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Can delay propagation be predicted? A case study of the three largest New York area airports using neural networks
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.
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Tony Diana is the Acting Division Manager, Outreach at the US Federal Aviation Administration (FAA). He received his doctorate in policy analysis and quantitative management from the University of Maryland, Baltimore County. He is involved in the communication of progress in modernisation programmes at US airports, metroplexes and airspaces. Prior to that position, he was Division Manager, NextGen Performance in the Office of NextGen Performance and Outreach and Deputy Division Manager, Forecasting and Performance Analysis, in the Office of Aviation Policy and Plans of the FAA, where he managed the aviation system performance metrics data warehouse. At the Maryland Aviation Administration, he was involved in performance measurement and route development. Tony’s main interests are performance evaluation and benchmarking as well as the study of delay. He is a certified Lean Sigma Master Black Belt and a certified Project Management Professional.