撞车
运输工程
先进的交通管理系统
交通事故
智能交通系统
计算机科学
道路交通安全
计算机安全
交通冲突
浮动车数据
工程类
道路交通
交通拥挤
程序设计语言
作者
Victor Adewopo,Nelly Elsayed,Zag Elsayed,Murat Özer,Victoria Wangia-Anderson,Ahmed Abdelgawad
出处
期刊:Cornell University - arXiv
日期:2023-07-22
标识
DOI:10.48550/arxiv.2307.12128
摘要
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.
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