  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
https://github.com/Traffic-Alpha/LLM-Assisted-Light.
