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

Sun, X, Zhang, H, Tian, F and Yang, L 2018, 'The Use of a Machine Learning Method to Predict the Real-Time Link Travel Time of Open-Pit Trucks', Mathematical Problems in Engineering, vol. 2018, pp. 1-14.


Document type: Journal Article
Collection: Journal Articles

Title The Use of a Machine Learning Method to Predict the Real-Time Link Travel Time of Open-Pit Trucks
Author(s) Sun, X
Zhang, H
Tian, F
Yang, L
Year 2018
Journal name Mathematical Problems in Engineering
Volume number 2018
Start page 1
End page 14
Total pages 14
Publisher Hindawi
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 k-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%.
Subject Mining Engineering
DOI - identifier 10.1155/2018/4368045
Copyright notice © 2018 Xiaoyu Sun et al. Creative Commons Attribution License.
ISSN 1024-123X
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