Traffic incident duration prediction via a deep learning framework for text description encoding

Grigorev, A Mihaita, A-S Saleh, K Piccardi, M

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Field	Value	Language
dc.contributor.author	Grigorev, A
dc.contributor.author	Mihaita, A-S
dc.contributor.author	Saleh, K
dc.contributor.author	Piccardi, M https://orcid.org/0000-0001-9250-6604
dc.date.accessioned	2023-01-10T22:34:55Z
dc.date.available	2023-01-10T22:34:55Z
dc.date.issued	2022-09-18
dc.identifier.citation	arXiv, 2022
dc.identifier.uri	http://hdl.handle.net/10453/164830
dc.language	en
dc.relation.ispartof	arXiv
dc.relation.isbasedon	10.48550/arxiv.2209.08735
dc.rights	info:eu-repo/semantics/embargoedAccess
dc.title	Traffic incident duration prediction via a deep learning framework for text description encoding
dc.type	Preprint
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Strength - GBDTC - Global Big Data Technologies
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Electrical and Data Engineering
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/A/DRsch The Data Science Institute
utslib.copyright.status	embargoed	*
utslib.copyright.embargo	2024-10-12T00:00:00+1000Z
dc.date.updated	2023-01-10T22:34:49Z

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