# CHATATC: Large Language Model-Driven Conversational Agents for Supporting Strategic Air Traffic Flow Management

arXiv: https://arxiv.org/abs/2402.14850 · HTML: https://mlsanity.com/paper/2402.14850/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Placed in General LLM agent frameworks and benchmarks, one of 16 directions in LLM agents and tool use.
- Builds on 5 of its 31 references that are in the map.
- First posted on arXiv 20 Feb 2024.

## Placed in

- LLM agents and tool use / [General LLM agent frameworks and benchmarks](https://mlsanity.com/agents/general-llm-agent-frameworks-and-benchmarks/)

## Builds on (most cited first, 5 of 5)

- [Chain-of-Thought Prompting Elicits Reasoning in Large Language Models](https://mlsanity.com/paper/2201.11903/) (2022)
- [Sparks of Artificial General Intelligence: Early experiments with GPT-4](https://mlsanity.com/paper/2303.12712/) (2023)
- [How Knowledge Workers Think Generative AI Will (Not) Transform Their Industries](https://mlsanity.com/paper/2310.06778/) (2023)
- [GPT Models in Construction Industry: Opportunities, Limitations, and a Use Case Validation](https://mlsanity.com/paper/2305.18997/) (2023)
- [AviationGPT: A Large Language Model for the Aviation Domain](https://mlsanity.com/paper/2311.17686/) (2023)

## Built on by (most cited first, 5 of 5)

- [Automatic Control With Human-Like Reasoning: Exploring Language Model Embodied Air Traffic Agents](https://mlsanity.com/paper/2409.09717/) (2024)
- [PilotBench: A Benchmark for General Aviation Agents with Safety Constraints](https://mlsanity.com/paper/2604.08987/) (2026)
- [Towards Automated Air Traffic Safety Assessment Around Non-Towered Airports Using Large Language Models](https://mlsanity.com/paper/2605.12332/) (2026)
- [Air Traffic Control Using Large Language Models: Prompt Engineering, Architecture, and Evaluation](https://mlsanity.com/paper/2608.19299/) (2026)
- [FLY-EVAL++: An Evidence-Driven Evaluation Protocol for Safety-Constrained Flight Prediction with Large Language Models](https://mlsanity.com/paper/2609.04021/) (2026)

## Abstract (arXiv)

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.