IDEAS home Printed from https://ideas.repec.org/a/gam/jdataj/v10y2025i8p119-d1707743.html
   My bibliography  Save this article

From Raw GPS to GTFS: A Real-World Open Dataset for Bus Travel Time Prediction

Author

Listed:
  • Aigerim Mansurova

    (Big Data and Blockchain Technologies Research and Innovation Center, Astana IT University, 020000 Astana, Kazakhstan)

  • Aigerim Mussina

    (Department of Computer Science, Al-Farabi Kazakh National University, 71 al-Farabi Avenue, 050040 Almaty, Kazakhstan)

  • Sanzhar Aubakirov

    (Department of Computer Science, Al-Farabi Kazakh National University, 71 al-Farabi Avenue, 050040 Almaty, Kazakhstan)

  • Aliya Nugumanova

    (Big Data and Blockchain Technologies Research and Innovation Center, Astana IT University, 020000 Astana, Kazakhstan)

  • Didar Yedilkhan

    (Smart City Research and Innovation Center, Astana IT University, 020000 Astana, Kazakhstan)

Abstract

The data descriptor introduces an open, high-resolution dataset of real-world bus operations in Astana, Kazakhstan, captured from GPS trajectories between July and September 2024. The data covers three high-frequency routes and have been processed into a GTFS format, enabling direct use with existing transit modeling tools. Unlike typical static GTFS feeds, this dataset provides empirically observed dwell times, run times, and travel times, offering a detailed snapshot of operational variability in urban bus systems. The dataset supports applications in machine learning–based travel time prediction, timetable optimization, and transit reliability analysis, especially in settings where live feeds are unavailable. By releasing this dataset publicly, we aim to promote transparent, data-driven transport research in emerging urban contexts.

Suggested Citation

  • Aigerim Mansurova & Aigerim Mussina & Sanzhar Aubakirov & Aliya Nugumanova & Didar Yedilkhan, 2025. "From Raw GPS to GTFS: A Real-World Open Dataset for Bus Travel Time Prediction," Data, MDPI, vol. 10(8), pages 1-16, July.
    • Handle: RePEc:gam:jdataj:v:10:y:2025:i:8:p:119-:d:1707743
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2306-5729/10/8/119/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2306-5729/10/8/119/
    Download Restriction: no
    ---><---

    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:gam:jdataj:v:10:y:2025:i:8:p:119-:d:1707743. 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: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    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.