IncidentResponseGPT: Generating Traffic Incident Response Plans with Generative Artificial Intelligence
CoRR(2024)
摘要
Traffic congestion due to road incidents poses a significant challenge in
urban environments, leading to increased pollution, economic losses, and
traffic congestion. Efficiently managing these incidents is imperative for
mitigating their adverse effects; however, the complexity of urban traffic
systems and the variety of potential incidents represent a considerable
obstacle. This paper introduces IncidentResponseGPT, an innovative solution
designed to assist traffic management authorities by providing rapid, informed,
and adaptable traffic incident response plans. By integrating a Generative AI
platform with real-time traffic incident reports and operational guidelines,
our system aims to streamline the decision-making process in responding to
traffic incidents. The research addresses the critical challenges involved in
deploying AI in traffic management, including overcoming the complexity of
urban traffic networks, ensuring real-time decision-making capabilities,
aligning with local laws and regulations, and securing public acceptance for
AI-driven systems. Through a combination of text analysis of accident reports,
validation of AI recommendations through traffic simulation, and implementation
of transparent and validated AI systems, IncidentResponseGPT offers a promising
approach to optimizing traffic flow and reducing congestion in the face of
traffic incidents. The relevance of this work extends to traffic management
authorities, emergency response teams, and municipal bodies, all integral
stakeholders in urban traffic control and incident management. By proposing a
novel solution to the identified challenges, this research aims to develop a
framework that not only facilitates faster resolution of traffic incidents but
also minimizes their overall impact on urban traffic systems.
更多查看译文
AI 理解论文
溯源树
样例
生成溯源树,研究论文发展脉络
Chat Paper
正在生成论文摘要