关键词: 高速公路；冻雨灾害；路面状态识别；时空风险预测；机器学习

Abstract: To ensure vehicle operation safety under freezing rain conditions and improve the capability of pavement condition identification and risk early warning for expressways under freezing rain hazards, this paper takes the Xihan Section of the Beijing-Kunming Expressway as the research object. By integrating road environmental characteristics, meteorological characteristics and pavement monitoring data, an XGBoost-based identification model for freezing rain hazards is constructed, in which pavement conditions are classified into five categories: dry, wet, snow-covered, icy, and freezing rain. Through integrated analysis of meteorological elements, road environment and pavement condition data, intelligent identification and prediction of pavement conditions under different hazardous weather conditions are realized. The results show that the overall accuracy,weighted F1-score and area under curve (AUC) of the model reach 0.910 2, 0.912 0 and 0.932 6 respectively, demonstrating favorable performance in identifying freezing rain conditions. Analysis indicates that temperature, humidity, altitude and precipitation are the primary factors affecting freezing rain formation. High-risk areas are mainly concentrated in sections such as high-altitude bridges, long steep longitudinal slopes and tunnel exits, with higher risks from nighttime to early morning. The research findings can provide a reference for freezing rain hazard monitoring, risk early warning for key road sections and traffic safety management of expressways.

Key words: expressways; freezing rain hazards; pavement condition identification; spatio-temporal risk prediction; machine learning