An Ensemble Deep Learning Model for Short-Term Road Surface Temperature Prediction

Abstract

In winter, the ice and snow on the asphalt pavement reduce the friction coefficient of the pavement, which may lead to serious traffic accidents and large-scale congestion. Taking preventive measures to ensure traffic safety by accurately predicting road surface temperature is an economical and environmentally friendly solution. However, road surface temperature (RST) prediction is a challenging task due to the complicated uncertainty and periodicity. To improve the accuracy of RST prediction, this paper aims to propose an advanced ensemble deep learning model using a gated recurrent unit (GRU) network and long short-term memory (LSTM) network. The ensemble model predicts RST by extracting the periodicity of RST and incorporating the lag and accumulation effects of meteorological factors. To verify the applicability of the ensemble model, RST data and climatic data were collected from a road weather station in Jiangsu, China. Extensive experiments are conducted including predictions for 1, 3, and 6 h ahead. The results demonstrated that the performance of the proposed ensemble deep learning model is validated for 1-, 3-, and 6-h nowcasts of RST, with mean absolute error (MAE) of 0.345, 0.833, and 1.743, respectively, and the prediction accuracy was higher than that of the baseline models [convolutional neural networks (CNN)-LSTM networks, support vector regression (SVR), and backpropagation neural network (BP) networks].