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dc.contributor.authorDu, Wen_US
dc.contributor.authorLi, Ben_US
dc.contributor.authorChen, Jen_US
dc.contributor.authorLv, Yen_US
dc.contributor.authorLi, Yen_US
dc.date.accessioned2023-01-10T12:13:10Z
dc.date.issued2023en_US
dc.identifier.issn1939-1390en_US
dc.identifier.urihttps://qmro.qmul.ac.uk/xmlui/handle/123456789/83591
dc.format.extent217 - 224en_US
dc.relation.ispartofIEEE INTELLIGENT TRANSPORTATION SYSTEMS MAGAZINEen_US
dc.subjectComplexity theoryen_US
dc.subjectAtmospheric modelingen_US
dc.subjectSpatiotemporal phenomenaen_US
dc.subjectPredictive modelsen_US
dc.subjectDeep learningen_US
dc.subjectCorrelationen_US
dc.subjectAir traffic controlen_US
dc.titleA Spatiotemporal Hybrid Model for Airspace Complexity Predictionen_US
dc.typeArticle
dc.rights.holder© 2022 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
dc.identifier.doi10.1109/MITS.2022.3204099en_US
pubs.author-urlhttps://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=PARTNER_APP&SrcAuth=LinksAMR&KeyUT=WOS:000862362900001&DestLinkType=FullRecord&DestApp=ALL_WOS&UsrCustomerID=612ae0d773dcbdba3046f6df545e9f6aen_US
pubs.issue2en_US
pubs.notesNot knownen_US
pubs.publication-statusPublisheden_US
pubs.volume15en_US
rioxxterms.funderDefault funderen_US
rioxxterms.identifier.projectDefault projecten_US
qmul.funderTRANSIT: Towards a Robust Airport Decision Support System for Intelligent Taxiing::Engineering and Physical Sciences Research Councilen_US
qmul.funderTRANSIT: Towards a Robust Airport Decision Support System for Intelligent Taxiing::Engineering and Physical Sciences Research Councilen_US


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