Preventing Black Ice Caused by Freezing Rain and Frost on Sensorless Rural Highways: Winter Maintenance in South...

Black ice poses a significant threat to drivers during winter due to its low visibility. Winter road maintenance personnel face continuous challenges in preventing its formation because of its high unpredictability. To address this issue, this paper proposes a practical strategy for effective winter road maintenance aimed at preventing black ice caused by freezing rain and frost. The strategy comprises three phases: black ice prediction, stakeholder notification, and anti-icing chemical application. The core of the strategy involves predicting black ice on rural highways that lack localized road weather sensors. Specifically, the prediction model relies exclusively on atmospheric data. The Extreme Gradient Boosting (XGBoost) algorithm was employed for prediction, achieving precision, recall, and F1 scores of 0.99 and outperforming Random Forest and Deep Neural Network models, which achieved F1 scores of 0.96 and 0.97, respectively. The XGBoost model's hyperparameters were optimized using the DEPSO algorithm, improving its F1 score by approximately 0.03. Furthermore, a feature importance analysis was conducted to determine the relative contribution of various meteorological variables to black ice formation. To effectively disseminate predictive alerts to maintenance personnel and drivers, a smartphone-based system was developed. Finally, optimal spread rates for anti-icing chemicals, calibrated to pavement temperatures, are presented. The methodology proposed in this study can significantly enhance the efficiency of winter road maintenance on rural highways where localized road weather data are unavailable.