Title | Active Learning Metamodels for ATM Simulation Modeling |
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Authors | Riss, C., Antunes, F., Gurtner, G., Camara Pereira, F., Delgado, L. and Azevedo, C. |
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Type | Conference paper |
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Abstract | Transportation systems are particularly prone to exhibiting overwhelming complexity on account of the numerous involved variables and their interrelationships, unknown stochastic phenomena, and ultimately human behavior. Simulation approaches are commonly used tools to describe and study such intricate real-world systems. Despite their obvious advantages,simulation models can still end up being quite complex themselves. The field of Air Traffic Management (ATM) modeling is no stranger to such concerns, as it traditionally involves laborious and systematic 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 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. |
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Keywords | Active Learning |
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| Simulation Metamodeling |
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| Air Traffic Management Simulation Modeling |
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| Gaussian Processes |
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Year | 2021 |
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Conference | 11th SESAR Innovation Days |
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Publisher | SESAR |
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Publisher's version | File Access Level Open (open metadata and files) |
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Publication dates |
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Published | 07 Dec 2021 |
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Project | NOSTROMO |
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Funder | SESAR Joint Undertaking under Horizon 2020 research and innovation programme |
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Web address (URL) of conference proceedings | https://www.sesarju.eu/node/3438 |
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Web address (URL) | https://www.sesarju.eu/node/3438 |
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File | File Access Level Open (open metadata and files) |
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