# Can ChatGPT Enable ITS? The Case of Mixed Traffic Control via Reinforcement Learning

arXiv: https://arxiv.org/abs/2306.08094 · HTML: https://mlsanity.com/paper/2306.08094/

## Key facts

- Cited by 20 papers in the map (data through 14 Oct 2026).
- Builds on 23 of its 48 references that are in the map.
- First posted on arXiv 13 Jun 2023.

## Builds on (most cited first, 23 of 23)

- [GPT-4 Technical Report](https://mlsanity.com/paper/2303.08774/) (2023)
- [Training language models to follow instructions with human feedback](https://mlsanity.com/paper/2203.02155/) (2022)
- [Proximal Policy Optimization Algorithms](https://mlsanity.com/paper/1707.06347/) (2017)
- [LLaMA: Open and Efficient Foundation Language Models](https://mlsanity.com/paper/2302.13971/) (2023)
- [Training a Helpful and Harmless Assistant with Reinforcement Learning from Human Feedback](https://mlsanity.com/paper/2204.05862/) (2022)
- [Sparks of Artificial General Intelligence: Early experiments with GPT-4](https://mlsanity.com/paper/2303.12712/) (2023)
- [PaLM-E: An Embodied Multimodal Language Model](https://mlsanity.com/paper/2303.03378/) (2023)
- [Guiding Pretraining in Reinforcement Learning with Large Language Models](https://mlsanity.com/paper/2302.06692/) (2023)
- [Foundation Models for Decision Making: Problems, Methods, and Opportunities](https://mlsanity.com/paper/2303.04129/) (2023)
- [ChatGPT Makes Medicine Easy to Swallow: An Exploratory Case Study on Simplified Radiology Reports](https://mlsanity.com/paper/2212.14882/) (2022)
- [Deep Reinforcement Learning for Intelligent Transportation Systems: A Survey](https://mlsanity.com/paper/2005.00935/) (2020)
- [Do Embodied Agents Dream of Pixelated Sheep: Embodied Decision Making using Language Guided World Modelling](https://mlsanity.com/paper/2301.12050/) (2023)
- [Can an Embodied Agent Find Your "Cat-shaped Mug"? LLM-Guided Exploration for Zero-Shot Object Navigation](https://mlsanity.com/paper/2303.03480/) (2023)
- [Flow: A Modular Learning Framework for Mixed Autonomy Traffic](https://mlsanity.com/paper/1710.05465/) (2017)
- [Language Instructed Reinforcement Learning for Human-AI Coordination](https://mlsanity.com/paper/2304.07297/) (2023)
- [Reinforcement Learning for Mixed Autonomy Intersections](https://mlsanity.com/paper/2111.04686/) (2021)
- [ADAPS: Autonomous Driving Via Principled Simulations](https://mlsanity.com/paper/1907.08874/) (2019)
- [Predicting Station-Level Bike-Sharing Demands Using Graph Convolutional Neural Network](https://mlsanity.com/paper/2004.08723/) (2020)
- [A Generic Graph Sparsification Framework using Deep Reinforcement Learning](https://mlsanity.com/paper/2112.01565/) (2021)
- [Black-box Adversarial Attacks on Network-wide Multi-step Traffic State Prediction Models](https://mlsanity.com/paper/2110.08712/) (2021)
- [Learning to Control DC Motor for Micromobility in Real Time with Reinforcement Learning](https://mlsanity.com/paper/2108.00138/) (2021)
- [AutoJoin: Efficient Adversarial Training against Gradient-Free Perturbations for Robust Maneuvering via Denoising Autoencoder and Joint Learning](https://mlsanity.com/paper/2205.10933/) (2022)
- [Efficient Quality-Diversity Optimization through Diverse Quality Species](https://mlsanity.com/paper/2304.07425/) (2023)

## Built on by (most cited first, 20 of 20)

- [TrafficGPT: Viewing, Processing and Interacting with Traffic Foundation Models](https://mlsanity.com/paper/2309.06719/) (2023)
- [Exploring the Roles of Large Language Models in Reshaping Transportation Systems: A Survey, Framework, and Roadmap](https://mlsanity.com/paper/2503.21411/) (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)
- [Mixture-of-Personas Language Models for Population Simulation](https://mlsanity.com/paper/2504.05019/) (2025)
- [BIGCity: A Universal Spatiotemporal Model for Unified Trajectory and Traffic State Data Analysis](https://mlsanity.com/paper/2412.00953/) (2024)
- [EnduRL: Enhancing Safety, Stability, and Efficiency of Mixed Traffic Under Real-World Perturbations Via Reinforcement Learning](https://mlsanity.com/paper/2311.12261/) (2023)
- [CARL: Congestion-Aware Reinforcement Learning for Imitation-based Perturbations in Mixed Traffic Control](https://mlsanity.com/paper/2404.00796/) (2024)
- [CityGPT: Towards Urban IoT Learning, Analysis and Interaction with Multi-Agent System](https://mlsanity.com/paper/2405.14691/) (2024)
- [TrafficGPT: Towards Multi-Scale Traffic Analysis and Generation with Spatial-Temporal Agent Framework](https://mlsanity.com/paper/2405.05985/) (2024)
- [Large-Scale Mixed-Traffic and Intersection Control using Multi-agent Reinforcement Learning](https://mlsanity.com/paper/2504.04691/) (2025)
- [Learning to Change: Choreographing Mixed Traffic Through Lateral Control and Hierarchical Reinforcement Learning](https://mlsanity.com/paper/2403.14879/) (2024)
- [Exploring Transformer-Augmented LSTM for Temporal and Spatial Feature Learning in Trajectory Prediction](https://mlsanity.com/paper/2412.13419/) (2024)
- [Analyzing Behaviors of Mixed Traffic via Reinforcement Learning at Unsignalized Intersections](https://mlsanity.com/paper/2312.05325/) (2023)
- [Neighbor-Aware Reinforcement Learning for Mixed Traffic Optimization in Large-scale Networks](https://mlsanity.com/paper/2412.12622/) (2024)
- [LDGNet: A Lightweight Difference Guiding Network for Remote Sensing Change Detection](https://mlsanity.com/paper/2504.05062/) (2025)
- [GraphTrafficGPT: Enhancing Traffic Management Through Graph-Based AI Agent Coordination](https://mlsanity.com/paper/2507.13511/) (2025)
- [Multi-Objective Reinforcement Learning for Large-Scale Mixed Traffic Control](https://mlsanity.com/paper/2512.11247/) (2025)
- [Learning to Design City-scale Transit Routes](https://mlsanity.com/paper/2512.19767/) (2025)
- [Large Language Models in Transportation Systems Management and Operations: From Text Reasoning to Multi-modal Decision Support](https://mlsanity.com/paper/2606.00991/) (2026)

## Abstract (arXiv)

The surge in Reinforcement Learning (RL) applications in Intelligent Transportation Systems (ITS) has contributed to its growth as well as highlighted key challenges. However, defining objectives of RL agents in traffic control and management tasks, as well as aligning policies with these goals through an effective formulation of Markov Decision Process (MDP), can be challenging and often require domain experts in both RL and ITS. Recent advancements in Large Language Models (LLMs) such as GPT-4 highlight their broad general knowledge, reasoning capabilities, and commonsense priors across various domains. In this work, we conduct a large-scale user study involving 70 participants to investigate whether novices can leverage ChatGPT to solve complex mixed traffic control problems. Three environments are tested, including ring road, bottleneck, and intersection. We find ChatGPT has mixed results. For intersection and bottleneck, ChatGPT increases number of successful policies by 150% and 136% compared to solely beginner capabilities, with some of them even outperforming experts. However, ChatGPT does not provide consistent improvements across all scenarios.