IDEAS home Printed from https://ideas.repec.org/a/eee/transe/v182y2024ics1366554524000048.html
   My bibliography  Save this article

Improving passenger travel efficiency through a dynamic autonomous non-stop rail transit system

Author

Listed:
  • Wu, Pei-Yang
  • Guo, Ren-Yong
  • Lin, Zhiyuan
  • Liu, Ronghui
  • Xu, Pu

Abstract

This study proposes a dynamic autonomous non-stop rail transit (DANRT) system to improve the travel efficiency of passengers in urban rail transit (URT) systems and solves a pertinent carriage scheduling problem derived from the DANRT system using a mathematical programming model. In the DANRT system, passengers traveling to the same destination are allocated to the same carriage(s), and each carriage can be attached to and detached from trains using the modular autonomous vehicle (MAV) technology effortlessly, which enables all trains to run non-stop throughout the focused operation period. We offer a cost-effective design for the DANRT system. To ensure safe and efficient operations, a mathematical model is proposed for the carriage scheduling problem in the DANRT system, where the number and destinations of carriages required by each station are determined. A linearization and segmentation method for the model is proposed. To examine the effectiveness of the DANRT system, we compare the travel efficiency of passengers in the DANRT system with that in the traditional system by using the origin-destination distribution of passengers on the Batong Line of Beijing Subway. The results demonstrate that passengers in DANRT can save about 2.9 % to 8.6 % of travel time compared with the traditional system. Finally, we conclude several observations and operational characteristics of the DANRT system by numerical experiments.

Suggested Citation

  • Wu, Pei-Yang & Guo, Ren-Yong & Lin, Zhiyuan & Liu, Ronghui & Xu, Pu, 2024. "Improving passenger travel efficiency through a dynamic autonomous non-stop rail transit system," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 182(C).
    • Handle: RePEc:eee:transe:v:182:y:2024:i:c:s1366554524000048
    DOI: 10.1016/j.tre.2024.103414
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S1366554524000048
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.tre.2024.103414?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.

    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:eee:transe:v:182:y:2024:i:c:s1366554524000048. 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: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/wps/find/journaldescription.cws_home/600244/description#description .

    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.