Hdl Handle:
http://hdl.handle.net/10545/618790
Title:
Aircraft taxi time prediction: Comparisons and insights
Authors:
Ravizza, Stefan; Chen, Jun; Atkin, Jason A. D. ( 0000-0002-7187-4982 ) ; Stewart, Paul ( 0000-0001-8902-1497 ) ; Burke, Edmund K.
Abstract:
The predicted growth in air transportation and the ambitious goal of the European Commission to have on-time performance of flights within 1 min makes efficient and predictable ground operations at airports indispensable. Accurately predicting taxi times of arrivals and departures serves as an important key task for runway sequencing, gate assignment and ground movement itself. This research tests different statistical regression approaches and also various regression methods which fall into the realm of soft computing to more accurately predict taxi times. Historic data from two major European airports is utilised for cross-validation. Detailed comparisons show that a TSK fuzzy rule-based system outperformed the other approaches in terms of prediction accuracy. Insights from this approach are then presented, focusing on the analysis of taxi-in times, which is rarely discussed in literature. The aim of this research is to unleash the power of soft computing methods, in particular fuzzy rule-based systems, for taxi time prediction problems. Moreover, we aim to show that, although these methods have only been recently applied to airport problems, they present promising and potential features for such problems.
Affiliation:
University of Lincoln
Citation:
Ravizza, S. et al (2016) 'Aircraft taxi time prediction: Comparisons and insights' 14:397 Applied Soft Computing
Publisher:
Elsevier
Journal:
Applied Soft Computing
Issue Date:
Jan-2014
URI:
http://hdl.handle.net/10545/618790
DOI:
10.1016/j.asoc.2013.10.004
Additional Links:
http://linkinghub.elsevier.com/retrieve/pii/S1568494613003384
Type:
Article
Language:
en
ISSN:
15684946
Sponsors:
The research was part-funded as part of the EPSRC Research Grant 'Integrating and Automating Airport Operations' The authors would like to thank the stakeholders involved, namely Swedavia AB and Flughafen Zürich AG, respectively, who provided the real datasets from their airports.
Appears in Collections:
Institute for Innovation in Sustainable Engineering

Full metadata record

DC FieldValue Language
dc.contributor.authorRavizza, Stefanen
dc.contributor.authorChen, Junen
dc.contributor.authorAtkin, Jason A. D.en
dc.contributor.authorStewart, Paulen
dc.contributor.authorBurke, Edmund K.en
dc.date.accessioned2016-08-25T11:13:12Z-
dc.date.available2016-08-25T11:13:12Z-
dc.date.issued2014-01-
dc.identifier.citationRavizza, S. et al (2016) 'Aircraft taxi time prediction: Comparisons and insights' 14:397 Applied Soft Computingen
dc.identifier.issn15684946-
dc.identifier.doi10.1016/j.asoc.2013.10.004-
dc.identifier.urihttp://hdl.handle.net/10545/618790en
dc.description.abstractThe predicted growth in air transportation and the ambitious goal of the European Commission to have on-time performance of flights within 1 min makes efficient and predictable ground operations at airports indispensable. Accurately predicting taxi times of arrivals and departures serves as an important key task for runway sequencing, gate assignment and ground movement itself. This research tests different statistical regression approaches and also various regression methods which fall into the realm of soft computing to more accurately predict taxi times. Historic data from two major European airports is utilised for cross-validation. Detailed comparisons show that a TSK fuzzy rule-based system outperformed the other approaches in terms of prediction accuracy. Insights from this approach are then presented, focusing on the analysis of taxi-in times, which is rarely discussed in literature. The aim of this research is to unleash the power of soft computing methods, in particular fuzzy rule-based systems, for taxi time prediction problems. Moreover, we aim to show that, although these methods have only been recently applied to airport problems, they present promising and potential features for such problems.en
dc.description.sponsorshipThe research was part-funded as part of the EPSRC Research Grant 'Integrating and Automating Airport Operations' The authors would like to thank the stakeholders involved, namely Swedavia AB and Flughafen Zürich AG, respectively, who provided the real datasets from their airports.en
dc.language.isoenen
dc.publisherElsevieren
dc.relation.urlhttp://linkinghub.elsevier.com/retrieve/pii/S1568494613003384en
dc.rightsAn error occurred on the license name.en
dc.rightsArchived with thanks to Applied Soft Computingen
dc.rights.uriAn error occurred getting the license - uri.en
dc.subjectDecision Supporten
dc.subjectFuzzy Logicen
dc.subjectAirport Ground Movementen
dc.subjectLow carbonen
dc.titleAircraft taxi time prediction: Comparisons and insightsen
dc.typeArticleen
dc.contributor.departmentUniversity of Lincolnen
dc.identifier.journalApplied Soft Computingen
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