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Travel Time Prediction in a Multimodal Freight Transport Relation Using Machine Learning Algorithms

Author

Listed:
  • Nikolaos Servos

    (Bosch Connected Industry, Robert Bosch Manufacturing Solutions GmbH, Leitzstrasse 47, 70469 Stuttgart, Germany)

  • Xiaodi Liu

    (Bosch Connected Industry, Robert Bosch Manufacturing Solutions GmbH, Leitzstrasse 47, 70469 Stuttgart, Germany)

  • Michael Teucke

    (BIBA—Bremer Institut für Produktion und Logistik GmbH, University of Bremen, Hochschulring 20, 28359 Bremen, Germany)

  • Michael Freitag

    (BIBA—Bremer Institut für Produktion und Logistik GmbH, University of Bremen, Hochschulring 20, 28359 Bremen, Germany
    Faculty of Production Engineering, University of Bremen, Badgasteiner Straße 1, 28359 Bremen, Germany)

Abstract

Accurate travel time prediction is of high value for freight transports, as it allows supply chain participants to increase their logistics quality and efficiency. It requires both sufficient input data, which can be generated, e.g., by mobile sensors, and adequate prediction methods. Machine Learning (ML) algorithms are well suited to solve non-linear and complex relationships in the collected tracking data. Despite that, only a minority of recent publications use ML for travel time prediction in multimodal transports. We apply the ML algorithms extremely randomized trees (ExtraTrees), adaptive boosting (AdaBoost), and support vector regression (SVR) to this problem because of their ability to deal with low data volumes and their low processing times. Using different combinations of features derived from the data, we have built several models for travel time prediction. Tracking data from a real-world multimodal container transport relation from Germany to the USA are used for evaluation of the established models. We show that SVR provides the best prediction accuracy, with a mean absolute error of 17 h for a transport time of up to 30 days. We also show that our model performs better than average-based approaches.

Suggested Citation

  • Nikolaos Servos & Xiaodi Liu & Michael Teucke & Michael Freitag, 2019. "Travel Time Prediction in a Multimodal Freight Transport Relation Using Machine Learning Algorithms," Logistics, MDPI, vol. 4(1), pages 1-22, December.
    • Handle: RePEc:gam:jlogis:v:4:y:2019:i:1:p:1-:d:301825
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    References listed on IDEAS

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    1. Sjoerd van der Spoel & Chintan Amrit & Jos van Hillegersberg, 2017. "Predictive analytics for truck arrival time estimation: a field study at a European distribution centre," International Journal of Production Research, Taylor & Francis Journals, vol. 55(17), pages 5062-5078, September.
    2. Xiaoyu Sun & Hang Zhang & Fengliang Tian & Lei Yang, 2018. "The Use of a Machine Learning Method to Predict the Real-Time Link Travel Time of Open-Pit Trucks," Mathematical Problems in Engineering, Hindawi, vol. 2018, pages 1-14, April.
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    Cited by:

    1. Julian Vasilev & Rosen Nikolaev & Tanka Milkova, 2023. "Transport Task Models with Variable Supplier Availabilities," Logistics, MDPI, vol. 7(3), pages 1-12, July.

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