Generative artificial intelligence (AI) and large language models (LLMs) have gained rapid popularity through publicly available tools such as ChatGPT. The adoption of LLMs for personal and professional use is fueled by the natural interactions between human users and computer applications such as ChatGPT, along with powerful summarization and text generation capabilities. Given the widespread use of such generative AI tools, in this work we investigate how these tools can be deployed in a non-safety critical, strategic traffic flow management setting. Specifically, we train an LLM, CHATATC, based on a large historical data set of Ground Delay Program (GDP) issuances, spanning 2000-2023 and consisting of over 80,000 GDP implementations, revisions, and cancellations. We test the query and response capabilities of CHATATC, documenting successes (e.g., providing correct GDP rates, durations, and reason) and shortcomings (e.g,. superlative questions). We also detail the design of a graphical user interface for future users to interact and collaborate with the CHATATC conversational agent.

翻译：生成式人工智能与大语言模型通过ChatGPT等公开工具迅速普及。大语言模型在个人与专业领域的应用，得益于人类用户与ChatGPT等计算机应用之间的自然交互，以及其强大的文本摘要与生成能力。鉴于此类生成式AI工具的广泛使用，本研究探讨了如何将其部署于非安全关键的战略性交通流量管理场景中。具体而言，我们基于大规模历史地面延误程序数据集（涵盖2000年至2023年，包含超过80,000次GDP实施、修订与取消记录）训练了一个名为CHATATC的大语言模型。我们测试了CHATATC的查询与响应能力，记录了其成功案例（如正确提供GDP执行率、持续时间和原因）与不足之处（例如处理最高级比较问题）。同时，我们详细介绍了为未来用户设计的图形用户界面，以实现与CHATATC对话智能体的交互协作。