In our decision tree analysis, we tried to predict the course of individual behavior patterns in different event scenarios. These included circumstances where people did not flee and others where they fled by vehicle, on foot or by some other means. With an accuracy of almost 67 percent, our model showed a remarkable ability to predict accurately in most cases. This accuracy highlights the overall effectiveness of the model in evaluating and interpreting different behavioral responses in different event contexts. On the other hand, a closer examination of the confusion matrix revealed some misclassifications. The algorithm correctly predicted 676 escape episodes and successfully identified 37 cases where people did not escape, but it also misclassified several cases. In particular, the model predicted an escape in 125 cases when it did not actually occur. However, 33 cases incorrectly predicted escape on foot and 136 cases incorrectly indicated escape by car. These misclassifications highlight specific parts of the model that need to be improved to increase its reliability and forecast accuracy.