Eva Puntero Parla
Research Subjects
Research Interests
Eva Puntero Parla has a total of 4 co-authors. Here are the top co-authors by number of shared publications:
- Danlin Zheng Zhang
CRIDA A.I.E., Avenida de Aragón 330, CP28022 Madrid, Spain
1 shared publication - Manuel Villegas Díaz
Airspace systems, air transport and airports department, Universidad Politécnica de Madrid, Madrid, Spain
1 shared publication - Rosa María Arnaldo Valdés
Department of Aerospace Systems, Air Transport and Airports, School of Aerospace Engineering, Universidad Politécnica de Madrid (UPM), 28040 Madrid, Spain
1 shared publication - Víctor Fernando Gómez Comendador
Department of Aerospace Systems, Air Transport and Airports, School of Aerospace Engineering, Universidad Politécnica de Madrid (UPM), 28040 Madrid, Spain
1 shared publication
Latest Publications
- Open Access
Bayesian Network Modelling of ATC Complexity Metrics for Future SESAR Demand and Capacity Balance Solutions
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.
Open AccessBayesian Network Modelling of ATC Complexity Metrics for Future SESAR Demand and Capacity Balance Solutions
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.