PURPOSES : This study was conducted to prevent road thinning ice caused by abnormal weather conditions. METHODS : The appropriate amount of de-icer spread rate was verified by presenting the appropriate amount of snow removal agent spraying criteria for the thickness of the water film, owing to abnormal weather phenomena (fog, frost), and applying the standards to the site. Furthermore, we present a method to utilize residual salt, by quantifying the surface state changes according to the amount of deicer. RESULTS : Precautionary spread experiments to prevent road thin ice caused by abnormal weather conditions, indicated no freezing from 7.6g/m2 at 2℃-4℃ but 11.1g/m2 was suggested as a step higher considering external environmental variables. The amount of spraying was presented in two sections of rainfall(freezing rain). It is 17.7g/m2 at 0-7℃, 33.3g/m2 at -7~ -15℃, and 44.4g/m2 and 51.1g/m2 at non-urban, respectively. CONCLUSIONS : The criteria were divided into air temperature and road temperature standards, so that they could be distributed according to the temperature standards that meet the conditions, and the criteria presented were confirmed to be effective in preventing road thinning ice. If the road manager adopts Safety Line, which is suggested by utilizing the amount of residual salt on the road, it is believed that it can help determine the additional deicer.

PURPOSES : In this study, systematic road snow-removal capabilities were estimated based on previous historical data for road-snowremoval works. The final results can be used to aid decision-making strategies for cost-effective snow-removal works by regional offices. METHODS: First, road snow-removal historical data from the road snow-removal management system (RSMS), operated by the Ministry of Land, Infrastructure and Transport, were employed to determine specific characteristics of the snow-removal capabilities by region. The actual owned amount and actual used amount of infrastructure were analyzed for the past three years. Second, the regional offices were classified using K-means clustering into groups “close”to one another. Actual used snow-removal infrastructure was determined from the number of snow-removal working days. Finally, the correlation between the de-icing materials used and infrastructure was analyzed. Significant differences were found among the amounts of used infrastructure depending on snowfall intensity for each regional office during the past three years. RESULTS: The results showed that the amount of snow-removal infrastructure used for low heavy-snowfall intensity did not appear to depend on the amount of heavy snowfall, and therefore, high variation is observed in each area. CONCLUSIONS: This implies that the final analysis results will be useful when making decisions on snow-removal works.