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Winter road safety procedures are crucial for maintaining safe operating conditions and daily transportation activities without impedance or risk to the population. Typically, road surface salting mitigates ice build-up; however, road surface temperature (RST) forecasting with mathematical models performs poorly where the geographic location and climate cannot be generalized or described models trained with data from sensors in unrepresentative geographic locations. Additionally, modeling interactions among meteorological, geographical, and physical road characteristics can prove challenging. This study proposes using deep neural networks to model the nonlinear interactions of the above features, thereby creating a better model for forecasting RST by up to twelve hours into the future.
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