# Geospatial Simulation System of Mountain Area Black Ice Accidents
## 6 Conclusions  
In this study, a method to estimate black ice risk regions along the mountain roads was proposed, considering South Korea's geographical and meteorological characteristics. To achieve this, we estimated the areas prone to black ice formation with limited thawing after sunrise by using the hourly shade and terrain information. Afterward, the specific risk segments were detected by considering the slope angle of the roads. The proposed system was tested in two regions where black ice occurs frequently. The results confirmed that the risk levels vary with the surrounding terrain even on the same road segment; thus, they additionally verified the likelihood of subsequent traffic accidents. Moreover, the proposed system anticipated 71% of traffic accidents caused by black ice during the testing period.  
\begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Categories**} & \multicolumn{2}{c}{**2015**} & \multicolumn{2}{c}{**2016**} & \multicolumn{2}{c}{**2017**} & \multicolumn{2}{c}{**2018**} & \multicolumn{2}{c}{**2019**} \\ \cline{2-11}  & **Black Ice** & **Snow** & **Black Ice** & **Snow** & **Black Ice** & **Snow** & **Black Ice** & **Snow** & **Black Ice** & **Snow** \\ \hline The cases of traffic accidents & 859 & 514 & 1140 & 623 & 1365 & 690 & 1358 & 894 & 478 & 163 \\ \hline The number of deaths & 41 & 6 & 43 & 12 & 38 & 16 & 24 & 10 & 24 & 2 \\ \hline The number of injuries & 1591 & 914 & 2221 & 1062 & 2374 & 1127 & 2402 & 1500 & 941 & 311 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Traffic accident statistics by road condition.  
The results show that the system can contribute significantly to preventing black-ice-related traffic accidents.  
The current study had certain limitations. For example, we exclusively considered the effects of shade brought by the adjacent terrains, whereas other shade effects from road facilities, such as soundproof walls and tunnels, were not considered. Moreover, several studies have reported significant shade effects over the bridges in the absence of geothermal heat, which was not reflected in this study. Additional data for evaluating the accuracy of the proposed method are required. However, with increasing public interest in black ice and the initial stage of establishing relevant data, the visual and qualitative assessments performed in this study are plausible.  
Research towards developing a real-time warning system is underway by testing vehicles equipped with sensors on high-risk roads (i.e., for black ice formation) as part of the recent response to black ice. Combining meteorological, terrain, and sensor information and data-driven analytical techniques such as machine learning or deep learning should enable more accurate predictions and warning systems in the future. The results of this study will be used for real-time prioritization of the target regions during the operation of survey vehicles.  
Conceptualization, J.-K.L.; methodology, J.-K.L. and Y.H.; software and data analysis, J.-K.L., Y.H. and J.P.; data curation, J.P.; writing--original draft preparation, J.-K.L.; writing--review and editing, Y.H. and J.P. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the Korea Institute of Civil Engineering and Building Technology Research Project (Research on Smart Construction Technology for Leading the future construction industry and Creating new market) [2022].  
Not applicable.  
The authors declare no conflict of interest.