국내 도로포장에서는 겨울철 결빙으로 인한 교통사고가 자주 발생하고 있다. 특히, 최근의 기후변화에 따른 동절기 사고건수가 증가 하고 있다. 이러한 문제를 해결하기 위해 다양한 도로노면 결빙 재료 및 공법, 기술들이 개발되고 현장에 적용되고 있다. 그러나 도로 노면 결빙 및 공법 관련 기술들에 대한 성능평가가 어려운 실정이다. 따라서, 도로환경에서 발생되는 환경조건 등의 모사를 통한 도로 노면 결빙 공법 및 기술의 성능평가를 위한 실증실험시설이 필요하다. 본 연구에서는 국내 동절기 도로포장에 대한 기본적인 환경조 건을 분석하여 이를 기반으로 도로노면 결빙 관련 환경모사가 가능한 실증실험시설을 구축하였다. 도로노면 결빙에 영향을 주는 요소 는 대기온도, 노면온도, 습도, 풍속, 기압 등의 다양한 인자가 있다. 이러한 요소중에 실증실험시설 구축에서는 대기온도, 노면온도, 습 도, 풍속을 모사할 수 있도록 하였다. 도로노면 결빙에 있어 노면온도가 중요한 요소로 작용할 수 있기에 챔버내에 시편의 온도를 제 어할 수 있는 내부챔버를 추가로 구성하였다. 또한, 도로노면 결빙을 유발할 수 있는 어는비 분사장치 및 온․습도 변화에 따른 시편 의 모니터링을 위해 열화상 카메라를 이용하도록 구성하였다. 도로노면 결빙 실증실험시설이 효율적으로 운영된다면 다양하게 개발되 는 기술들에 대한 실험을 통해 성능평가가 가능할 것으로 판단되며, 향후 이를 기반으로 실증실험시설의 운영에 대한 매뉴얼 및 평가 방법 등을 마련하는 것이 필요하다.

겨울철 국내 도로 결빙으로 인한 교통사고가 증가하는 추세를 보이고 있으며 2018년~2022년까지 총 4,609건의 결빙 교통사고가 발 생하였다. 결빙 교통사고의 치사율은 2.3으로 일반적인 교통사고와 비교하여 높은 치사율을 보이며 최근 5년(2018~2022)동안 결빙 교 통사고로 인하여 107명이 사망자와 7,728명의 부상자가 발생하였다. 현재 국토교통부에서 제시한 결빙 취약구간 평가기준표에 따라 결 빙 위험 구간을 지정하고 있으나, 해당 기준은 결빙의 주요 요인으로 고려되는 기상조건을 충분히 반영하지 못하고 있다. 도로 결빙은 노면온도가 0℃ 이하이며 노면에 수분이 공급될 때 형성되며 기온, 구름량, 풍속, 풍향, 상대습도, 강수량 등의 기상인자들이 복합적으 로 작용하여 결빙이 발생한다는 점을 고려하였을 때, 기상 특성은 도로 결빙의 주요 인자로 판단된다. 따라서 국내 결빙 취약구간 평 가기준의 개선이 필요하며 본 연구의 목적은 국내 결빙 교통사고 데이터를 분석하고 결빙이 형성되는 기상 조건을 구체화하는 것이다. 분석을 위한 데이터로 2018년~2022년까지 5년동안 발생한 결빙사고 사례와 기상청 방재기상관측소(AWS)에서 제공하는 기상 데이터 를 적용하였다. 이후, 박스도표, 확률밀도함수 등의 통계분석을 적용하여 결빙 형성 기상 조건을 구체화하였다. 이를 통하여 기존 결빙 취약구간 평가기준의 기상학적 개선 방향성을 제시할 수 있으며 더 나아가 도로 결빙 예측 로직 개발을 기대할 수 있다.

PURPOSES : The purpose of this study is to statistically analyze the meteorological factors that contribute to the formation of road surface icing based on actual cases of icing accidents and provide directions for improving icing evaluation criteria. METHODS : In this study, we collected cases of domestic road icing accidents by searching news articles with the keyword ‘icing collision accidents’. Subsequently, we determined the latitude, longitude, and altitude of accident locations using satellite map service. We applied the Inverse Distance Weighting (IDW) method and temperature lapse rate to estimate meteorological data at each location. Finally, statistical analysis was conducted for temperature, humidity, and precipitation occurrence using probability density functions. RESULTS : As a result, road icing accident data points with identifiable location coordinates were collected. Among these, temperature, humidity, and precipitation occurrence from Automated Weather Stations (AWS) data were selected for analysis. During the process of correcting meteorological factors using the Inverse Distance Weighting (IDW) method, the optimal Weighting Exponent (p) that minimizes the error was determined and applied. The results showed that accidents occurring in the morning indicated the highest accident occurrence rate. The average temperature at the time of the accidents was -1.4°C, with a humidity level of 85.1%. Precipitation was observed at the time of the accident in 19 cases. CONCLUSIONS : Icing on pavement can occur not only under extreme weather conditions but also under typical meteorological conditions. Typically, icing can occur when the relative humidity is above 70%. Accordingly, for future improvements in the evaluation criteria for icing-prone areas by the Ministry of Land, Infrastructure and Transport, it is possible to incorporate the temperature and humidity ranges that generally lead to icing, taking into account climate characteristics.

본 연구는 도로 노면결빙 판정 알고리즘에 대해 알고리즘을 개선하고 실제 현장 측정 자료와 알고리즘 예측값을 비교하였을 때 알고리즘에 대한 적중률을 분석하였다. 분석을 위하여 포천시 신북면 금 동리의 도로 및 기상을 측정하였다. 알고리즘은 기존 도로 결빙 알고리즘을 선정하여 실제 결빙 조건 및 측정 수치에 맞춰 4차 알고리즘까지 개선하였다. 최종적으로 응결에 의한 결빙, 강수에 의한 결빙, 적설에 의한 결빙, 결빙상태의 지속, 풍속에 의한 결빙 5개의 알고리즘을 제작하였다. 포천 현장에서 알고리즘을 활용하여 예측할 경우 경우 결빙 적중률이 93.22%까지 개선되었다. 결빙 알고리즘에 대한 조합 비율에 대 해 도출하였을 때 응결에 의한 결빙과 결빙상태의 지속에 대한 알고리즘이 96%를 차지하였다.

PURPOSES: The purpose of this study is to identify the key factors that influence a driver’s trust in road-freezing risk information METHODS : A survey was designed and conducted in the metropolitan area to analyze drivers’perception towards road-freezing risk information. A binary logistic regression model was developed based on the survey responses. The statistically significant model coefficients were determined and their odds ratios were calculated. Finally, they were considered as the key factors influencing drivers’trust in roadfreezing risk information. RESULTS : The developed binary logistic regression model was validated through statistical tests. The final results indicated that previous experiences of accidents on slippery roads, as well as route choice patterns are statistically significant, having 95% confidence levels. CONCLUSIONS : Previous experiences of accidents on slippery roads and road choice patterns are the factors that most significantly influence drivers’trust in road-freezing risk information. The findings of this study will be used as fundamental data in establishing tailored road-freezing risk mitigation procedures and services.

PURPOSES: According to accident statistics for road built in 2004, the ratio of accidents on frozen roads to normal roads is 0.9%, whereas the fatality ratio is 2.7%. The risk of accidents on frozen roads is very high. Measures taken every year to prevent traffic accidents of frozen roads in the winter season are still insufficient. Additionally, measures have been established mainly on rural roads. Therefore, for urban roads, analyses and measures to prevent accidents are lacking. In this study, data on accidents on frozen roads was used to search for the causes behind these accidents and measures to reduce accidents have been recommended. METHODS: In this study, collected data from the TAMS (Traffic Accident Management System), which were collected by the Seoul National Police Agency was used. The data were divided into vehicle, people, and condition of road. The analytical model used here was the Logistic Regression Model, which is frequently used for traffic safety and accident analysis. This study uses the odds ratio analysis to search for variables related to frozen road traffic accidents in each category. A total of 18 out of 47 variables were found to be the causes of accidents. RESULTS: From the results of the comparative analysis of 18 variables, the category of the condition of the road was found to be the most critical. Contrary to expectations, more accidents occurred in clear weather than in other conditions. Accidents on bridges occurred frequently, and its odds ratio was the highest compared with other road types. When BPT is operated, the probability of accidents on frozen roads is lower than in general conditions, and accidents occurred frequently on roads with less than four lanes. CONCLUSIONS : Based on the results of this study, suggestions for reducing the risk of future domestic road accidents in freezing conditions are indicated as follows. First, it is necessary to perform a technical review of the urban road traffic accidents caused by frozen roads. Second, it is necessary to establish criteria for the study of the road environment based on the major causes of road accidents on frozen roads. Third, improvements in urban road environmental factors should be made.

Anti-icing system can reduce traffic accidents and congestion by quickly removing the frozen road surface area. there is no decision criteria for determining application of the Anti-icing system in Korea. In this study, we proposed the decision criteria for determining application of the anti-icing system based on weather and road conditions, i.e., geometric and topographic conditions. Regional climate survey, Solar Radiation analysis, and dynamic vehicle simulation considered road geometry was conducted to standardize the installation method of anti-icing system. Also, we have developed a software that can be determined an installation of anti-icing system.

The road icing forecast and snow detection system using state evaluation algorithm of multi sensor optimizes snow melting system to control spread time and amount of chemical spread application This system operates integrated of contact/non-contact sensor and infrared camera. The state evaluation algorithm of the system evaluates road freezing danger condition and snowfall condition (snowfall intensity also) using acquired data such as temperature/humidity, moisture detection and result of image signal processing from field video footage. In the field experiment, it proved excellent and reliable evaluated result of snowfall state detection rate of 89% and wet state detection rate of 94%.