Machine Learning Approaches to Estimate Road Surface Temperature Variation along Road Section in Real-Time for Winter Operation

2019 ◽  
Vol 18 (2) ◽  
pp. 343-355
Author(s):  
Choong Heon Yang ◽  
Duk Geun Yun ◽  
Jin Guk Kim ◽  
Gunwoo Lee ◽  
Seoung Bum Kim
Keyword(s):  
Machine Learning ◽  
Surface Temperature ◽  
Real Time ◽  
Temperature Variation ◽  
Road Surface ◽  
Learning Approaches ◽  
Road Section ◽  
Road Surface Temperature ◽  
Winter Operation ◽  
Surface Temperature Variation

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>

Analysis of Road Surface Temperature Change Patterns using Machine Learning Algorithms

2017 ◽  
Vol 19 (2) ◽  
pp. 35-44 ◽  
Author(s):  
Choong Heon Yang ◽  
Seoung Bum Kim ◽  
Chun Joo Yoon ◽  
Jin Guk Kim ◽  
Jae Hong Park ◽  
...  
Keyword(s):  
Machine Learning ◽  
Surface Temperature ◽  
Temperature Change ◽  
Learning Algorithms ◽  
Road Surface ◽  
Machine Learning Algorithms ◽  
Surface Temperature Change ◽  
Road Surface Temperature

Predicting surface temperature variation in urban settings using real-time weather forecasts

Urban Climate ◽  
2017 ◽  
Vol 20 ◽  
pp. 192-201 ◽  
Author(s):  
Maryam Karimi ◽  
Brian Vant-Hull ◽  
Rouzbeh Nazari ◽  
Megan Mittenzwei ◽  
Reza Khanbilvardi
Keyword(s):  
Surface Temperature ◽  
Real Time ◽  
Temperature Variation ◽  
Urban Settings ◽  
Weather Forecasts ◽  
Surface Temperature Variation

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
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