Please use this identifier to cite or link to this item: http://hdl.handle.net/1893/31986
Full metadata record
DC FieldValueLanguage
dc.contributor.authorWang, Xinweien_UK
dc.contributor.authorBrownlee, Alexander E Ien_UK
dc.contributor.authorWoodward, John Ren_UK
dc.contributor.authorWeiszer, Michalen_UK
dc.contributor.authorMahfouf, Mahdien_UK
dc.contributor.authorChen, Junen_UK
dc.date.accessioned2020-11-21T01:07:01Z-
dc.date.available2020-11-21T01:07:01Z-
dc.date.issued2021-03en_UK
dc.identifier.other102892en_UK
dc.identifier.urihttp://hdl.handle.net/1893/31986-
dc.description.abstractTaxiing remains a major bottleneck at many airports. Recently, several approaches to allocating efficient routes for taxiing aircraft have been proposed. The routing algorithms underpinning these approaches rely on accurate prediction of the time taken to traverse each segment of the taxiways. Many features impact on taxi time, including the route taken, aircraft category, operational mode of the airport, traffic congestion information, and local weather conditions. Working with real-world data for several international airports, we compare multiple prediction models and investigate the impact of these features, drawing conclusions on the most important features for accurately modelling taxi times. We show that high accuracy can be achieved with a small subset of the features consisting of those generally important across all airports (departure/arrival, distance, total turns, average speed and numbers of recent aircraft), and a small number of features specific to particular target airports. Moving from all features to this small subset results in less than a 1 percentage-point drop in movements correctly predicted within 1, 3 and 5 minutes.en_UK
dc.language.isoenen_UK
dc.publisherElsevieren_UK
dc.relationWang X, Brownlee AEI, Woodward JR, Weiszer M, Mahfouf M & Chen J (2021) Aircraft taxi time prediction: Feature importance and their implications. Transportation Research Part C: Emerging Technologies, 124, Art. No.: 102892. https://doi.org/10.1016/j.trc.2020.102892en_UK
dc.rightsThis item has been embargoed for a period. During the embargo please use the Request a Copy feature at the foot of the Repository record to request a copy directly from the author. You can only request a copy if you wish to use this work for your own research or private study. Accepted refereed manuscript of: Wang X, Brownlee A, Woodward J, Weiszer M, Mahfouf M & Chen J (2021) Aircraft taxi time prediction: Feature importance and their implications. Transportation Research Part C: Emerging Technologies, 124, Art. No.: 102892. https://doi.org/10.1016/j.trc.2020.102892 © 2020, Elsevier. Licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International http://creativecommons.org/licenses/by-nc-nd/4.0/en_UK
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/en_UK
dc.subjectair traffic managementen_UK
dc.subjectfeature importanceen_UK
dc.subjectmachine learningen_UK
dc.subjectpredictionen_UK
dc.subjecttaxi timeen_UK
dc.titleAircraft taxi time prediction: Feature importance and their implicationsen_UK
dc.typeJournal Articleen_UK
dc.rights.embargodate2021-12-20en_UK
dc.rights.embargoreason[Taxi_Time_Prediction_for_repo.pdf] Publisher requires embargo of 12 months after formal publication.en_UK
dc.identifier.doi10.1016/j.trc.2020.102892en_UK
dc.citation.jtitleTransportation Research Part C: Emerging Technologiesen_UK
dc.citation.issn0968-090Xen_UK
dc.citation.volume124en_UK
dc.citation.publicationstatusPublisheden_UK
dc.citation.peerreviewedRefereeden_UK
dc.type.statusAM - Accepted Manuscripten_UK
dc.contributor.funderEngineering and Physical Sciences Research Councilen_UK
dc.contributor.funderEngineering and Physical Sciences Research Councilen_UK
dc.contributor.funderEngineering and Physical Sciences Research Councilen_UK
dc.author.emailalexander.brownlee@stir.ac.uken_UK
dc.citation.date19/12/2020en_UK
dc.contributor.affiliationQueen Mary, University of Londonen_UK
dc.contributor.affiliationComputing Scienceen_UK
dc.contributor.affiliationQueen Mary, University of Londonen_UK
dc.contributor.affiliationQueen Mary, University of Londonen_UK
dc.contributor.affiliationUniversity of Sheffielden_UK
dc.contributor.affiliationQueen Mary, University of Londonen_UK
dc.identifier.isiWOS:000646029800005en_UK
dc.identifier.scopusid2-s2.0-85098463120en_UK
dc.identifier.wtid1683284en_UK
dc.contributor.orcid0000-0003-2892-5059en_UK
dc.date.accepted2020-11-20en_UK
dcterms.dateAccepted2020-11-20en_UK
dc.date.filedepositdate2020-11-20en_UK
rioxxterms.apcnot requireden_UK
rioxxterms.typeJournal Article/Reviewen_UK
rioxxterms.versionAMen_UK
local.rioxx.authorWang, Xinwei|en_UK
local.rioxx.authorBrownlee, Alexander E I|0000-0003-2892-5059en_UK
local.rioxx.authorWoodward, John R|en_UK
local.rioxx.authorWeiszer, Michal|en_UK
local.rioxx.authorMahfouf, Mahdi|en_UK
local.rioxx.authorChen, Jun|en_UK
local.rioxx.projectProject ID unknown|Engineering and Physical Sciences Research Council|http://dx.doi.org/10.13039/501100000266en_UK
local.rioxx.freetoreaddate2021-12-20en_UK
local.rioxx.licencehttp://www.rioxx.net/licenses/under-embargo-all-rights-reserved||2021-12-19en_UK
local.rioxx.licencehttp://creativecommons.org/licenses/by-nc-nd/4.0/|2021-12-20|en_UK
local.rioxx.filenameTaxi_Time_Prediction_for_repo.pdfen_UK
local.rioxx.filecount1en_UK
local.rioxx.source0968-090Xen_UK
Appears in Collections:Computing Science and Mathematics Journal Articles

Files in This Item:
File Description SizeFormat 
Taxi_Time_Prediction_for_repo.pdfFulltext - Accepted Version2.5 MBAdobe PDFView/Open


This item is protected by original copyright



A file in this item is licensed under a Creative Commons License Creative Commons

Items in the Repository are protected by copyright, with all rights reserved, unless otherwise indicated.

The metadata of the records in the Repository are available under the CC0 public domain dedication: No Rights Reserved https://creativecommons.org/publicdomain/zero/1.0/

If you believe that any material held in STORRE infringes copyright, please contact library@stir.ac.uk providing details and we will remove the Work from public display in STORRE and investigate your claim.