This work investigates the system performance characteristics of centralized and decentralized strategies for air traffic separation. A centralized separation strategy and two decentralized separation strategies, implemented as constant-speed heading-change maneuvers, were simulated for randomized horizontal traffic patterns at various traffic densities. Human decision-making of controllers and pilots were not modeled. The centralized strategy represents a controller-oriented separation system generating coordinated resolution advisories that emphasize system-level stability. The decentralized strategies represent user-oriented separation systems generating independent resolution advisories that emphasize aircraft-level efficiency. Results from numerical experiments indicate that system stability and efficiency both degrade as traffic density increases, for all separation strategies. Although decentralized separation strategies can give rise to a significant domino effect, the resulting drop in system efficiency (relative to a centralized strategy that suppresses the domino effect) is quite small for traffic densities up to a certain threshold density. Introducing even a limited stability emphasis
The estimation of the capacity of an airspace region during weather events is an important part of air traffic management. This problem must be solved ahead of time with predicted traffic demands and weather forecasts. In order to capture the uncertainty of the weather, a stochastic weather model is used. We investigate the problem of estimating the maximum capacity of an airspace region by analyzing the sector airspace geometry and a stochastic weather model. Using algorithms for computing geometric flow capacity in 2-dimensional regions, we compute the maximum capacity for an airspace having a given (deterministic) set of weather constraints. Then, we extend our results to a stochastic weather model, obtaining analytical results for weather constraints that form constraints along a line segment (e.g., placed along the flow bottleneck or along a squall line) and obtaining simulation results for a more general two-dimensional stochastic weather model. Our results allow us to determine the probability distribution of the throughput capacity of an airspace, given a probabilistic weather forecast.
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