2023/07/22 by Victor Adewopo, Adewopo, Victor, Nelly Elsayed +9
Engineering · #Artificial Intelligence (cs.AI) #Computer Vision and Pattern Recognition (cs.CV) #Computers and Society (cs.CY) #FOS: Computer and information sciences #IoT and GPS-based Vehicle Safety Systems #Machine Learning (cs.LG) #Traffic Prediction and Management Techniques #Vehicular Ad Hoc Networks (VANETs)
paper · pdf · doi:10.48550/arxiv.2307.12128
openalex publication_date 2023/07/22 · openalex created_date 2023/07/26 · openalex updated_date 2026/07/28
Accident detection and traffic analysis is a critical component of smart city and autonomous transportation systems that can reduce accident frequency, severity and improve overall traffic management. This paper presents a comprehensive analysis of traffic accidents in different regions across the United States using data from the National Highway Traffic Safety Administration (NHTSA) Crash Report Sampling System (CRSS). 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 spontaneously. Integrating the proposed framework with emergency services will harness the power of traffic cameras and machine learning algorithms to create an efficient solution for responding to traffic accidents and reducing human errors. Advanced intelligence technologies, such as the proposed accident detection systems in smart cities, will improve traffic management and traffic accident severity. Overall, this study provides valuable insights into traffic accidents in the US and presents a practical solution to enhance the safety and efficiency of transportation systems.