IDEAS home Printed from https://ideas.repec.org/a/taf/tjrtxx/v12y2024i1p76-101.html
   My bibliography  Save this article

Metro train delay-recovery strategy considering passenger waiting time and energy consumption: a real-world case study

Author

Listed:
  • Wenxin Li
  • Qingwei Zhong
  • Qiyuan Peng
  • Jing Liu
  • Chao Ma

Abstract

Based on actual delay-recovery strategies (ADRS) on site only considers reducing the delays and ignores the operation cost of the enterprise. This study proposes optimized delay-recovery strategies (ODRS) to reduce the impact of delay on passengers and increase energy efficiency simultaneously. A bi-objective optimization model is used to deal with different initial delay scenarios and minimize the additional waiting time of passengers (AWTP) and total energy consumption (TEC). In the solving process, the non-dominated sorting genetic algorithm-II (NSGA-II) is used to solve the models, which can get the effective Pareto frontier solutions. Finally, Chengdu Metro is taken as the numerical experiments to verify the performance of the models. The results show that the ODRS can effectively reduce the AWTP and TEC than the ADRS. Furthermore, with the increase of initial delay time, the optimization effect of ODRS will be more obvious.

Suggested Citation

  • Wenxin Li & Qingwei Zhong & Qiyuan Peng & Jing Liu & Chao Ma, 2024. "Metro train delay-recovery strategy considering passenger waiting time and energy consumption: a real-world case study," International Journal of Rail Transportation, Taylor & Francis Journals, vol. 12(1), pages 76-101, January.
    • Handle: RePEc:taf:tjrtxx:v:12:y:2024:i:1:p:76-101
    DOI: 10.1080/23248378.2022.2143918
    as

    Download full text from publisher

    File URL: http://hdl.handle.net/10.1080/23248378.2022.2143918
    Download Restriction: Access to full text is restricted to subscribers.
    File URL: https://libkey.io/10.1080/23248378.2022.2143918?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    More about this item

    Statistics

    Access and download statistics

    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:taf:tjrtxx:v:12:y:2024:i:1:p:76-101. 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.

    We have no bibliographic references for this item. You can help adding them by using 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: Chris Longhurst (email available below). General contact details of provider: http://www.tandfonline.com/tjrt20 .

    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.