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Descripción
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| Airports are considered complex system in which the coexistence of different actors competing and collaborating for the same resources under operational time uncertainties can cause a poor performance on the overall ATM (Air Traffic Management) system. In order to facilitate the process of decision making to mitigate the propagation of perturbations through the different airport processes a causal model relying on machine learning, using data mining algorithms has been implemented to predict feasible states. This paper introduces a new approach for modelling causal relationships, which can be used for further analysing of feasible scenarios by means of simulation techniques. The state space analysis of reachable airport states is a relevant approach to validate the causal model using a huge amount of historical data for predictive purposes. | |
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Internacional
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Si |
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Nombre congreso
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European Modeling and Simulation Symposium. Multidisciplinary Modeling and Simulation Multiconference (I3M 2017) |
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Tipo de participación
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960 |
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Lugar del congreso
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Revisores
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Si |
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ISBN o ISSN
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978-88-97999-85-0 |
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DOI
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Fecha inicio congreso
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18/09/2017 |
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Fecha fin congreso
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20/09/2017 |
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Desde la página
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288 |
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Hasta la página
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295 |
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Título de las actas
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Proceedings of the European Modeling and Simulation Symposium, 2017 |