Can machines learn how to forecast taxi-out time? A comparison of predictive models applied to the case of Seattle/Tacoma International Airport
| dc.contributor.author | Diana, Tony | |
| dc.date.accessioned | 2021-09-09T17:16:30Z | |
| dc.date.available | 2021-09-09T17:16:30Z | |
| dc.date.issued | 2018-10-15 | |
| dc.description.abstract | This 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. | en |
| dc.description.uri | https://www.sciencedirect.com/science/article/pii/S136655451830543X#! | en |
| dc.format.extent | 16 pages | en |
| dc.genre | journal articles | en |
| dc.identifier | doi:10.13016/m2fyqd-rvov | |
| dc.identifier.citation | Diana, 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.2018.10.003 | en |
| dc.identifier.uri | https://doi.org/10.1016/j.tre.2018.10.003 | |
| dc.identifier.uri | http://hdl.handle.net/11603/22980 | |
| dc.language.iso | en | en |
| dc.publisher | Elsevier | en |
| dc.relation.isAvailableAt | The University of Maryland, Baltimore County (UMBC) | |
| dc.relation.ispartof | UMBC Data Science Collection | |
| dc.rights | Public Domain Mark 1.0 | * |
| dc.rights | This 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. Law | |
| dc.rights | This 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. | en |
| dc.rights.uri | http://creativecommons.org/publicdomain/mark/1.0/ | * |
| dc.title | Can machines learn how to forecast taxi-out time? A comparison of predictive models applied to the case of Seattle/Tacoma International Airport | en |
| dc.type | Text | en |
| dcterms.creator | https://orcid.org/0000-0002-6692-1131 | en |