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

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Abstract

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 6 min 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.

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Author information

Authors and Affiliations

  1. School of Electronic and Information Engineering, Beijing Jiaotong University, Beijing, 100044, China

    ShiHong Wang, TianLe Wang, Tao Tang & TaoGang Hou

  2. School of Integrated Circuit Science and Engineering, Beihang University, Beijing, 100191, China

    ShiHong Wang

  3. School of Mechanical Engineering and Automation, Beihang University, Beijing, 100191, China

    Xuan Pei & Hao Wang

  4. CATS Highway Engineering & Technology Co., Ltd., Beijing, 100029, China

    Qiang Zhu

Authors
  1. ShiHong Wang
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  2. TianLe Wang
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  3. Xuan Pei
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  4. Hao Wang
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  5. Qiang Zhu
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  6. Tao Tang
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  7. TaoGang Hou
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Corresponding author

Correspondence to TaoGang Hou.

Additional information

This work was 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), and the Beijing Laboratory for Urban Mass Transit (Grant No. 353203535). The authors would like to acknowledge the database support from the China Academy of Transportation Science, the drawings from Siyang Zhang of Beihang University, and the suggestions from Dr. Shuai Zhang of Beihang University.

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Wang, S., Wang, T., Pei, X. et al. Highway icing time prediction with deep learning approaches based on data from road sensors. Sci. China Technol. Sci. 66, 1987–1999 (2023). https://doi.org/10.1007/s11431-022-2230-8

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  • DOI: https://doi.org/10.1007/s11431-022-2230-8

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