Can delay propagation be predicted? A case study of the three largest New York area airports using neural networks

Tony Diana
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Tony Diana: Manager of the Outreach Division at Federal Aviation Administration, NextGen Collaboration and Messaging Office, USA

Journal of Airport Management, 2010, vol. 4, issue 2, 170-177

Abstract: Neural networks were used to assess and compare the importance of selected operational factors on delay propagation. The study is based on the cases of the three largest New York area airports for summer (June to August) 2007 and 2008. These airports were selected as they have consistently ranked among the top ten most delayed US airports. A delay propagates if a flight departs late from Airport A, arrives late at Airport B and subsequently departs late from Airport B to its next destination. The results suggest that taxi delays and late gate departures from any New York area airport are difficult to recover, thus increasing the possibility of delay propagation. The study concludes with a discussion of the policy implications of its findings as well as the implications for airline and airport analysts to help minimise delay propagation.

Keywords: delay propagation; neural networks; airport operations; delay (search for similar items in EconPapers)
JEL-codes: M1 M10 R4 R40 (search for similar items in EconPapers)
Date: 2010
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Persistent link: https://EconPapers.repec.org/RePEc:aza:jam000:y:2010:v:4:i:2:p:170-177

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