Effect of Trajectory Prediction Uncertainty on a Probablistic Debunching Concept for Inbound Air Traffic

Abstract

The airspace in western Europe is one of the most complex airspaces globally. With the air space already operating near maximum capacity, innovations are needed to increase airspace efficiency. This research aims to design and test a debunching concept for inbound air traffic to increase the efficiency of the arrival traffic stream. A probabilistic debunching concept is created for the Initial Approach Fix using Probability Density Functions fitted on the errors in predicted arrival times provided by the EUROCONTROL Enhanced Tactical Flow Management System Flight Data messages (EFD). A bunching probability is detected when two or more aircraft have a probability of arriving at the Initial Approach fix simultaneously or within the required Wake Category Separation. A debuncher is created using a Constrained Genetic Algorithm that decreases the bunching probability by imposing en-route delay on arriving air traffic. The results show that the bunching probability increases with shorter prediction horizons as the uncertainty in the arrival estimates decreases. It is shown that with the probabilistic debunching method, it is possible to decrease the delay in the Arrival Manager by imposing delay en-route. However, at prediction ranges before 40 minutes before arrival at the IAF, the decrease in the AMAN delay is not consistent, indicating that the trajectory prediction uncertainty at this range is too high. Furthermore, the decrease in the AMAN delay is often lower than the imposed delay by the debuncher, showing that the decrease in AMAN delay comes at the cost of extra delay en-route. It is concluded that when the uncertainty in the trajectory predictions is decreased, the effectiveness of the debuncher increases and the effect on the Arrival Manager is improved, indicating improved arrival efficiency.