Winter Road Surface Condition Forecasting

This study has attempted to address a challenging problem in winter road maintenance, namely road surface condition (RSC) forecasting. A novel conceptual framework for short-term road surface condition forecasting is proposed. This framework is designed to consider all important conditional factors, including weather, traffic, and maintenance operations. Salt applications are modeled by considering a history instead of one single-time interval of salting operations. In this way, the variation of snow/ice melting speed caused by both residual salt amounts and salt-contaminant mixing state is effectively incorporated in the forecasting model, enabling accurate short-term forecasting for contaminant layers. This approach practically circumvents a major limitation of previous studies, making the postsalting RSC forecasting more reliable and accurate. Under this model framework, several advanced time series modeling methodologies are introduced into the analysis in order to capture the highly complex interactions between RSC measures and conditional factors. Those methodologies, especially the univariate and multivariate integrated autoregressive moving average (ARIMA) methods, are for the first time applied to the winter RSC evolution process. The forecasting errors of surface temperature and contaminant layer depths are all found to be small. The calibrated models are simple in structure, easy to interpret, and mostly consistent with physical knowledge. Compared to existing models, the proposed models provide extra flexibility for refactory, tuning, and deployment.