In this work we describe a novel approach for the prediction of the flight plan to be sent by airspace users during the pre-tactical phase of Air Traffic Flow and Capacity Management (ATFCM). The proposed approach uses machine learning algorithms to extract airspace user preferences in terms of route characteristics, allowing the prediction of new routes not observed during the model training phase. We present the results obtained from applying this approach to short and medium range KLM flights for 52 weeks. Results show that the proposed solution is robust, scalable and capable of reducing the number of wrong predictions provided by the current Network Manager operational solution by 24.3% (4.5% increment on accuracy).Manuel Mateos´ PhD is funded by the 1st SESAR ENGAGE
KTN Call for PhDs and is developed in collaboration between
Nommon and the Technical University of Catalonia. This PhD
study has received funding from the SESAR Joint Undertaking
under the European Union’s Horizon 2020 research and
innovation programme under grant agreement No 783287.Peer ReviewedPostprint (published version
