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• W2288748218 abstract "Accurate estimation of taxi-out time in the presence of uncertainties in the National Airspace System (NAS) is essential for the development of a more efficient air traffic management system. The dynamic nature of operations in the NAS indicates that traditional regression methods characterized by constant parameters would be inadequate to capture variations in taxi-out time across a day. In this paper, we describe how to build a taxi- out time estimation model using Reinforcement Learning, and identify factors that influence taxi-out time through the day. Taxi-out time predictions for a flight are made 15 minutes in advance of scheduled gate pushback time. Results from a case study of Detroit International Airport (DTW), Tampa International Airport (TPA), and John F. Kennedy International Airport (JFK) are presented and analyzed. Transportation System (NGATS), the Federal Aviation Administration (FAA) is looking towards a modernized Air Traffic Control (ATC) system, with increased levels of automation. Such a system would need to be supported by taxi- out time predictions in real-time, as the airport system and the rest of the NAS evolves. The taxi-out time estimation algorithm proposed in this work is a data-driven approach that adapts to changing airport dynamics, and is a step towards meeting this need for real-time predictability. The method is based on a reinforcement learning scheme, and has its roots in stochastic dynamic programming." @default.
• W2288748218 created "2016-06-24" @default.
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• W2288748218 date "2009-01-01" @default.
• W2288748218 modified "2023-09-27" @default.
• W2288748218 title "Application of Reinforcement Learning Algorithms for Predicting Taxi-out Times" @default.
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