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The study illustrates how the synergy between AI technologies and IoT-enabled sensing and communication capabilities can significantly improve the capabilities of accident detection, reduce emergency response times and contribute to overall road safety.
Traffic accidents are still a major threat to public safety, causing a lot of deaths, damage to property, and problems in society around the world. Intelligent, data-driven, and proactive traffic management systems have been made possible by the fast development of AI and ML, which has revolutionized traditional road safety practice. This paper presents a comprehensive review of recent AI-based approaches for enhancing road safety, with emphasis on accident prediction, driver behavior analysis, and connected vehicle technologies. In addition, the review examines the major factors contributing to road accidents and discusses the Safe System approach as a framework for improving transportation safety. Current challenges, including data quality, model interpretability, cybersecurity, privacy preservation, and regulatory constraints, are critically analyzed to highlight existing research limitations. Furthermore, emerging research directions explored as potential solutions for developing robust, scalable, and trustworthy intelligent transportation systems. The findings indicate that AI-driven technologies have considerable potential to improve accident prevention, traffic efficiency, and decision-making while supporting the development of safer and more sustainable road transportation systems.
