IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v17y2025i6p2703-d1615203.html
   My bibliography  Save this article

An Integrated Approach to Schedule Passenger Train Plans and Train Timetables Economically Under Fluctuating Passenger Demands

Author

Listed:
  • Chang Han

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

  • Leishan Zhou

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

  • Zixi Bai

    (School of Logistics, Beijing Wuzi University, Beijing 101149, China)

  • Wenqiang Zhao

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

  • Lu Yang

    (Shudao Investment Group Co., Ltd., Chengdu 610000, China)

Abstract

High-speed railways (HSRs), with their advantages of safety, energy conservation, and convenience, are increasingly becoming the preferred mode of transportation. Railway operators schedule full-schedule timetables to operate as many trains and serve as many passengers as possible. However, due to the fluctuation in passenger demands, it is not necessary to operate all trains in full-schedule timetable, which results in high operation costs and too much energy consumption. Based on this, we propose an integrated approach to schedule passenger train plans and train timetables by selecting trains to operate from the full-schedule timetable, adjusting their stopping scheme and operation sequence to reduce operation costs and energy consumption and contribute to sustainable development. In the scheduling process, both operation costs and passenger service quality are considered, and a two-objective model is established. An algorithm is designed based on Non-dominated Sorting Genetic Algorithms-II (NSGA-II) to solve the model, containing techniques for acceleration that utilize overtaking patterns, in which overtaking chromosomes are used to illustrate the train operation sequence, and parallel computing, in which the decoding process is computed in parallel. A set of Pareto fronts are obtained to offer a diverse set of results with different operation costs and passenger service quality. The model and algorithm are verified by cases based on the Beijing–Shanghai HSR line. The results indicate that compared to the full-schedule timetable, the operation costs under three sets of passenger demands decreased by 35.4%, 27.7%, and 15.7% on average. Compared to the genetic algorithm with weighting multiple objectives and NSGA-II without acceleration techniques, the algorithm proposed in this paper with the two acceleration techniques of utilizing overtaking patterns and parallel computing can significantly accelerate the solution process, with an average reduction of 42.9% and 38.3% in calculation time, indicating that the approach can handle the integrated scheduling problem economically and efficiently.

Suggested Citation

  • Chang Han & Leishan Zhou & Zixi Bai & Wenqiang Zhao & Lu Yang, 2025. "An Integrated Approach to Schedule Passenger Train Plans and Train Timetables Economically Under Fluctuating Passenger Demands," Sustainability, MDPI, vol. 17(6), pages 1-31, March.
  • Handle: RePEc:gam:jsusta:v:17:y:2025:i:6:p:2703-:d:1615203
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/17/6/2703/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/2071-1050/17/6/2703/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. 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.
    2. 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.
    3. U. Brännlund & P. O. Lindberg & A. Nõu & J.-E. Nilsson, 1998. "Railway Timetabling Using Lagrangian Relaxation," Transportation Science, INFORMS, vol. 32(4), pages 358-369, November.
    4. 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.
    5. Li, Wenqing & Ni, Shaoquan, 2022. "Train timetabling with the general learning environment and multi-agent deep reinforcement learning," Transportation Research Part B: Methodological, Elsevier, vol. 157(C), pages 230-251.
    6. 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.
    7. 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)

    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. 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.
    3. 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.
    4. Yu-Jun Zheng, 2018. "Emergency Train Scheduling on Chinese High-Speed Railways," Transportation Science, INFORMS, vol. 52(5), pages 1077-1091, October.
    5. 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.
    6. 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.
    7. 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.
    8. 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.
    9. 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.
    10. 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.
    11. 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.
    12. 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.
    13. E. Ursavas & Stuart X. Zhu, 2018. "Integrated Passenger and Freight Train Planning on Shared-Use Corridors," Service Science, INFORMS, vol. 52(6), pages 1376-1390, December.
    14. Barrena, Eva & Canca, David & Coelho, Leandro C. & Laporte, Gilbert, 2014. "Single-line rail rapid transit timetabling under dynamic passenger demand," Transportation Research Part B: Methodological, Elsevier, vol. 70(C), pages 134-150.
    15. 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.
    16. 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.
    17. Juan Mesa & Francisco Ortega & Miguel Pozo, 2014. "Locating optimal timetables and vehicle schedules in a transit line," Annals of Operations Research, Springer, vol. 222(1), pages 439-455, November.
    18. 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.
    19. Li, Feng & Gao, Ziyou & Li, Keping & Yang, Lixing, 2008. "Efficient scheduling of railway traffic based on global information of train," Transportation Research Part B: Methodological, Elsevier, vol. 42(10), pages 1008-1030, December.
    20. Meng, Lingyun & Zhou, Xuesong, 2011. "Robust single-track train dispatching model under a dynamic and stochastic environment: A scenario-based rolling horizon solution approach," Transportation Research Part B: Methodological, Elsevier, vol. 45(7), pages 1080-1102, August.

    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:17:y:2025:i:6:p:2703-:d:1615203. 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.