Abstract Once an urban rail transit (URT) system breaks down or is deliberately damaged, it will cause tremendous pressure on the whole urban transportation system. Hence, its ability to deal with incidents has become an important research field. Existing studies seldom consider the entire cycle of an incident and ignore changes in passenger travel behavior during emergency events. In this paper, resilience is defined as the ability of absorbing, resisting and recovering when the URT network fails. We propose a multi-dimensional evaluation model for measuring the resilience by calculating multiple abilities with consideration of multi-source data including passenger flow, train diagram, passenger travel choice behavior, network topology, etc. Finally, we present a case study based on real-world data from the Beijing Subway network to illustrate the effectiveness and applicability of the proposed model. Through the resilience analysis, we recognize the critical stations, measure emergency recovery ability and provide necessary information support for reducing the risk of URT incidents.

    Highlights The proposed multi-dimensional model evaluates the network resilience from three aspects: absorption ability, resistance ability and recovery ability. Multi-source data including passenger flow, train diagram, passenger travel choice behavior, network topology, etc., are considered in the proposed evaluation model. A real-world large-scale case study of the Beijing Subway network is analyzed in the paper.


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    Titel :

    Measuring the resilience of an urban rail transit network: A multi-dimensional evaluation model


    Beteiligte:
    Ma, Zhiao (Autor:in) / Yang, Xin (Autor:in) / Wu, Jianjun (Autor:in) / Chen, Anthony (Autor:in) / Wei, Yun (Autor:in) / Gao, Ziyou (Autor:in)

    Erschienen in:

    Transport Policy ; 129 ; 38-50


    Erscheinungsdatum :

    2022-10-05


    Format / Umfang :

    13 pages




    Medientyp :

    Aufsatz (Zeitschrift)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch




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