Resiliency assessment of urban rail transit networks: Shanghai metro as an example

Publikation: Beitrag in FachzeitschriftArtikelForschungPeer-Review

Autoren

  • Dong-ming Zhang
  • Fei Du
  • Hongwei Huang
  • Fan Zhang
  • Bilal M. Ayyub
  • Michael Beer

Externe Organisationen

  • Tongji University
  • The University of Liverpool
  • Bureau Veritas Investment (Shanghai) Co.
  • University of Maryland
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Details

OriginalspracheEnglisch
Seiten (von - bis)230-243
Seitenumfang14
FachzeitschriftSafety Science
Jahrgang106
Frühes Online-Datum31 März 2018
PublikationsstatusVeröffentlicht - Juli 2018

Abstract

This paper presents a general framework to assess the resilience of large and complex metro networks by quantitatively analyzing its vulnerability and recovery rapidity within unifying metrics and models. The connectivity performance of network is indicated by the network efficiency. The resilience of a metro network can be associated to the network performance loss triangle over the relevant timeline from the occurrence of a random or intentional disruption to full recovery. The proposed resilience model is applied to the Shanghai metro network with its 303 stations and 350 links as an example. The quantitative vulnerability analysis shows that the Shanghai metro with its L-space type of topology has a strong robustness regarding connectivity under random disruption but severe vulnerability under intentional disruption. This result is typical for small-world and scale-free networks such as the Shanghai metro system, as can be shown by a basic topological analysis. Considering the case of one disrupted metro station, both the vulnerability and resilience of the network depend not only on the node degree of the disrupted station but also on its contribution to connectivity of the whole network. Analyzing the performance loss triangle and the associated cost from loss of operational income and repair measures, an appropriate recovery strategy in terms of the optimum recovery sequence of stations and the optimum duration can be identified in a structured manner, which is informative and helpful to decision makers.

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Resiliency assessment of urban rail transit networks: Shanghai metro as an example. / Zhang, Dong-ming; Du, Fei; Huang, Hongwei et al.
in: Safety Science, Jahrgang 106, 07.2018, S. 230-243.

Publikation: Beitrag in FachzeitschriftArtikelForschungPeer-Review

Zhang D, Du F, Huang H, Zhang F, Ayyub BM, Beer M. Resiliency assessment of urban rail transit networks: Shanghai metro as an example. Safety Science. 2018 Jul;106:230-243. Epub 2018 Mär 31. doi: 10.1016/j.ssci.2018.03.023
Zhang, Dong-ming ; Du, Fei ; Huang, Hongwei et al. / Resiliency assessment of urban rail transit networks : Shanghai metro as an example. in: Safety Science. 2018 ; Jahrgang 106. S. 230-243.
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abstract = "This paper presents a general framework to assess the resilience of large and complex metro networks by quantitatively analyzing its vulnerability and recovery rapidity within unifying metrics and models. The connectivity performance of network is indicated by the network efficiency. The resilience of a metro network can be associated to the network performance loss triangle over the relevant timeline from the occurrence of a random or intentional disruption to full recovery. The proposed resilience model is applied to the Shanghai metro network with its 303 stations and 350 links as an example. The quantitative vulnerability analysis shows that the Shanghai metro with its L-space type of topology has a strong robustness regarding connectivity under random disruption but severe vulnerability under intentional disruption. This result is typical for small-world and scale-free networks such as the Shanghai metro system, as can be shown by a basic topological analysis. Considering the case of one disrupted metro station, both the vulnerability and resilience of the network depend not only on the node degree of the disrupted station but also on its contribution to connectivity of the whole network. Analyzing the performance loss triangle and the associated cost from loss of operational income and repair measures, an appropriate recovery strategy in terms of the optimum recovery sequence of stations and the optimum duration can be identified in a structured manner, which is informative and helpful to decision makers.",
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AU - Zhang, Dong-ming

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AU - Huang, Hongwei

AU - Zhang, Fan

AU - Ayyub, Bilal M.

AU - Beer, Michael

N1 - Funding Information: This study is financially supported by the Natural Science Foundation Committee Programs (Grant nos. 51278381 and 51538009 ). The support is gratefully acknowledged. We thank Dr. Rulu Wang and Mr. Hua Shao from Shanghai Metro Co., Ltd. for their support collecting field data.

PY - 2018/7

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