# TrafficGPT: Viewing, Processing and Interacting with Traffic Foundation Models

arXiv: https://arxiv.org/abs/2309.06719 · HTML: https://mlsanity.com/paper/2309.06719/

## Key facts

- Cited by 45 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 36 references that are in the map.
- First posted on arXiv 13 Sep 2023.

## Builds on (most cited first, 10 of 10)

- [Training language models to follow instructions with human feedback](https://mlsanity.com/paper/2203.02155/) (2022)
- [Chain-of-Thought Prompting Elicits Reasoning in Large Language Models](https://mlsanity.com/paper/2201.11903/) (2022)
- [ReAct: Synergizing Reasoning and Acting in Language Models](https://mlsanity.com/paper/2210.03629/) (2022)
- [Visual ChatGPT: Talking, Drawing and Editing with Visual Foundation Models](https://mlsanity.com/paper/2303.04671/) (2023)
- [Mathematical Capabilities of ChatGPT](https://mlsanity.com/paper/2301.13867/) (2023)
- [MultiModal-GPT: A Vision and Language Model for Dialogue with Humans](https://mlsanity.com/paper/2305.04790/) (2023)
- [XuanYuan 2.0: A Large Chinese Financial Chat Model with Hundreds of Billions Parameters](https://mlsanity.com/paper/2305.12002/) (2023)
- [Prompt to Transfer: Sim-to-Real Transfer for Traffic Signal Control with Prompt Learning](https://mlsanity.com/paper/2308.14284/) (2023)
- [Can ChatGPT Enable ITS? The Case of Mixed Traffic Control via Reinforcement Learning](https://mlsanity.com/paper/2306.08094/) (2023)
- [City-scale synthetic individual-level vehicle trip data](https://mlsanity.com/paper/2206.03273/) (2022)

## Built on by (most cited first, 30 of 45)

- [LLM4Drive: A Survey of Large Language Models for Autonomous Driving](https://mlsanity.com/paper/2311.01043/) (2023)
- [Position: What Can Large Language Models Tell Us about Time Series Analysis](https://mlsanity.com/paper/2402.02713/) (2024)
- [Open-TI: Open Traffic Intelligence with Augmented Language Model](https://mlsanity.com/paper/2401.00211/) (2023)
- [Urban Generative Intelligence (UGI): A Foundational Platform for Agents in Embodied City Environment](https://mlsanity.com/paper/2312.11813/) (2023)
- [Empowering Autonomous Driving with Large Language Models: A Safety Perspective](https://mlsanity.com/paper/2312.00812/) (2023)
- [Towards Knowledge-driven Autonomous Driving](https://mlsanity.com/paper/2312.04316/) (2023)
- [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)
- [Delving into Multi-modal Multi-task Foundation Models for Road Scene Understanding: From Learning Paradigm Perspectives](https://mlsanity.com/paper/2402.02968/) (2024)
- [Integrating LLMs with ITS: Recent Advances, Potentials, Challenges, and Future Directions](https://mlsanity.com/paper/2501.04437/) (2025)
- [How Can Time Series Analysis Benefit From Multiple Modalities? A Survey and Outlook](https://mlsanity.com/paper/2503.11835/) (2025)
- [From Text to Transformation: A Comprehensive Review of Large Language Models' Versatility](https://mlsanity.com/paper/2402.16142/) (2024)
- [AccidentGPT: Accident Analysis and Prevention from V2X Environmental Perception with Multi-modal Large Model](https://mlsanity.com/paper/2312.13156/) (2023)
- [Large Language Models for Human-like Autonomous Driving: A Survey](https://mlsanity.com/paper/2407.19280/) (2024)
- [UrbanLLM: Autonomous Urban Activity Planning and Management with Large Language Models](https://mlsanity.com/paper/2406.12360/) (2024)
- [V2X-LLM: Enhancing V2X Integration and Understanding in Connected Vehicle Corridors](https://mlsanity.com/paper/2503.02239/) (2025)
- [IFAR: Multi-Perspective and Multi-Level Causal Discovery with LLMs](https://mlsanity.com/paper/2409.05559/) (2024)
- [Generating and Evolving Reward Functions for Highway Driving with Large Language Models](https://mlsanity.com/paper/2406.10540/) (2024)
- [What can LLM tell us about cities?](https://mlsanity.com/paper/2411.16791/) (2024)
- [XLM for Autonomous Driving Systems: A Comprehensive Review](https://mlsanity.com/paper/2409.10484/) (2024)
- [UrbanPlanBench: A Comprehensive Urban Planning Benchmark for Evaluating Large Language Models](https://mlsanity.com/paper/2504.21027/) (2025)
- [When, Where, and What? A Novel Benchmark for Accident Anticipation and Localization with Large Language Models](https://mlsanity.com/paper/2407.16277/) (2024)
- [TransCompressor: LLM-Powered Multimodal Data Compression for Smart Transportation](https://mlsanity.com/paper/2411.16020/) (2024)
- [Reimagining Urban Science: Scaling Causal Inference with Large Language Models](https://mlsanity.com/paper/2504.12345/) (2025)
- [Large Language Model Powered Intelligent Urban Agents: Concepts, Capabilities, and Applications](https://mlsanity.com/paper/2507.00914/) (2025)
- [IncidentResponseGPT: Generating Traffic Incident Response Plans with Generative Artificial Intelligence](https://mlsanity.com/paper/2404.18550/) (2024)
- [CityGPT: Towards Urban IoT Learning, Analysis and Interaction with Multi-Agent System](https://mlsanity.com/paper/2405.14691/) (2024)
- [Enhancing Travel Choice Modeling with Large Language Models: A Prompt-Learning Approach](https://mlsanity.com/paper/2406.13558/) (2024)
- [Independent Mobility GPT (IDM-GPT): A Self-Supervised Multi-Agent Large Language Model Framework for Customized Traffic Mobility Analysis Using Machine Learning Models](https://mlsanity.com/paper/2502.18652/) (2025)
- [Urban Computing in the Era of Large Language Models](https://mlsanity.com/paper/2504.02009/) (2025)

## Abstract (arXiv)

With the promotion of chatgpt to the public, Large language models indeed showcase remarkable common sense, reasoning, and planning skills, frequently providing insightful guidance. These capabilities hold significant promise for their application in urban traffic management and control. However, LLMs struggle with addressing traffic issues, especially processing numerical data and interacting with simulations, limiting their potential in solving traffic-related challenges. In parallel, specialized traffic foundation models exist but are typically designed for specific tasks with limited input-output interactions. Combining these models with LLMs presents an opportunity to enhance their capacity for tackling complex traffic-related problems and providing insightful suggestions. To bridge this gap, we present TrafficGPT, a fusion of ChatGPT and traffic foundation models. This integration yields the following key enhancements: 1) empowering ChatGPT with the capacity to view, analyze, process traffic data, and provide insightful decision support for urban transportation system management; 2) facilitating the intelligent deconstruction of broad and complex tasks and sequential utilization of traffic foundation models for their gradual completion; 3) aiding human decision-making in traffic control through natural language dialogues; and 4) enabling interactive feedback and solicitation of revised outcomes. By seamlessly intertwining large language model and traffic expertise, TrafficGPT not only advances traffic management but also offers a novel approach to leveraging AI capabilities in this domain. The TrafficGPT demo can be found in https://github.com/lijlansg/TrafficGPT.git.