## 5 Conclusions  
This study demonstrates the effectiveness of a geohash-based prediction model in estimating the locations of traffic incidents. The algorithms produced accurate and dependable estimations of incident locations. The suggested algorithm for predicting traffic incidents achieved a substantial accuracy rate of 90.81%. The use of geohash areas, combined with the consideration of all variables affecting traffic events, greatly influenced the success of the model. Analysis of geohash areas revealed that machine learning algorithms achieved remarkable success in predicting traffic incident locations through the use of geohash-area-based geographical data.  
RF, SVM, and k-NN are generally more complex and flexible models. RF works as an ensemble learning method. In this case, it involves creating a strong learner by using multiple weak learners (DT). Ensemble learning increases overall performance by combining different learning strategies. A single tree model such as DT may overfit the training data, leading to overfitting. RF is more resistant to overfitting because it trains trees with random samples and features. For this reason, it gave the best results. SVM and k-NN are generally more flexible and powerful in terms of feature selection. This allows the model to make better use of important features in the dataset.  
The prediction model enables dynamic geohash-based predictions. It captures real-time data, allowing for dynamic prediction of traffic incident scenes. Taking into account different time zones is also significant for the model's ability to quickly adapt to traffic situations. The study's findings indicate that precise analysis of geographic data, coupled with efficient utilization of geohash-based machine learning algorithms, can substantially aid in forming strategies to mitigate adverse consequences of traffic incidents. These results extend fresh insights to traffic managers and emergency responders on more effective management of traffic incidents to enhance the safety of drivers.  
The model's ability to successfully forecast can be used for automatic alerts and emergency management when integrated into traffic management systems. For example, when high probabilities of traffic incidents are detected in a given area, the system can automatically send alerts and mobilize emergency teams. Predicted traffic incident locations give traffic managers the chance to respond to incidents in advance. In this case, it can help traffic management systems utilize their resources more effectively and better plan emergency responses.  
If real-time data from various variables, which may impact traffic accidents and incidents involving cities, traffic conditions, vehicles, and people, are incorporated into the model along with the flow of real-time data collected from various institutions' databases, traffic events that are likely to occur within specific time frames can be predicted with high accuracy rates, apart from the 74 variables already included in the model. This can significantly improve traffic management and safety by optimizing traffic flow within cities and reducing adverse effects caused by traffic incidents. The prediction model will assist traffic supervisors and emergency response teams in precisely intervening at crucial sites and efficiently managing personnel during traffic incidents.