Road Weather Condition Prediction Based on Numerical Weather Prediction and Machine Learning Technology
PU Xiushu
LIU Xinchao
SONG Yixuan
GUO Rong
GUO Jie
Abstract：Road weather conditions are closely related to traffic safety,as slippery and icy roads can easily lead to accidents.Therefore,accurate and timely predictions of road weather conditions are essential.The data used in the present study included the observation data of road weather conditions from three ground observation stations along the Yakang highway and the 24-hour numerical weather prediction data for the corresponding area.Based on a decision tree model,the corresponding relationship between numerical weather prediction results and various types of road weather conditions was established,enabling predictions of road weather conditions for the next 24 hours.The results show that,for the five types of road weather conditions at three ground observation stations,the average accuracy of cross-validation for our proposed model was 89.79%.In the extrapolation experiment,the average accuracy of prediction for the next 6 hours was 64.73%,for the next 12 hours was 77.30%,for the next 18 hours was 80.19%,and for the next 24 hours was 70.41%.Our research method effectively achieved continuous spatial coverage and long-term prediction of road weather conditions,providing important reference information for traffic safety,public travel decision-making,and weather forecasting services.
Keywords：road weather conditionprediction modeldecision treemachine learningmeteorological service
Publication Date：2024-12-28
Online Publishing Date：2025-08-15(First online date of this platform, not the publication date of the document)
Pages：12( 993-1004 )