Adaptive traffic signal control (TSC) has demonstrated strong effectiveness in managing dynamic traffic flows. However, conventional methods often struggle when unforeseen traffic incidents occur (e.g., accidents and road maintenance), which typically require labor-intensive and inefficient manual interventions by traffic police officers. Large Language Models (LLMs) appear to be a promising solution thanks to their remarkable reasoning and generalization capabilities. Nevertheless, existing works often propose to replace existing TSC systems with LLM-based systems, which can be (i) unreliable due to the inherent hallucinations of LLMs and (ii) costly due to the need for system replacement. To address the issues of existing works, we propose a hierarchical framework that augments existing TSC systems with LLMs, whereby a virtual traffic police agent at the upper level dynamically fine-tunes selected parameters of signal controllers at the lower level in response to real-time traffic incidents. To enhance domain-specific reliability in response to unforeseen traffic incidents, we devise a self-refined traffic language retrieval system (TLRS), whereby retrieval-augmented generation is employed to draw knowledge from a tailored traffic language database that encompasses traffic conditions and controller operation principles. Moreover, we devise an LLM-based verifier to update the TLRS continuously over the reasoning process. Our results show that LLMs can serve as trustworthy virtual traffic police officers that can adapt conventional TSC methods to unforeseen traffic incidents with significantly improved operational efficiency and reliability.

翻译：自适应交通信号控制（TSC）在管理动态交通流方面已展现出显著成效。然而，当突发交通事件（如交通事故和道路施工）发生时，传统方法往往难以应对，通常需要交通警察进行劳动密集且效率低下的手动干预。大语言模型（LLMs）凭借其卓越的推理和泛化能力，似乎是一种有前景的解决方案。然而，现有研究常提议用基于LLM的系统取代现有TSC系统，这可能存在以下问题：（i）由于LLM固有的幻觉问题而不可靠；（ii）因需要系统替换而导致成本高昂。为解决现有研究的不足，我们提出了一种分层框架，利用LLMs增强现有TSC系统：上层虚拟交通警察智能体根据实时交通事件，动态微调下层信号控制器的选定参数。为提升应对突发交通事件的领域特定可靠性，我们设计了一种自优化的交通语言检索系统（TLRS），通过检索增强生成技术，从包含交通状况与控制器操作原理的定制化交通语言数据库中获取知识。此外，我们设计了一个基于LLM的验证器，在推理过程中持续更新TLRS。实验结果表明，LLMs能够作为可信赖的虚拟交通警察，使传统TSC方法适应突发交通事件，并显著提升运行效率与可靠性。