This paper presents a new Hybrid Demand and Capacity Balance model for the air traffic network optimization problem, in which Lagrangian air traffic measures are calculated for individual flights. The core of the algorithm is a Pseudo Eulerian-Lagrangian flow model, which works with aggregated flights. This new research line allows near-optimal individual Air Traffic Flow Management (ATFM) measures to be obtained in a short computational time. Queue synchronized outputs in each constraint area (airspace/elementary volumes and airports) are computed by the flow optimization algorithm. Using the appropriate conversion algorithms, the input and output of the Hybrid model are Lagrangian. This new model is flexible enough to include new air traffic concepts. Delays can be computed in each elementary volume and airport. The air traffic measures will be given to airspace users as a set of space-time constraints on the overloaded airspace/airport area. The new model allows user preferences to be taken into account at two different levels: inter-flow and in-flow preferences. A set of Key Performance Indicators (KPIs) in line with the future air traffic system Operational Concept (SESAR ConOps in Europe, NextGen in EEUU) are defined. KPIs are demonstrated to be easily obtained from the model. Finally, a set of simulations of actual air traffic data show the performance of the proposed method.
Theme: Network and Strategic Traffic Flow Optimization
Keywords: hybrid demand and capacity balance, network flow optimization, queue synchronization, target time of arrival, user preferences
Juan José Rebollo
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