Date: January, 23rd, 11h00
Place: Université Libre de Bruxelles
Abstract: Optimization models for Air Traffic Flow Management (ATFM) select, for each flight, suitable trajectories with the aim of reducing congestion of both airports and en-route sectors, and maximizing the Air Traffic Management system efficiency. Recent models try to include as accurate as possible information on airspace capacity as well as on Airspace Users route preferences and priorities, as suggested by recent SESAR (Single European Sky ATM Research) programs. In this direction, we analyze flight trajectories queried from Eurocontrol DDR2 data source. We learn homogeneous trajectories via clustering, and we apply data analytics (mainly based on tree classifiers, support vector machines and multiple regression) to explore the relation between grouped trajectories and potential choice-determinants such as length, time, en-route charges, fuel consumption, aircraft type, airspace congestion etc. The associations are evaluated and the predictive value of determinants is validated and analyzed. For any given origin-destination pair, this ultimately leads to determining a set of flight trajectories and information on related Airspace Users’ preferences that feed optimization models for ATFM.