An Ensemble Deep Learning Model for Short-Term Road Surface Temperature Prediction
Publication: Journal of Transportation Engineering, Part B: Pavements
Volume 149, Issue 1
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].
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Data Availability Statement
Some of the data, models, or code generated or used during the study are available from the corresponding author by request (code for ensemble model, data analysis method and road weather station data).
Acknowledgments
This work was jointly supported by the General Program of Key Science and Technology in Transportation, the Ministry of Transport, PRC (Grant Nos. 2018-MS4-102 and ZL-2018-04), the Science and Technology Demonstration Project of the Ministry of Transport, PRC (Grant No. 2017-09), the Science and Technology Innovation Program of the Department of Transportation, Yunnan Province, China (Nos. 2019303 and 2021-06), Yunnan Fundamental Research Projects (No. 202101AT070693), and Yunnan Key Laboratory of Digital Communications (Grant No. 202205AG070008).
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© 2022 American Society of Civil Engineers.
History
Received: Mar 23, 2022
Accepted: Sep 20, 2022
Published online: Nov 16, 2022
Published in print: Mar 1, 2023
Discussion open until: Apr 16, 2023
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