CHATATC: Large Language Model-Driven Conversational Agents for
  Supporting Strategic Air Traffic Flow Management
Sinan Abdulhak Wayne Hubbard Karthik Gopalakrishnan Max Z. Li
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.