AI on the Road: A Comprehensive Analysis of Traffic Accidents and
  Accident Detection System in Smart Cities
Victor Adewopo Nelly Elsayed Zag Elsayed Murat Ozer Victoria Wangia-Anderson Ahmed Abdelgawad
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.