## 4 Results
Twelve traffic incident locations that should be in cluster 6 were found to be in cluster 4,  
Figure 5: RF model confusion matrix. which are not close to each other. Although the model was unable to fully distinguish them, it achieved an acceptable 86% accuracy rate, representing a significant outcome for the ten cluster assignments. In clusters 1, 2, and 5, incorrect predictions were confused with only one cluster. Cluster 9 was confused with the highest number of different clusters, with a total of three. This indicates that the model's incorrect predictions were primarily due to confusion between regions that are close to each other. Conversely, predictions for regions that are relatively distant from each other were more accurate. A total of 1023 traffic accident-incidents that occurred in four different time intervals, (8:00 a.m.-1:59 p.m., 2:00 p.m.-7:59 p.m., 8:00 p.m.-1:59 a.m., and 2:00 a.m.-7:59 a.m.) in ten different geohash areas over a period of 16 months were used as test data. Table 8 displays the actual and predicted results of the traffic incidents that occurred in these various time periods and locations. Upon examining the table, it becomes clear that 29 out of 33 traffic incidents that took place between 8:00 a.m. and 1:59 p.m. in geohash field number 5 were accurately forecasted. Thirty-three traffic events occurred at various times during the test period in the geohash area 1. During the first time period between 08:00 and 13:59 in the fifth region, our model successfully predicted 29 of the events. According to the table, the prediction success rate for 10 traffic incidents in time zone 1 is 92.48%. Out of the 27 incorrect predictions made in time zones, the largest amount occurred in the geohash field no. 7, with a total of 8 incorrect predictions. Notably, these incorrect predictions occurred within two adjacent geohash areas that produced the most errors. In time zone 2, ten locations where traffic incidents are likely to occur were predicted with an accuracy rate of 90.16%. Among them, 44 incorrect predictions were made for the traffic incident location, and the majority of the errors occurred in geohash field number 3,  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Geohash Area**} & \multirow{2}{*}{**Actual/ Predicted**} & **1 st** & **2 nd** & **3 rd** & **4 th** & \\  & & **Time** & **Time** & **Time** & **Time** & **Time** & **Total** \\  & & **Zone** & **Zone** & **Zone** & **Zone** & **Zone** & \\ \hline \multirow{2}{*}{0} & Actual & 38 & 68 & 15 & 3 & 124 \\  & Predicted & 38 & 62 & 14 & 3 & 117 \\  & Actual & 50 & 52 & 32 & 6 & 140 \\  & Predicted & 50 & 51 & 32 & 6 & 139 \\  & Actual & 36 & 39 & 18 & 8 & 101 \\  & Predicted &  
with 11 incorrect predictions. These predictions erroneously estimated the incident location in geohash field 0. Here, as in nearby areas previously observed, the proximity of these locations contributes to the occurrence of these errors. Ten traffic incidents were accurately predicted at a rate of 91.57% in time zone 3. However, the accuracy rate dropped to 82.35% in time zone 4, which corresponds to the early morning hours with relatively little traffic. This suggests that the prediction success decreases due to the scarcity of traffic events during this period. Compared to other time intervals, the success rate of learning from traffic events is relatively low during the fourth hour.