Survey of Artificial Intelligence Approaches for Traffic Accident Analysis, Prediction, And Prevention

Abstract

Road traffic accidents are a major public safety concern, causing substantial fatalities, injuries, and economic losses every year. The increasing availability of traffic data and advances in Artificial Intelligence (AI) have accelerated the development of intelligent techniques for traffic accident analysis, prediction, and prevention. This paper presents a comprehensive survey of AI-based approaches for traffic accident analysis, covering traditional statistical methods, Machine Learning (ML), Deep Learning (DL), computer vision, and Intelligent Transportation Systems (ITS). The survey discusses the role of AI in accident risk prediction, smart traffic monitoring, and intelligent transportation infrastructure, along with conventional accident prevention strategies. Furthermore, recent literature is systematically reviewed to compare commonly used algorithms, application domains, advantages, and existing limitations. The paper also examines key enabling technologies, including IoT, connected vehicles, GPS, wireless communication, and traffic surveillance systems, that support real-time accident prevention. Finally, current research challenges and emerging trends are discussed to provide insights into future intelligent traffic accident prevention systems. This survey provides a structured overview of current research and highlights emerging directions for developing more accurate, explainable, and scalable AI-driven traffic accident prevention systems.