AI on the Road: A Comprehensive Analysis of Traffic Accidents and Autonomous Accident Detection System in Smart Cities
Victor Adewopo, Nelly Elsayed, Zag ElSayed, Murat Özer, Victoria Wangia-Anderson, Ahmed Abdelgawad
Abstract
Accident detection and traffic analysis are critical components of smart city and autonomous transportation systems that can reduce accident frequency and severity and improve overall traffic management. This paper presents a comprehensive analysis of traffic accidents in the United States using data from the National Highway Traffic Safety Administration (NHTSA) Crash Report Sampling System (CRSS). This study analyzed traffic accident trends, including accidents, accident severity in different regions across the US, and the percentage distribution of different types of collisions. To address the challenges of accident detection and traffic analysis, this paper proposes a framework that uses traffic surveillance cameras and action recognition systems to detect and respond to traffic accidents quickly and accurately. The proposed framework harnesses the power of traffic cameras, data processing, and machine learning algorithms to create an efficient solution for identifying and responding to traffic accidents. The system’s integration with emergency services and first responders can reduce the response time of law enforcement officers and eliminate reporting errors. The use of advanced technologies, such as the proposed framework, and accident detection systems in smart cities can improve traffic management and reduce the risks associated with accidents. This system can potentially save more human lives and property in the United States. Overall, this study provides valuable insights into traffic accidents in the US and presents a practical solution that can enhance the safety and efficiency of transportation systems.