최근 급격한 기후 변화로 인해 도로 교통사고의 발생 빈도가 증가하고 있으며, 특히 겨울철에 자주 발생하는 도로 살얼음(블랙아이 스) 현상이 주요 원인 중 하나로 지목되고 있다. 도로살얼음의 형성 메커니즘은 다양한 요인에 따라 복합적으로 작용하며, 당시의 도 로 기상 조건과 도로의 기하학적 구조에 따라 얼음의 형태 및 강도가 결정된다. 그중에서도 도로 노면 온도는 도로살얼음 형성에 중 요한 요소로, 여러 나라에서 겨울철 교통안전 평가를 위한 주요 지표로 사용되고 있다. 그러나 현재 도로 노면 온도에 대한 명확한 정 의가 부족할 뿐만 아니라, 측정 방법에 따라 계측 편차와 온도 손실 등 여러 한계가 존재해 정확한 온도 측정이 어려운 실정이다. 이 에 본 연구는 지중 깊이에 따른 온도 데이터와 도로 기상 데이터를 결합하여 보다 정밀한 도로 노면 온도 예측 방법을 제시하는 것을 목적으로 한다. 연구를 위해 지중 깊이 2cm, 3cm, 4cm, 5cm, 7cm, 9cm, 15cm, 20cm에 각각 온도 센서를 설치하였으며, 기상 데이터는 해당 지점에서 2m 떨어진 AWS(Automatic Weather System)를 통해 대기 온도, 습도, 강수량, 일사량 등의 정보를 수집하였다. 이를 바 탕으로 지중 온도와 기상 조건의 상관관계를 활용하여 노면 온도를 예측하는 방법론을 도출하였다. 본 연구의 결과는 도로 노면 온도 예측의 정확성을 향상시킬 뿐만 아니라, 새로운 접근 방식을 통해 노면 온도의 정의를 재정립하는 데 기여할 것으로 기대된다.

PURPOSES : Due to the frequent occurrence of accidents on icy roads during nighttime, it would be advantageous to notify road managers and drivers about the most perilous areas. This would allow road managers to treat the icy roads with de-icing chemicals and enable drivers to be better prepared for potential hazards. Essential information about pavement temperature is required to identify icy spots on the road. METHODS : With the goal of estimating nighttime pavement temperature on the National Highways in Korea using atmospheric data, the current study investigated a widely recognized forecasting method known as deep neural network (DNN). To achieve this objective, the input data for the models were gathered from the weather agency's website. The dataset comprised of relative humidity, air temperature, dew point temperature, as well as the differences in air temperature and humidity between two consecutive days. RESULTS : In order to assess the effectiveness of the built DNN model, a comparison was made using baseline pavement temperature data gathered through an infrared-based pavement temperature sensor installed in a highway patrol car. The results indicated that the DNN model achieved a mean absolute error (MAE) of 0.42 and a root mean square error (RMSE) of 0.62. In comparison, a conventional regression model yielded an MAE of 2.07 and an RMSE of 2.64. Thus, the DNN model demonstrated superior performance in comparison to the conventional regression model. CONCLUSIONS : Considering the increasing focus on preventive maintenance, these newly developed prediction models can be implemented proactively as a preventive measure against icing. This proactive approach has the potential to significantly improve traffic safety on winter roads.

PURPOSES : The increasing heat wave warnings during the summer season in Korea have significant impacts on daily life and industry as a whole, especially in urban areas (such as areas with asphalt and sidewalk pavements). Heat waves directly affect urban heat island and heat dome phenomena. Various urban temperature reduction measures are being discussed to reduce urban heat islands and heat dome phenomena and to improve citizen safety against summer heat waves; suggestions include thermal packaging, rooftop greening, and expansion of vegetation areas. There is a lack of analysis on the methodology for increasing the road spraying effect during summer heat waves (e.g., there is no systematic engineering study on the effect from reducing the temperature of the road spraying during a heat wave in the city) and on the types of road pavements in the city. In addition, as the asphalt pavements of roadways and block pavements installed in sidewalks account for a considerable portion of all pavements, this study provides a more systematic and scientific approach and procedures for reducing temperatures through road spraying in the city by tracking the effects of heat waves. METHODS : In this preliminary experiment, four types of road pavement materials were selected as test specimens: asphalt test specimens (AP- 300 mm × 300 mm × 50 mm), concrete test specimens (CP-300 mm × 300 mm × 50 mm), impermeable blocks (IB-200 mm × 200 mm × 60 mm), and self-permeable blocks (PB-200 mm × 200 mm × 60 mm). As a test method to evaluate the size and duration of each spray effect package type, the surface temperature of each specimen was measured using thermal imaging cameras every 20 min after spraying at the maximum temperature point of each specimen, and the average surface temperature was analyzed based on the collected temperature data. In addition, to conduct a quantitative analysis of the effect of reducing the surface temperature of road pavements by road spraying in summer, field tests were conducted on asphalt roads and watertight blocks for sidewalks. RESULTS : As a result of the comparative analysis of the spray effect under a 36 ℃ air temperature based on a heat wave warning, the surface temperatures were, from high to low, the asphalt (68.8 ℃), concrete (59.1 ℃), impermeable block (57.3 ℃), and permeable block (58.7 ℃). The asphalt pavement had the greatest effect on the heat island and heat dome phenomena. From measuring the temperature reduction effect and sustainability of each type of road pavement, the surface temperature reduction effects were ranked in the following order: water-permeable block (Δ18.0 ℃), asphalt test piece (Δ17.5 ℃), concrete test piece (Δ12.2 ℃), and water-permeable block (over 240 min). In the report pitching block, the average road surface temperature reduction between the pore recovery and treatment was expected to continue to decrease by approximately -4.3 ℃ on the day of work and approximately -2.4 ℃ on the next day. The expected effect of the temperature reduction owing to simple spraying on the surface of the pore block was evaluated to be limited to the day. CONCLUSIONS : In the road spray effect analysis conducted on the common asphalt road, there was a slight difference in the initial temperature reduction size as the test specimen was measured, but the surface temperature difference between the non-spray section and spray section tended to be approximately Δ3°C after 140 minutes of spraying. Therefore, it was determined that the asphalt pavement temperature reduction plan through road spraying in urban areas in summer would be the most effective if it was repeated twice or more in an hour (between 13:00 and 14:00) on the day of the heat wave.

PURPOSES : The purpose of this study was to develop techniques for forecasting black ice using historical pavement temperature data collected by patrol cars and concurrent atmospheric data provided by the Korea Meteorological Administration. METHODS : To generate baseline data, the physical principle that ice forms when the pavement temperature is negative and lower than the dew-point temperature was exploited. To forecast frost-induced black ice, deep-learning algorithms were created using air, pavement, and dew point temperatures, as well as humidity, wind speed, and the z-value of the historical pavement temperature of the target segment. RESULTS : The suggested forecasting models were evaluated against baseline data generated by the above-mentioned physical principle using pavement temperature and atmospheric data gathered on a national highway in the vicinity of Young-dong in the Chungcheongbukdo province. The accuracies of the forecasting models for the bridge and roadway segments were 94% and 90%, respectively, indicating satisfactory results. CONCLUSIONS : Preventive anti-icing maintenance activities, such as applying anti-icing chemicals or activating road heating systems before roadways are covered with ice (frost), could be possible with the suggested methodologies. As a result, traffic safety on winter roads, especially at night, could be enhanced.

PURPOSES : This aim of this study is to develop a model for predicting road surface temperature using an LSTM network to predict road surface temperature associated with road icing. METHODS : A long short-term memory (LSTM) neural network suitable for time-series data with time correlation is used in the analysis. Moreover, an optimal neural network architecture is designed via hyperparameter search and verification using learning and validation data. Finally, the generalization performance is evaluated based on the RMSE using unseen data as test data. RESULTS : The results show that the predicted data are similar to the actual road surface temperature patterns , and that the network appears to be generalized. CONCLUSIONS : The LSTM model improves the accuracy and generalization of road surface temperature prediction, as compared with other machine learning models.

PURPOSES: This study aimed to evaluate the performance of a model developed for road surface temperature change pattern in reflecting specific road characteristics. Three types of road sections were considered, namely, basic, tunnel, and soundproof tunnel. METHODS: A thermal mapping system was employed to collect actual road surface temperature and locational data of the survey vehicle. Data collection was conducted 12 times from 05:30 am to 06:30 am on the test route, which is an uninterrupted flow facility. A total of 9010 road surface temperature data were collected, and half of these were selected based on a random selection process. The other half was used to evaluate the performance of the model. The model used herein is based on machine learning algorithms. The mean absolute error (MAE) was used to evaluate the accuracy of the estimation performance of the model. RESULTS: The MAE was calculated to determine the difference between the estimated and the actual road surface temperature. A MAE of 0.48℃ was generated for the overall test route. The basic section obtained the smallest error whereas that of the tunnel was relatively high. CONCLUSIONS: The road surface temperature change is closely related to the air temperature. The process of data pre-processing is very important to improve the estimation accuracy of the model. Lastly, it was difficult to determine the influence of the data collection date on the estimation of the road surface temperature change pattern due to the same weather conditions.

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.

겨울철에는 눈, 우박 등의 원인으로 교통사고가 많이 발생한다. 이런 겨울철에 차량이 주행하여 발생하는 열은 도로환경의 안정성에 영향을 끼친다. 본 연구에서는 도로기상정보시스템(RWIS; Road Weather Information System)에서 제공하는 실시간 기상관측 데이터를 이용하여 차량 주행시 발생하는 열이 노면온도변화에 어떠한 영향을 끼치는지 알아보도록 한다. 노면은 태양복사에너지와 대기장파복사에너지를 흡수하고, 현열, 잠열, 지구장파에너지 및 포장열을 방출한다. 차량이 통행하는 노면에서의 열수지 방정식으로 나타내면 식(1)과 같다.    Δ       (1) ：노면의 체적 열용량 [J/㎥/K] ,   ：노면 온도 [K] , Δ : 노면 표층 두께 [m],  ：순복사 플럭스 [W/㎡] ：자연 바람에 의한 현열 플럭스 [W/㎡], ：잠열 플럭스 [W/㎡], ：포장열 플럭스 [W/㎡] 본 연구에서는 노면온도 관측을 위해 기상센서 시스템을 구축되어 있는 제3경인고속도로 목감IC 전방을 선정하여 적용하였다. 데이터는 24시간(2014년 12월 2일 0시 ∼ 24시) 동안 1분 간격으로 수집하였다. 주요 입력 자료(기온, 습도, 기압, 풍속, 노면온도 등)는 기상센서를 이용한 관측 데이터를 사용하였고, 태양복사량 등은 기상 개발연구원의 예측 데이터를 이용하였다. 노면의 열수지에서 차량열의 영향을 알아보기 위해 각각의 열수지 성분 절대값의 합계에 대한 차량열 플럭스( 및   ,   , )의 각 비율을 로 나타내었다. 이는 식 (2), (3)과 같다.                  (2)                   or  or  (3) 차량 복사열이 일사량과 교통량이 많은 낮에는 노면에 미치는 영향이 감소하고, 일사의 영향이 없는 야간과 새벽에는  _   와 비슷한 비율을 된다. 차량풍 현열이 노면의 열수지에 끼치는 영향( _ , △)는 0:00∼8:00까지는 5∼7%로 작지만, 그 이후 일사의 영향에 의한 기온, 노면온도 차이의 증가, 교통량의 증가로 인해 최대 30.0%로 급격히 증가한다. 또한 17:00부터 일사량의 감소로 기온과 노면온도 차이가 감소되어 노면 열 수지에 끼치는 영향이 급격히 감소한다. 주행하는 자동차에서 방사되는 열을 바탕으로 차량열 플럭스의 시간변화에 따른 노면에서의 영향을 평가할 수 있었다. 겨울철 노면온도에 있어서 차량열의 영향은 46.5 ∼75.9%이며, 타이어 마찰열, 차량 복사열 및 차량풍 현열와 전체 열 플럭스의 비율은 각각 최대 약 30%에 달했다. 즉, 건조한 노면상태에서 주행하는 차량의 발생열이 노면온도에 큰 영향을 미친다.

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.

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.

기상 조건을 고려하여 교통사고 발생 원인과의 관계를 분석한 연구에서는 갑작스러운 온도 또는 기상 상 태의 변화는 교통사고에 영향을 준다는 결론을 제시하고 있다. 특히, 동절기에 기온이 떨어지는 경우, 도로 결빙에 의한 교통사고가 발생할 가능성이 높으며, 도로 표면에서 발생하는 결빙 현상은 운전자가 육안으로 쉽게 관측 할 수 없는 한계를 가진다. 따라서, 노면에 발생하는 결빙은 교통사고의 원인으로 작용 할 수 있 기 때문에, 결빙이 발생한 구간에서 운전자의 주의를 높여 줄 수 있는 대안 등이 요구된다. 결빙 현상은 야 간과 같이 온도가 낮아지는 시간에 발생하므로, 결빙을 예측하기 위해서는 노면 온도에 대한 정보가 필요하 다. 그러나, 노면온도 변화에 직･간접적으로 영향을 주는 대기 기상정보는 거시적(5km*5km 단위) 범위에 서 정보가 제공되므로, 운전자가 주행하는 도로망에 대한 정확한 정보를 제공하는데 어려움이 따른다. 따라 서, 본 연구에서는 운전자가 주행하는 도로망에 대기 온도를 제공 및 예측하기 위해 대기온도, 대기습도, 풍 향 등을 고려한 기상정보와 노면 온도 자료를 활용하여 노면 온도를 예측하는 모형을 개발하고자 한다. 노면 온도 예측 모형을 개발하기 위하여 Thermal Mapping 장비가 장착된 차량을 이용하였으며, Thermal Mapping 장비를 통해 노면온도, 대기온도, 대기습도를 측정한다. 또한, 시스템적으로 GPS가 연계되어 있어서, 정확한 위치 정보의 취득이 가능하며, 노면온도에 영향을 미치는 기상정보는 기상청에 서 수집하여 제공하는 온도, 습도, 풍속 등을 이용하였다. 또한, 지형의 요소를 교량부, 산지부, 평지부, 해안, 내륙을 구분하여 지형적인 요소도 반영 될 수 있도록 하였다. 예측 모형은 비선형 분석 모형을 사용 하여 노면 예측 정확도에 적합한 알고리즘을 사용할 계획이며, 본 연구의 결과는 기상과 관련된 교통안전 관련 연구에 활용될 것으로 기대한다.

This study examined test piece production and on-the-spot test results of power supply system of road surface temperature sensor module that was put to forecast road freezing at bridge section. The temperature sensor module was produced to replace sensitive sensor (temperature and humidity sensor) of road surface that was put by wire system, and to supply power on-the-spot by itself, And, the study researched optimization considering power consumption for self power generation and sensor operation.