Predicting Sector Complexity Using Machine Learning

Wieland, Frederick; Rebollo, Juan Luis Moreno; Gibbs, Mary; Churchill, Andrew

doi:10.2514/6.2022-3754

Cited by 4 publications

(1 citation statement)

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“…Li [19] employed an unsupervised learning approach for the airspace complexity evaluation; results showed that it outperformed state-of-the-art methods in terms of airspace complexity evaluation accuracy. Finally, Wieland [20] showed that ML approaches can help determine the importance of each complexity feature in predicting airspace capacity.…”

Section: Related Workmentioning

confidence: 99%

Using Reinforcement Learning to Improve Airspace Structuring in an Urban Environment

Ribeiro

Ellerbroek

2022

Aerospace

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Current predictions on future drone operations estimate that traffic density orders of magnitude will be higher than any observed in manned aviation. Such densities redirect the focus towards elements that can decrease conflict rate and severity, with special emphasis on airspace structures, an element that has been overlooked within distributed environments in the past. This work delves into the impacts of different airspace structures in multiple traffic scenarios, and how appropriate structures can increase the safety of future drone operations in urban airspace. First, reinforcement learning was used to define optimal heading range distributions with a layered airspace concept. Second, transition layers were reserved to facilitate the vertical deviation between cruising layers and conflict avoidance. The effects of traffic density, non-linear routes, and vertical deviation between layers were tested in an open-source airspace simulation platform. Results show that optimal structuring catered to the current traffic scenario improves airspace usage by correctly segmenting aircraft according to their flight routes. The number of conflicts and losses of minimum separation was reduced versus using a single, uniform airspace structure for all traffic scenarios, thus enabling higher airspace capacity.

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Section: Related Workmentioning

confidence: 99%