Logo
ScienceStack
Table of Contents
Fetching ...
Sign In

LLMLight: Large Language Models as Traffic Signal Control Agents

Siqi Lai, Zhao Xu, Weijia Zhang, Hao Liu, Hui Xiong

TL;DR

This work introduces LLMLight, a framework that uses domain-tuned large language models as traffic signal control agents to achieve human-like reasoning, improved generalization, and interpretability in urban networks. Central to the approach is LightGPT, a specialized backbone LLM trained via imitation fine-tuning and critic-guided policy refinement, enabling effective control policies with lower cost than large generalist models. Across ten real-world and synthetic datasets, LLMLight demonstrates state-of-the-art or competitive performance against traditional methods, RL baselines, and general LLMs, while maintaining robustness in transfer, scalability, and extreme-traffic scenarios. The method emphasizes interpretability through step-by-step reasoning and proposes deployment considerations, including privacy, regulatory aspects, and real-world integration, marking a significant step toward practical LLM-enabled intelligent transportation systems.

Abstract

Traffic Signal Control (TSC) is a crucial component in urban traffic management, aiming to optimize road network efficiency and reduce congestion. Traditional TSC methods, primarily based on transportation engineering and reinforcement learning (RL), often struggle with generalization abilities 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 conducted on ten real-world and synthetic datasets, along with evaluations by fifteen human experts, demonstrate the exceptional effectiveness, generalization ability, and interpretability of LLMLight with LightGPT, outperforming nine baseline methods and ten advanced LLMs.

LLMLight: Large Language Models as Traffic Signal Control Agents

TL;DR

This work introduces LLMLight, a framework that uses domain-tuned large language models as traffic signal control agents to achieve human-like reasoning, improved generalization, and interpretability in urban networks. Central to the approach is LightGPT, a specialized backbone LLM trained via imitation fine-tuning and critic-guided policy refinement, enabling effective control policies with lower cost than large generalist models. Across ten real-world and synthetic datasets, LLMLight demonstrates state-of-the-art or competitive performance against traditional methods, RL baselines, and general LLMs, while maintaining robustness in transfer, scalability, and extreme-traffic scenarios. The method emphasizes interpretability through step-by-step reasoning and proposes deployment considerations, including privacy, regulatory aspects, and real-world integration, marking a significant step toward practical LLM-enabled intelligent transportation systems.

Abstract

Traffic Signal Control (TSC) is a crucial component in urban traffic management, aiming to optimize road network efficiency and reduce congestion. Traditional TSC methods, primarily based on transportation engineering and reinforcement learning (RL), often struggle with generalization abilities 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 conducted on ten real-world and synthetic datasets, along with evaluations by fifteen human experts, demonstrate the exceptional effectiveness, generalization ability, and interpretability of LLMLight with LightGPT, outperforming nine baseline methods and ten advanced LLMs.
Paper Structure (51 sections, 6 equations, 18 figures, 11 tables)

This paper contains 51 sections, 6 equations, 18 figures, 11 tables.

Table of Contents

  1. Introduction
  2. Preliminary
  3. LLM for Traffic Signal Control
  4. Task Formulation
  5. Workflow of LLMLight
  6. Observation Feature Construction
  7. Commonsense Augmented Prompt Construction
  8. Reasoning and Action Execution
  9. Backbone LLM Training
  10. Trajectory Collection and Filtering
  11. Imitation Fine-tuning
  12. Critic-guided Policy Refinement
  13. Experiments
  14. Experimental Settings
  15. Datasets
  16. ...and 36 more sections

Figures (18)

  • Figure 1: The workflow of LLMLight.
  • Figure 2: The prompt template of LLMLight and decision-making process of the backbone LLM. In this scenario, the intersection connects four lane sections, including north, south, east, and west.
  • Figure 3: The training procedure of LightGPT.
  • Figure 4: The transferability of different methods.
  • Figure 5:
  • ...and 13 more figures

Theorems & Definitions (2)

  • definition 1
  • definition 2