AI-BASED COLLISION PREDICTION AND PREVENTION SYSTEMS IN URBAN TRAFFIC

Abstract

Urban traffic congestion and road accidents have emerged as critical global challenges due to rapid urbanization, population growth, and the increasing number of vehicles on roads. According to the World Health Organization, approximately 1.19 million people lose their lives each year as a result of road traffic accidents, while millions more suffer serious injuries and long-term disabilities. These alarming statistics highlight the urgent need for intelligent and proactive traffic safety solutions. Traditional traffic monitoring systems primarily operate on reactive mechanisms, responding only after risky situations become evident. Such systems often rely on fixed rule-based algorithms, basic sensors, and manual monitoring, which limit their ability to predict potential collisions in advance. To address these limitations, this study proposes an Artificial Intelligence (AI)-based collision prediction and prevention system specifically designed for complex urban traffic environments. The proposed framework integrates Convolutional Neural Networks (CNN) for real-time vehicle detection and object recognition, along with Long Short-Term Memory (LSTM) networks for trajectory forecasting and time-series analysis of vehicle movement patterns. By combining spatial and temporal data analysis, the system can estimate collision probability several seconds before impact, enabling early intervention. Simulation based evaluation demonstrates that the AI-driven system achieves an overall accuracy of 94.6% and reduces collision rates by nearly 30% compared to conventional traffic systems. Furthermore, the model shows improved precision and recall, indicating both reliable detection and reduced false alarms. These findings emphasize the potential of AI-powered predictive traffic management systems to enhance road safety, optimize traffic flow, and support the development of intelligent transportation infrastructures within smart cities.