标题： 使用生成式人工智能的交通事件响应计划：第一部分——构建事件响应基准 显示英文标题

标题： Automated Traffic Incident Response Plans using Generative Artificial Intelligence: Part 1 -- Building the Incident Response Benchmark

摘要： 交通事故仍然是全球范围内的一个重要公共安全问题，澳大利亚在2024年记录了1300起道路死亡事件，这是12年来的最高数字。同样，美国每年报告大约600万起车祸，这在快速响应时间和运营管理方面带来了重大挑战。 传统的响应协议依赖于人为决策，在关键时刻，每一分钟都影响到安全结果和网络性能，这可能导致潜在的不一致性和延迟。 为了解决这个问题，我们提出了一种新的事故响应基准，该基准使用生成式人工智能自动生成即将到来的交通事件的响应计划。 我们的方法旨在通过建议适当的行动来显著减少事件解决时间，例如根据特定事件特征调整可变信息标志部署、车道关闭以及紧急资源分配。 首先，所提出的这种方法使用来自性能测量系统（PeMS）的真实世界事件报告作为训练和评估数据。我们从这些报告中提取历史上实施的动作，并将其与AI生成的响应计划进行比较，这些计划建议具体行动，如车道关闭、可变信息标志公告和/或派遣适当的应急资源。 其次，模型评估显示，像GPT-4o和Grok 2这样的先进生成式AI模型与专家解决方案具有更好的一致性，这通过最小化的汉明距离（平均为2.96-2.98）和低加权差异（约0.27-0.28）得以证明。相比之下，尽管Gemini 1.5 Pro记录了最少的遗漏行动，但其极高数量的不必要的行动（1547次对比GPT-4o的225次）表明了过度触发的策略，这降低了整体计划效率。 显示英文摘要

摘要： Traffic incidents remain a critical public safety concern worldwide, with Australia recording 1,300 road fatalities in 2024, which is the highest toll in 12 years. Similarly, the United States reports approximately 6 million crashes annually, raising significant challenges in terms of a fast reponse time and operational management. Traditional response protocols rely on human decision-making, which introduces potential inconsistencies and delays during critical moments when every minute impacts both safety outcomes and network performance. To address this issue, we propose a novel Incident Response Benchmark that uses generative artificial intelligence to automatically generate response plans for incoming traffic incidents. Our approach aims to significantly reduce incident resolution times by suggesting context-appropriate actions such as variable message sign deployment, lane closures, and emergency resource allocation adapted to specific incident characteristics. First, the proposed methodology uses real-world incident reports from the Performance Measurement System (PeMS) as training and evaluation data. We extract historically implemented actions from these reports and compare them against AI-generated response plans that suggest specific actions, such as lane closures, variable message sign announcements, and/or dispatching appropriate emergency resources. Second, model evaluations reveal that advanced generative AI models like GPT-4o and Grok 2 achieve superior alignment with expert solutions, demonstrated by minimized Hamming distances (averaging 2.96-2.98) and low weighted differences (approximately 0.27-0.28). Conversely, while Gemini 1.5 Pro records the lowest count of missed actions, its extremely high number of unnecessary actions (1547 compared to 225 for GPT-4o) indicates an over-triggering strategy that reduces the overall plan efficiency.