Validation of a road surface temperature prediction model using real-time weather forecasts

2012 ◽  
Vol 16 (7) ◽  
pp. 1289-1294 ◽  
Author(s):  
Choong Heon Yang ◽  
Duk-Geun Yun ◽  
Jung Gon Sung
Keyword(s):  
Prediction Model ◽  
Surface Temperature ◽  
Real Time ◽  
Road Surface ◽  
Temperature Prediction ◽  
Weather Forecasts ◽  
Road Surface Temperature

scholarly journals Secure and Accurate Road Weather Services - The Belgian SARWS project

2021 ◽  
Author(s):  
Sylvain Watelet ◽  
Joris Van den Bergh ◽  
Maarten Reyniers ◽  
Wim Casteels ◽  
Toon Bogaerts ◽  
...  
Keyword(s):  
Surface Temperature ◽  
Real Time ◽  
Air Temperature ◽  
Vehicle Dynamics ◽  
Large Scale ◽  
Road Surface ◽  
Sensor Data ◽  
Weather Forecasts ◽  
Road Surface Temperature ◽  
Weather Stations

<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>

Road surface temperature prediction based on gradient extreme learning machine boosting

2018 ◽  
Vol 99 ◽  
pp. 294-302 ◽  
Author(s):  
Bo Liu ◽  
Shuo Yan ◽  
Huanling You ◽  
Yan Dong ◽  
Yong Li ◽  
...  
Keyword(s):  
Surface Temperature ◽  
Extreme Learning Machine ◽  
Road Surface ◽  
Temperature Prediction ◽  
Road Surface Temperature ◽  
Learning Machine

scholarly journals Effects of Speeds and Weights of Travelling Vehicles on the Road Surface Temperature

2021 ◽  
pp. 100077
Author(s):  
Samim Mustafa ◽  
Hidehiko Sekiya ◽  
Aya Hamajima ◽  
Iwao Maeda ◽  
Shuichi Hirano
Keyword(s):  
Surface Temperature ◽  
Road Surface ◽  
The Road ◽  
Road Surface Temperature ◽  
On The Road

scholarly journals Applying the METRo model for road-condition forecasting in Norway 

2021 ◽  
Author(s):  
Stephanie Mayer ◽  
Fabio Andrade ◽  
Torge Lorenz ◽  
Luciano de Lima ◽  
Anthony Hovenburg ◽  
...  
Keyword(s):  
Surface Temperature ◽  
Heat Dissipation ◽  
Road Traffic ◽  
Weather Forecast ◽  
Road Surface ◽  
Balance Model ◽  
Weather Station ◽  
The Road ◽  
Road Surface Temperature ◽  
Road Condition

<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>

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