Using the disaster spreading theory to analyze the cascading failure of urban rail transit network. (November 2021)

• Record Type: Journal Article
• Authors: Huang, Wencheng; Zhou, Bowen; Yu, Yaocheng; Sun, Hao; Xu, Pengpeng
• Is Part Of: Reliability engineering & system safety. Volume 215(2021)
• Publication Date: 2021-11
• Subjects: Urban rail transit network -- Cascading failure -- Disaster spreading theory -- Self-recovery factor -- Failures spreading mechanism -- Node attribute value; Reliability (Engineering) -- Periodicals; System safety -- Periodicals; Industrial safety -- Periodicals; Fiabilité -- Périodiques; Sécurité des systèmes -- Périodiques; Sécurité du travail -- Périodiques; 620.00452
• DOI: 10.1016/j.ress.2021.107825
• Languages: English
• ISSNs: 0951-8320

Abstract

Highlights: The cascading failure model is proposed based on disaster spreading theory. Five kinds of influence factors are considered in the model. Failures caused by attacks and by passenger flow congestion are considered respectively. Eight simulation scenarios are established. Five emergency resources allocation strategies are proposed. Abstract: A new dynamic model is established to formulate the cascading failure in the urban rail transit network based on the disaster spreading theory. Firstly, the weighted urban rail transit network by considering the time cost of each effective path is established, the transfer station and turn back station on the topological network are handled specifically, the Dijkstra algorithm is designed to solve the shortest path of each Origin-Destination. Then, the cascading failure model based on disaster spreading theory is established. Five factors including the failure evolution process with time, self-recovery ability of the nodes, failures spreading mechanism, passenger volume changes and the internal random noises by other influence factors are fully considered in this model. Finally, a real-world case study is conducted by using Chengdu Metro Network as the background. Eight simulation scenarios are established, the output is statistical number of failed stations. The results show that, the failed stations number has the greatest scale when fixed transfer stations are attacked. There is no obvious functional relationship between the scale of failure stations and self-recovery factor, and there is a positive correlation between self-recovery factor and cascading failure scale. Based on the results, five emergency resources allocation strategies are proposed.