IDEAS home Printed from https://ideas.repec.org/a/taf/marpmg/v53y2026i2p183-199.html

Deep learning-based estimation of truck Turn Around Time at container port

Author

Listed:
  • Byongchan Shin
  • Yusung Min
  • Gunwoo Lee
  • Hyounseok Yang
  • Bumchul Cho

Abstract

In South Korea, most import and export cargo are mainly transported by sea. With so much cargo being transported through the ports, congestion from trucks is particularly severe at city ports, which are located close to cities. Accordingly, port terminals require systematic and efficient terminal operation, since congestion leads to problems such as air pollution. This study aims to estimate the Turn Around Time (TAT) of trucks at container terminals. Accurate truck TAT estimations have the potential to mitigate congestion, since truck drivers can avoid congested times. The dataset was constructed by combining truck DTG (Digital Tacho Graph) data collected in Korea in 2021, shipping in port data collected from major ports in Korea such as Busan New Port and Incheon Port, and weather data. Several artificial intelligence models were used to estimate the TAT, and their performance evaluation indicators were compared. From the comparison results, the artificial intelligence model with the best estimation performance was identified. As a result of estimating TAT through artificial intelligence models, Busan New Port had the highest TAT estimation accuracy. Through this study, we found that it is possible to estimate TAT using DTG data, which is installed in most Korean trucks.

Suggested Citation

  • Byongchan Shin & Yusung Min & Gunwoo Lee & Hyounseok Yang & Bumchul Cho, 2026. "Deep learning-based estimation of truck Turn Around Time at container port," Maritime Policy & Management, Taylor & Francis Journals, vol. 53(2), pages 183-199, February.
    • Handle: RePEc:taf:marpmg:v:53:y:2026:i:2:p:183-199
    DOI: 10.1080/03088839.2025.2470437
    as

    Download full text from publisher

    File URL: http://hdl.handle.net/10.1080/03088839.2025.2470437
    Download Restriction: Access to full text is restricted to subscribers.
    File URL: https://libkey.io/10.1080/03088839.2025.2470437?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

    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:marpmg:v:53:y:2026:i:2:p:183-199. 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/TMPM20 .

    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.