• Journal of Korean Society of Transportation
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• v.32 no.6
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• pp.686-693
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• 2014

This study presents a model that enables to predict road surface temperature using neural network theory. Historical road surface temperature data were collected from Road Weather Information System. They used for the calibration of the model. The neural network was designed to predict surface temperature after 1-hour, 2-hour, and 3-hour from now. The developed model was performed on Cheongwon-Sangju highway to test. As a result, the standard deviation of the difference of the predicted and observed was $1.27^{\circ}C$, $0.55^{\circ}C$ and $1.43^{\circ}C$, respectively. Also, comparing the predicted surface temperature and the actual data, R2 was found to be 0.985, 0.923, and 0.903, respectively. It can be concluded that the explanatory power of the model seems to be high.

• Journal of Korean Society of Transportation
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• v.29 no.2
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• pp.123-131
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• 2011

This study proposes a model for road surface temperature prediction on basis of the heat-energy balance equation between atmosphere and road surface. The overall model is consisted of two types of modules: 1) Canopy 1 is used to describe heat transfer between soil surface and atmosphere; and 2) Canopy 2 can reflect the characteristics of pavement type. Input data used in the model run is obtained from the Korea Meteorological For model validation, the observed and predicted surface temperature data are compared using data collected on MoonEui Bridge along CheongWon-Sangju Expressway, and the comparison is made on winter and other seasons separately. Analysis results show that average difference between two temperatures lies within ${\pm}2^{\circ}C$ which is considered as appropriate from a micrometeorology point of view. The model proposed in this paper can be adopted as a useful tool in practical applications for winter maintenance. This study being a fundamental research is anticipated to be a starting point for further development of robust surface road temperature prediction algorithms.

• Journal of the Korean Applied Science and Technology
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• v.37 no.6
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• pp.1507-1516
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• 2020

This study analyzed the scenario for road freezing section by applying the road freezing evaluation algorithm. To apply road freezing algorithm, the influencing factors on road freezing were reviewed. Observation data from four points, Mokgam IC, Jeongneung tunnel, Seongsan bridge, and Yeomchang bridge were used for analysis. All observatories are installed on the expressway, and they are classified for the analysis of road freezing characteristics. When the difference between the road surface temperature and dew-point temperature of the road freezing evaluation algorithm was 3℃ or less, the section where road freezing occurred was checked. In addition, road freezing evaluation was derived through the change of the road surface condition and water film thickness of the freezing section.

• Proceedings of the Korea Concrete Institute Conference
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• 2010.05a
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• pp.391-392
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• 2010

The frozen road is effected as major cause of car accident in winter. Especially, road surface freezing on the highway can lead to fatal accident. The accident by frozen road can effectively reduced by prevent road surface freezing before it frozen as evaluate road surface condition. Therefore, this study installed thermometer in each depth of concrete pavement for evaluate road surface conditions which freezing chronically. The result of this study will be used as preliminary data for predict before freezing.

• Proceedings of the KOR-KST Conference
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• 1998.10a
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• pp.342-351
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• 1998

노면표시는 운전자에게 시선유도와 각종 규제 및 지시에 대한 정보를 제공함으로써 교통안전 및 소통에 도움을 주는 시설로써 주야간의 시인성 확보가 중요하며 이를 위해서는 반사화가 필요하다. 노면표시의 반사성능은 유리알 (Glass Bead)의 함량 및 종류, 용융온도, 도료의 색도등 각 영향인자에 의해 결정되지만 현재는 시공법 및 관련 연구의 미흡으로 현행기준의 최하수준을 상회하는 정도로 제공되고 있다. 따라서 본 연구에서는 노면표시의 시공에 관계되는 각 요인중 반사성능가 내구성에 영향을 미치는 주요인자 및 반사성능을 최적화하기 위한 인자별 최적조합을 도출하였다. 연구결과 유리알 살포량이 중량비로 25%-30%, 용융시 온도가 $188^{\circ}C$$\pm$$10^{\circ}C$일 때 노면표면시의 반사성능이 최적화됨을 밝혀내었다. 또한 유리알의 품질개선과 함께 황색 노면표시의 재귀반사 휘도계수 기준을 현재 기준보다 상향조정할 필요성이 있음을 제안하였다.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.14 no.2
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• pp.30-38
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• 2015

In this study, to improve effectiveness of road management services and the safety of the road in winter, road surface temperature prediction model was developed. We have utilized the existing input data of meteorological data and additional traffic data. This Road surface temperature prediction model was utilizing a Heat-Balance Method additionally considering amount of traffic that produce heat radiation by vehicle-tire friction. This improved model was compared to the based model to check into influence of traffic affecting the road surface temperature. There were verified by comparing the real observed road surface temperature of the third Gyeong-In highway and road surface temperature from the two models. As a result, the error of real observed and the predicted value (RMSE) was found to average $1.97^{\circ}C$. Observed road surface temperature was dramatically affected by the sunlight from 6 a.m. to 2 p.m. and degree of influence decreases after that. The predictive value of the model is lower than the observed value in the afternoon, and higher at night. These results appear due to the shielding of solar radiation caused by the vehicle in the afternoon and at night, the vehicle appeared to cause thermal heat supply.

• Proceedings of the Korea Water Resources Association Conference
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• 2023.05a
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• pp.263-263
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• 2023

블랙아이스는 눈에 쉽게 구분되지 않아 많은 교통사고를 초래하고 있다. 한국교통연구원 교통사고분석시스템에 따르면, 2017년부터 2021년까지 5년간의 서리/결빙으로 인한 교통사고 사망자는 122명, 적설로 인한 교통사고 사망자는 40명으로, 블랙아이스는 적설에 비해 위험성이 높은 것으로 나타난다. 과거의 다양한 연구에서 블랙아이스 생성조건을 기압과 한기 축적등의 조건에서 예측해왔지만, 이러한 기상학적 모델은 봄철 해빙기의 일교차로 인한 눈의 해동과 재냉각과 같은 다양한 기상 조건에서의 블랙아이스 탐지가 어렵다는 한계가 있어 최근에는 이미지 판별과 딥러닝모델(YOLO 등)을 기반으로 한 센서가 제시되고 있다. 그러나, 이러한 방법은 충분한 컴퓨팅 자원이 뒷받침되어야 하며, 블랙아이스 탐지까지 걸리는 속도가 빠르지 못한 편으로, 블랙아이스 초입 구간에서의 제동에 취약하다는 잠재적인 약점을 가지고 있다. 그러므로 본 연구에서는 블랙아이스의 주 원인인 서리나 어는비가 발생하기 위해서 주변 공기가 이슬점 온도 이하, 노면온도와 이슬점이 어는점보다 낮아야 함을 이용, IoT 센서 모듈을 통해 Magnus 방정식으로 계산한 이슬점 온도와 노면 온도를 사용하는 이동식 블랙아이스 추정 장치를 제시한다. 본 장치는 대기압, 온도, 습도로부터 계산된 이슬점 온도와 노면 온도를 통한 서리발생 가능성과 대기 온도, 노면 온도를 통해 어는비의 발생환경 여부를 계산한다. 본 연구 결과를 통해 블랙아이스 추정과 기상정보 생산을 동시에 가능케 하며, 추정 결과를 통합 수집서버에 전송함으로서 운전자에게 전방 블랙아이스 위험 구간을 조기에 전달하는 시스템과 이를 관리하기 위한 인프라를 구축하여 운전 시 결빙 미끄러짐 사고를 저감하고자 한다.

• 한국방재학회:학술대회논문집
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• 2010.02a
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• pp.89.2-89.2
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• 2010

고속도로에서 주행속도가 높아지게 되면, 도로의 노면 상태에 따라 차량의 안전과 쾌적한 운전자의 환경이 변화될 수 있다. 이처럼 도로의 노면 상태를 결정하는 주된 인자는 도로의 평탄성과 소성변형에 의한 노면의 요철이라고 할 수 있다. 평탄하지 못한 도로를 자동차가 고속으로 주행하게 되면, 자동차의 속도에 의한 도로와의 마찰이 발생하여 자동차에는 매우 큰 흔들림이 발생하게 된다. 또한, 도로의 경우에도 자동차의 차축과 도로면에서 발생하는 충격에 의해 미세한 진동이 발생하게 된다. 그리고 광섬유 브래그 격자(FBG)센서는 외부에서 작용하는 매우 미세한 물리량에 의한 변화의 측정이 가능한 매우 우수한 계측 센서로 사용이 가능하기 때문에 온도보상형 광섬유진동센서를 제작하였고, 이를 고속도로 포장면의 평탄성 모니터링에 활용하고자 하였다.

• Journal of the Korean Association of Geographic Information Studies
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• v.23 no.4
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• pp.156-172
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• 2020

The purpose of this study was to predict road surface temperature using high-resolution solar radiation data. The road surface temperature prediction model (RSTPM) was applied to predict road surface temperature; this model was developed based on the heat-balance method. In addition, using SOLWEIG (SOlar and LongWave Environmental Irradiance Geometry-model), the shadow patterns caused by the terrain effects were analyzed, and high-resolution solar radiation data with 10 m spatial resolution were calculated. To increase the accuracy of the shadow patterns and solar radiation, the day that was modeled had minimal effects from fog, clouds, and precipitation. As a result, shadow areas lasted for a long time at the entrance and exit of a tunnel, and in a high-altitude area. Furthermore, solar radiation clearly decreased in areas affected by shadows, which was reflected in the predicted road surface temperatures. It was confirmed that the road surface temperature should be high at topographically open points and relatively low at higher altitude points. The results of this study could be used to forecast the freezing of sections of road surfaces in winter, and to inform decision making by road managers and drivers.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.17 no.6
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• pp.121-132
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• 2018

Weather is an important factor affecting roadway transportation in many aspects such as traffic flow, driver 's driving patterns, and crashes. This study focuses on the relationship between weather and road surface condition and develops a model to estimate the road surface condition using machine learning. A road surface sensor was attached to the probe vehicle to collect road surface condition classified into three categories as 'dry', 'moist' and 'wet'. Road geometry information (curvature, gradient), traffic information (link speed), weather information (rainfall, humidity, temperature, wind speed) are utilized as variables to estimate the road surface condition. A variety of machine learning algorithms examined for predicting the road surface condition, and a two - stage classification model based on 'Random forest' which has the highest accuracy was constructed. 14 days of data were used to train the model and 2 days of data were used to test the accuracy of the model. As a result, a road surface state prediction model with 81.74% accuracy was constructed. The result of this study shows the possibility of estimating the road surface condition using the existing weather and traffic information without installing new equipment or sensors.