Cart (Loading....) | Create Account
Close category search window

Estimating Taxi-out times with a reinforcement learning algorithm

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$31 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

4 Author(s)
Balakrishna, P. ; Center for Air Transp. Syst. Res., George Mason Univ., Fairfax, VA ; Ganesan, R. ; Sherry, L. ; Levy, B.S.

Flight delays have a significant impact on the nationpsilas economy. Taxi-out delays in particular constitute a significant portion of the block time of a flight. In the future, it can be expected that accurate predictions of dasiawheels-offpsila time may be used in determining whether an aircraft can meet its allocated slot time, thereby fitting into an en-route traffic flow. Without an accurate taxi-out time prediction for departures, there is no way to effectively manage fuel consumption, emissions, or cost. Dynamically changing operations at the airport makes it difficult to accurately predict taxi-out time. This paper describes a method for estimating average taxi-out times at the airport in 15 minute intervals of the day and at least 15 minutes in advance of aircraft scheduled gate push-back time. A probabilistic framework of stochastic dynamic programming with a learning-based solution strategy called Reinforcement Learning (RL) has been applied. Historic data from the Federal Aviation Administrationpsilas (FAA) Aviation System Performance Metrics (ASPM) database were used to train and test the algorithm. The algorithm was tested on John F. Kennedy International airport (JFK), one of the busiest, challenging, and difficult to predict airports in the United States that significantly influences operations across the entire National Airspace System (NAS). Due to the nature of departure operations at JFK the prediction accuracy of the algorithm for a given day was analyzed in two separate time periods (1) before 4:00 P.M and (2) after 4:00 P.M. On an average across 15 days, the predicted average taxi-out times matched the actual average taxi-out times within plusmn5 minutes for about 65 % of the time (for the period before 4:00 P.M) and 53 % of the time (for the period after 4:00 P.M). The prediction accuracy over the entire day within plusmn5 minutes range of accuracy was about 60 %. Further, application of the RL algorithm to estimate taxi-out times at airports- - with multi-dependent static surface surveillance data will likely improve the accuracy of prediction. The implications of these results for airline operations and network flow planning are discussed.

Published in:

Digital Avionics Systems Conference, 2008. DASC 2008. IEEE/AIAA 27th

Date of Conference:

26-30 Oct. 2008

Need Help?

IEEE Advancing Technology for Humanity About IEEE Xplore | Contact | Help | Terms of Use | Nondiscrimination Policy | Site Map | Privacy & Opting Out of Cookies

A not-for-profit organization, IEEE is the world's largest professional association for the advancement of technology.
© Copyright 2014 IEEE - All rights reserved. Use of this web site signifies your agreement to the terms and conditions.