## 3 Results and Discussion  
Machine learning algorithms are used to classify data with high accuracy. In this study, more than one machine learning algorithm was preferred. In this way, better results can be obtained by using the data of the best classification algorithm. In order to determine the algorithm that performs the best classification, it is necessary to examine the accuracy values and error scales. The higher the precision, recall, F-measure, and ROC area values of an algorithm, the better its performance. However, the MAE and RMSE values, which are error values, should be low, and the kappa statistic values should be high. If there is an imbalance in these results, the dataset may not be suitable for that algorithm. For this reason, it is necessary to examine both the accuracy values and the error scales of the algorithms. The accuracy values obtained in each algorithm for the nine classes created according to the road slope and pavement surface conditions are shown in Figure 4 below. When the accuracy values for all three algorithms were examined, it was seen that the J48 algorithm gives the best results. In particular, the F-measure value, which is an important accuracy criterion, was 93%. This value shows that the algorithm has a very suitable structure for the dataset. It can be seen that the J48 algorithm gives the best results in terms of precision, recall and ROC area values, which are also accuracy criteria. On the other hand, although the Naive Bayes algorithm shows good results, it has lower accuracy values than the J48 algorithm. It can be seen that the algorithm with the lowest accuracy value among all three algorithms is the multilayer perceptron. After examining the accuracy values of the algorithms, it was necessary to examine the error criteria. It can be said that the lower the MAE and RMSE values of an algorithm, the more accurate the results. The error value of the algorithm with a high kappa statistic value is also low. The kappa statistic, MAE and RMSE values of each algorithm are shown in Figure 5 below. Looking at the error criteria for all three algorithms, we see that the J48 algorithm has MAE values of 8% and RMSE values of 21%. These values are lower than the error criteria of both Naive Bayes and the multilayer perceptron. In addition, it can be seen that the algorithm that gives the best result in terms of kappa statistical value is the J48 algorithm with 85%. When all the values of accuracy and error criteria were examined, it was shown that the J48 algorithm has a better performance. Using the classification values of this algorithm, an accident risk density map of the Erzurum province was created using ArcGIS software. The accident risk map that may occur in winter months, taking into account the slope and surface conditions of the road, is shown in Figure 6 below. Figure 6 highlights the areas in Erzurum with varying levels of accident risk during the winter months. The map employs the Inverse Distance Weighting (IDW) method to estimate risk intensity based on key factors such as road slope, surface conditions and historical accident data. The darker regions on the map correspond to zones with the highest accident risk, primarily due to steep slopes and icy conditions. Conversely, lighter areas represent regions with relatively lower risk. When the road sections where the average number of accidents is between 6 and 8 are examined, it was determined that the  
Figure 4: Accuracy values of the algorithms. Figure 5: Error scales of the algorithms. slope is high, and ice is frequent on this road surface in winter months. In a small part of the sections where the average number of accidents is between 4.5 and 6, it was determined that the roads have an average slope, and ice is frequently encountered in winter months, while in most of them, the slope is high and snowing is frequently encountered in winter months.