Predicting Traffic Accidents Using Artificial Intelligence: A Machine Learning Approach

Traffic accidents are a major global concern, resulting in significant human and economic losses. This study aims to explore the predictive capabilities of machine learning algorithms in identifying the factors that contribute to such accidents. Utilizing a comprehensive dataset that includes weather, road type, time of day, traffic density, speed limits, and driver attributes (e.g., age and alcohol consumption), we evaluated various classifiers including logistic regression, decision trees, naïve Bayes, KNN, and random forest. Experimental results show that KNN achieved the highest prediction accuracy. This work demonstrates the potential of ensemble and classification techniques for improving traffic safety through proactive accident prediction.