  Nowadays, huge efforts are made to modernize the air traffic management
systems to cope with uncertainty, complexity and sub-optimality. An answer is
to enhance the information sharing between the stakeholders. This paper
introduces a framework that bridges the gap between air traffic management and
air traffic control on the one hand, and bridges the gap between the ground,
the approach and the en-route centers on the other hand. An original system is
presented, that has three essential components: the trajectory models, the
optimization process, and the monitoring process. The uncertainty of the
trajectory is modeled with a Bayesian Network, where the nodes are associated
to two types of random variables: the time of overflight on metering points of
the airspace, and the traveling time of the routes linking these points. The
resulting Bayesian Network covers the complete airspace, and Monte- Carlo
simulations are done to estimate the probabilities of sector congestion and
delays. On top of this trajectory model, an optimization process minimizes
these probabilities by tuning the parameters of the Bayesian trajectory model
related to overflight times on metering points. The last component is the
monitoring process, that continuously updates the situation of the airspace,
modifying the trajectories uncertainties according to actual positions of
aircraft. After each update, a new optimal set of overflight times is computed,
and can be communicated to the controllers as clearances for the aircraft
pilots. The paper presents a formal specification of this global optimization
problem, whose underlying rationale was derived with the help of air traffic
controllers at Thales Air Systems.
