IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v13y2021i5p2538-d506453.html
   My bibliography  Save this article

An Efficient Hybrid Approach for Scheduling the Train Timetable for the Longer Distance High-Speed Railway

Author

Listed:
  • Zeyu Wang

    (Department of Transportation Management Engineering, School of Traffic and Transportation, Beijing Jiaotong University, Beijing 100044, China)

  • Leishan Zhou

    (Department of Transportation Management Engineering, School of Traffic and Transportation, Beijing Jiaotong University, Beijing 100044, China)

  • Bin Guo

    (State Research Center of Rail Transit Technology Education and Service, Beijing Jiaotong University, Beijing 100044, China)

  • Xing Chen

    (Nanchang Metro, Nanchang 330038, China)

  • Hanxiao Zhou

    (Department of Transportation Management Engineering, School of Traffic and Transportation, Beijing Jiaotong University, Beijing 100044, China)

Abstract

Compared with other modes of transportation, a high-speed railway has energy saving advantages; it is environmentally friendly, safe, and convenient for large capacity transportation between cities. With the expansion of the high-speed railway network, the operation of high-speed railways needs to be improved urgently. In this paper, a hybrid approach for quickly solving the timetable of high-speed railways, inspired by the periodic model and the aperiodic model, is proposed. A space–time decomposition method is proposed to convert the complex passenger travel demands into service plans and decompose the original problem into several sub-problems, to reduce the solving complexity. An integer programming model is proposed for the sub-problems, and then solved in parallel with CPLEX. After that, a local search algorithm is designed to combine the timetables of different periods, considering the safety operation constraints. The hybrid approach is tested on a real-world case study, based on the Beijing–Shanghai high-speed railway (HSR), and the results show that the train timetable calculated by the approach is superior to the real-world timetable in many indexes. The hybrid approach combines the advantages of the periodic model and the aperiodic model; it can deal with the travel demands of passengers well and the solving speed is fast. It provides the possibility for flexible adjustment of a timetable and timely response to the change of passenger travel demands, to avoid the waste of transportation resources and achieve sustainable development.

Suggested Citation

  • Zeyu Wang & Leishan Zhou & Bin Guo & Xing Chen & Hanxiao Zhou, 2021. "An Efficient Hybrid Approach for Scheduling the Train Timetable for the Longer Distance High-Speed Railway," Sustainability, MDPI, vol. 13(5), pages 1-22, February.
    • Handle: RePEc:gam:jsusta:v:13:y:2021:i:5:p:2538-:d:506453
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/13/5/2538/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2071-1050/13/5/2538/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Matthew E. H. Petering & Mojtaba Heydar & Dietrich R. Bergmann, 2016. "Mixed-Integer Programming for Railway Capacity Analysis and Cyclic, Combined Train Timetabling and Platforming," Transportation Science, INFORMS, vol. 50(3), pages 892-909, August.
    2. Wenliang Zhou & Xiaorong You & Wenzhuang Fan, 2020. "A Mixed Integer Linear Programming Method for Simultaneous Multi-Periodic Train Timetabling and Routing on a High-Speed Rail Network," Sustainability, MDPI, vol. 12(3), pages 1-34, February.
    3. Zhou, Wenliang & Tian, Junli & Xue, Lijuan & Jiang, Min & Deng, Lianbo & Qin, Jin, 2017. "Multi-periodic train timetabling using a period-type-based Lagrangian relaxation decomposition," Transportation Research Part B: Methodological, Elsevier, vol. 105(C), pages 144-173.
    4. Sels, P. & Dewilde, T. & Cattrysse, D. & Vansteenwegen, P., 2016. "Reducing the passenger travel time in practice by the automated construction of a robust railway timetable," Transportation Research Part B: Methodological, Elsevier, vol. 84(C), pages 124-156.
    5. Zhang, Yongxiang & Peng, Qiyuan & Yao, Yu & Zhang, Xin & Zhou, Xuesong, 2019. "Solving cyclic train timetabling problem through model reformulation: Extended time-space network construct and Alternating Direction Method of Multipliers methods," Transportation Research Part B: Methodological, Elsevier, vol. 128(C), pages 344-379.
    6. Zhou, Xuesong & Zhong, Ming, 2007. "Single-track train timetabling with guaranteed optimality: Branch-and-bound algorithms with enhanced lower bounds," Transportation Research Part B: Methodological, Elsevier, vol. 41(3), pages 320-341, March.
    7. Ghoseiri, Keivan & Szidarovszky, Ferenc & Asgharpour, Mohammad Jawad, 2004. "A multi-objective train scheduling model and solution," Transportation Research Part B: Methodological, Elsevier, vol. 38(10), pages 927-952, December.
    8. Leo G. Kroon & Leon W. P. Peeters, 2003. "A Variable Trip Time Model for Cyclic Railway Timetabling," Transportation Science, INFORMS, vol. 37(2), pages 198-212, May.
    9. Jozef Gasparik & Milan Dedik & Lukas Cechovic & Peter Blaho, 2020. "Estimation of Transport Potential in Regional Rail Passenger Transport by Using the Innovative Mathematical-Statistical Gravity Approach," Sustainability, MDPI, vol. 12(9), pages 1-13, May.
    10. Robenek, Tomáš & Azadeh, Shadi Sharif & Maknoon, Yousef & de Lapparent, Matthieu & Bierlaire, Michel, 2018. "Train timetable design under elastic passenger demand," Transportation Research Part B: Methodological, Elsevier, vol. 111(C), pages 19-38.
    11. Sparing, Daniel & Goverde, Rob M.P., 2017. "A cycle time optimization model for generating stable periodic railway timetables," Transportation Research Part B: Methodological, Elsevier, vol. 98(C), pages 198-223.
    12. Odijk, Michiel A., 1996. "A constraint generation algorithm for the construction of periodic railway timetables," Transportation Research Part B: Methodological, Elsevier, vol. 30(6), pages 455-464, December.
    13. Leo G. Kroon & Leon W. P. Peeters & Joris C. Wagenaar & Rob A. Zuidwijk, 2014. "Flexible Connections in PESP Models for Cyclic Passenger Railway Timetabling," Transportation Science, INFORMS, vol. 48(1), pages 136-154, February.
    14. Zhou, Xuesong & Zhong, Ming, 2005. "Bicriteria train scheduling for high-speed passenger railroad planning applications," European Journal of Operational Research, Elsevier, vol. 167(3), pages 752-771, December.
    15. Higgins, A. & Kozan, E. & Ferreira, L., 1996. "Optimal scheduling of trains on a single line track," Transportation Research Part B: Methodological, Elsevier, vol. 30(2), pages 147-161, April.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Limsawasd, Charinee & Athigakunagorn, Nathee & Khathawatcharakun, Phattadon & Boonmee, Atiwat, 2022. "Skip-Stop Strategy Patterns optimization to enhance mass transit operation under physical distancing policy due to COVID-19 pandemic outbreak," Transport Policy, Elsevier, vol. 126(C), pages 225-238.
    2. Yi Liu & Senbin Yu & Chaoyang Zhang & Peiran Zhang & Yang Wang & Liang Gao, 2022. "Critical Percolation on Temporal High-Speed Railway Networks," Mathematics, MDPI, vol. 10(24), pages 1-8, December.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Zhang, Yongxiang & Peng, Qiyuan & Yao, Yu & Zhang, Xin & Zhou, Xuesong, 2019. "Solving cyclic train timetabling problem through model reformulation: Extended time-space network construct and Alternating Direction Method of Multipliers methods," Transportation Research Part B: Methodological, Elsevier, vol. 128(C), pages 344-379.
    2. Zhou, Wenliang & Tian, Junli & Xue, Lijuan & Jiang, Min & Deng, Lianbo & Qin, Jin, 2017. "Multi-periodic train timetabling using a period-type-based Lagrangian relaxation decomposition," Transportation Research Part B: Methodological, Elsevier, vol. 105(C), pages 144-173.
    3. Zhang, Yongxiang & Peng, Qiyuan & Lu, Gongyuan & Zhong, Qingwei & Yan, Xu & Zhou, Xuesong, 2022. "Integrated line planning and train timetabling through price-based cross-resolution feedback mechanism," Transportation Research Part B: Methodological, Elsevier, vol. 155(C), pages 240-277.
    4. Burdett, R.L. & Kozan, E., 2010. "A disjunctive graph model and framework for constructing new train schedules," European Journal of Operational Research, Elsevier, vol. 200(1), pages 85-98, January.
    5. Yu-Jun Zheng, 2018. "Emergency Train Scheduling on Chinese High-Speed Railways," Transportation Science, INFORMS, vol. 52(5), pages 1077-1091, October.
    6. Limsawasd, Charinee & Athigakunagorn, Nathee & Khathawatcharakun, Phattadon & Boonmee, Atiwat, 2022. "Skip-Stop Strategy Patterns optimization to enhance mass transit operation under physical distancing policy due to COVID-19 pandemic outbreak," Transport Policy, Elsevier, vol. 126(C), pages 225-238.
    7. Mo, Pengli & D’Ariano, Andrea & Yang, Lixing & Veelenturf, Lucas P. & Gao, Ziyou, 2021. "An exact method for the integrated optimization of subway lines operation strategies with asymmetric passenger demand and operating costs," Transportation Research Part B: Methodological, Elsevier, vol. 149(C), pages 283-321.
    8. Xueqiao Yu & Maoxiang Lang & Wenhui Zhang & Shiqi Li & Mingyue Zhang & Xiao Yu, 2019. "An Empirical Study on the Comprehensive Optimization Method of a Train Diagram of the China High Speed Railway Express," Sustainability, MDPI, vol. 11(7), pages 1-30, April.
    9. Xie, J. & Wong, S.C. & Zhan, S. & Lo, S.M. & Chen, Anthony, 2020. "Train schedule optimization based on schedule-based stochastic passenger assignment," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 136(C).
    10. Wenliang Zhou & Xiaorong You & Wenzhuang Fan, 2020. "A Mixed Integer Linear Programming Method for Simultaneous Multi-Periodic Train Timetabling and Routing on a High-Speed Rail Network," Sustainability, MDPI, vol. 12(3), pages 1-34, February.
    11. Yin, Jiateng & Yang, Lixing & Tang, Tao & Gao, Ziyou & Ran, Bin, 2017. "Dynamic passenger demand oriented metro train scheduling with energy-efficiency and waiting time minimization: Mixed-integer linear programming approaches," Transportation Research Part B: Methodological, Elsevier, vol. 97(C), pages 182-213.
    12. Wenliang Zhou & Junli Tian & Jin Qin & Lianbo Deng & TangJian Wei, 2015. "Optimization of Multiperiod Mixed Train Schedule on High-Speed Railway," Discrete Dynamics in Nature and Society, Hindawi, vol. 2015, pages 1-14, April.
    13. Zhou, Wenliang & Teng, Hualiang, 2016. "Simultaneous passenger train routing and timetabling using an efficient train-based Lagrangian relaxation decomposition," Transportation Research Part B: Methodological, Elsevier, vol. 94(C), pages 409-439.
    14. Tian, Xiaopeng & Niu, Huimin, 2020. "Optimization of demand-oriented train timetables under overtaking operations: A surrogate-dual-variable column generation for eliminating indivisibility," Transportation Research Part B: Methodological, Elsevier, vol. 142(C), pages 143-173.
    15. Albrecht, Amie & Howlett, Phil & Pudney, Peter & Vu, Xuan & Zhou, Peng, 2016. "The key principles of optimal train control—Part 1: Formulation of the model, strategies of optimal type, evolutionary lines, location of optimal switching points," Transportation Research Part B: Methodological, Elsevier, vol. 94(C), pages 482-508.
    16. Li, Feng & Sheu, Jiuh-Biing & Gao, Zi-You, 2014. "Deadlock analysis, prevention and train optimal travel mechanism in single-track railway system," Transportation Research Part B: Methodological, Elsevier, vol. 68(C), pages 385-414.
    17. Julia Lange & Frank Werner, 2018. "Approaches to modeling train scheduling problems as job-shop problems with blocking constraints," Journal of Scheduling, Springer, vol. 21(2), pages 191-207, April.
    18. Zhang, Chuntian & Gao, Yuan & Yang, Lixing & Gao, Ziyou & Qi, Jianguo, 2020. "Joint optimization of train scheduling and maintenance planning in a railway network: A heuristic algorithm using Lagrangian relaxation," Transportation Research Part B: Methodological, Elsevier, vol. 134(C), pages 64-92.
    19. Lee, Yusin & Chen, Chuen-Yih, 2009. "A heuristic for the train pathing and timetabling problem," Transportation Research Part B: Methodological, Elsevier, vol. 43(8-9), pages 837-851, September.
    20. Sparing, Daniel & Goverde, Rob M.P., 2017. "A cycle time optimization model for generating stable periodic railway timetables," Transportation Research Part B: Methodological, Elsevier, vol. 98(C), pages 198-223.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jsusta:v:13:y:2021:i:5:p:2538-:d:506453. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.