Browsing by Author "Saez Nieto, Francisco"

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    Short-term air traffic flow and capacity management measures in multi-airport systems.
    (2019-05) Amaro Carmona, Manuel Angel; Saez Nieto, Francisco; Jia, Huamin
    Dynamic Demand and Capacity Balancing (dDCB) focuses on reducing the existent gap between the Air Traffic Flow and Capacity Management (ATFCM) and the Air Traffic Control (ATC) activities by introducing a more dynamic management of the airspace resources. This dynamism could be achieved by the application of Short-Term ATFCM Measures (STAM) that consists of detecting potential hotspots, identifying the flights producing the complexity, and applying minor changes to selected flights. This thesis presents a research about the application of STAM in a Multi-Airport System (MAS). Firstly, it is proposed an Operational Concept (OpsCon) designed to apply those STAMs that suggest changes in the take-off time of selected flights (temporal displacements in the planned trajectory). The operational concept is tested by real-time simulations (including the human- in-the-loop) with the objective of evaluating the performance of the ground ATCOs while dealing with most of the uncertainties produced before take-off. Subsequently, it is proposed a methodology that characterizes and evaluates the performance of the aircraft operation in a complex systemized TMA based on the study of its standard routes and their actual traffic in order to reduce the uncertainties after take-off. The process is composed of two main components. The first component identifies recurrent deviation patterns by comparing the Spatio-Temporal (S-T) differences between the actual and planned trajectories. The second component identifies and characterizes concurrence events based on the analysis of the standard routes and the along-track deviation derived from the first component with the objective to analyse the causes that produce recurrent patterns in the terminal airspace. The developed framework is applied to a study case of a representative MAS. The quantitative effectiveness of the framework is derived by simulations using historical traffic data samples of the London TMA.