Probabilistic occupancy counts and flight criticality measures in air traffic management

Airspace congestion is a major challenge for future European air traffic management. When air traffic control believes that a sector will exceed its maximal capacity, a regulation is applied to it, which limits the number of aircraft entering the sector. These actions have a large cost because they affect all the flights that cross the sector. Moreover, they are based on the partial data available to the controller and do not take into account the network situation. First, a probabilistic framework for modeling air traffic occupancy count and sector congestion is proposed. This allows more precise information on the probability of sector overload to be provide to air traffic control. Second, based on this framework, metrics for individual flights are defined that measure their impact on the congestion of the whole network. These metrics are intended to be used in demand and capacity balancing tools, allowing for optimized choices for the whole network. Numerical experiments are presented for one day of European data, which include 33,219 flights in 1991 sectors. The simulations advocate the metrics and show how actions taken on selected flights have a positive impact on the network congestion.