Traffic congestion in metropolitan areas presents a formidable challenge with far-reaching economic, environmental, and societal ramifications. Therefore, effective congestion management is imperative, with traffic signal control (TSC) systems being pivotal in this endeavor. Conventional TSC systems, designed upon rule-based algorithms or reinforcement learning (RL), frequently exhibit deficiencies in managing the complexities and variabilities of urban traffic flows, constrained by their limited capacity for adaptation to unfamiliar scenarios. In response to these limitations, this work introduces an innovative approach that integrates Large Language Models (LLMs) into TSC, harnessing their advanced reasoning and decision-making faculties. Specifically, a hybrid framework that augments LLMs with a suite of perception and decision-making tools is proposed, facilitating the interrogation of both the static and dynamic traffic information. This design places the LLM at the center of the decision-making process, combining external traffic data with established TSC methods. Moreover, a simulation platform is developed to corroborate the efficacy of the proposed framework. The findings from our simulations attest to the system's adeptness in adjusting to a multiplicity of traffic environments without the need for additional training. Notably, in cases of Sensor Outage (SO), our approach surpasses conventional RL-based systems by reducing the average waiting time by $20.4\%$. This research signifies a notable advance in TSC strategies and paves the way for the integration of LLMs into real-world, dynamic scenarios, highlighting their potential to revolutionize traffic management. The related code is available at \href{https://github.com/Traffic-Alpha/LLM-Assisted-Light}{https://github.com/Traffic-Alpha/LLM-Assisted-Light}.

翻译：城市区域的交通拥堵带来了深远的经济、环境和社会影响，因此有效的拥堵管理至关重要，而交通信号控制（TSC）系统在其中发挥着关键作用。传统基于规则算法或强化学习（RL）的TSC系统，由于对陌生场景的适应能力有限，在处理城市交通流的复杂性和多变性方面常常存在不足。针对这些局限性，本文提出了一种创新方法，将大型语言模型（LLMs）集成到TSC中，利用其先进的推理和决策能力。具体而言，我们提出了一种混合框架，该框架通过一套感知和决策工具增强LLMs，从而能够查询静态和动态交通信息。这种设计将LLM置于决策过程的核心，结合外部交通数据与既定TSC方法。此外，我们开发了一个仿真平台来验证所提框架的有效性。仿真结果证明，该系统无需额外训练即可适应多种交通环境。值得注意的是，在传感器中断（SO）场景下，我们的方法将平均等待时间降低了20.4%，优于传统基于RL的系统。这项研究标志着TSC策略的重大进展，并为将LLMs集成到真实世界动态场景中铺平了道路，凸显了其革新交通管理的潜力。相关代码可在\href{https://github.com/Traffic-Alpha/LLM-Assisted-Light}{https://github.com/Traffic-Alpha/LLM-Assisted-Light}获取。