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.

1. 서론
2. 결빙사고 데이터 수집
    2.1. 결빙사고 사례 조사
    2.2. 결빙사고 기상 데이터 수집
3. 기상 데이터 보정
    3.1. 공간보간(Spatial Interpolation)
    3.2. 기상인자별 공간보간 기법 결정
4. 결빙사고 데이터 통계 분석
    4.1. 결빙사고 발생시각
    4.2. 사고 당시 기온
    4.3. 사고 당시 습도
    4.4. 사고 당시 강수유무
5. 결론
    5.1. 결론
    5.2. 향후 연구방향
감사의 글

• 김효원(학생회원 · 인하대학교 스마트시티공학과 석사과정) | Kim Hyo Won
• 최문규(학생회원 · 인하대학교 스마트시티공학과 박사과정) | Choi Moon Gyu
• 김세호(학생회원 · 인하대학교 스마트시티공학과 석사박사통합과정) | Kim Se Ho
• 김병준(정회원 · 인하대학교 토목공학과 박사과정) | Kim Byoung Jun
• 백승범(정회원 · 인하대학교 토목공학과 박사과정) | Baek Seung Beom
• 정진훈(정회원 · 인하대학교 사회인프라공학과 교수 · 공학박사) | Jeong Jin Hoon (Professor Department of Civil Engineering, Inha University, 100 Inha-ro, Michuhol-gu, Incheon 22212, Korea) 교신저자