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Predicting on-time departures: A comparison of static with dynamic Bayesian networks in the case of Newark Liberty International Airport
Journal of Airport Management,
13
(3),
271-290
(2019)
Abstract
This paper proposes to evaluate how a network of interrelated operational variables influence the percentage of on-time gate departures in the case of Newark Liberty International airport (EWR). It compared a static with a dynamic Bayesian network model to determine which one is the most likely to balance bias versus variance, which are two key elements in machine learning. Both models featured high variance and low bias, which limits generalisation beyond the training set. Nevertheless, both models stressed the significance of surface congestion in limiting the percentage of on-time gate departures, even when gate departure times are compared with those in the flight plan.
Keywords: Bayesian networks; taxi operations; punctuality; machine learning
The full article is available to subscribers to the journal.
Author's Biography
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.
