Abstract
A road weather observation system (RWOS), which is used for observing the weather and surface conditions of roads, cannot be installed for all sections of the road to provide high-resolution road weather information because the associated devices are expensive. Furthermore, because the road condition information that is most closely related to safe driving is weather conditions, e.g. ice, snow, and rain, road conditions should be determined by a nodelink when a high-resolution automatic weather system (AWS) is applied. Therefore, in this research, an algorithm was proposed for determining real-time road surface conditions using ensemble learning. Learning data was organized using time-series mapping of observed data from the RWOS and the AWS. The road condition determination model uses machine learning. As a result, weather conditions from nodelinks on major roads in Seoul, South Korea, were calculated and presented. Road conditions were determined and provided to drivers in real-time based on the corresponding data.
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