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.

翻译：事故检测与交通分析是智慧城市和自主交通系统的关键组成部分，能够降低事故频率、减轻事故严重程度，并改善整体交通管理。本文利用美国国家公路交通安全管理局（NHTSA）的事故报告采样系统（CRSS）数据，对美国不同地区的交通事故进行了全面分析。为应对事故检测与交通分析的挑战，本文提出了一种框架，该框架利用交通监控摄像头和动作识别系统，自动检测并响应交通事故。将所提框架与应急服务集成，将充分利用交通摄像头和机器学习算法的能力，创建高效应对交通事故并减少人为失误的解决方案。先进智能技术（如所提出的智慧城市事故检测系统）将改善交通管理和交通事故严重程度。总体而言，本研究为美国交通事故提供了重要见解，并提出了一种切实可行的解决方案，以提升交通系统的安全性和效率。