Applied metamodelling for ATM performance simulations

TL;DR

This paper introduces XALM, a novel framework combining active learning and explainability techniques to improve the efficiency and interpretability of ATM simulation metamodels, aiding decision-making in air traffic management.

Contribution

The paper presents XALM, a new active learning-based framework that enhances ATM simulation metamodels with better predictive performance and explainability, reducing computational costs.

Findings

XALM achieves predictive accuracy comparable to XGBoost with fewer simulations.

XALM provides superior interpretability through SHAP explanations.

Application to real-world ATM scenario demonstrates practical effectiveness.

Abstract

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.