Details
| Original language | English |
|---|---|
| Pages (from-to) | 230-243 |
| Number of pages | 14 |
| Journal | Safety Science |
| Volume | 106 |
| Early online date | 31 Mar 2018 |
| Publication status | Published - Jul 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.
Keywords
- Network resilience, Node connectivity, Robustness, Shanghai metro, Topology, Vulnerability
ASJC Scopus subject areas
- Engineering(all)
- Safety, Risk, Reliability and Quality
- Social Sciences(all)
- Safety Research
- Medicine(all)
- Public Health, Environmental and Occupational Health
Sustainable Development Goals
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In: Safety Science, Vol. 106, 07.2018, p. 230-243.
Research output: Contribution to journal › Article › Research › peer review
}
TY - JOUR
T1 - Resiliency assessment of urban rail transit networks
T2 - Shanghai metro as an example
AU - Zhang, Dong-ming
AU - Du, Fei
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
Y1 - 2018/7
N2 - 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.
AB - 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.
KW - Network resilience
KW - Node connectivity
KW - Robustness
KW - Shanghai metro
KW - Topology
KW - Vulnerability
UR - http://www.scopus.com/inward/record.url?scp=85044599047&partnerID=8YFLogxK
U2 - 10.1016/j.ssci.2018.03.023
DO - 10.1016/j.ssci.2018.03.023
M3 - Article
AN - SCOPUS:85044599047
VL - 106
SP - 230
EP - 243
JO - Safety Science
JF - Safety Science
SN - 0925-7535
ER -