Applying the METRo model for road-condition forecasting in Norway

According to the 14^th Annual Road Safety Performance Index Report by the European Transport Safety Council, annually more than 100,000 accidents occur on European roads, of which 22,660 people lost their lives in 2019. The factors contributing to road traffic accidents are commonly grouped into three categories: environment, vehicle or driver. The European accident research and safety report 2013 by Volvo states in about 30% of accidents contributing factors could be attributed to weather and environment leading for example to unexpected changes in road friction, such as black ice. In this work, we are developing a solution to forecast road conditions in Norway by applying the Model of the Environment and Temperature of Roads – METRo, which is a surface energy balance model to predict the road surface temperature. In addition, METRo includes modules for water accumulation at the surface (liquid and frozen) and vertical heat dissipation (Crevier and Delage, 2001). The road condition is forecasted for a given pair of latitude, longitude and desired forecast time. Data from the closest road weather station and postprocessed weather forecast are used to initialize METRo and provide boundary conditions to the road weather forecast. The weather forecasts are obtained from the THREDDS service and the road weather station data from the FROST service, both provided by MET Norway. We develop algorithms to obtain the data from these services, process them to match the METRo model input requirements and send them to METRo’s pre-processing algorithms, which combine observations and forecast data to initialize the model. In a case study, we will compare short-term METRo forecasts with observations obtained by road weather stations and with observations retrieved by car-mounted environmental sensors (e.g., road surface temperature). This work is part of the project AutonoWeather - Enabling autonomous driving in winter conditions through optimized road weather interpretation and forecast financed by the Research Council of Norway in 2020.