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 : Snow removal is one of the principal components in winter road maintenance. The two commonly used methods are mechanical removal and chemical removal. Mechanical removal pushes accumulated snow off the roadway using snow plows. Chemical removal involves the application of chemicals such as NaCl2 (salt), CaCl2, MgCl2, etc., to liquefy the snow on the road. However, chemicals are known to pose negative effects on the environment and road infrastructure, so it is emphasized that only an appropriate amount of chemicals should be applied. Hence, in this study, extensive field experiments were performed to determine the appropriate amounts of chemicals required for each road surface temperature group. METHODS : The experiments were carried out at a road weather proving ground, located in Yeoncheon where road weather (including snowfall) can be artificially created. Four surface temperature groups were predetermined, according to the characteristics of de-icing chemicals on snow. For each temperature group, four different amounts of pre-wetted salt were applied to find the optimal rate for each group. RESULTS : As a consequence, the amount of recommended chemicals for each temperature group was found to be an average of 27.2g/ m2, which is 7.7g/m2 (22%) lower than the corresponding amount presented in the current Korean guidelines. CONCLUSIONS : Applying the results of this study to snow and ice control tasks enables the minimization of the negative impacts of de-icing chemicals, but still maintaining road safety and mobility.

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.