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

Xiaoyu Sun,Hang Zhang,Lei Yang,Fengliang Tian

doi:10.1155/2018/4368045

Xiaoyu Sun, Hang Zhang + Show 2 more

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https://doi.org/10.1155/2018/4368045

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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%.

Highlights

At present, shovel-truck systems (STSs) are commonly used in open-pit mining operations [1,2,3], especially for large open-pit mines
The results show that the travel time prediction (TTP) models based on support vector machine (SVM) and random forest (RF) are better than that based on k-nearest neighbors (kNN)
2,246,746 historical records from March 2017 were exported from the open-pit automated truck dispatching system (OPATDS) database of the Fushun West Open-pit Mine (FWOM)

Summary

Introduction

Shovel-truck systems (STSs) are commonly used in open-pit mining operations [1,2,3], especially for large open-pit mines. This is because STSs do not require extensive infrastructure in conjunction with a high mining intensity [4]. Almost all large open-pit mines are trying to optimize truck dispatching to achieve lower costs and higher mining efficiency [7,8,9,10]. Mining efficiency has increased through the integration of some truck dynamic dispatching principles (TDDPs) into the OPATDS [9, 11, 12]. The TDDPs rely heavily on an accurate truck cycle time [7, 8, 10], and one of its fundamental techniques is predicting the travel time of the trucks [11,12,13,14]

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Conclusion