Combined prediction model of truck multi-section travel time in open-pit mine based on velocity field

Abstract

Due to the complexity of road in open-pit mine, the existing truck travel time prediction methods are difficult in the actual deployment. This leads to the truck optimal scheduling system only realizing scheduling instead of optimization. A combined prediction model of truck multi-section travel time in open pit mine based on velocity field is proposed. The open-pit mine road is divided into multiple road sections. the random forest algorithm is used to construct the unit prediction model to predict the travel time of the truck in each section. Then the predicted values of the unit prediction models are accumulated to obtain the travel time predicted value of the truck on in the composite road section. In order to improve the prediction precision, the average velocity of the truck is taken as an influence factor of the travel time. The velocity field is constructed according to the collected velocity information of the truck. The average value of the truck velocity at all points on a road section is calculated, which is approximate to the average velocity of the truck on the road section, and the average velocity is input into the unit prediction model. Based on the data of truck schedule information in truck dispatching system of Yimin Open-pit Mine, the combined prediction model is trained, and the prediction precision and real-time performance of the model are tested. The results show that the combined prediction model of truck travel time in multiple sections of open-pit mine based on velocity field has high prediction precision for truck travel time in composite road sections. The average absolute error percentage is 4.81%, which is more than 2% lower than the single prediction model based on random forest algorithm. The operation time of the combined prediction model is less than 1 s, which can realize the real-time prediction of truck travel time