Highway icing time prediction with deep learning approaches based on data from road sensors  

作　　者：WANG ShiHong WANG TianLe PEI Xuan WANG Hao ZHU Qiang TANG Tao HOU TaoGang 

机构地区：[1]School of Electronic and Information Engineering,Beijing Jiaotong University,Beijing 100044,China [2]School of Integrated Circuit Science and Engineering,Beihang University,Beijing 100191,China [3]School of Mechanical Engineering and Automation,Beihang University,Beijing 100191,China [4]CATS Highway Engineering&Technology Co.,Ltd.,Beijing 100029,China

出　　处：《Science China(Technological Sciences)》2023年第7期1987-1999,共13页中国科学（技术科学英文版）

基　　金：supported by the Fundamental Research Funds for the Central Universities (Grant No.2020JBM265);the Beijing Natural Science Foundation (Grant No.3222016);the National Natural Science Foundation of China (Grant No.62103035);the China Postdoctoral Science Foundation(Grant No.2021M690337);the Beijing Laboratory for Urban Mass Transit (Grant No.353203535)。

摘　　要：In harsh climates,highway icing poses a hazard to traffic safety and increases road maintenance costs.It is of great significance to predict when the highway icing may occur and take a preventive plan.However,there are few studies on highway icing time prediction due to the scarcity and complexity of data.In this study,variables of icing temperature,friction,ice percentage,road surface temperature,water film height,saline concentration,and road condition were collected by road sensors distributed on a highway in China.A large-scale time series highway surface information dataset called HighwayIce is formed.Furthermore,a deep learning approach called IceAlarm,composed of long short-term memory neural network(LSTM),multilayer perceptron(MLP),and residual connection,has been developed to predict when the highway will ice.The LSTM is used to process dynamic variables,the MLP is used to process static variables,and the fully-connected layers with residual connections are used to make a deep fusion.The experimental results show that the average mean absolute error before icing using the IceAlarm model is about 6min and outperforms all baseline models.The HighwayIce dataset and IceAlarm model can help improve the prediction accuracy and efficiency of forecasting real-world road icing time,therefore reducing the impact of icy road conditions on traffic.

关 键 词：road icing time prediction road surface condition multilayer perceptron(MLP) long short-term memory(LSTM) residual connection 

分 类 号：TP212[自动化与计算机技术—检测技术与自动化装置] TP18[自动化与计算机技术—控制科学与工程] U418.41[交通运输工程—道路与铁道工程]