## 6 Conclusions and Discussion  
In this study, we used a traffic accident dataset, which included accidents in Seoul from 2010 to 2018, to identify the major factors and types that affect the severity of traffic accidents. To create a good classification, less frequent or skewed data were pre-processed by being removed and re-grouped, and analyzed using XGBoost, Logistic Regression, and \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Variables** & **Cluster 1** & **Cluster 2** & **Cluster 3** \\ \hline Season & Spring & Spring & Summer \\  & (26.5 \%) & (26.9 \%) & (27.3 \%) \\ Time & Day & Day & Day \\  & (47.5 \%) & (52.0 \%) & (56.6 \%) \\ Day of the week & Friday & Saturday & Friday \\  & (16.1 \%) & (17.2 \%) & (16.7 \%) \\ Accident type & Side collision & Rear-end collision & Other \\  & (57.6 \%) & (46.3 \%) & (53.0 \%) \\ Violation of law & Non-compliance with & Non-compliance with & Non-compliance with \\  & safe-driving obligation (45.3 \%) & safe-driving obligation (59.8 \%) & safe-driving obligation (92.1 \%) \\ Road surface & Dry & Dry & Dry \\  & (98.3 \%) & (96.2 \%) & (99.4 \%) \\ Weather & Sunny & Sunny & Sunny \\  & (97.6 \%) & (95.7 \%) & (98.4 \%) \\ Road type & At an intersection & Single road & Single road \\  & (61.1 \%) & (95.5 \%) & (95.0 \%) \\ Perpetrator’s vehicle type & Passenger car & Passenger car & Passenger car \\  & (87.9 \%) & (86.5 \%) & (77.2 \%) \\ Perpetrator’s gender & Male & Male & Male \\  & (90.4 \%) & (89.9 \%) & (84.9 \%) \\ Perpetrator’s age & Middle-aged & Middle-aged & Middle-aged \\  & (43.7 \%) & (40.0 \%) & (40.7 \%) \\ Victim’s vehicle type & Passenger car & Passenger car & Pedestrian \\  & (80.3 \%) & (75.2 \%) & (100 \%) \\ Victim’s gender & Male & Male & Male \\  & (89.1 \%) & (85.9 \%) & (54.2 \%) \\ Victim’s age & Middle-aged & Old-aged & Middle-aged \\  & (38.9 \%) & (36.8 \%) & (25.1 \%) \\ \hline \hline \end{tabular}
\end{table} 
Table 11: Characteristics of three major clusters. DBSCAN, which are the representative methodologies widely used in the field. In the XGBoost results, the case where the perpetrator violated the signal or the victim was riding a two-wheeled vehicle was also found to be an important variable in judging a serious traffic accident. In addition, the case where the victim and perpetrator's vehicle type was a passenger car had a significant influence in judging the slight accident. In logistic regression, the top and bottom 10 variables were analyzed according to the regression coefficient values to identify factors affecting the severity of traffic accidents. As a result, the perpetrator's violation of the law was found to affect serious traffic accidents in the order of speeding, two-wheeler or motorized bicycle, elderly, pedestrian, and bicycle. In contrast, environmental factors did not significantly affect traffic accidents. The clustering analysis results derived the top three clusters, represented by in-intersection side-crashes, rear-end collision, and clusters for pedestrians. Considering the three methodologies as a whole, environmental factors such as season, day of the week, and weather were found to be insignificant on the severity of traffic accidents. On the other hand, it is worth noting that variables for pedestrians appear in common among all of the three approaches, which would suggest establishing more preventive measures against pedestrian accidents, in order to reduce the fatal injury by the traffic accidents in Seoul. In practice, actual traffic accidents are caused by a combination of more specific and diverse factors than the variables in the dataset used in this study. For example, in this study, variables such as speeding in violation of the law may vary depending on how fast the analysis was conducted. Factors such as driver's vision or seat belt wearing may also affect the outcome. If data including more diverse information are available, a more specific analysis will be possible; thus, an active data opening policy is needed.