Air traffic sector demand and capacity balancing is an important process to enable safe and efficient flight execution. In current operations, demand and capacity are determined based on schedules and flight plans. In reality, disruptions to flights create a different situation that may not have been anticipated by the Air Navigation Service Provider. This can ultimately cause unnecessary network regulations. This research aims to improve air traffic sector demand forecasting, by exploring machine learning-based trajectory prediction. In light of the Trajectory Based Operations concept that is developed within ATM research, a trajectory-based approach is taken to improve demand forecasts. Using available flight status messages from the Eurocontrol Network Manager, and actual recorded trajectories, a transformer neural network was built that could generatively predict flight trajectories. This model could accurately generate trajectories, outperforming the flight plan and other neural network approaches by a large margin. For demand prediction, the introduction of improved trajectories may provide only marginal improvements.
Graduated: November 2023