Diana, Tony2021-09-092021-09-092018-10-15Diana, Tony; Can machines learn how to forecast taxi-out time? A comparison of predictive models applied to the case of Seattle/Tacoma International Airport; Transportation Research Part E: Logistics and Transportation Review, Volume 119, Pages 149-164, 15 October, 2018; https://doi.org/10.1016/j.tre.2018.10.003https://doi.org/10.1016/j.tre.2018.10.003http://hdl.handle.net/11603/22980This study compares the performance of ensemble machine learning, ordinary least-squared and penalized algorithms to predict taxi-out time at two different periods of NextGen capability implementation. In the pre-sample, ordinary least-squared and ridge models performed better than other ensemble learning models. However, the gradient boosting model provided the lowest root mean squared errors in the post-sample. No algorithm fits data better in all cases. This paper recommends selecting the model that provides the best balance between bias and variance.16 pagesen-USThis item is likely protected under Title 17 of the U.S. Copyright Law. Unless on a Creative Commons license, for uses protected by Copyright Law, contact the copyright holder or the author.Public Domain Mark 1.0This work was written as part of one of the author's official duties as an Employee of the United States Government and is therefore a work of the United States Government. In accordance with 17 U.S.C. 105, no copyright protection is available for such works under U.S. LawText