Applying the METRo model for road-condition forecasting in Norway

<p>According to the 14<sup>th</sup> 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 <em>Model of the Environment and Temperature of Roads – METRo</em>, 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 <em>AutonoWeather - Enabling autonomous driving in winter conditions through optimized road weather interpretation and forecast</em> financed by the Research Council of Norway in 2020. </p>

Secure and Accurate Road Weather Services - The Belgian SARWS project

<p>For the generation of accurate warnings for dangerous road conditions, road weather models typically depend on observations from road weather stations (RWS) at fixed locations along roads and highways. Observations at higher resolution in space and time have the potential to provide more localized, real-time weather warnings. The rise of connected vehicles with onboard sensing capabilities opens up exciting new opportunities in this field. For this purpose, a heterogeneous group of industrial stakeholders and researchers consisting of more than thirty partners from seven countries including Belgium, initiated the CELTIC-NEXT project "Secure and Accurate Road Weather Services" (SARWS). The goal of SARWS is to provide real-time weather services by expanding observational data from traditional RWS sources with data from large-scale vehicle fleets. The Belgian consortium consists of Verhaert New Products & Services, Be-Mobile, Inuits, bpost, imec - IDLab (University of Antwerp) and the Royal Meteorological Institute of Belgium (RMI). Within the Belgian consortium, the focus is on the use of vehicle data to enable real-time warning services for potentially dangerous local weather and road surface conditions. The vehicle fleet consists of cars of the Belgian Post Group (bpost) in the region around Antwerp, and will consist of 15 cars by the end of summer 2021. Data on vehicle dynamics, such as wheel speed, are gathered from the vehicle's CAN bus, while an additional installed sensor box collects air temperature, relative humidity and road surface temperature observations. Data on wipers and fog light activation, and camera images are also collected.</p><p>We present the Belgian SARWS setup, data flow, and the developed data distribution platform. We discuss validation results for 2021, comparing car sensor observations to close RWS and weather stations, focusing mainly on air temperature, humidity and road surface temperature, and show the need for calibration and bias correction. We also demonstrate an experimental version of the RMI road weather model that provides short-term road weather forecasts for 50-meter road segments, using car sensor data for initialization, and compare with road weather forecasts at nearby station locations. We also demonstrate machine learning approaches that are explored to detect weather information from the vehicle dynamics.</p>

Numerical Analysis of Temperature Reduction Effect of Permeable Pavements

Permeable pavements can effectively reduce the urban road surface temperatures. To study the cooling effect of holding water on the road surface under the comprehensive influence of the external water and heat environment, a numerical model established by a finite element analysis software and an indoor test were used to verify the temperature change behavior of the road surface under different heating temperatures and holding water conditions. The results show that the average error between the numerical model and indoor test is 3.5%, and the model reliability is high. Under the same conditions, the change in thickness of the permeable pavement surface and base course has a negligible effect on the temperature of the road surface. For every 100 J/(kg/°C) increase in the specific heat capacity of the upper surface course and lower surface course, the maximum daily road surface temperature can be reduced by approximately 2.1°C and 0.4°C, respectively. The road surface temperature shows a similar pattern when the thermal conductivity increases. Under dry conditions, the maximum daily road surface temperature can be reduced by approximately 3.4°C and 4.3°C for surface-permeable and fully permeable pavements, respectively. This study provides reference suggestions for optimizing the selection and design of urban road pavement structures from the perspectives of permeable pavement design, material parameters, and structure type.