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.

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 Verification of Road Surface Temperature Forecasts Assimilating Data from Mobile Sensors

2019 ◽  
Vol 34 (3) ◽  
pp. 539-558 ◽  
Author(s):  
Virve Karsisto ◽  
Lauri Lovén
Keyword(s):  
Surface Temperature ◽  
Winter Season ◽  
Forecast Accuracy ◽  
Road Surface ◽  
Weather Station ◽  
The Road ◽  
Road Surface Temperature ◽  
Weather Model ◽  
Statistical Correction ◽  
On The Road

ABSTRACT The advances in communication technologies have made it possible to gather road condition information from moving vehicles in real time. However, data quality must be assessed and its effects on the road weather forecasts analyzed before using the new data as input in forecasting systems. Road surface temperature forecasts assimilating mobile observations in the initialization were verified in this study. In addition to using measured values directly, different statistical corrections were applied to the mobile observations before using them in the road weather model. The verification results are compared to a control run without surface temperature measurements and to a control run that utilized interpolated values from surrounding road weather stations. Simulations were done for the period 12 October 2017–30 April 2018 for stationary road weather station points in southern Finland. Road surface temperature observations from the stations were used in the forecast verification. According to the results, the mobile observations improved the accuracy of road surface temperature forecasts when compared to the first control run. The statistical correction methods had a positive effect on forecast accuracy during the winter, but the effect varied during spring when the daily temperature variation was strong. In the winter season, the forecasts based on the interpolated road surface temperature values and the forecasts utilizing mobile observations with statistical correction had comparable accuracy. However, the tested area has high road weather station density and not much elevation variation, so results might have been different in more varying terrain.

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>

scholarly journals Accounting for anthropic energy flux of traffic in winter urban road surface temperature simulations with TEB model

2015 ◽  
Vol 8 (6) ◽  
pp. 4737-4779
Author(s):  
A. Khalifa ◽  
M. Marchetti ◽  
L. Bouilloud ◽  
E. Martin ◽  
M. Bues ◽  
...  
Keyword(s):  
Surface Temperature ◽  
Numerical Models ◽  
Road Surface ◽  
Winter Maintenance ◽  
Urban Road ◽  
The Road ◽  
Inappropriate Use ◽  
Road Surface Temperature ◽  
The Cost ◽  
The Town

Abstract. A forecast of the snowfall helps winter coordination operating services, reducing the cost of the maintenance actions, and the environmental impacts caused by an inappropriate use of de-icing. In order to determine the possible accumulation of snow on pavement, the forecast of the road surface temperature (RST) is mandatory. Physical numerical models provide such forecast, and do need an accurate description of the infrastructure along with meteorological parameters. The objective of this study was to build a reliable urban RST forecast with a detailed integration of traffic in the Town Energy Balance (TEB) numerical model for winter maintenance. The study first consisted in generating a physical and consistent description of traffic in the model with all the energy interactions, with two approaches to evaluate the traffic incidence on RST. Experiments were then conducted to measure the traffic effect on RST increase with respect to non circulated areas. These field data were then used for comparison with forecast provided by this traffic-implemented TEB version.

scholarly journals Detection of Coordinate Based Accident-Prone Areas on Road Surface using Machine Learning Methods

2020 ◽  
Vol 12 (3) ◽  
pp. 19-25
Author(s):  
Anitha Kumari Dara ◽  
Dr. A. Govardhan
Keyword(s):  
Spatial Data ◽  
Surface Defects ◽  
Road Accident ◽  
Road Surface ◽  
The Other ◽  
Work Outcomes ◽  
Traffic Density ◽  
Surface Conditions ◽  
The Road ◽  
On The Road

The growth in the road networks in India and other developing countries have influenced the growth in transport industry and other industries, which depends on the road network for operations. The industries such as postal services or mover services have influenced the similar growths in these industries as well. However, the dependency of these industries is high on the road surface conditions and any deviation on the road surface conditions can also influence the performance of the services provided by the mentioned services. Nonetheless, the conditions of the road surface are one of the prime factors for road safety and number of evidences are found, which are discussed in subsequent sections of this work, that the bad road surface conditions are increasing the road accidents. Several parallel research attempts are deployed in order to find out, the regions where the road surface conditions are not proper, and the traffic density is higher. Nevertheless, outcomes of these parallel works are highly criticised due to the lack of accuracy in detection of the road surface defects, detection of accurate location of the defects and detection of the traffic density data from various sources. Thus, this work proposes a novel framework for detection of the road defect and further mapping to the spatial data coordinates resulting into the detection of the accident-prone zones or accident affinities of the roads. This work deploys a self-adjusting parametric coefficient-based regression model for detection of the risk factors of the road defects and in the other hand, extracts the traffic density of the road regions and further maps the accident affinities. This work outcomes into 97.69% accurate detection of the road accident affinity and demonstrates less complexity compared with the other parallel research outcomes

scholarly journals Comparison of algorithms for road surface temperature prediction

2018 ◽  
Vol 2 (3) ◽  
pp. 212-224
Author(s):  
Bo Liu ◽  
Libin Shen ◽  
Huanling You ◽  
Yan Dong ◽  
Jianqiang Li ◽  
...  
Keyword(s):  
Surface Temperature ◽  
Prediction Method ◽  
Temperature Data ◽  
Road Surface ◽  
Machine Learning Algorithms ◽  
Gradient Boosting ◽  
Data Set ◽  
Content Type ◽  
The Road ◽  
Road Surface Temperature

Purpose The influence of road surface temperature (RST) on vehicles is becoming more and more obvious. Accurate predication of RST is distinctly meaningful. At present, however, the prediction accuracy of RST is not satisfied with physical methods or statistical learning methods. To find an effective prediction method, this paper selects five representative algorithms to predict the road surface temperature separately. Design/methodology/approach Multiple linear regressions, least absolute shrinkage and selection operator, random forest and gradient boosting regression tree (GBRT) and neural network are chosen to be representative predictors. Findings The experimental results show that for temperature data set of this experiment, the prediction effect of GBRT in the ensemble algorithm is the best compared with the other four algorithms. Originality/value This paper compares different kinds of machine learning algorithms, observes the road surface temperature data from different angles, and finds the most suitable prediction method.

Justification Constructions of the Road Pavement Under Conditions of Changing Road Surface Temperature

2021 ◽  
pp. 161-172
Author(s):  
Anatoly Novik ◽  
Igor Drozdetskiy ◽  
Pavel Petukhov ◽  
Nikita Labusov ◽  
Vasilina Novik ◽  
...  
Keyword(s):  
Surface Temperature ◽  
Road Surface ◽  
The Road ◽  
Road Pavement ◽  
Road Surface Temperature

scholarly journals Comparing the Performance of Two Road Weather Models in the Netherlands

2017 ◽  
Vol 32 (3) ◽  
pp. 991-1006 ◽  
Author(s):  
Virve Karsisto ◽  
Sander Tijm ◽  
Pertti Nurmi
Keyword(s):  
The Netherlands ◽  
Surface Temperature ◽  
Weather Forecasting ◽  
Freezing Point ◽  
Road Surface ◽  
The Road ◽  
Road Surface Temperature ◽  
Weather Model ◽  
The Difference ◽  
Comparable Quality

Abstract High-quality road condition forecasts are a prerequisite for road authorities to ensure wintertime road safety. Harsh winter conditions can cause problems for traffic not only in countries where snowy winters are common but also in regions where the temperature drops below the freezing point occasionally. This study reports on the evaluation of the Royal Netherlands Meteorological Institute’s (KNMI) new road weather forecasting model by comparing it with the Finnish Meteorological Institute’s (FMI) road weather model, both run for 321 Dutch road weather stations, four times daily (0300, 0900, 1500, and 2100 UTC) during the test period, 15 January–28 February 2015. Road surface temperature forecasts by both models were evaluated against observations. The KNMI model produced slightly more accurate forecasts than the FMI model. The main reason for the difference is probably due to the optimization of the physical properties of the KNMI model for the Netherlands, whereas the FMI model is designed for quite different Finnish wintertime meteorological conditions. However, in general the road surface temperature forecasts were of quite comparable quality.

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