# Geospatial Simulation System of Mountain Area Black Ice Accidents
## 4 Results  
Figure 9 shows the system architecture of the proposed method that combines meteorological, spatial, and road structure information. First, the spatial information required for the hillshade simulation was acquired using the web portal of the National Spatial Data Infrastructure (operated by the Ministry of Land, Infrastructure, and Transport) [23]. The altitude angle and azimuth angle of the sun, required for the shade analysis per time zone, were acquired from the portal operated by KASCI. The structure information required for calculating the slope angle of a road was obtained from RMS. Currently, the system does not provide API functions for data delivery, thereby requiring the user to directly search for the necessary information. However, KMA's web service provides Open-API functions, which enable an automatic collection of weather information for a specific location and time. The collected data was integrated into ArcGIS v10.1. The analysis was conducted using the method proposed in this study. The proposed black ice estimation system was tested on two 50 km by 50 km regions, which was the testing area around Sokcho, Gangwon-do. The road segments in the testing areas were resampled every 50 m because the length of the road section for slope estimation provided by RMS was 50 m. The proposed method was then applied for each resampled segment. Figure 10 shows the testing area and a black ice risk map estimated using weather, surrounding terrain, and road structure information for March 1 2020, at 09:00. As shown in Figure 4, the northeastern area of Korea is usually a mountainous area and is close to the East Sea. Therefore, humidity is high in winter, frequently causing weather phenomena such as fog, and the temperature is low in the morning with significant daily temperature variations, thereby increasing the risk of the occurrence of black ice. Consequently, the estimation based exclusively on meteorological information resulted in possible black ice formation in all the road segments. However, the results do not provide specific road safety \begin{table}
\begin{tabular}{c c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Type**} & \multicolumn{3}{c}{**Number of**} & \multicolumn{8}{c}{**Observation Element**} \\ \cline{3-11}  & \multicolumn{2}{c}{**Stations**} & \multicolumn{1}{c}{**DT**} & **WD** & **WS** & **T** & **RH** & **P** & **PE** & **SC** & **LE** & **SE** \\ \hline \multirow{3}{*}{AWS} & KMA & 494 & O & O & O & O & O & O & O & X & X & X \\ \cline{2-11}  & SK tehx & 1089 & O & O & O & O & O & O & O & O & X \\ \cline{2-11}  & Seoul & 26 & O & O & O & O & O & O & O & X & X \\ \hline \multicolumn{11}{c}{ASOS} & 96 & O & O & O & O & O & O & O & O & O & O \\ \hline \multicolumn{11}{c}{DT: date time; WD: wind direction; WS: wind speed; T: temperature; RH: relative humidity; P: precipitation; PE: precipitation existence; SC: sky cord; LE: lightning existence; SE: snow existence.} \\ \end{tabular}
\end{table} 
Table 1: Features of weather observation equipment. and warning information for drivers. Therefore, a hazard warning sign was installed, based on previous traffic accidents, to inform drivers about the hazard. The application of the proposed method resulted in a more specific identification of segments where thawing by solar heat was limited by shadows at certain times (see Case 2, Figure 10). Further, segments with a high risk of slipping when stopping a vehicle (Case 3) could be determined by considering the slope angle of each road segment. A comparison of the results with the terrain shades indicates that, even on the same road segment, there may be a significant difference in the likelihood of black ice formation. The effect of shadows was pronounced in the upper cases where the road profile was relatively simple, whereas a combination of Cases 1, 2, and 3 was present in the lower cases where the road profile was relatively complex.