Deep learning for intelligent traffic sensing and prediction: recent
advances and future challenges

Field	Value	Language
dc.contributor.author	Nanda, P https://orcid.org/0000-0002-5748-155X
dc.contributor.author	Fan, X
dc.contributor.author	He, X
dc.date.accessioned	2020-09-09T03:57:20Z
dc.date.available	2020-09-09T03:57:20Z
dc.date.issued	2020-09-03
dc.identifier.citation	CCF Transactions on Pervasive Computing and Interaction, 2020
dc.identifier.uri	http://hdl.handle.net/10453/142579
dc.description.abstract	With the emerging concepts of smart cities and intelligent transportation systems, accurate traffic sensing and prediction have become critically important to support urban management and traffic control. In recent years, the rapid uptake of the Internet of Vehicles and the rising pervasiveness of mobile services have produced unprecedented amounts of data to serve traffic sensing and prediction applications. However, it is significantly challenging to fulfill the computation demands by the big traffic data with ever-increasing complexity and diversity. Deep learning, with its powerful capabilities in representation learning and multi-level abstractions, has recently become the most effective approach in many intelligent sensing systems. In this paper, we present an up-to-date literature review on the most advanced research works in deep learning for intelligent traffic sensing and prediction.
dc.language	en
dc.publisher	Springer Link
dc.relation.ispartof	CCF Transactions on Pervasive Computing and Interaction
dc.rights	info:eu-repo/semantics/openAccess
dc.title	Deep learning for intelligent traffic sensing and prediction: recent advances and future challenges
dc.type	Journal Article
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Strength - CRIN - Realtime Information Networks
pubs.organisational-group	/University of Technology Sydney/Strength - INEXT - Innovation in IT Services and Applications
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
utslib.copyright.status	open_access	*
pubs.consider-herdc	false
dc.date.updated	2020-09-09T03:57:01Z
pubs.publication-status	Published online

Abstract:

With the emerging concepts of smart cities and intelligent transportation systems, accurate traffic sensing and prediction have become critically important to support urban management and traffic control. In recent years, the rapid uptake of the Internet of Vehicles and the rising pervasiveness of mobile services have produced unprecedented amounts of data to serve traffic sensing and prediction applications. However, it is significantly challenging to fulfill the computation demands by the big traffic data with ever-increasing complexity and diversity. Deep learning, with its powerful capabilities in representation learning and multi-level abstractions, has recently become the most effective approach in many intelligent sensing systems. In this paper, we present an up-to-date literature review on the most advanced research works in deep learning for intelligent traffic sensing and prediction.

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http://hdl.handle.net/10453/142579