## 5 Results
### Cluster Analysis through DBSCAN  
In DBSCAN, there are two input parameters: (i) eps and (ii) minPts. We determined hyper-parameters following the heuristic suggested by literature [26; 27]. MinPts is set to  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Variables** & **Regression Coefficient Values** \\ \hline Accident type = Backup collision & \(-\)1.0902 \\ Victim’s vehicle type = Passenger car & \(-\)0.4670 \\ Accident type = Passing on the edge of the road & \(-\)0.4471 \\ Victim’s age = Underage & \(-\)0.3909 \\ Road type = Crosswalk at an intersection & \(-\)0.3828 \\ Victim’s vehicle type = Lorry & \(-\)0.2660 \\ Perpetrator’s vehicle type = Passenger car & \(-\)0.2199 \\ Road type = Near an intersection & \(-\)0.2105 \\ Victim’s age = Youth & \(-\)0.2057 \\ Victim’s gender = Male & \(-\)0.1959 \\ \hline \hline \end{tabular}
\end{table}
Table 10: Ten variables leading to slight traffic accidents.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Variables** & **Regression Coefficient Values** \\ \hline Violation of law = Speeding & 1.8490 \\ Perpetrator’s vehicle type = Two-wheeler & 0.6885 \\ Victim’s vehicle type = Motorized bicycle & 0.6717 \\ Victim’s age = Elderly & 0.5820 \\ Victim’s vehicle type = Pedestrian & 0.5254 \\ Victim’s vehicle type = Bicycle & 0.5200 \\ Violation of law = Centreline violation & 0.4805 \\ Road type = Crossing & 0.4100 \\ Violation of law = Signal violation & 0.4005 \\ Perpetrator’s vehicle type = Heavy equipment & 0.3730 \\ \hline \hline \end{tabular}
\end{table}
Table 9: Ten Variables leading to serious traffic accidents.  
approximately twice the dimensionality, thus 30 for a 14-dimensional space. Then, the value of eps is estimated by plotting the distance to the (MinPts\(-\)1)th nearest neighbor for each of sampled points, sorted in descending order, and finding the distance to an \"elbow\" of the curve. As a result of DBSCAN, we derived three major clusters corresponding to 97.8 of the entire dataset.  
Table 11 shows the results of arranging the modes for each variable in each cluster after clustering. Given the high proportion of variables within the accident type = side collision and road type = at an intersection, cluster 1 appears to be a cluster for side collision accidents within intersections. Further, cluster 2 appears to be a cluster for rear-end collision accidents, given that the proportion of accident type = rear-end collision is high. In addition, cluster 3 is considered to be a cluster for pedestrian accidents, given that the proportion of the victim's vehicle type = pedestrian is 100%. In cluster 3, violation of law = non-compliance with safe-driving obligation was 92.1%, which was much higher than that of the other two clusters. It can be inferred that non-compliance with safe-driving obligation leads to many pedestrian accidents. Overall, the clustering results show that environmental factors do not significantly influence traffic accidents given the distribution ratio. This supports the previous results, in which environmental factors such as weather do not significantly impact the occurrence of traffic accidents in Seoul. In addition, it is also interesting to note that in all clusters, the gender of the victim is predominantly male.