Abstract
Truck travel time prediction is the basis for real-time optimal scheduling decision of open-pit trucks. Most prediction models for open-pit truck travel time mainly rely on a single machine learning algorithm or model to optimize the hyperparameters, which is difficult to accurately capture the composite characteristics of truck travel time data, affectting the accuracy of the prediction results. This paper proposed a travel time prediction method for open-pit trucks based on LSTM-TabTransformer. After eliminating the outliers in the truck travel time data set by applying the Paura criterion, the data set was non-dimensionalized, and the corresponding category data was processed by means of data diversion. Then, the self-attention mechanism of TabTransformer and gating mechanism of LSTM were used to capture the dynamic characteristics of the travel time variation rule at multiple levels. Finally, the dimensionality of the concatenated feature matrix was reduced by MLP, and the predicted truck travel time was output. Based on the truck travel time series data set collected from the example open-pit coal mine, the prediction experiment was carried out. The results show that the combined machine learning model LSTM-Tabtransformer proposed in this paper is suitable to process the complex truck travel time series data set and learn multiple features, which can reduce the volatility of relative absolute error (RAE) and relative square error (RSE) of travel time prediction, overcome the limitations of a single machine learning prediction model, effectively increase the sensitivity of learning features, and significantly improve the accuracy of truck travel time prediction in open-pit mines.