A Probabilistic Digital Twin of UK En Route Airspace for Training and Evaluating AI Agents for Air Traffic Control

2026-01-06

AIAA SCITECH 2026 Forum

AI Summary

This study addresses the critical gap in current AI research for air traffic control—namely, the absence of a safe, high-fidelity, and quantifiable virtual environment for training and evaluation. The authors present the first probabilistic digital twin system tailored to UK en-route airspace, integrating historical and real-time operational data with physics-informed machine learning models to faithfully reproduce realistic traffic scenarios and enable human–AI collaborative assessment. Innovatively, the framework incorporates a structured validation methodology grounded in trustworthiness and ethical safeguards, delivering a unified, high-speed, standardized testing platform capable of simulating up to 200× real-time speed. Through a Python Gym interface, an interactive human-in-the-loop interface, and quantitative performance metrics, the system facilitates rapid iteration of AI agents and controller-led capability evaluation in a high-fidelity airspace, laying the groundwork for advanced automation in air traffic management.

Technology Category

• Philosophy and Ethics of AI: Safety, Robustness & Trustworthiness
• Humans and AI: Human-in-the-loop Machine Learning
• Multiagent Systems: Adversarial Agents

Application Category

• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions

Abstract

This paper presents the first probabilistic Digital Twin of operational en route airspace, developed for the London Area Control Centre. The Digital Twin is intended to support the development and rigorous human-in-the-loop evaluation of AI agents for Air Traffic Control (ATC), providing a virtual representation of real-world airspace that enables safe exploration of higher levels of ATC automation. This paper makes three significant contributions: firstly, we demonstrate how historical and live operational data may be combined with a probabilistic, physics-informed machine learning model of aircraft performance to reproduce real-world traffic scenarios, while accurately reflecting the level of uncertainty inherent in ATC. Secondly, we develop a structured assurance case, following the Trustworthy and Ethical Assurance framework, to provide quantitative evidence for the Digital Twin's accuracy and fidelity. This is crucial to building trust in this novel technology within this safety-critical domain. Thirdly, we describe how the Digital Twin forms a unified environment for agent testing and evaluation. This includes fast-time execution (up to x200 real-time), a standardised Python-based ``gym''interface that supports a range of AI agent designs, and a suite of quantitative metrics for assessing performance. Crucially, the framework facilitates competency-based assessment of AI agents by qualified Air Traffic Control Officers through a Human Machine Interface. We also outline further applications and future extensions of the Digital Twin architecture.

Problem

• Digital Twin
• Air Traffic Control
• AI Agents
• Probabilistic Modeling
• Human-in-the-loop Evaluation

Innovation

• Probabilistic Digital Twin
• Physics-informed Machine Learning
• Air Traffic Control Automation
• Human-in-the-loop Evaluation
• Assurance Case