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Service-Oriented Load Balancing Approach to Alleviating Peak-Hour Congestion in a Metro Network Based on Multi-Path Accessibility

Author

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  • Zhiyuan Huang

    (Key Laboratory of Road and Traffic Engineering of the Ministry of Education, College of Transportation Engineering, Tongji University, Shanghai 201804, China)

  • Ruihua Xu

    (Key Laboratory of Road and Traffic Engineering of the Ministry of Education, College of Transportation Engineering, Tongji University, Shanghai 201804, China)

  • Wei (David) Fan

    (Key Laboratory of Road and Traffic Engineering of the Ministry of Education, College of Transportation Engineering, Tongji University, Shanghai 201804, China
    USDOT Center for Advanced Multimodal Mobility Solutions and Education, Department of Civil and Environmental Engineering, University of North Carolina at Charlotte, Charlotte, NC 28223, USA)

  • Feng Zhou

    (Key Laboratory of Road and Traffic Engineering of the Ministry of Education, College of Transportation Engineering, Tongji University, Shanghai 201804, China)

  • Wei Liu

    (Technology Center of Shanghai Shentong Metro Group Co. Ltd., Shanghai 201103, China)

Abstract

To further improve the service quality and reduce safety risks in current congested metro systems during peak hours, this paper presents a load balancing (LB) approach so that available capacity can be utilized more effectively in order to alleviate peak hour congestion. A set of under-utilized yet effective alternative routes were searched using a deletion algorithm (DA) in order to share the passenger loads on overcrowded metro line segments. An optimization model was constructed based on an improved route generalized time utility function considering the penalties of both in-vehicle congestion and transfers. A detailed load balancing solution was generated based on the proposed algorithm. A real-world example of three overloaded metro line segments in the Shanghai metro network were selected and used to verify the feasibility and validity of the developed load balancing method. The results show that the load balancing method can effectively reduce the overcrowding situation to a great extent. Finally, two prospective inducing schemes are discussed to help implement the load balancing solution in the actual metro system in an efficient and effective manner.

Suggested Citation

  • Zhiyuan Huang & Ruihua Xu & Wei (David) Fan & Feng Zhou & Wei Liu, 2019. "Service-Oriented Load Balancing Approach to Alleviating Peak-Hour Congestion in a Metro Network Based on Multi-Path Accessibility," Sustainability, MDPI, vol. 11(5), pages 1-16, March.
    • Handle: RePEc:gam:jsusta:v:11:y:2019:i:5:p:1293-:d:210142
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    References listed on IDEAS

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    1. Kim, Kyung Min & Hong, Sung-Pil & Ko, Suk-Joon & Kim, Dowon, 2015. "Does crowding affect the path choice of metro passengers?," Transportation Research Part A: Policy and Practice, Elsevier, vol. 77(C), pages 292-304.
    2. Shi, Jungang & Yang, Lixing & Yang, Jing & Gao, Ziyou, 2018. "Service-oriented train timetabling with collaborative passenger flow control on an oversaturated metro line: An integer linear optimization approach," Transportation Research Part B: Methodological, Elsevier, vol. 110(C), pages 26-59.
    3. Li, Shukai & Dessouky, Maged M. & Yang, Lixing & Gao, Ziyou, 2017. "Joint optimal train regulation and passenger flow control strategy for high-frequency metro lines," Transportation Research Part B: Methodological, Elsevier, vol. 99(C), pages 113-137.
    4. Li, Zheng & Hensher, David A., 2011. "Crowding and public transport: A review of willingness to pay evidence and its relevance in project appraisal," Transport Policy, Elsevier, vol. 18(6), pages 880-887, November.
    5. Xu, Xin-yue & Liu, Jun & Li, Hai-ying & Jiang, Man, 2016. "Capacity-oriented passenger flow control under uncertain demand: Algorithm development and real-world case study," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 87(C), pages 130-148.
    6. Joaquín de Cea & Enrique Fernández, 1993. "Transit Assignment for Congested Public Transport Systems: An Equilibrium Model," Transportation Science, INFORMS, vol. 27(2), pages 133-147, May.
    7. Raveau, Sebastián & Muñoz, Juan Carlos & de Grange, Louis, 2011. "A topological route choice model for metro," Transportation Research Part A: Policy and Practice, Elsevier, vol. 45(2), pages 138-147, February.
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    Cited by:

    1. Qiuchi Xue & Xin Yang & Jianjun Wu & Huijun Sun & Haodong Yin & Yunchao Qu, 2019. "Urban Rail Timetable Optimization to Improve Operational Efficiency with Flexible Routing Plans: A Nonlinear Integer Programming Model," Sustainability, MDPI, vol. 11(13), pages 1-26, July.
    2. Cai Jia & Shuyan Zheng & Hanqiang Qian & Bingxin Cao & Kaiting Zhang, 2022. "Analysis of Crowded Propagation on the Metro Network," Sustainability, MDPI, vol. 14(16), pages 1-12, August.
    3. Jairo Ortega & János Tóth & Tamás Péter, 2021. "A Comprehensive Model to Study the Dynamic Accessibility of the Park & Ride System," Sustainability, MDPI, vol. 13(7), pages 1-17, April.

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