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Assessing network vulnerability of heavy rail systems with the impact of partial node failures

Author

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  • Qian Ye

    (The University of Tennessee)

  • Hyun Kim

    (The University of Tennessee)

Abstract

Much of the literature in recent years has examined the vulnerability of transportation networks. To identify appropriate and operational measures of nodal centrality using connectivity in the case of heavy rail systems, this paper presents a set of comprehensive measures in the form of a Degree of Nodal Connection (DNC) index. The DNC index facilitates a reevaluation of nodal criticality among distinct types of transfer stations in heavy rail networks that present a number of multiple lines between stations. Specifically, a new classification of transfer stations—mandatory transfer, non-mandatory transfer, and end transfer—and a new measure for linkages—link degree and total link degree—introduces the characteristics of heavy rail networks when we accurately expose the vulnerability of a node. The concept of partial node failure is also introduced and compare the results of complete node failure scenarios. Four local and global indicators of network vulnerability are derived from the DNC index to assess the vulnerability of major heavy rail networks in the United States. Results indicate that the proposed DNC indexes can inform decision makers or network planners as they explore and compare the resilience of multi-hubs and multi-line networks in a comprehensive but accurate manner regardless of their network sizes.

Suggested Citation

  • Qian Ye & Hyun Kim, 2019. "Assessing network vulnerability of heavy rail systems with the impact of partial node failures," Transportation, Springer, vol. 46(5), pages 1591-1614, October.
  • Handle: RePEc:kap:transp:v:46:y:2019:i:5:d:10.1007_s11116-018-9859-6
    DOI: 10.1007/s11116-018-9859-6
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    Cited by:

    1. Bellè, Andrea & Zeng, Zhiguo & Duval, Carole & Sango, Marc & Barros, Anne, 2022. "Modeling and vulnerability analysis of interdependent railway and power networks: Application to British test systems," Reliability Engineering and System Safety, Elsevier, vol. 217(C).
    2. Qian Ye & Hyun Kim, 2019. "Partial Node Failure in Shortest Path Network Problems," Sustainability, MDPI, vol. 11(22), pages 1-21, November.
    3. Nan Zhang & Daniel J. Graham & Daniel Hörcher & Prateek Bansal, 2021. "A causal inference approach to measure the vulnerability of urban metro systems," Transportation, Springer, vol. 48(6), pages 3269-3300, December.
    4. Qingjie Qi & Yangyang Meng & Xiaofei Zhao & Jianzhong Liu, 2022. "Resilience Assessment of an Urban Metro Complex Network: A Case Study of the Zhengzhou Metro," Sustainability, MDPI, vol. 14(18), pages 1-19, September.
    5. Hong, Wei-Ting & Clifton, Geoffrey & Nelson, John D., 2022. "Rail transport system vulnerability analysis and policy implementation: Past progress and future directions," Transport Policy, Elsevier, vol. 128(C), pages 299-308.

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