  In this paper, we present a novel data-driven optimization approach for
trajectory based air traffic flow management (ATFM). A key aspect of the
proposed approach is the inclusion of airspace users' trajectory preferences,
which are computed from traffic data by combining clustering and classification
techniques. Machine learning is also used to extract consistent trajectory
options, while optimization is applied to resolve demand-capacity imbalances by
means of a mathematical programming model that judiciously assigns a feasible
4D trajectory and a possible ground delay to each flight. The methodology has
been tested on instances extracted from real air traffic data repositories.
With more than 32,000 flights considered, we solve the largest instances of the
ATFM problem available in the literature in short computational times that are
reasonable from the practical point of view. As a by-product, we highlight the
trade-off between preferences and delays as well as the potential benefits.
Indeed, computing efficient solutions of the problem facilitates a consensus
between network manager and airspace users. In view of the level of accuracy of
the solutions and of the excellent computational performance, we are optimistic
that the proposed approach may provide a significant contribution to the
development of the next generation of air traffic flow management tools.
