Latest revision as of 19:28, 22 March 2021

Abstract

In China, air traffic congestion has become increasingly prominent and tends to spread from terminal areas to en route networks. Accurate and objective traffic demand prediction could alleviate congestion effectively. However, the usual demand prediction is based on conjecture method of flying track, and the number of aircraft flying over a sector in a set time interval could be inferred through the location information of any aircraft track. In this paper, we proposed a probabilistic traffic demand prediction method by considering the deviations caused by random events, such as the change of departure or arrival time, the temporary change in route or altitude under severe weather conditions, and unscheduled cancellation for a flight. The probabilistic method quantifies these uncertain factors and presents numerical value with its corresponding probability instead of the deterministic number of aircraft in a sector during a time interval. The analysis results indicate that the probabilistic traffic demand prediction based on error distribution characteristics achieves an effective match with the realistic operation in airspace of central and southern China, which contributes to enhancing the implementation of airspace congestion risk management.

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The different versions of the original document can be found in:

• http://downloads.hindawi.com/journals/jat/2018/8184513.pdf under the license https://creativecommons.org/licenses/by

• http://dx.doi.org/10.1155/2018/8184513 under the license cc-by

• http://downloads.hindawi.com/journals/jat/2018/8184513.pdf,

http://downloads.hindawi.com/journals/jat/2018/8184513.xml,

http://dx.doi.org/10.1155/2018/8184513 under the license http://creativecommons.org/licenses/by/4.0

• http://downloads.hindawi.com/journals/jat/2018/8184513.pdf,

https://www.hindawi.com/journals/jat/2018/8184513,

https://academic.microsoft.com/#/detail/2886374619 under the license http://creativecommons.org/licenses/by/4.0/

• http://dx.doi.org/10.1155/2018/8184513,

https://doaj.org/toc/0197-6729,

https://doaj.org/toc/2042-3195