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.

翻译：道路事件导致的交通拥堵是城市环境中的一项重大挑战，会导致污染加剧、经济损失和交通拥堵加剧。有效管理这些事件对于减轻其不利影响至关重要；然而，城市交通系统的复杂性和潜在事件的多样性构成了相当大的障碍。本文介绍了IncidentResponseGPT，这是一种创新的解决方案，旨在通过提供快速、知情且适应性强的交通事件响应方案来协助交通管理当局。通过将生成式人工智能平台与实时交通事件报告和操作指南相结合，我们的系统旨在简化应对交通事件的决策过程。本研究解决了在交通管理中部署人工智能所涉及的关键挑战，包括克服城市交通网络的复杂性、确保实时决策能力、符合当地法律法规以及确保公众对人工智能驱动系统的接受度。通过结合事故报告的文本分析、通过交通仿真验证人工智能建议以及实施透明且经过验证的人工智能系统，IncidentResponseGPT为在交通事件面前优化交通流和减少拥堵提供了一种有前景的方法。这项工作的相关性延伸至交通管理当局、应急响应团队和市政机构，这些都是城市交通控制和事件管理中不可或缺的利益相关者。通过针对已识别的挑战提出新颖的解决方案，本研究旨在开发一个框架，该框架不仅有助于更快地解决交通事件，而且能最大限度地减少其对城市交通系统的整体影响。