Given the hazards posed by black ice, it is crucial to investigate the conditions that contribute to its formation. Two ensemble machinelearning algorithms, Random Forest (RF) and Extreme Gradient Boosting (XGBoost), were employed to forecast the occurrence of black ice using atmospheric data. Additionally, explainable artificial intelligence techniques, including Feature Importance (FI) and partial dependence Plot (PDP), were utilized to identify atmospheric conditions that significantly increase the likelihood of black ice formation. The machinelearning algorithms achieved a forecasting accuracy of 90%, demonstrating reliable performance. FI analysis revealed distinct key predictors between the algorithms: relative humidity was the most critical for RF, whereas wind speed was paramount for XGBoost. The PDP analysis identified the specific atmospheric conditions under which black ice was likely to form. This study provides detailed insights into the atmospheric precursors of frost/fog-induced black ice formation. These findings enable road managers to implement proactive winter road maintenance strategies, such as optimizing anti-icing patrol routes and displaying warnings on various message signs, thereby enhancing road safety.

ABSTRACT
1. Introductuon
2. Previous Works
3. Data
    3.1. Data Collection
    3.2. Data Aggregation
4. Black Ice Forecast
    4.1. Building Block of Machine Learning Models
    4.2. Reference Data for Model Evaluation
    4.3. Application of Black Ice Forecasting Models
    4.4. Assessment
5. Explainable Artificial Intelligence Analysis
    5.1. Feature Importance Analysis
    5.2. Partial Dependence Plot Analysis
6. Conclusions and Future Studies
ACKNOWLEDGMENTS
REFERENCES

• Jang Jinhwan(한국건설기술연구원 도로교통연구본부 연구위원) | 장진환 Corresponding author