  Traffic Signal Control (TSC) is a crucial component in urban traffic
management, aiming to optimize road network efficiency and reduce congestion.
Traditional methods in TSC, primarily based on transportation engineering and
reinforcement learning (RL), often exhibit limitations in generalization across
varied traffic scenarios and lack interpretability. This paper presents
LLMLight, a novel framework employing Large Language Models (LLMs) as
decision-making agents for TSC. Specifically, the framework begins by
instructing the LLM with a knowledgeable prompt detailing real-time traffic
conditions. Leveraging the advanced generalization capabilities of LLMs,
LLMLight engages a reasoning and decision-making process akin to human
intuition for effective traffic control. Moreover, we build LightGPT, a
specialized backbone LLM tailored for TSC tasks. By learning nuanced traffic
patterns and control strategies, LightGPT enhances the LLMLight framework
cost-effectively. Extensive experiments on nine real-world and synthetic
datasets showcase the remarkable effectiveness, generalization ability, and
interpretability of LLMLight against nine transportation-based and RL-based
baselines.
