系统仿真学报 ›› 2024, Vol. 36 ›› Issue (12): 2960-2970.doi: 10.16182/j.issn1004731x.joss.23-1266

• 论文 • 上一篇    

基于超图理论的城市轨道交通超网络级联失效仿真

韩紫金1, 钱名军1, 王玺宪2, 张凯悦1   

  1. 1.兰州交通大学 交通运输学院,甘肃 兰州 730070
    2.北京全路通信信号研究设计院集团有限公司,北京 110032
  • 收稿日期:2023-10-19 修回日期:2023-12-02 出版日期:2024-12-20 发布日期:2024-12-20
  • 通讯作者: 钱名军
  • 第一作者简介:韩紫金(1997-),男,硕士生,研究方向为复杂网络、交通网络优化。
  • 基金资助:
    甘肃省教育厅高等学校创新基金(2020A-038);兰州交通大学青年基金(2014029)

Simulation of Cascade Failure in Urban Rail Transit Hypernetworks Based on Hypergraph Theory

Han Zijin1, Qian Mingjun1, Wang Xixian2, Zhang Kaiyue1   

  1. 1.School of Traffic and Transportation, Lanzhou Jiaotong University, Lanzhou 730070, China
    2.CRSC Research & Design Institute Group Co. , Ltd. , Beijing 110032, China
  • Received:2023-10-19 Revised:2023-12-02 Online:2024-12-20 Published:2024-12-20
  • Contact: Qian Mingjun

摘要:

为提升城市轨道交通线网的抗毁性能,确保其在面临突发事件时的稳定运行和乘客安全,引入超图理论,构建基于超图的城市轨道交通超网络模型,并建立基于客流加权的非线性负载-容量级联失效模型。针对实际交通网络站点客流疏散过程,从网络层面和客流重要度考虑,提出了负载重分配机制。对实际交通网络站点停运时仍可接受负载现象,设计了节点状态判定模型。结果表明:超图分配机制更符合实际交通流传播,介数攻击对网络级联失效影响更大,适当增加节点容量参数可提高网络的鲁棒性,过载能力系数的变化不会对级联失效现象产生影响。

关键词: 超网络, 级联失效, 轨道交通, 鲁棒性, 复杂网络

Abstract:

In order to enhance the resilience of urban rail transit networks to ensure stable operations and passenger safety in the face of emergencies, hypergraph theory is introduced to construct a hypergraph based urban rail transit hypernetwork model, and a nonlinear load-capacity cascading failure model based on passenger flow weighting is established. In response to the passenger evacuation process at actual transportation network stations, a load redistribution mechanism is proposed, taking into consideration both the network level and the importance of passenger flow. To address scenarios where stations in actual traffic networks can still accommodate loads during shutdowns, a node status determination model is designed. The results indicate that the hypergraph allocation mechanism is more consistent with the actual propagation of traffic flow. In terms of network cascading failure, the centrality attack has a greater impact. Moreover, appropriately increasing the node capacity parameter significantly enhances the robustness of the network. The variation in overload capacity coefficient does not influence the occurrence of cascading failures.

Key words: hypernetworks, cascading failures, rail transit, robustness, complex network

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