• 대기
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• 제24권4호
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• pp.471-479
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• 2014

A road surface temperature prediction model (UM-Road) using input data of the Unified Model (UM) output and road physical properties is developed and verified with the use of the observed data at road weather information system. The UM outputs of air temperature, relative humidity, wind speed, downward shortwave radiation, net longwave radiation, precipitation and the road properties such as slope angles, albedo, thermal conductivity, heat capacity at maximum 7 depth are used. The net radiation is computed by a surface radiation energy balance, the ground heat flux at surface is estimated by a surface energy balance based on the Monin-Obukhov similarity, the ground heat transfer process is applied to predict the road surface temperature. If the observed road surface temperature exists, the simulated road surface temperature is corrected by mean bias during the last 24 hours. The developed UM-Road is verified using the observed data at road side for the period from 21 to 31 March 2013. It is found that the UM-Road simulates the diurnal trend and peak values of road surface temperature very well and the 50% (90%) of temperature difference lies within ${\pm}1.5^{\circ}C$ (${\pm}2.5^{\circ}C$) except for precipitation case.

• 한국도로학회논문집
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• 제18권4호
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• pp.1-8
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• 2016

PURPOSES : This study evaluates the reliability of the patterns of changes in the road surface temperature during winter using a statistical technique. In addition, a flexible road segmentation method is developed based on the collected road surface temperature data. METHODS : To collect and analyze the data, a thermal mapping system that could be attached to a survey vehicle along with various other sensors was employed. We first selected the test route based on the date and the weather and topographical conditions, since these factors affect the patterns of changes in the road surface temperature. Each route was surveyed a total of 10 times on a round-trip basis at the same times (5 AM to 6 AM). A correlation analysis was performed to identify whether the weather conditions reported for the survey dates were consistent with the actual conditions. In addition, we developed a method for dividing the road into sections based on the consecutive changes in the road surface temperature for use in future applications. Specifically, in this method, the road surface temperature data collected using the thermal mapping system was compared continuously with the average values for the various road sections, and the road was divided into sections based on the temperature. RESULTS : The results showed that the comparison of the reported and actual weather conditions and the standard deviation in the observed road surface temperatures could produce a good indicator of the reliability of the patterns of the changes in the road surface temperature. CONCLUSIONS : This research shows how road surface temperature data can be evaluated using a statistical technique. It also confirms that roads should be segmented based on the changes in the temperature and not using a uniform segmentation method.

• 한국안전학회지
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• 제32권6호
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• pp.150-156
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• 2017

A frozen road surface increases traffic accidents during the winter season. Hence, information on easily-frozen road sections and their specificities are required to prevent traffic accidents. Frozen road surfaces are determined by equipment measuring road surface temperatures. However, there are limitations in investigating the entire road network. Therefore, it is imperative to develop new methods that effectively determine road surface freezing risks. Meteorologically, road surfaces are frozen when the actual temperature cools down to the dew point temperature. Under this condition, there is likely to be frost if relative humidity reaches 100% and frozen road surfaces as the temperature gets lower. Meteorological characteristics give us an alternative to a direct measurement road surface temperature to estimate risks of road surface freezing. Based on the clues, the relationship between severity of traffic accidents and temperature changes is empirically investigated using Paju weather data. The results reveal that as the temperature gets lower and changes in current temperature are relatively small, the severity of traffic accidents become higher. In addition, the same is true when the difference between current temperature and the dew point temperature is relatively small, as it increases possibilities of road surface freezing. Future studies must investigate how current temperature and the dew point temperature affect road surface freezing and thereby establish a time-space scope to estimate possible road surface freezing sections using only weather and road material type data. This would provide invaluable information for predicting and preventing frozen road accidents based on weather patterns.

• 한국도로학회논문집
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• 제20권2호
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• pp.127-135
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• 2018

PURPOSES : This study develops various models that can estimate the pattern of road surface temperature changes using machine learning methods. METHODS : Both a thermal mapping system and weather forecast information were employed in order to collect data for developing the models. In previous studies, the authors defined road surface temperature data as a response, while vehicular ambient temperature, air temperature, and humidity were considered as predictors. In this research, two additional factors-road type and weather forecasts-were considered for the estimation of the road surface temperature change pattern. Finally, a total of six models for estimating the pattern of road surface temperature changes were developed using the MATLAB program, which provides the classification learner as a machine learning tool. RESULTS : Model 5 was considered the most superior owing to its high accuracy. It was seen that the accuracy of the model could increase when weather forecasts (e.g., Sky Status) were applied. A comparison between Models 4 and 5 showed that the influence of humidity on road surface temperature changes is negligible. CONCLUSIONS : Even though Models 4, 5, and 6 demonstrated the same performance in terms of average absolute error (AAE), Model 5 can be considered the optimal one from the point of view of accuracy.

• 한국도로학회논문집
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• 제19권2호
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• pp.35-44
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• 2017

PURPOSES: This study suggests a specific methodology for the prediction of road surface temperature using vehicular ambient temperature sensors. In addition, four kind of models is developed based on machine learning algorithms. METHODS : Thermal Mapping System is employed to collect road surface and vehicular ambient temperature data on the defined survey route in 2015 and 2016 year, respectively. For modelling, all types of collected temperature data should be classified into response and predictor before applying a machine learning tool such as MATLAB. In this study, collected road surface temperature are considered as response while vehicular ambient temperatures defied as predictor. Through data learning using machine learning tool, models were developed and finally compared predicted and actual temperature based on average absolute error. RESULTS : According to comparison results, model enables to estimate actual road surface temperature variation pattern along the roads very well. Model III is slightly better than the rest of models in terms of estimation performance. CONCLUSIONS : When correlation between response and predictor is high, when plenty of historical data exists, and when a lot of predictors are available, estimation performance of would be much better.

• 대기
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• 제31권5호
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• pp.525-537
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• 2021

Black ices on road surfaces in winter tend to cause severe and terrible accidents. It is very difficult to detect black ice events in advance due to their localities as well as sensitivities to surface and upper meteorological variables. This study develops a methodology to detect the road sections vulnerable to black ice with the use of road surface temperature data obtained from a mobile road weather observation vehicle. The 7 experiments were conducted on the route from Nam-Wonju IC to Nam-Andong IC (132.5 km) on the Jungang Expressway during the period from December 2020 to February 2021. Firstly, temporal road surface temperature data were converted to the spatial data with a 50 m resolution. Then, the spatial road surface temperature was normalized with zero mean and one standard deviation using a simple normalization, a linear de-trend and normalization, and a low-pass filter and normalization. The resulting road thermal map was calculated in terms of road surface temperature differences. A road ice index was suggested using the normalized road temperatures and their horizontal differences. Road sections vulnerable to black ice were derived from road ice indices and verified with respect to road geometry and sky view, etc. It was found that black ice could occur not only over bridges, but also roads with a low sky view factor. These results are expected to be applicable to the alarm service for black ice to drivers.

• 한국응용과학기술학회지
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• 제37권6호
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• pp.1507-1516
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• 2020

본 연구는 도로 노면결빙 판정 알고리즘을 적용하여 도로결빙 구간에 대한 시나리오를 분석하였다. 도로결빙 알고리즘 적용을 위해서, 도로 결빙에 대한 영향인자를 검토하고, 분석을 위해서 목감IC, 정릉터널, 성산대교, 염창교 등 4지점의 관측자료를 활용하였다. 관측소는 모두 고속화도로에 설치되어 있으며, 도로 결빙 특성 분석을 위하여 분류하였다. 도로 결빙 판정 알고리즘의 노면온도-노점온도 차가 3℃ 이하일 때 도로결빙 발생 구간을 확인하고 결빙 구간의 노면상태와 수막두께 변화를 통해 도로 결빙 판정을 도출하였다.

• 한국조경학회지
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• 제39권3호
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• pp.107-116
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• 2011

도시의 온도상승의 주요 인자 중 하나가 도시부 도로이다. 본 연구의 목적은 도시도로 녹지가 표면온도의 저감에 영향을 미치는 요인을 파악하는 것이다. 이를 위해 서울시 도로 18개 지점을 선정하여 도로횡단구성요소(주변토지이용, 차도, 보도, 식수대, 중앙분리대)별로 열화상 카메라로 표면온도를 조사하고, 도시도로 녹지 면적을 측정하였다. 도시도로 녹지의 도로 표면온도 저감에 미치는 영향을 분석한 결과 도로 주변토지이용이 녹지 및 오픈스페이스일 경우 도로온도에 미치는 영향이 가장 크며, 식생중앙분리대의 면적, 식수대의 면적 순으로 표면온도를 저감시키는 것으로 나타났다. 도로주변 뿐만 아니라 도로설계의 구성요소 내에서 식수대, 중앙분리대 등의 녹지면적 증가는 도로의 표변온도 저감에 영향을 미친다. 도시의 고온화현상을 완화하기 위하여 도시도로 녹지 면적을 증가시키는 것도 한 방편이다.

• 한국환경과학회지
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• 제31권4호
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• pp.329-339
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• 2022

Better understanding the mechanism of black ice occurrence on the road in winter is necessary to reduce the socio-economic damage it causes. In this study, intensive observations of road weather elements and surface information under the influence of synoptic high-pressure patterns (22^nd December, 2020 and 29^th January, and 25^th February, 2021) were carried out using a mobile observation vehicle. We found that temperature and road surface temperature change is significantly influenced by observation time, altitude and structure of the road, surrounding terrain, and traffic volume, especially in tunnels and bridges. In addition, even if the spatial distribution of temperature and road surface temperature for the entire observation route is similar, there is a difference between air and road surface temperatures due to the influence of current weather conditions. The observed road temperature, air temperature and air pressure in Nongong Bridge were significantly different to other fixed road weather observation points.

• 한국콘텐츠학회논문지
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• 제11권11호
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• pp.42-48
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• 2011

본 논문에서는 교량구간의 도로 결빙예측 및 감지를 위한 시스템 설계를 제안하였다. 센서 노드의 하드웨어는 마이크로프로세서, 온도 센서, 습도 센서, 그리고 Zigbee 무선 통신으로 구성되었다. 관제센터의 소프트웨어는 관제센터에 수집된 교량 온도, 습도 데이터로 관찰하기 위하여 구현되었다. 교량 노면의 결빙은 노면의 온도가 이슬점 온도 이하이면서 영하일 때 발생한다. 제안된 시스템을 이용하여 도로면의 온도 및 습도 분포를 측정하였다. 측정 데이터는 도로 결빙이 발생하는 시점을 예측하기 위하여 사용되었다. 실제 결빙되는 것보다 최소 30분 이전에 결빙시점을 예측하여 경고가 이루어진다. 이 결과로 결빙으로 인한 교통사고를 방지하기 위하여 사용 할 수 있다.