2026-04-19T19:18:02Zhttps://recercat.cat/oai/request

oai:recercat.cat:2117/4274272026-02-04T08:18:37Zcom_2072_1033col_2072_452950

00925njm 22002777a 4500 dc Vouros, George author Ioannidis, Ioannis author Santipantakis, Georgios author Tranos, Theodore author Blekas, Konstantinos author Melgosa Farrés, Marc author Prats Menéndez, Xavier author 2024-11-12 Complex microscopic simulation models of strategic Air Traffic Management (ATM) performance assessment and decision-making are hindered by several factors. One of the most important is the existence of hidden parameters—such as aircraft take-off weight (TOW) and the selected cost index (CI)—which, if known, would allow for more effective performance modeling methodologies for assessing Key Performance Indicators (KPIs) at various levels of abstraction/detail, e.g., system-wide, or at the level of individual flights. This research proposes a data-driven methodology for the estimation of flights’ hidden parameters combining mechanistic and advanced Artificial Intelligence/Machine Learning (AI/ML) models. Aiming at microsimulation models, our goal is to study the effect of these estimations on the prediction of flights’ KPIs. In so doing, we propose a novel methodology according to which data-driven methods are trained given optimal trajectories (produced by mechanistic models) corresponding to known hidden parameter values, with the aim of predicting hidden parameters’ values of unseen trajectories. The results show that estimations of hidden parameters support the accurate prediction of KPIs regarding the efficiency of flights: fuel consumption, gate-to-gate time and distance flown. Postprint (published version) Àrees temàtiques de la UPC::Aeronàutica i espai Prediction Hidden parameters KPIs Take-off weight Cost index Deep learning Simulation Machine-learning methods estimating flights’ hidden parameters for the prediction of KPIs