Airspace congestion is a major challenge for future European ATM. When air traffic control (ATC) 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, as 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, we propose a probabilistic framework for modeling air traffic occupancy count and sector congestion. This allows us to provide ATC with more precise information on the probability of sector overload. Second, based on this framework, we define metrics for individual flights 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. We present numerical experiments for one day of European data, which included 33, 219 flights in 1, 991 elementary sectors. The simulations advocate our metrics and show how actions taken on selected flights have a positive impact on the network congestion.