Abstract | Transportation systems are particularly prone to exhibiting overwhelming complexity on account of the numerous involved variables, corresponding interrelationships, and the unpredictability of human behavior. Simulation approaches are commonly used tools to describe and study such intricate real-world systems. Despite their clear advantages, these models can too suffer from high complexity and computational hindrances, especially when designed along with fine detail. The field of Air Traffic Management (ATM) modeling is no stranger to such concerns, as it traditionally involves exhausting and manual-driven intense analyses built upon computationally heavy simulation models. This rather frequent shortcoming can be addressed by employing simulation metamodels combined with active learning strategies to approximate, via fast functions, the input-output mappings inherently defined by the simulation models in an efficient way. In this work, we propose an exploration framework that integrates active learning and simulation metamodeling in a single unified approach to address recurrent computational bottlenecks typically associated with intense performance impact assessments within the field of ATM. Our methodology is designed to systematically explore the simulation input space in an efficient and self-guided manner, ultimately providing ATM practitioners with meaningful insights concerning the simulation models under study. Using a fully developed state-of-the-art ATM simulator and employing a Gaussian Process as a metamodel, we show that active learning is indeed capable of enhancing both the modeling and performances of simulation metamodeling by strategically avoiding redundant computer experiments and predicting simulation outputs values given a pre-specified input region. |
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