Article
ROAD ACCIDENT ANALYSIS AND PREDICTION OF ACCIDENT SEVERITY
Road accidents are a critical public safety issue, especially in developing countries like India, where increasing vehicle density and poor traffic management contribute to high fatality rates. This project focuses on the development of an intelligent road accident analysis and accident severity prediction system using machine learning techniques. The system utilizes historical accident datasets to identify patterns and relationships among various factors such as road conditions, weather conditions, time of accident, driver behavior, vehicle type, and traffic density. Data preprocessing techniques including cleaning, normalization, and feature selection are applied to ensure accurate model performance. Multiple machine learning algorithms such as Decision Tree, K-Nearest Neighbors, Naïve Bayes, and AdaBoost are implemented and evaluated to determine the most effective model for predicting accident severity. The system classifies accidents into categories such as slight, serious, and fatal injuries, enabling better understanding of risk factors. Experimental results indicate that machine learning models outperform traditional statistical methods in predicting accident severity with higher accuracy. The proposed system provides data-driven insights that assist traffic authorities, policymakers, and emergency services in proactive decision-making and resource allocation. Furthermore, the system can be enhanced with real-time data integration and visualization dashboards to improve road safety strategies. Overall, this project contributes to reducing accident severity and improving transportation safety through intelligent prediction and analysis
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