Disturbance Feedback for Handling Uncertainty in Air Traffic Flow Management

This paper presents the novel application of disturbance feedback optimization techniques for uncertainty management in Air Traffic Flow Management (ATFM) problems. The efficiency of ATFM optimizations in preventing local demand-capacity imbalances is reliant on predictions of future capacity states. However these predictions are inherently uncertain due to factors such as weather effects and unscheduled demand.

A pre-existing ATFM flow based model is augmented to include feedback on the disturbances which perturb the weather scenario away from the nominal. Two formulations for modelling the feedback disturbance signal are explored.

Results are presented demonstrating the benefits, in terms of reduced delays, of incorporating feedback on the problem solutions over single-solution approaches. Some initial studies of the relative computational scaling properties are also presented, demonstrating that taking advantage, within the formulation, of linearly related scenarios can yield computational advantages. Directions for further computational improvement are also discussed.