RoadGuard AI: Intelligent Road Accident Risk Prediction Using Machine Learning and Real-Time Data Integration

This paper presents RoadGuard AI, a full-stack intelligent road accident risk prediction system that combines machine learning with real-time data integration. The system uses three real-world Kaggle datasets comprising over 3.5 million accident records from the United States, United Kingdom, and India to train and evaluate Logistic Regression, Random Forest, and Gradient Boosting classifiers. The Gradient Boosting model achieved 84.72% accuracy on the UK Road Safety dataset with 2 million records. The system integrates live GPS location detection, real-time weather data from Open-Meteo API, and reverse geocoding from OpenStreetMap to provide context-aware risk predictions. A novel smooth risk scoring formula using predict_proba output generates a continuous 0-100% risk score instead of discrete risk classes. The prototype is built using Flask (Python) backend with JWT authentication and SQLite database, and a React (Vite) frontend with an interactive risk dashboard. The system demonstrates that combining multiple global accident datasets with real-time environmental data can produce actionable, evidence-based road safety predictions.