Predicting requested flight levels with machine learning

The objective of this paper is to present a machine learning approach for the prediction of the Requested Flight Level received during the pre-tactical phase of the Air Traffic Flow and Capacity Management process. A set of machine learning models are proposed in order to determine which Requested Flight Level is the most likely to be filed by an airspace user for a certain origin-destination pair. Results show that the proposed system outperforms the pre-tactical traffic forecasting approach currently used by the European Network Manager in 60% of the 14,465 origin-destination pairs considered in the study, reducing the error of the current solution by 4.8%.Manuel Mateos’ PhD is funded by SESAR’s Engage Knowledge Transfer Network, which has received funding from the SESAR Joint Undertaking under the European Union’s Horizon 2020 research and innovation programme under grant agreement No 783287. The opinions expressed herein reflect the authors’ view only. Under no circumstances shall the SESAR Joint Undertaking be responsible for any use that may be made of the information contained herein. The authors would also like to acknowledge the support of the Spanish Centre for Industrial Development (CDTI) through the PRETA project (Grant no. IDI-20190029).Peer ReviewedPostprint (published version