# Towards Automated Air Traffic Safety Assessment Around Non-Towered Airports Using Large Language Models

arXiv: https://arxiv.org/abs/2605.12332 · HTML: https://mlsanity.com/paper/2605.12332/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 16 of its 32 references that are in the map.
- First posted on arXiv 12 May 2026.

## Builds on (most cited first, 16 of 16)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [LLaMA: Open and Efficient Foundation Language Models](https://mlsanity.com/paper/2302.13971/) (2023)
- [Chain-of-Thought Prompting Elicits Reasoning in Large Language Models](https://mlsanity.com/paper/2201.11903/) (2022)
- [Gemini 2.5: Pushing the Frontier with Advanced Reasoning, Multimodality, Long Context, and Next Generation Agentic Capabilities](https://mlsanity.com/paper/2507.06261/) (2025)
- [Mistral 7B](https://mlsanity.com/paper/2310.06825/) (2023)
- [Qwen2 Technical Report](https://mlsanity.com/paper/2407.10671/) (2024)
- [Gemma 2: Improving Open Language Models at a Practical Size](https://mlsanity.com/paper/2408.00118/) (2024)
- [A Survey on Multimodal Large Language Models](https://mlsanity.com/paper/2306.13549/) (2023)
- [Multi-Step Reasoning with Large Language Models, a Survey](https://mlsanity.com/paper/2407.11511/) (2024)
- [Neural Machine Translation: A Review and Survey](https://mlsanity.com/paper/1912.02047/) (2019)
- [Recent Advances in Speech Language Models: A Survey](https://mlsanity.com/paper/2410.03751/) (2024)
- [Call Me When Necessary: LLMs can Efficiently and Faithfully Reason over Structured Environments](https://mlsanity.com/paper/2403.08593/) (2024)
- [AviationGPT: A Large Language Model for the Aviation Domain](https://mlsanity.com/paper/2311.17686/) (2023)
- [CHATATC: Large Language Model-Driven Conversational Agents for Supporting Strategic Air Traffic Flow Management](https://mlsanity.com/paper/2402.14850/) (2024)
- [Automatic Control With Human-Like Reasoning: Exploring Language Model Embodied Air Traffic Agents](https://mlsanity.com/paper/2409.09717/) (2024)
- [Language Conditioning Improves Accuracy of Aircraft Goal Prediction in Non-Towered Airspace](https://mlsanity.com/paper/2509.14063/) (2025)

## Built on by (most cited first, 2 of 2)

- [Air Traffic Control Using Large Language Models: Prompt Engineering, Architecture, and Evaluation](https://mlsanity.com/paper/2608.19299/) (2026)
- [V2TATC: Joint Voice-Trajectory Embedding and Dataset for Air Traffic Controller Situational Awareness](https://mlsanity.com/paper/2608.28981/) (2026)

## Abstract (arXiv)

We investigate frameworks for post-flight safety analysis at non-towered airports using large language models (LLMs). Non-towered airports rely on the Common Traffic Advisory Frequency (CTAF) for air traffic coordination and experience frequent near mid-air collisions due to the pilot self-announcement communication protocol. We propose a general vision-language model (VLM) approach to analyze the transcribed CTAF radio communications in natural language, METeorological Aerodrome Report (METAR) weather data, Automatic Dependent Surveillance-Broadcast (ADS-B) flight trajectories, and Visual Flight Rules sectional charts of the airfield. We provide a preliminary study at Half Moon Bay Airport, with a qualitative real world case study and a quantitative evaluation using a new synthetic dataset of communications and weather modalities. We qualitatively evaluate our framework on real flight data using Gemini 2.5 Pro, demonstrating accurate identification of a right-of-way violation. The synthetic dataset is derived from real examples and includes a 12-category hazard taxonomy, and is used to benchmark three open-source (Qwen 2.5-7B, Mistral-7B, Gemma-2-9B) and three closed-source (GPT-4o, GPT-5.4, Claude Sonnet 4.6) LLM models on the subset of inputs related to CTAF and METAR. Even limited to CTAF and METAR inputs and open source LLMs, instances of our framework typically achieve a macro F1 score above 0.85 on a binary nominal/danger classification task. Future work includes a quantitative evaluation across all modalities and a larger number of real world examples. Taken together, our results suggest that VLM analysis of safety at non-towered airports may be a valuable future capability.