Machine Learning – Based Prediction of Traffic Road Accidents Risk In Vehicular

Abstract

This project, titled “Machine learning-based system for predicting traffic accident risk in vehicular networks” using real-time data such as traffic flow, weather, road conditions, and vehicle behavior. Traditional monitoring systems struggle with dynamic data, causing delays in risk assessment. Our approach integrates models like Random Forest, SVM, and Neural Networks, trained on historical and environmental data, to deliver accurate, real-time predictions. The system ensures data integrity and supports timely interventions, offering a scalable solution for enhancing road safety and intelligent traffic management. The key innovation lies in the integration of real-time data processing to provide timely and accurate risk assessments. Furthermore, we explore the use of advanced algorithms to enhance prediction accuracy, while minimizing computational cost and energy consumption. A series of performance evaluations show that our approach achieves high prediction accuracy, with low false-positive rates, and offers efficient solutions for real-time traffic accident prediction