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