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Vulnerability Analysis of Urban Rail Transit Network considering Cascading Failure Evolution
Author(s) -
Ranran Sun,
Guangyu Zhu,
Bing Liu,
Xiaolu Li,
Yiyuan Yang,
Jingxuan Zhang
Publication year - 2022
Publication title -
journal of advanced transportation
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.577
H-Index - 46
eISSN - 2042-3195
pISSN - 0197-6729
DOI - 10.1155/2022/2069112
Subject(s) - cascading failure , vulnerability (computing) , vulnerability assessment , beijing , betweenness centrality , computer science , urban rail transit , transport engineering , engineering , reliability engineering , computer security , electric power system , geography , centrality , china , psychology , power (physics) , physics , archaeology , mathematics , quantum mechanics , combinatorics , psychological resilience , psychotherapist
Vulnerability analysis is the premise of operational risk management and control for the large-scale and complex urban rail transit network (URTN) under the operation interruption of important stations. The temporary operation interruption of one station in an emergency may lead to the cascading failure and the paralysis of the whole URTN due to the load of other stations exceeding the limited capacity. The priority of important stations is proposed by combining its location and function in URTN. In addition, focusing on the analysis of the travel behaviour of passengers and the synergy of public transport networks, a novel cascading failure evolution model is established to simulate the cascading failure process of URTN under different attack scenarios. The vulnerability indicators are constructed to dynamically evaluate the vulnerability of URTN considering cascading failure evolution, which are different from the traditional vulnerability indicators based on complex network theory. Taking the Beijing urban rail transit network as an example, the dynamic simulation results show that the cascading failure of URTN is closely related to the temporal-spatial distribution of passenger flows and malicious attacks are more destructive than random attacks. Compared with the important stations with the largest betweenness or degree, the interrupted stations with largest intensity have a greater impact on the operational stability of URTN. Moreover, increasing the capacity coefficient of the station can reduce the vulnerability of URTN.

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