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Four Eulerian network models are implemented to model high al- titude air tra-c ∞ow. Three of the models use the framework of discrete time dynamical systems, while the fourth consists of a network of partial difieren- tial equations. The construction of these models is done using one year of air tra-c data. The four models are applied to high altitude tra-c for six Air Route Tra-c Control Centers in the National Airspace System and surround- ing airspace. Simulations are carried out for a full day of data for each of the models, to assess their predictive capabilities. The models' predictions are com- pared to the recorded ∞ight data. Several error metrics are used to characterize the relative accuracy of the models. The e-ciency of the respective models is also compared in terms of computational time and memory requirements for the scenarios of interest. Control strategies are designed and implemented on similar benchmark scenarios for two of the models. They use techniques such as adjoint-based optimization, as well as mixed integer linear programming. A discussion of the four models' structural difierences explains why one model may outperform another.

Comparison of the performance of four Eulerian network flow models for strategic air traffic management