduous and complicated system engineering. In order to ensure the safety of urban rail transit system operation and improve the ability of management departments to respond to emergencies, it is necessary to evaluate the risk status of rail transit operations and formulate corresponding control measures from the aspects of comprehensive application of technology and scientific decision-making. Based on the TEI@I methodology, this paper proposes a delay duration prediction model. The delay time is predicted by establishing a Bayesian network model. The statistical distribution model such as lognormal, Weibull and Gamma distribution is used to verify the prediction result of delay time. Based on the results, a prediction model of subway delay time is constructed. This statistical analysis method is used to calculate the probability of occurrence of the risk event, so as to analyze the risk status of the rail transit system. An empirical study on the Beijing rail transit system shows that the urban rail transit system in Beijing has a good operational status and the possibility of risk events is small. Among all types of risk events, driving accidents are most likely to occur, usually caused by a variety of factors, and the relevant departments should pay enough attention.

Key words: rail transit, risk management, system engineering, Bayesian network, TEI@I