# LLMLight: Large Language Models as Traffic Signal Control Agents

arXiv: https://arxiv.org/abs/2312.16044 · HTML: https://mlsanity.com/paper/2312.16044/

## Key facts

- Cited by 73 papers in the map (data through 14 Oct 2026).
- Placed in Multimodal and embodied agents, one of 16 directions in LLM agents and tool use.
- Builds on 20 of its 61 references that are in the map.
- First posted on arXiv 26 Dec 2023.

## Placed in

- LLM agents and tool use / [Multimodal and embodied agents](https://mlsanity.com/agents/multimodal-and-embodied-agents/)

## Builds on (most cited first, 20 of 20)

- [Proximal Policy Optimization Algorithms](https://mlsanity.com/paper/1707.06347/) (2017)
- [Llama 2: Open Foundation and Fine-Tuned Chat Models](https://mlsanity.com/paper/2307.09288/) (2023)
- [Scaling Laws for Neural Language Models](https://mlsanity.com/paper/2001.08361/) (2020)
- [Scaling Instruction-Finetuned Language Models](https://mlsanity.com/paper/2210.11416/) (2022)
- [Voyager: An Open-Ended Embodied Agent with Large Language Models](https://mlsanity.com/paper/2305.16291/) (2023)
- [RRHF: Rank Responses to Align Language Models with Human Feedback without tears](https://mlsanity.com/paper/2304.05302/) (2023)
- [AgentTuning: Enabling Generalized Agent Abilities for LLMs](https://mlsanity.com/paper/2310.12823/) (2023)
- [FireAct: Toward Language Agent Fine-tuning](https://mlsanity.com/paper/2310.05915/) (2023)
- [LanguageMPC: Large Language Models as Decision Makers for Autonomous Driving](https://mlsanity.com/paper/2310.03026/) (2023)
- [CoLight: Learning Network-level Cooperation for Traffic Signal Control](https://mlsanity.com/paper/1905.05717/) (2019)
- [Drive as You Speak: Enabling Human-Like Interaction with Large Language Models in Autonomous Vehicles](https://mlsanity.com/paper/2309.10228/) (2023)
- [A Survey on Traffic Signal Control Methods](https://mlsanity.com/paper/1904.08117/) (2019)
- [Penetrative AI: Making LLMs Comprehend the Physical World](https://mlsanity.com/paper/2310.09605/) (2023)
- [AttendLight: Universal Attention-Based Reinforcement Learning Model for Traffic Signal Control](https://mlsanity.com/paper/2010.05772/) (2020)
- [Practical Adversarial Attacks on Spatiotemporal Traffic Forecasting Models](https://mlsanity.com/paper/2210.02447/) (2022)
- [Efficient Pressure: Improving efficiency for signalized intersections](https://mlsanity.com/paper/2112.02336/) (2021)
- [UUKG: Unified Urban Knowledge Graph Dataset for Urban Spatiotemporal Prediction](https://mlsanity.com/paper/2306.11443/) (2023)
- [Irregular Traffic Time Series Forecasting Based on Asynchronous Spatio-Temporal Graph Convolutional Network](https://mlsanity.com/paper/2308.16818/) (2023)
- [Robust Spatiotemporal Traffic Forecasting with Reinforced Dynamic Adversarial Training](https://mlsanity.com/paper/2306.14126/) (2023)
- [A Preference-aware Meta-optimization Framework for Personalized Vehicle Energy Consumption Estimation](https://mlsanity.com/paper/2306.14421/) (2023)

## Built on by (most cited first, 30 of 73)

- [Uncertainty Quantification and Confidence Calibration in Large Language Models: A Survey](https://mlsanity.com/paper/2503.15850/) (2025)
- [AgentMove: A Large Language Model based Agentic Framework for Zero-shot Next Location Prediction](https://mlsanity.com/paper/2408.13986/) (2024)
- [Foundation Models for Spatio-Temporal Data Science: A Tutorial and Survey](https://mlsanity.com/paper/2503.13502/) (2025)
- [Towards Urban General Intelligence: A Review and Outlook of Urban Foundation Models](https://mlsanity.com/paper/2402.01749/) (2024)
- [Integrating LLMs with ITS: Recent Advances, Potentials, Challenges, and Future Directions](https://mlsanity.com/paper/2501.04437/) (2025)
- [Traffic-R1: Reinforced LLMs Bring Human-Like Reasoning to Traffic Signal Control Systems](https://mlsanity.com/paper/2508.02344/) (2025)
- [ARROW: An Adaptive Rollout and Routing Method for Global Weather Forecasting](https://mlsanity.com/paper/2510.09734/) (2025)
- [CrashSage: A Large Language Model-Centered Framework for Contextual and Interpretable Traffic Crash Analysis](https://mlsanity.com/paper/2505.07853/) (2025)
- [TraveLLM: Could you plan my new public transit route in face of a network disruption?](https://mlsanity.com/paper/2407.14926/) (2024)
- [USTBench: Benchmarking and Dissecting Spatiotemporal Reasoning of LLMs as Urban Agents](https://mlsanity.com/paper/2505.17572/) (2025)
- [Multi-level Value Alignment in Agentic AI Systems: Survey and Perspectives](https://mlsanity.com/paper/2506.09656/) (2025)
- [Large Language Model Powered Intelligent Urban Agents: Concepts, Capabilities, and Applications](https://mlsanity.com/paper/2507.00914/) (2025)
- [Beyond Words: Evaluating Large Language Models in Transportation Planning](https://mlsanity.com/paper/2409.14516/) (2024)
- [VLMLight: Safety-Critical Traffic Signal Control via Vision-Language Meta-Control and Dual-Branch Reasoning Architecture](https://mlsanity.com/paper/2505.19486/) (2025)
- [SUMO-MCP: Leveraging the Model Context Protocol for Autonomous Traffic Simulation and Optimization](https://mlsanity.com/paper/2506.03548/) (2025)
- [Urban-R1: Reinforced MLLMs Mitigate Geospatial Biases for Urban General Intelligence](https://mlsanity.com/paper/2510.16555/) (2025)
- [Aligning LLM agents with human learning and adjustment behavior: a dual agent approach](https://mlsanity.com/paper/2511.00993/) (2025)
- [AgentSUMO: An Agentic Framework for Interactive Simulation Scenario Generation in SUMO via Large Language Models](https://mlsanity.com/paper/2511.06804/) (2025)
- [TrafficSimAgent: A Hierarchical Agent Framework for Autonomous Traffic Simulation with MCP Control](https://mlsanity.com/paper/2512.20996/) (2025)
- [OPTIAGENT: A Physics-Driven Agentic Framework for Automated Optical Design](https://mlsanity.com/paper/2602.23761/) (2026)
- [Urban Computing in the Era of Large Language Models](https://mlsanity.com/paper/2504.02009/) (2025)
- [LLM-ODDR: A Large Language Model Framework for Joint Order Dispatching and Driver Repositioning](https://mlsanity.com/paper/2505.22695/) (2025)
- [SymLight: Exploring Interpretable and Deployable Symbolic Policies for Traffic Signal Control](https://mlsanity.com/paper/2511.05790/) (2025)
- [Chat2SPaT: A Large Language Model Based Tool for Automating Traffic Signal Control Plan Management](https://mlsanity.com/paper/2507.05283/) (2025)
- [EvolveSignal: A Large Language Model Powered Coding Agent for Discovering Traffic Signal Control Strategies](https://mlsanity.com/paper/2509.03335/) (2025)
- [An LLM-Powered Cooperative Framework for Large-Scale Multi-Vehicle Navigation](https://mlsanity.com/paper/2510.07825/) (2025)
- [SVBRD-LLM: Self-Verifying Behavioral Rule Discovery for Autonomous Vehicle Identification](https://mlsanity.com/paper/2511.14977/) (2025)
- [Evolutionary Discovery of Heuristic Policies for Traffic Signal Control](https://mlsanity.com/paper/2511.23122/) (2025)
- [Virtual Traffic Police: Large Language Model-Augmented Traffic Signal Control for Unforeseen Incidents](https://mlsanity.com/paper/2601.15816/) (2026)
- [Human-Centric Traffic Signal Control for Equity: A Multi-Agent Action Branching Deep Reinforcement Learning Approach](https://mlsanity.com/paper/2602.02959/) (2026)

## Abstract (arXiv)

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.