Route redundancy-based approach to identify the critical stations in metro networks: A mean-excess probability measure

Weiwei Jing, Xiangdong Xu and Yichao Pu

Reliability Engineering and System Safety, 2020, vol. 204, issue C

Abstract: Identifying the critical elements of metro networks attracts a growing attention due to the significant impact of accidents on metro systems. The existing measures can be divided into two types: the localized measures (e.g., flow, centrality, etc.) in a normal network state and the impact-based measures by assuming failure scenarios to conduct before-and-after analysis. In this paper, we develop a new method to identify the critical stations in metro systems based on the concept of route redundancy. Different from the localized measures, route redundancy describes the origin-destination effective connections under potential disruptions explicitly from travelers’ perspective. Compared with the impact-based measures, the proposed method does not need to enumerate disruption scenarios and to reevaluate the resultant network performances. Specifically, the mean-excess criticality probability is proposed as a risk measure to calculate the criticality of each station in a metro network. A realistic case study based on the Shanghai metro network is conducted to demonstrate the features of the proposed method. The results indicate that the critical stations are not necessarily transfer stations or those with a high degree, and the important stations based on betweenness, passenger flow and network efficiency are not necessarily critical for the network redundancy. The proposed method could assist in a cost-effective resource allocation and an informed decision-making for strategically enhancing the metro network resiliency.

Keywords: Metro network; Critical station; Route redundancy; Mean-excess criticality probability (search for similar items in EconPapers)
Date: 2020
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Citations: View citations in EconPapers (15)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:reensy:v:204:y:2020:i:c:s0951832020307055

DOI: 10.1016/j.ress.2020.107204

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