Vulnerability assessments of weighted urban rail transit networks with integrated coupled map lattices

Jianhua Zhang, Ziqi Wang, Shuliang Wang, Wenchao Shao, Xun Zhao and Weizhi Liu

Reliability Engineering and System Safety, 2021, vol. 214, issue C

Abstract: This paper proposes an integrated coupled map lattices (ICML) to assess the node states and study the vulnerability of weighted urban rail transit networks (URTNs) subjected to the external disturbance R. Meanwhile, the passenger flow is refined as the passenger in-flow and out-flow of the station, and a new passenger flow redistribution rule is proposed to discuss the cascading failures of URTNs. Moreover, three parameters are adopted to analyze the vulnerability characteristics of URTNs subjected to cascading failures and Nanjing metro network is taken as the example to verify the feasibility and effectiveness of the presented model. The results show that there is a linear relationship between the node betweenness and edge betweenness, and URTNs are very vulnerable subjected to malicious attacks and become more and more vulnerable with the increase of the external disturbance. Furthermore, we find that the largest degree node-based attack (LDA) is more likely to result in cascading failures and the highest betweenness node-based attack (HBA) can cause the most damage to URTNs subjected malicious attacks. We also discover that the external disturbance R=2 is the critical threshold to smash the Nanjing metro network subjected to the largest degree node-based attack, and R=2.3 is the critical threshold to crush Nanjing metro network subjected to the highest betweenness node-based attack and the largest strength node-based attack.

Keywords: Vulnerability assessments; Flow distribution; Integrated coupled map lattices; Disturbance intensity; Urban rail transit networks (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (22)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:reensy:v:214:y:2021:i:c:s0951832021002428

DOI: 10.1016/j.ress.2021.107707

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