Spatiotemporal analysis of road surface temperature (RST) and building wall temperature (BWT) and its relation to the traffic volume at Jorhat urban environment, India

2020 ◽  
Author(s):  
Rituraj Neog ◽  
Shukla Acharjee ◽  
Jiten Hazarika
Keyword(s):  
Surface Temperature ◽  
Urban Environment ◽  
Wall Temperature ◽  
Spatiotemporal Analysis ◽  
Traffic Volume ◽  
Road Surface ◽  
Road Surface Temperature ◽  
Building Wall

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>

Remote measuring system of road surface temperature based on dual window infrared detector

2020 ◽  
Vol 49 (6) ◽  
pp. 20190455
Author(s):  
程寅 Yin Cheng ◽  
刘建国 Jianguo Liu ◽  
桂华侨 Huaqiao Gui ◽  
陆亦怀 Yihuai Lu ◽  
魏秀丽 Xiuli Wei
Keyword(s):  
Surface Temperature ◽  
Infrared Detector ◽  
Road Surface ◽  
Measuring System ◽  
Road Surface Temperature ◽  
Dual Window

scholarly journals Modeling Road Surface Temperature from Air Temperature and Geographical Parameters—Implication for the Application of Floating Car Data in a Road Weather Forecast Model

2019 ◽  
Vol 58 (5) ◽  
pp. 1023-1038 ◽  
Author(s):  
Yumei Hu ◽  
Esben Almkvist ◽  
Torbjörn Gustavsson ◽  
Jörgen Bogren
Keyword(s):  
Surface Temperature ◽  
Air Temperature ◽  
Winter Season ◽  
Road Surface ◽  
View Factor ◽  
Traffic Intensity ◽  
Weather Patterns ◽  
Floating Car Data ◽  
Thermal Mapping ◽  
Road Surface Temperature

AbstractPrecise forecasts of road surface temperature (RST) and road conditions allow winter roads to be maintained efficiently. The upcoming “big data” application known as “floating car data” (FCD) provides the opportunity to improve road weather forecasts with measurements of air temperature Ta from in-car sensors. The research thus far with regard to thermal mapping has mainly focused on clear and calm nights, which occur rarely and during low traffic intensity. It is expected that more than 99% of the FCD will be collected during conditions other than clear and calm nights. Utilizing 32 runs of thermal mapping and controlled Ta surveys carried out on mostly busy roads over one winter season, it was possible to simulate the use of Ta and geographical parameters to reflect the variation of RST. The results show that the examined route had several repeatable thermal fingerprints during times of relatively high traffic intensity and with different weather patterns. The measurement time, real-time weather pattern, and previous weather patterns influenced the spatial pattern of thermal fingerprints. The influence of urban density and altitude on RST can be partly seen in their relationship with Ta, whereas the influence of shading and sky-view factor was only seen for RST. The regression models with Ta included explained up to 82% of the RST distribution and outperformed models that are based only on the geographical parameters by as much as 30%. The performance of the models denotes the possible utility of Ta from FCD, but further investigation is needed before moving from controlled Ta measurements to Ta from FCD.

scholarly journals Road Surface Condition Forecasting in France

2009 ◽  
Vol 48 (12) ◽  
pp. 2513-2527 ◽  
Author(s):  
L. Bouilloud ◽  
E. Martin ◽  
F. Habets ◽  
A. Boone ◽  
P. Le Moigne ◽  
...  
Keyword(s):  
Surface Temperature ◽  
Initial Conditions ◽  
Meteorological Data ◽  
Surface Condition ◽  
Road Surface ◽  
Snow Layer ◽  
Surface Conditions ◽  
The Road ◽  
Road Surface Temperature ◽  
On The Road

Abstract A numerical model designed to simulate the evolution of a snow layer on a road surface was forced by meteorological forecasts so as to assess its potential for use within an operational suite for road management in winter. The suite is intended for use throughout France, even in areas where no observations of surface conditions are available. It relies on short-term meteorological forecasts and long-term simulations of surface conditions using spatialized meteorological data to provide the initial conditions. The prediction of road surface conditions (road surface temperature and presence of snow on the road) was tested at an experimental site using data from a comprehensive experimental field campaign. The results were satisfactory, with detection of the majority of snow and negative road surface temperature events. The model was then extended to all of France with an 8-km grid resolution, using forcing data from a real-time meteorological analysis system. Many events with snow on the roads were simulated for the 2004/05 winter. Results for road surface temperature were checked against road station data from several highways, and results for the presence of snow on the road were checked against measurements from the Météo-France weather station network.

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