Prediction of ATFM impact for individual flights: A machine learning approach

Air Traffic Flow Management regulations consist of issuing pre-departure ground delays to deal with demandcapacity imbalances in the airspace, smoothing out the demand at congested infrastructures. This results in discrepancies between the initially planned and executed (regulated) flights, negatively impacting operational efficiency and economic performance. To mitigate downstream effects, airspace users need to assess the severity and impact of these regulations when defining their operational plan. It is crucial to anticipate these potential network issues and plan for mitigation measures during the pre-tactical phase (the day before operations (D-1)). This article presents four independent machine learning models to estimate the impact of Air Traffic Flow Management regulations for individual flights during this pre-tactical phase. An integrated view is proposed to combine the results of the different models, displaying the appropriate level of information. Two models are designed to provide information about the probability of a given flight being affected by Air Traffic Flow Management regulations and the reason for this regulation (airport or airspace congestion). These models have reported accuracy levels between 82% and 87%. The remaining models estimate the impact of the delay (amount of Air Traffic Flow Management delay issued to the flight if regulated), with a Mean Absolute Error of 9.5 min when predicting this amount of delay. The SHapely Additive exPlanations analysis is used to identify the most important factors for detecting Air Traffic Flow Management regulations for individual flights during the pre-tactical phase from a data-driven perspective. An integrated view is proposed to create a system which combines the results of the four different models, displaying the appropriate level of information and avoiding an overload of information. Finally, the results are compared against models considering only static or post-operational data.