Impact of Trajectories’ Uncertainty in Existing ATC Complexity Methodologies and Metrics for DAC and FCA SESAR Concepts

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.

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.

Sectorization and Configuration Transition in Airspace Design

Current airspace is sectorized according to some predefined rules that are not flexible. To facilitate utilizing the airspace more efficiently, methods to design sectors need to be promoted. In this paper, we propose an undirected graph cut-based approach that employs a memetic local search-embedded constrained evolution algorithm, NSGA-II, to generate nondominated airspace configurations. We also propose a new concave hull-based method to automatically depict sector boundaries. In addition, we also study the configuration transition problem. We define the similarity of the two different configurations and calculate their similarity with a bisection diagram and a minimum cost flow algorithm. We build a forward network to represent configuration transitions across several consecutive time periods and use multiobjective dynamic programming to determine a series of nondominated configuration links from the first period to the end. We test our approaches by simulation in high-altitude airspace controlled by Beijing Area Control Center. The results show that our sectorization method outperforms the current configuration in practice, providing a lower sector number, lower intersector flow, more balanced workload distribution among the different sectors, and no constraint violations, so that the proposed approach shows its significant potential as practical applications for dynamic airspace configuration.