交通运输系统工程与信息

交通运输系统工程与信息 ›› 2020, Vol. 20 ›› Issue (1): 12-18.

• 综合交通运输体系论坛 • 上一篇    下一篇

轨道交通网络级联失效影响范围研究

熊志华*1，姚智胜2   

  1. 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； 2. 北京市城市规划设计研究院，北京 100045
  • 收稿日期:2019-09-23 修回日期:2019-11-20 出版日期:2020-02-25 发布日期:2020-03-02
  • 作者简介:熊志华(1979-)，女，江西南昌人，副教授，博士.
  • 基金资助:

    国家自然科学基金/ National Natural Science Foundation of China(71501011)；国家重点研发计划/National Key Research and Development Program of China(2019YFF0301403)；中央高校基本科研业务费专项资金/ Fundamental Research Funds for the Central Universities of Ministry of Education of China(2019JBM041).

Influence Scope of Cascading Failure on Rail Transit System

XIONG Zhi-hua1, YAO Zhi-sheng2   

  1. 1. Key Laboratory of Transport Industry of Big Data Application Technologies for Comprehensive Transport, Beijing Jiaotong University, Beijing 100044, China; 2. Beijing Municipal Institute of City Planning and Design, Beijing 100045, China
  • Received:2019-09-23 Revised:2019-11-20 Online:2020-02-25 Published:2020-03-02

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摘要：

轨道交通系统因多因素干扰引发客流集中、车站拥挤，并相继传播至周边车站，形成级联失效. 把握拥挤在轨道交通网络的传播范围，是减少交通突发事件影响的基础. 基于轨道交通拥挤传播机理分析，将物理结构、网络初始交通状态、客流量融合进耦合映像格子模型 (Coupled Map Lattice，CML)中，通过历史客流数据量化参数，构建轨道交通拥挤传播模型，展开3 种不同情景下拥挤传播过程分析. 实例验证结果表明，轨道交通车站的初始状态、耦合系数对拥挤传播影响显著，网络结构对拥挤传播范围的影响并不明显. 根据网络结构、初始值和耦合系数，可以掌握拥挤传播规模，识别拥挤车站的影响范围，有助于提高轨道交通系统的可靠性.

关键词: 城市交通, 拥挤传播, 耦合映像格子, 轨道交通, 客流, 影响阈值

Abstract:

Passenger aggregation, station congestion and congestion spread in the rail transit system infect by multiple factors and lead to cascading failure that means congestion spread to the whole network. To estimate the congestion propagation range of rail transit is an effective way to reduce the impact of uncertainty. The coupled map lattices (CML) model was proposed in this paper. Based on the principle of congestion propagation on rail transit system, the parameters such as physical structure, the initial transportation states and the volume of passenger flow, were combined into CML model and obtained by the historical passenger flow data. Three scenes were discussed under different parameters combinations. It can be shown that the initial states and coupling coefficient have significant influence on the range of congestion propagation. The range has no significant relation with the physical structure. The scale and range of congestion station can be obtained through CML model with the respondent network, initial states and coupling coefficients. It is benefic for improving the reliability of rail transit system.

Key words: urban traffic, congestion propagation, coupled map lattices (CML), rail transit, passenger flow, influence threshold

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