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Enhancing metro network resilience via localized integration with bus services

Author

Listed:
  • Jin, Jian Gang
  • Tang, Loon Ching
  • Sun, Lijun
  • Lee, Der-Horng

Abstract

This paper advances the field of network disruption analysis by introducing an application to a multi-modal transport network, capitalizing on the redundancies and improved connectivity of an integrated metro-bus network. Metro network resilience to disruptions can be enhanced by leveraging on public bus services. To ensure better acceptance among operators and commuters, we focus on introducing localized integration with bus services instead of designing an entirely new bus network to achieve the desired resilience to potential disruptions. This is accomplished by increasing the capacity of bus services that run in parallel with affected metro lines as well as those connecting to different metro lines. Our analysis starts with a network representation to model the integrated metro and bus system. A two-stage stochastic programming model is further developed to assess the intrinsic metro network resilience as well as to optimize the localized integration with bus services. The approach is applied to a case study based on the Singapore public transit system and actual travel demand data. The results show that the metro network resilience to disruptions can be enhanced significantly from localized integration with public bus services.

Suggested Citation

  • Jin, Jian Gang & Tang, Loon Ching & Sun, Lijun & Lee, Der-Horng, 2014. "Enhancing metro network resilience via localized integration with bus services," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 63(C), pages 17-30.
  • Handle: RePEc:eee:transe:v:63:y:2014:i:c:p:17-30
    DOI: 10.1016/j.tre.2014.01.002
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    References listed on IDEAS

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    1. Lin, Cheng-Chang & Chen, Sheu-Hua, 2008. "An integral constrained generalized hub-and-spoke network design problem," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 44(6), pages 986-1003, November.
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    4. Zeng, Amy Z. & Durach, Christian F. & Fang, Yan, 2012. "Collaboration decisions on disruption recovery service in urban public tram systems," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 48(3), pages 578-590.
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    Cited by:

    1. repec:eee:reensy:v:158:y:2017:i:c:p:58-72 is not listed on IDEAS
    2. repec:eee:reensy:v:152:y:2016:i:c:p:137-150 is not listed on IDEAS
    3. Sun, Huijun & Wu, Jianjun & Wu, Lijuan & Yan, Xiaoyong & Gao, Ziyou, 2016. "Estimating the influence of common disruptions on urban rail transit networks," Transportation Research Part A: Policy and Practice, Elsevier, vol. 94(C), pages 62-75.
    4. repec:eee:ejores:v:266:y:2018:i:1:p:1-15 is not listed on IDEAS
    5. repec:eee:reensy:v:145:y:2016:i:c:p:47-61 is not listed on IDEAS
    6. Michaelides, Panayotis G. & Konstantakis, Konstantinos N. & Milioti, Christina & Karlaftis, Matthew G., 2015. "Modelling spillover effects of public transportation means: An intra-modal GVAR approach for Athens," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 82(C), pages 1-18.
    7. Jin, Jian Gang & Lu, Linjun & Sun, Lijun & Yin, Jingbo, 2015. "Optimal allocation of protective resources in urban rail transit networks against intentional attacks," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 84(C), pages 73-87.
    8. repec:eee:reensy:v:149:y:2016:i:c:p:34-43 is not listed on IDEAS
    9. Yang, Xin & Chen, Anthony & Ning, Bin & Tang, Tao, 2017. "Bi-objective programming approach for solving the metro timetable optimization problem with dwell time uncertainty," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 97(C), pages 22-37.

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