用大模型辅助高速公路事故处置，提升决策效率与可靠性。
Automating the loop in traffic incident management on highway
- 融合大模型与优化算法，兼顾自然语言交互与决策稳定性。
- 实测显示混合方案在关键指标上优于纯大模型方案。
- 适合交通指挥中心等对准确性和一致性要求高的场景。
高效的交通事件管理对于保障安全、减少拥堵和缩短应急响应时间至关重要。传统高速公路事件管理高度依赖无线电室操作员在高压力环境下快速做出决策。本文提出一种创新解决方案，通过将大语言模型（LLMs）集成到交通事件管理决策支持系统中，以增强和辅助人工决策。我们引入两种方法：(1) 大模型+优化混合方案，结合自然语言交互的灵活性与优化技术的稳健性；(2) 全大模型方案，仅依靠大模型能力自主生成决策。我们在意大利高速公路公司（Autostrade per l'Italia）的历史事件数据上测试了所提方案。实验结果表明，尽管两种方法均具潜力，但大模型+优化方案展现出更优的可靠性，特别适用于对一致性和准确性要求极高的关键应用。本研究揭示了大模型在实现可访问、数据驱动的决策支持方面，对高速公路事件管理的变革潜力。
原文摘要 · Abstract (English)
Effective traffic incident management is essential for ensuring safety, minimizing congestion, and reducing response times in emergency situations. Traditional highway incident management relies heavily on radio room operators, who must make rapid, informed decisions in high-stakes environments. This paper proposes an innovative solution to support and enhance these decisions by integrating Large Language Models (LLMs) into a decision-support system for traffic incident management. We introduce two approaches: (1) an LLM + Optimization hybrid that leverages both the flexibility of natural language interaction and the robustness of optimization techniques, and (2) a Full LLM approach that autonomously generates decisions using only LLM capabilities. We tested our solutions using historical event data from Autostrade per l'Italia. Experimental results indicate that while both approaches show promise, the LLM + Optimization solution demonstrates superior reliability, making it particularly suited to critical applications where consistency and accuracy are paramount. This research highlights the potential for LLMs to transform highway incident management by enabling accessible, data-driven decision-making support.
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