## 4 Results  
Performance metrics such as accuracy, precision, recall, and F1-score were computed to evaluate the effectiveness of the DT, k-NN, RF, and SVM algorithms employed. The results of the study are presented in Table 7, which highlights the performance of the four distinct machine learning models. Table 7 displays that although random forest (RF) offers the highest accuracy rate of 90,8% for predicting traffic incident locations, other machine learning algorithms also deliver notable performance. The sequence of algorithm success in classification was RF, SVM, DT, and k-NN. The close distribution of traffic events within the 10 geohash clusters prevented over-learning of the training dataset, resulting in similar macro and micro precision, recall, and F1-scores. In RF, Precision_macro minimizes error costs to 8.5% and attains a success rate of 91.5%. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Performance Metric** & **DT** & **k-NN** & **RF** & **SVM** \\ \hline accuracy & 0.874 & 0.786 & 0.908 & 0.875 \\ balanced accuracy & 0.867 & 0.774 & 0.905 & 0.872 \\ precision micro & 0.874 & 0.786 & 0.908 & 0.875 \\ precision macro & 0.878 & 0.790 & 0.915 & 0.876 \\ recall micro & 0.874 & 0.786 & 0.908 & 0.875 \\ recall macro & 0.867 & 0.774 & 0.905 & 0.872 \\ f1 micro & 0.874 & 0.786 & 0.908 & 0.875 \\ f1 macro & 0.870 & 0.775 & 0.907 & 0.873 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Test results. When evaluating the other performance metrics and situations where the correct and incorrect identifications were reversed, a 9% error was observed. Guessing the truth accurately and identifying errors in the wrong place showed significant performance in differentiating the clusters. This finding will be a valuable point of reference for further studies in the literature. MCC is an evaluation metric that takes into account the imbalance between classes, especially in unbalanced classification problems. MCC value takes a value between \(-1\) and 1. In our study, the MCC value of the RF model was calculated as 0.93. The closer the value of MCC is to 1, the better the performance of the RF model is. Additionally, the Roc_Auc graph of the RF model is given in Appendix B. Figure 5 displays the confusion matrix for the RF model, which produced the most favorable outcomes. Confusion matrices for the other three models are included in Appendix B. The confusion matrix depicted in Figure 5 illustrates how clusters representing geob-hash areas are distributed according to the test results obtained from 1023 traffic data. The confusion matrix shows a high number of correctly predicted instances, indicating the model's success in predicting traffic event locations. Each class has a relatively high number of correctly predicted instances, demonstrating successful identification. From the test dataset, it is evident that the first geobash region had the highest number of traffic incidents reported with 140 occurrences, while the eighth geobash region had the lowest count of traffic events with only 67 incidents. A prediction accuracy of 99% was achieved as 139 out of 140 traffic incident locations in the first cluster were predicted correctly. The cluster indices with accuracy rates exceeding 90% for predicting traffic incident locations are 1, 2, 4, 0, 8, and 9, from the largest to the smallest. On the other hand, clusters 7, 3, 6, and 5 have accuracy rates lower than 90% when arranged numerically from the least to the most accurate. It was observed that cluster 7 had an accuracy rate of only 80%, and cluster 4 presented significant difficulty in predicting the traffic incident. It is understood that these two clusters are neighboring locations bordering each other, which explains the mixing situation. The close proximity of the regions allows for a relaxed approach, resulting in an 80% accuracy rate. Upon examining cluster 3, 15 traffic incidents that should have occurred were found to be in cluster 0 due to the two geobash fields being very close to each other.