RCD is made possible by machine learning, ruled-based models and big data
RCD is made possible by combining machine learning, ruled-based algorithms, and vast amounts of real-life road and environment data. Our models and big data enable us to understand the present and predict the future.
By using any road network and dynamically segmenting it, RCD internally models the road network including its surrounding topography (Road section characteristics). Using a proprietary model and arrangement for collection and processing of live and historical data from different data sources:
Stationary data source
Dynamic data source
Meteorological data source
RCD algorithms model the current and forecasted road condition down to a road segment resolution of 25 meters per segment. The method is unparalleled as it uses extensive knowledge of the microclimate and the surrounding topography when forecasting the road status.