Controlling air traffic congestion by predicting flight departure delays and duration : integrating machine learning sampling techniques and deep learning approaches

Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/81398

DC Field	Value	Language
dc.creator	Khan, WA	en_US
dc.creator	Chung, SH	en_US
dc.creator	Ma, HL	en_US
dc.date.accessioned	2019-09-24T00:53:20Z	-
dc.date.available	2019-09-24T00:53:20Z	-
dc.identifier.uri	http://hdl.handle.net/10397/81398	-
dc.description	Transportation Science and Logistics Conference 2020 (TSL 2020), Washington DC, USA, 27-29 May 2020 (cancelled)	en_US
dc.description.sponsorship	Department of Industrial and Systems Engineering	en_US
dc.language.iso	en	en_US
dc.publisher	INFORMS	en_US
dc.rights	Posted with permission of the author.	en_US
dc.title	Controlling air traffic congestion by predicting flight departure delays and duration : integrating machine learning sampling techniques and deep learning approaches	en_US
dc.type	Conference Paper	en_US
dc.identifier.spage	1	en_US
dc.identifier.epage	5	en_US
dcterms.accessRights	open access	en_US
dcterms.bibliographicCitation	Proceedings of the TSL Second Triennial Conference, 2020, 128, p.1-5.	en_US
dcterms.issued	2020	-
dc.relation.conference	Transportation Science and Logistics Conference (TSL)	en_US
dc.identifier.artn	128	en_US
dc.description.validate	202006 bcma	en_US
dc.description.oa	Version of Record	en_US
dc.identifier.FolderNumber	a0436-n01	en_US
dc.description.pubStatus	Published	en_US
dc.description.oaCategory	Copyright retained by author	en_US
Appears in Collections:	Conference Paper

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