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

Four Eulerian network models are implemented to model high altitude air traffic flow. Three of the models use the framework of discrete time dynamical systems, while the fourth consists of a network of partial differential equations. The construction of these models is done using one year of air traffic data. The four models are applied to high altitude traffic for six Air Route Traffic Control Centers in the National Air space System and surrounding 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 compared to the recorded flight data. Several error metrics are used to characterize the relative accuracy of the models. The efficiency 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 differences explains why one model may outperform another.