IDEAS home Printed from https://ideas.repec.org/a/taf/transp/v48y2025i1p90-110.html
   My bibliography  Save this article

Decision Model of Taxi Drivers at High-Speed Railway Stations Based on Profit Priority

Author

Listed:
  • Bin Lv
  • Xiangshuo Meng
  • Qixiang Chen

Abstract

After dropping off passengers at the high-speed railway (HSR) station, taxi drivers face a crucial decision: whether to stay at the station or return empty. To solve the decision problem, this paper establishes a weighting model utilizing the Analytic Hierarchy Process (AHP) combined with the entropy weight method. This model determines the significance of various factors influencing drivers’ decisions. Subsequently, a decision model for taxi drivers at the HSR station is established by comparing the profitability of staying at the station versus leaving. Furthermore, the sensitivity analysis method is employed to quantitatively assess the model's dependency on influencing factors to confirm the model's effectiveness. Utilizing GPS trajectory data from Lanzhou taxis, this paper validates the decision-making model. The findings suggest that in the afternoon and evening, drivers are more likely to wait in the parking lot for passengers, whereas in the morning, they are more inclined to return empty.

Suggested Citation

  • Bin Lv & Xiangshuo Meng & Qixiang Chen, 2025. "Decision Model of Taxi Drivers at High-Speed Railway Stations Based on Profit Priority," Transportation Planning and Technology, Taylor & Francis Journals, vol. 48(1), pages 90-110, January.
  • Handle: RePEc:taf:transp:v:48:y:2025:i:1:p:90-110
    DOI: 10.1080/03081060.2024.2360138
    as

    Download full text from publisher

    File URL: http://hdl.handle.net/10.1080/03081060.2024.2360138
    Download Restriction: Access to full text is restricted to subscribers.

    File URL: https://libkey.io/10.1080/03081060.2024.2360138?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:transp:v:48:y:2025:i:1:p:90-110. 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/GTPT20 .

    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.