PURPOSES :This study evaluates the reasonableness of the recommended amount of deicing chemicals based on historical data for snow removal. The result can be used to aid decision-making for the reservation of cost-effective de-icing chemicals.METHODS :First, the recommended amount of de-icing chemical to use and historical usage data were evaluated to identify specific usage characteristics for each region. Road maintenance length and snow-removal working days were analyzed over the past five winter seasons. Next, differences in the recommended amount of chemical to use and actual use were compared using the Kolmogorov-Smirnov test. Last, the two types of data were analyzed using a chi-square test to verify if the two distributions of variation pattern are statistically significant. We found that there are significant differences between the data from each region during the past five winter seasons.RESULTS :The results showed that the equation for calculating the amount of de-icing chemical to use appears to be revised.CONCLUSIONS :The results imply that the equation for calculating the amount of de-icing chemical to apply as a national standard is very important when the public agency makes decisions related to snow-removal.

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.