Predicting Block Time: An Application of Quantile Regression
| dc.contributor.author | Diana, Tony | |
| dc.date.accessioned | 2021-09-09T14:32:20Z | |
| dc.date.available | 2021-09-09T14:32:20Z | |
| dc.date.issued | 2012-08 | |
| dc.description.abstract | Airlines face three types of delay that make it difficult to build robust schedules and to support block time predictability. Block time is the time elapsed from gate departure to gate arrival and refers to the time when blocks are off the wheels at the departure airport to the time they are back on at the destination airport. These delays can be induced (i.e., ground delays), propagated, or stochastic. With capacity constrained at major airports and regulators facing greater public pressure to alleviate congestion and tarmac delays, aviation practitioners have renewed their interest in the predictability of block time. This study presents a methodology based on the case study of the Seattle/Tacoma International (SEA) and Oakland International airport (OAK) city pair to determine the predictability of block time. The methodology based on quantile regression models is appropriate for a skewed distribution where analysts are interested in the impact of selected operational variables on the conditional mean of block times at given percentiles. Quantile regression provides a measure of on-time performance based on the percentile results that show the most significance and best fit. | en_US |
| dc.description.uri | https://ageconsearch.umn.edu/record/207326 | en_US |
| dc.format.extent | 16 pages | en_US |
| dc.genre | journal articles | en_US |
| dc.identifier | doi:10.13016/m2jwb4-o6mk | |
| dc.identifier.citation | Diana, Tony; Predicting Block Time: An Application of Quantile Regression; Journal of the Transportation Research Forum, Vol. 51, No. 3, pp. 39-53, August 2012; http://dx.doi.org/10.22004/ag.econ.207326 | en_US |
| dc.identifier.uri | http://dx.doi.org/10.22004/ag.econ.207326 | |
| dc.identifier.uri | http://hdl.handle.net/11603/22970 | |
| dc.language.iso | en_US | en_US |
| dc.publisher | AgEcon | en_US |
| dc.relation.isAvailableAt | The University of Maryland, Baltimore County (UMBC) | |
| dc.relation.ispartof | UMBC Data Science Collection | |
| 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_US |
| 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.uri | http://creativecommons.org/publicdomain/mark/1.0/ | * |
| dc.title | Predicting Block Time: An Application of Quantile Regression | en_US |
| dc.type | Text | en_US |
| dcterms.creator | https://orcid.org/0000-0002-6692-1131 | en_US |