Abstract
The most relevant SESAR 2020 solutions dealing with future Capacity Management processes are Dynamic Airspace Configuration (DAC) and Flight Centric ATC (FCA). Both concepts, DAC and FCA, rely on traffic flow complexity assessment. For this reason, complexity assessments processes, methods and metrics, become one of the main constraints to deal with the growing demand and increasing airspace capacity. The aim of this work is to identify the influence of trajectories’ uncertainty in the quality of the predictions of complexity of traffic demand and the effectiveness of Demand Capacity Balance (DCB) airspace management processes, in order to overcome the limitations of existing complexity assessment approaches to support Capacity Management processes in a Trajectory-Based Operations (TBO) environment. This paper presents research conducted within COTTON project, sponsored by the SESAR Joint Undertaking and EU’s Horizon 2020 research and innovation program. The main objective is to deliver innovative solutions to maximize the performance of the Capacity Management procedures based on information in a TBO environment.
Highlights
The most relevant SESAR 2020 solutions [2,3] dealing with future Capacity Management processes are Dynamic Airspace Configuration (DAC) [4,5] and Flight Centric ATC (FCA) [6]
Step 1: The first step focuses on the analysis of the Trajectory-Based Operations (TBO) environment, describing the operating
TBO is the based for consistent aircraft trajectory and flight information in collaborative decision-making process regarding the flight
Summary
Operations (TBO) [1] philosophy.The most relevant SESAR 2020 solutions [2,3] dealing with future Capacity Management processes are Dynamic Airspace Configuration (DAC) [4,5] and Flight Centric ATC (FCA) [6]. They are in charge of a set of aircraft throughout their trajectory a given airspace (or from TMA to TMA), while at the same time different controllers manage another set of aircraft sharing the same airspace Both solutions, DAC and FCA, rely on traffic flow complexity assessment processes [7], methods [8,9,10,11,12,13,14,15] and metrics [16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32], and integrate predicted workload function and confidence index
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