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Optimizing Bus Bridging Services in Response to Disruptions of Urban Transit Rail Networks

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  • Jian Gang Jin

    (School of Naval Architecture, Ocean and Civil Engineering; and State Key Laboratory of Ocean Engineering, Shanghai Jiao Tong University, Shanghai 200240, China)

  • Kwong Meng Teo

    (Department of Industrial and Systems Engineering, National University of Singapore, Republic of Singapore 117576)

  • Amedeo R. Odoni

    (Operations Research Center, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139)

Abstract

With growing dependence of many cities on urban mass transit, even limited disruptions of public transportation networks can lead to widespread confusion and significant productivity losses. A need exists for systematic approaches to developing efficient responses to minimize such negative impacts. We present an optimization-based approach that responds to degradations of urban transit rail networks by introducing smartly designed bus bridging services that take into consideration commuter travel demand at the time of the disruption. The approach consists of three fundamental steps, namely, (1) a column generation procedure to dynamically generate demand-responsive candidate bus routes, (2) a path-based multicommodity network flow model to identify the most effective combination of these candidate bus routes, and (3) another optimization-based procedure to determine simultaneously the optimal allocation of available vehicle resources among the selected routes and corresponding headways. The approach is applied to two case studies defined using actual data. The results show that the proposed approach can be carried out efficiently and that adding nonintuitive bus routes to the standard bus bridging services can significantly reduce the average travel delay. Moreover, the approach distributes delay more equitably. Many realistic operating constraints can also be handled.

Suggested Citation

  • Jian Gang Jin & Kwong Meng Teo & Amedeo R. Odoni, 2016. "Optimizing Bus Bridging Services in Response to Disruptions of Urban Transit Rail Networks," Transportation Science, INFORMS, vol. 50(3), pages 790-804, August.
  • Handle: RePEc:inm:ortrsc:v:50:y:2016:i:3:p:790-804
    DOI: 10.1287/trsc.2014.0577
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    References listed on IDEAS

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    Cited by:

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    2. Aya Aboudina & Alaa Itani & Ehab Diab & Siva Srikukenthiran & Amer Shalaby, 2021. "Evaluation of bus bridging scenarios for railway service disruption management: a users’ delay modelling tool," Public Transport, Springer, vol. 13(3), pages 457-481, October.
    3. Jiateng Yin & Lixing Yang & Andrea D’Ariano & Tao Tang & Ziyou Gao, 2022. "Integrated Backup Rolling Stock Allocation and Timetable Rescheduling with Uncertain Time-Variant Passenger Demand Under Disruptive Events," INFORMS Journal on Computing, INFORMS, vol. 34(6), pages 3234-3258, November.
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    6. Chen, Yao & An, Kun, 2021. "Integrated optimization of bus bridging routes and timetables for rail disruptions," European Journal of Operational Research, Elsevier, vol. 295(2), pages 484-498.
    7. Pan, Hanchuan & Yang, Lixing & Liang, Zhe, 2023. "Demand-oriented integration optimization of train timetabling and rolling stock circulation planning with flexible train compositions: A column-generation-based approach," European Journal of Operational Research, Elsevier, vol. 305(1), pages 184-206.
    8. Zhang, Ping & Sun, Huijun & Qu, Yunchao & Yin, Haodong & Jin, Jian Gang & Wu, Jianjun, 2021. "Model and algorithm of coordinated flow controlling with station-based constraints in a metro system," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 148(C).
    9. Zheng, Hankun & Sun, Huijun & Kang, Liujiang & Dai, Peiling & Wu, Jianjun, 2023. "Multi-route coordination for bus systems in response to road disruptions," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 179(C).
    10. Muren, & Zhang, Shiyuan & Hua, Lianlian & Yu, Bo, 2022. "Peak-easing strategies for urban subway operations in the context of COVID-19 epidemic," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 161(C).
    11. Liang, Jinpeng & Wu, Jianjun & Gao, Ziyou & Sun, Huijun & Yang, Xin & Lo, Hong K., 2019. "Bus transit network design with uncertainties on the basis of a metro network: A two-step model framework," Transportation Research Part B: Methodological, Elsevier, vol. 126(C), pages 115-138.
    12. Yinfei Feng & Zhichao Cao & Silin Zhang, 2022. "Shuttle Bus Timetable Adjustment in Response to Behind-Schedule Commuter Railway Disturbance," Sustainability, MDPI, vol. 14(24), pages 1-24, December.
    13. Mo, Baichuan & Koutsopoulos, Haris N. & Zhao, Jinhua, 2022. "Inferring passenger responses to urban rail disruptions using smart card data: A probabilistic framework," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 159(C).
    14. Pan, Hanchuan & Liu, Zhigang & Yang, Lixing & Liang, Zhe & Wu, Qiang & Li, Sijie, 2021. "A column generation-based approach for integrated vehicle and crew scheduling on a single metro line with the fully automatic operation system by partial supervision," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 152(C).
    15. Mo, Baichuan & Koutsopoulos, Haris N. & Shen, Zuo-Jun Max & Zhao, Jinhua, 2023. "Robust path recommendations during public transit disruptions under demand uncertainty," Transportation Research Part B: Methodological, Elsevier, vol. 169(C), pages 82-107.
    16. Kuo, Yong-Hong & Leung, Janny M.Y. & Yan, Yimo, 2023. "Public transport for smart cities: Recent innovations and future challenges," European Journal of Operational Research, Elsevier, vol. 306(3), pages 1001-1026.
    17. Tang, Junqing & Xu, Lei & Luo, Chunling & Ng, Tsan Sheng Adam, 2021. "Multi-disruption resilience assessment of rail transit systems with optimized commuter flows," Reliability Engineering and System Safety, Elsevier, vol. 214(C).

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