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Bayesian Network Modelling of ATC Complexity Metrics for Future SESAR Demand and Capacity Balance Solutions

1
Air Transport and Airports Department, School of Aerospace Engineering, Technical University of Madrid (UPM), 28040 Madrid, Spain
2
ATM Research and Development Reference Centre (CRIDA), 28022 Madrid, Spain
*
Author to whom correspondence should be addressed.
Entropy 2019, 21(4), 379; https://doi.org/10.3390/e21040379
Received: 16 February 2019 / Revised: 29 March 2019 / Accepted: 5 April 2019 / Published: 8 April 2019
(This article belongs to the Special Issue Bayesian Inference and Information Theory)
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Abstract

Demand & Capacity Management solutions are key SESAR (Single European Sky ATM Research) research projects to adapt future airspace to the expected high air traffic growth in a Trajectory Based Operations (TBO) environment. These solutions rely on processes, methods and metrics regarding the complexity assessment of traffic flows. However, current complexity methodologies and metrics do not properly take into account the impact of trajectories’ uncertainty to the quality of complexity predictions of air traffic demand. This paper proposes the development of several Bayesian network (BN) models to identify the impacts of TBO uncertainties to the quality of the predictions of complexity of air traffic demand for two particular Demand Capacity Balance (DCB) solutions developed by SESAR 2020, i.e., Dynamic Airspace Configuration (DAC) and Flight Centric Air Traffic Control (FCA). In total, seven BN models are elicited covering each concept at different time horizons. The models allow evaluating the influence of the “complexity generators” in the “complexity metrics”. Moreover, when the required level for the uncertainty of complexity is set, the networks allow identifying by how much uncertainty of the input variables should improve. View Full-Text
Keywords: Bayesian networks; complexity; uncertainty; TBO; SESAR Capacity Management; DCB; DAC; FCA Bayesian networks; complexity; uncertainty; TBO; SESAR Capacity Management; DCB; DAC; FCA
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Gomez Comendador, V.F.; Arnaldo Valdés, R.M.; Villegas Diaz, M.; Puntero Parla, E.; Zheng, D. Bayesian Network Modelling of ATC Complexity Metrics for Future SESAR Demand and Capacity Balance Solutions. Entropy 2019, 21, 379.

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