IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v15y2023i2p1122-d1027822.html
   My bibliography  Save this article

Prediction of Shipping Cost on Freight Brokerage Platform Using Machine Learning

Author

Listed:
  • Hee-Seon Jang

    (Department of Industrial & Management Engineering/Intelligence & Manufacturing Research Center, Kyonggi University, Suwon 16227, Republic of Korea)

  • Tai-Woo Chang

    (Department of Industrial & Management Engineering/Intelligence & Manufacturing Research Center, Kyonggi University, Suwon 16227, Republic of Korea)

  • Seung-Han Kim

    (Hwamulman Co. Ltd., Gwangju 12777, Republic of Korea)

Abstract

Not having an exact cost standard can present a problem for setting the shipping costs on a freight brokerage platform. Transport brokers who use their high market position to charge excessive commissions can also make it difficult to set rates. In addition, due to the absence of a quantified fare policy, fares are undervalued relative to the labor input. Therefore, vehicle owners are working for less pay than their efforts. This study derives the main variables that influence the setting of the shipping costs and presents the recommended shipping cost given by a price prediction model using machine learning methods. The cost prediction model was built using four algorithms: multiple linear regression, deep neural network, XGBoost regression, and LightGBM regression. R-squared was used as the performance evaluation index. In view of the results of this study, LightGBM was chosen as the model with the greatest explanatory power and the fastest processing. Furthermore, the range of the predicted shipping costs was determined considering realistic usage patterns. The confidence interval was used as the method of calculation for the range of the predicted shipping costs, and, for this purpose, the dataset was classified using the K-fold cross-validation method. This paper could be used to set the shipping costs on freight brokerage platforms and to improve utilization rates.

Suggested Citation

  • Hee-Seon Jang & Tai-Woo Chang & Seung-Han Kim, 2023. "Prediction of Shipping Cost on Freight Brokerage Platform Using Machine Learning," Sustainability, MDPI, vol. 15(2), pages 1-14, January.
  • Handle: RePEc:gam:jsusta:v:15:y:2023:i:2:p:1122-:d:1027822
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/15/2/1122/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/2071-1050/15/2/1122/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Marjan Sternad, 2019. "Cost Calculation In Road Freight Transport," Business Logistics in Modern Management, Josip Juraj Strossmayer University of Osijek, Faculty of Economics, Croatia, vol. 19, pages 215-225.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Alp, Osman & Tan, Tarkan & Udenio, Maximiliano, 2022. "Transitioning to sustainable freight transportation by integrating fleet replacement and charging infrastructure decisions," Omega, Elsevier, vol. 109(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:gam:jsusta:v:15:y:2023:i:2:p:1122-:d:1027822. 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.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with 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.