The use of Air traffic management (ATM) simulators for planing and operations can be challenging due to their modelling complexity. This paper presents XALM (eXplainable Active Learning Metamodel), a three-step framework integrating active learning and SHAP (SHapley Additive exPlanations) values into simulation metamodels for supporting ATM decision-making. XALM efficiently uncovers hidden relationships among input and output variables in ATM simulators, those usually of interest in policy analysis. Our experiments show XALM's predictive performance comparable to the XGBoost metamodel with fewer simulations. Additionally, XALM exhibits superior explanatory capabilities compared to non-active learning metamodels. Using the `Mercury' (flight and passenger) ATM simulator, XALM is applied to a real-world scenario in Paris Charles de Gaulle airport, extending an arrival manager's range and scope by analysing six variables. This case study illustrates XALM's effectiveness in enhancing simulation interpretability and understanding variable interactions. By addressing computational challenges and improving explainability, XALM complements traditional simulation-based analyses. Lastly, we discuss two practical approaches for reducing the computational burden of the metamodelling further: we introduce a stopping criterion for active learning based on the inherent uncertainty of the metamodel, and we show how the simulations used for the metamodel can be reused across key performance indicators, thus decreasing the overall number of simulations needed.

翻译：空中交通管理（ATM）仿真器在规划和运营中的应用因其建模复杂性而面临挑战。本文提出XALM（可解释主动学习元模型），一种将主动学习和SHAP（SHapley Additive exPlanations）值集成到仿真元模型以支持ATM决策的三步框架。XALM能高效揭示ATM仿真器中输入与输出变量间的隐藏关系——这些关系通常是政策分析中的关注重点。实验表明，XALM的预测性能与XGBoost元模型相当，但所需仿真次数更少。此外，相较于非主动学习元模型，XALM展现出更优的解释能力。通过使用“Mercury”（航班与旅客）ATM仿真器，XALM被应用于巴黎戴高乐机场的真实场景，通过分析六个变量扩展了进场管理器的覆盖范围与维度。该案例研究证明了XALM在增强仿真可解释性与理解变量交互方面的有效性。通过解决计算挑战并提升可解释性，XALM补充了传统基于仿真的分析方法。最后，我们讨论了两种进一步降低元建模计算负担的实用方案：一是引入基于元模型固有不确定性的主动学习停止准则，二是展示如何跨关键性能指标复用元模型构建所需的仿真结果，从而减少总体仿真次数。