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Hourly road pavement surface temperature forecasting using deep learning models
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DOI：10.1016/j.jhydrol.2021.126877.png)
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Abstract
En 中文
Road authorities in cold climates regularly apply salt on roads, during winter, to ensure public safety. Pavement surface temperature is a significant parameter affecting snow and ice melting at the onset of a storm. Road temperature below the freezing point of the applied brine causes ice to form on the road surface. Excessive application of salt on the road can have adverse environmental impacts, especially to soil and water quality. Therefore, forecasting pavement temperature can optimize road salt application, while reducing costs, improving public safety, and reducing environmental impacts. This research aims to develop a reliable and accurate pavement surface temperature prediction tool using machine learning techniques. This study employs advanced deep neural network (DNN) learning techniques to predict pavement surface temperature for road salt management purposes. To validate the proposed methodology, this work used hourly solar radiation and air temperature data from Environment Canada, and pavement surface temperature data collected from the Road Weather Information System (RWIS), for sites around the city of Toronto, Ontario. The performance of the proposed DNN model, that integrates a Convolutional Neural Network (CNN) with a Long Short-Term Memory (LSTM), on pavement surface temperature forecasting, was evaluated against four other comparative machine learning methods, LSTM, Convolutional-LSTM (ConvLSTM), Sequence-to-Sequence (Seq2Seq), and Wavelet neural network (Wavenet) models. A dataset of 10,895 samples was collected as an hourly pavement surface temperature record in three timeframes from November 2009 to March 2014 on Highway 401 in southern Ontario, Canada. Experiments included predictions for 1-, 2-, 4- and 6-hours ahead, using as input features air temperature, solar radiation, and present pavement temperature. The proposed pavement temperature forecasting approach resulting from this investigation suggests our new approach is more accurate than previous models. These results reveal that the CNN-LSTM outperforms the four other models and creates predictions closer to the true pavement surface temperature.
Keywords:
Machine learning
Pavement surface temperature
Deep learning
CNN-LSTM
ConvLSTM
LSTM
Seq2Seq
Wavenet
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