AirTrafficGen: Configurable Air Traffic Scenario Generation with Large Language Models

• URL: http://arxiv.org/abs/2508.02269v1
• Date: Mon, 04 Aug 2025 10:21:47 GMT
• Title: AirTrafficGen: Configurable Air Traffic Scenario Generation with Large Language Models
• Authors: Dewi Sid William Gould, George De Ath, Ben Carvell, Nick Pepper,
• Abstract summary: We introduce an end-to-end approach to generate complex Air Traffic Control scenarios.<n>Our method uses a purpose-built, graph-based representation to encode sector topology.<n>We show that state-of-the-art models like Gemini 2.5 Pro and OpenAI o3 can generate high-traffic scenarios.
• Score: 0.004807514276707785
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The manual design of scenarios for Air Traffic Control (ATC) training is a demanding and time-consuming bottleneck that limits the diversity of simulations available to controllers. To address this, we introduce a novel, end-to-end approach, AirTrafficGen, that leverages large language models (LLMs) to automate and control the generation of complex ATC scenarios. Our method uses a purpose-built, graph-based representation to encode sector topology (including airspace geometry, routes, and fixes) into a format LLMs can process. Through rigorous benchmarking, we show that state-of-the-art models like Gemini 2.5 Pro and OpenAI o3 can generate high-traffic scenarios whilst maintaining operational realism. Our engineered prompting enables fine-grained control over interaction presence, type, and location. Initial findings suggest these models are also capable of iterative refinement, correcting flawed scenarios based on simple textual feedback. This approach provides a scalable alternative to manual scenario design, addressing the need for a greater volume and variety of ATC training and validation simulations. More broadly, this work showcases the potential of LLMs for complex planning in safety-critical domains.

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