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.

翻译：本文提出了首个面向伦敦区域管制中心运行航路空域的概率数字孪生模型。该数字孪生旨在支持空中交通管制AI智能体的开发及严格的人机协同评估，通过构建真实空域的虚拟映射，为探索更高层级的ATC自动化提供安全实验环境。本研究作出三项重要贡献：首先，我们展示了如何将历史与实时运行数据与基于物理机理的飞机性能概率机器学习模型相结合，在精确反映ATC固有不确定性的同时复现真实交通场景。其次，我们遵循可信伦理保障框架构建结构化保障案例，为数字孪生的准确性与保真度提供量化证据，这对在安全关键领域建立对该新兴技术的信任至关重要。第三，我们阐述了该数字孪生如何构建统一的智能体测试评估环境，包括支持最高200倍实时速率的快速仿真、基于Python的标准化"gym"接口（兼容多种AI智能体架构）以及性能评估指标体系。关键创新在于，该框架通过人机交互界面支持持证空中交通管制员对AI智能体开展基于胜任能力的评估。最后，我们展望了该数字孪生架构的拓展应用与未来发展方向。