IDEAS home Printed from https://ideas.repec.org/a/eme/dtspps/dts-01-2023-0006.html
   My bibliography  Save this article

Data-driven identification and analysis of passenger riding paths in megacity metro system

Author

Listed:
  • Lianghui Xie
  • Zhenji Zhang
  • Robin Qiu
  • Daqing Gong

Abstract

Purpose - The paper aims to identify and analyze passengers’ riding paths for providing better operational support for digital transformation in megacity metro systems. Design/methodology/approach - The authors develop a method to leverage certain passengers’ deterministic riding paths to corroborate other passengers’ uncertain paths. Using Automatic Fare Collection data and train schedules, a witness model is built to recover the actual riding paths for passengers whose paths are unknown otherwise. The identification and analysis of passenger riding paths between three different types of origin–destination) pairs reveal the complexity of passenger path choice. Findings - The results show that passenger path choice modeling is usually characterized by complexity, experience and partial blindness. Some passengers choose paths that are not optimal due to their experience and limited access to overall metro system information. These passengers could be the subject of improved path guidance in light of riding efficiency improved through digital transformation. Originality/value - This research contributes to the improvement of metro management and operations by leveraging ongoing digital transformation in megacity metro systems. Based on the riding paths and trip chains of a large number of individual passengers identified by the proposed method, metro operation management could prevent risks in areas with concentrated passenger flow in advance, optimally adjust train schedules on a daily basis and deliver real-time riding guidance station by station, which would greatly improve megacity metro systems’ service safety, quality and operational efficacy over time.

Suggested Citation

  • Lianghui Xie & Zhenji Zhang & Robin Qiu & Daqing Gong, 2023. "Data-driven identification and analysis of passenger riding paths in megacity metro system," Digital Transformation and Society, Emerald Group Publishing Limited, vol. 2(3), pages 316-339, July.
    • Handle: RePEc:eme:dtspps:dts-01-2023-0006
    DOI: 10.1108/DTS-01-2023-0006
    as

    Download full text from publisher

    File URL: https://www.emerald.com/insight/content/doi/10.1108/DTS-01-2023-0006/full/html?utm_source=repec&utm_medium=feed&utm_campaign=repec
    Download Restriction: no
    File URL: https://www.emerald.com/insight/content/doi/10.1108/DTS-01-2023-0006/full/pdf?utm_source=repec&utm_medium=feed&utm_campaign=repec
    Download Restriction: no
    File URL: https://libkey.io/10.1108/DTS-01-2023-0006?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
    ---><---

    Citations

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


    Cited by:

    1. Shang, Pan & Xiong, Yufan & Guo, Jifu & Xian, Kai & Yu, Yun & Xu, Han, 2024. "A modeling framework to integrate frequency - and schedule-based passenger assignment approaches for coordinated path choice and space-time trajectory estimation based on multi-source observations," Transportation Research Part B: Methodological, Elsevier, vol. 183(C).

    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:eme:dtspps:dts-01-2023-0006. 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: Emerald Support (email available below). General contact details of provider: .

    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.