Volume 39 Issue 3
Jun.  2021
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Article Navigation >  Journal of Transport Information and Safety  > 2021  >  39(3): 41-49
ZHAO Ruilin, MOU Haibo, XIAO Ding, YANG Jingfeng. A Contrastive Analysis of Survivability of Urban Rail Network Based on Complex Network Theory[J]. Journal of Transport Information and Safety, 2021, 39(3): 41-49. doi: 10.3963/j.jssn.1674-4861.2021.03.006
Citation: ZHAO Ruilin, MOU Haibo, XIAO Ding, YANG Jingfeng. A Contrastive Analysis of Survivability of Urban Rail Network Based on Complex Network Theory[J]. Journal of Transport Information and Safety, 2021, 39(3): 41-49. doi: 10.3963/j.jssn.1674-4861.2021.03.006
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A Contrastive Analysis of Survivability of Urban Rail Network Based on Complex Network Theory

doi: 10.3963/j.jssn.1674-4861.2021.03.006

  • School of Traffic and Transportation, Lanzhou Jiaotong University, Lanzhou 730070, China

  • Received Date: 2020-11-22
    Abstract
  • The metro networks of 10 typical cities in China are selected to analyze the resilience of different metro networks to emergencies.The complex network theory is used to analyze the resilience of networks under simulated attacks.Pajek software is used to construct an abstract road network of topological space Space-L.Quantitative evaluation indicators are set to systematically analyze the survivability of the network under random attacks and deliberate attacks of accumulated nodes.The improved network efficiency formula is used to analyze the influence of each node on the network under the single-node deliberate attack.The results show that the metro network is a scale-free network.Based on node degree, network efficiency, and the maximum connection sub-grap, the proportions of failed sites in the two networks reach 10.44%and 11.09%, 17.99%and 18.39%, and 13.27%and 12.92%under random attacks, and the net crashes.Under cumulative-node deliberate attacks, the proportions of failed sites in the two networks reach 5.22%and 5.17%, 4.3%and 3.19%, and 4.23%and 2.43%, and the net crashes.Compared with the failure node at the intersection of divergent lines, the node at the apex of the triangle or the mesh structure is more invulnerable to the network.

     

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