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The Use of a Machine Learning Method to Predict the Real-Time Link Travel Time of Open-Pit Trucks

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  • Xiaoyu Sun
  • Hang Zhang
  • Fengliang Tian
  • Lei Yang

Abstract

Accurate truck travel time prediction (TTP) is one of the critical factors in the dynamic optimal dispatch of open-pit mines. This study divides the roads of open-pit mines into two types: fixed and temporary link roads. The experiment uses data obtained from Fushun West Open-pit Mine (FWOM) to train three types of machine learning (ML) prediction models based on -nearest neighbors (kNN), support vector machine (SVM), and random forest (RF) algorithms for each link road. The results show that the TTP models based on SVM and RF are better than that based on kNN. The prediction accuracy calculated in this study is approximately 15.79% higher than that calculated by traditional methods. Meteorological features added to the TTP model improved the prediction accuracy by 5.13%. Moreover, this study uses the link rather than the route as the minimum TTP unit, and the former shows an increase in prediction accuracy of 11.82%.

Suggested Citation

  • 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.
    • Handle: RePEc:hin:jnlmpe:4368045
    DOI: 10.1155/2018/4368045
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    Cited by:

    1. 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.
    2. Wang, Qian & Gu, Qinghua & Li, Xuexian & Xiong, Naixue, 2024. "Comprehensive overview: Fleet management drives green and climate-smart open pit mine," Renewable and Sustainable Energy Reviews, Elsevier, vol. 189(PA).

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