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.

翻译：随着ChatGPT向公众推广，大型语言模型确实展现出惊人的常识、推理和规划能力，频繁提供富有洞察力的指导。这些能力为其在城市交通管理与控制中的应用提供了重要前景。然而，大型语言模型在处理交通问题时存在困难，尤其是在处理数值数据和与仿真交互方面，这限制了它们解决交通相关挑战的潜力。与此同时，存在专门的交通基础模型，但这些模型通常针对特定任务设计，输入-输出交互有限。将这些模型与大型语言模型相结合，为增强其解决复杂交通问题并提供洞察性建议的能力提供了机遇。为弥合这一差距，我们提出了TrafficGPT——ChatGPT与交通基础模型的融合。这一整合带来了以下关键改进：1) 赋予ChatGPT查看、分析和处理交通数据的能力，并为城市交通系统管理提供洞察性决策支持；2) 促进广泛复杂任务的智能分解，并逐步利用交通基础模型完成这些任务；3) 通过自然语言对话辅助人类在交通控制中的决策制定；4) 实现交互式反馈并征求修订结果。通过将大型语言模型与交通专业知识无缝融合，TrafficGPT不仅推进了交通管理，还为在该领域利用人工智能能力提供了一种新颖方法。TrafficGPT演示可在https://github.com/lijlansg/TrafficGPT.git 查看。