Machine learning in aviation: Is it possible to predict when an ATC Sector or Air Traffic Flow will be regulated?

Abstract

Abstract Due to the increasing complexity of airspace, the ATC system does not have sufficient capacity to cope with aircraft demand. For this reason, the ATFCM system needs to implement more and more measures to balance capacity and demand. These measures are the ATFCM regulations. In this paper, a methodology to predict ATFCM capacity regulations based on a machine learning model is proposed. This model will try to predict whether an ATC sector will be regulated or not at a specific time based on the time of prediction and certain operating variables in the sector. A test has been carried out in the LECMPAU sector of Spanish airspace. With results of 91% accuracy in predicting whether the sector will be regulated or not and a logical explainability, it can be concluded that with the sufficient historical operation and certain operational variables, it is possible to predict when an ATFCM regulation capacity will appear in the airspace. It can also be concluded that without a detailed knowledge of the operation in a sector, it is possible to make this prediction because patterns can be found in historical behaviour.