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}{this https URL}.

本研究介绍了一种创新方法，将大型语言模型（LLMs）与交通信号控制系统相结合，提供先进的推理和决策能力，以应对都市交通流量的复杂性和变动性。通过在感知和决策工具套件中引入 LLMs，并将其放置在决策过程的核心位置，以结合外部交通数据和现有的交通信号控制方法，该方法在多种交通环境下表现出无需额外训练即可适应的高效性。在传感器故障情况下，相比传统基于强化学习（RL）的系统，该方法减少了平均等待时间 20.4%。本研究的进展为交通信号控制策略带来重大突破，并为将 LLMs 整合到真实、动态场景中铺平了道路，突显其革命性改变交通管理的潜力。