Full-scale pre-tactical route prediction machine learning to increase pre-tactical demand forecast accuracy
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Inclou dades d'ús des de 2022
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hdl:2117/341212
Tipus de documentText en actes de congrés
Data publicació2020
Condicions d'accésAccés obert
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Abstract
The objective of this paper is to present an artificial intelligence-based methodology to predict the Flight plans that will be received during the pre-tactical phase of the Air Traffic Flow and Capacity Management (ATFCM) process. For this purpose, input features equivalent to those of EUROCONTROL’s PREDICT solution are fed to a Multinomial Logistic Regression algorithm over pre-clustered air routes in order to determine which route cluster is the most likely to be filed by an airspace user within each OD-pair. Results show that this procedure is capable of outperforming the current PREDICT solution in almost 40% of the 5,699 OD pairs considered and reducing current solution’s error by 11%, showing good and scalable prediction capabilities.
CitacióMateos, M. [et al.]. Full-scale pre-tactical route prediction machine learning to increase pre-tactical demand forecast accuracy. A: International Conference on Research in Air Transportation. "Proceedings of 9th ICRAT 2020 - International Conference on Research in Air Transportation". 2020,
Altres identificadorshttp://icrat.org/icrat/upcoming-conference/papers/
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2020_Mateos_ICRAT_route-prediction.pdf | 642,3Kb | Visualitza/Obre |