IDEAS home Printed from https://ideas.repec.org/a/zbw/jumsac/320456.html
   My bibliography  Save this article

Waiting time estimation for ride-hailing fleets using graph neural networks

Author

Listed:
  • Sadid, Hashmatullah

Abstract

Ride-hailing services are part of intermodal transport systems, allowing passengers to use various transport modes for their trip. The optimal choice for a request in the intermodal system depends on the passenger's waiting time for the ride-hailing service. Estimating this waiting time is crucial for efficient system operation. The prediction of waiting time depends on the spatial dependency of the transport network and traffic flow elements. Graph neural network (GNN) approaches have gained attention for capturing spatial dependencies in various applications, though less attention has been given to ride-hailing waiting time prediction. The aim of this master thesis is to implement a GNN-based method to predict waiting time for ridehailing requests in the network. Simulation-based waiting time data is used for model training and validation. MATSim is chosen for generating waiting time data under different demand and supply scenarios. Graph Convolutional Network (GCN) and Gated Attention Network (GAT) are used as prediction models. Regression and MLP methods are used as baselines to compare model performance. Results show GCN outperforms regression by 15%, while GAT performs 14% better than regression.

Suggested Citation

  • Sadid, Hashmatullah, 2025. "Waiting time estimation for ride-hailing fleets using graph neural networks," Junior Management Science (JUMS), Junior Management Science e. V., vol. 10(2), pages 462-490.
  • Handle: RePEc:zbw:jumsac:320456
    DOI: 10.5282/jums/v10i2pp462-490
    as

    Download full text from publisher

    File URL: https://www.econstor.eu/bitstream/10419/320456/1/1928903339.pdf
    Download Restriction: no

    File URL: https://libkey.io/10.5282/jums/v10i2pp462-490?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
    ---><---

    More about this item

    Keywords

    ;
    ;
    ;

    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:zbw:jumsac:320456. 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: ZBW - Leibniz Information Centre for Economics (email available below). General contact details of provider: https://jums.academy/en/ .

    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.