Data Fusion for MaaS: Opportunities and Challenges

Wu, J; Zhou, L; Cai, C; Shen, J; Lau, SK; Yong, J

Data Fusion for MaaS: Opportunities and Challenges

Wu, J Zhou, L Cai, C Shen, J

Lau, SK Yong, J

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Publication Type:: Conference Proceeding
Citation:: Proceedings of the 2018 IEEE 22nd International Conference on Computer Supported Cooperative Work in Design, CSCWD 2018, 2018, pp. 184 - 189
Issue Date:: 2018-09-13

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Field	Value	Language
dc.contributor.author	Wu, J	en_US
dc.contributor.author	Zhou, L	en_US
dc.contributor.author	Cai, C	en_US
dc.contributor.author	Shen, J https://orcid.org/0000-0002-9403-7140	en_US
dc.contributor.author	Lau, SK	en_US
dc.contributor.author	Yong, J	en_US
dc.date.available	2020-09-14T19:06:05Z
dc.date.issued	2018-09-13	en_US
dc.identifier.citation	Proceedings of the 2018 IEEE 22nd International Conference on Computer Supported Cooperative Work in Design, CSCWD 2018, 2018, pp. 184 - 189	en_US
dc.identifier.isbn	9781538614822	en_US
dc.identifier.uri	http://hdl.handle.net/10453/131248
dc.description.abstract	© 2018 IEEE. Computer Supported Cooperative Work (CSCW) in design is an essential facilitator for the development and implementation of smart cities, where modern cooperative transportation and integrated mobility are highly demanded. Owing to greater availability of different data sources, data fusion problem in intelligent transportation systems (ITS) has been very challenging, where machine learning modelling and approaches are promising to offer an important yet comprehensive solution. In this paper, we provide an overview of the recent advances in data fusion for Mobility as a Service (MaaS), including the basics of data fusion theory and the related machine learning methods. We also highlight the opportunities and challenges on MaaS, and discuss potential future directions of research on the integrated mobility modelling.	en_US
dc.relation.ispartof	Proceedings of the 2018 IEEE 22nd International Conference on Computer Supported Cooperative Work in Design, CSCWD 2018	en_US
dc.relation.isbasedon	10.1109/CSCWD.2018.8465224	en_US
dc.rights	info:eu-repo/semantics/openAccess
dc.title	Data Fusion for MaaS: Opportunities and Challenges	en_US
dc.type	Conference Proceeding
utslib.for	0805 Distributed Computing	en_US
pubs.embargo.period	Not known	en_US
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/Faculty of Engineering and Information Technology/School of Information, Systems and Modelling
utslib.copyright.status	open_access	*
pubs.publication-status	Published	en_US

Abstract:

© 2018 IEEE. Computer Supported Cooperative Work (CSCW) in design is an essential facilitator for the development and implementation of smart cities, where modern cooperative transportation and integrated mobility are highly demanded. Owing to greater availability of different data sources, data fusion problem in intelligent transportation systems (ITS) has been very challenging, where machine learning modelling and approaches are promising to offer an important yet comprehensive solution. In this paper, we provide an overview of the recent advances in data fusion for Mobility as a Service (MaaS), including the basics of data fusion theory and the related machine learning methods. We also highlight the opportunities and challenges on MaaS, and discuss potential future directions of research on the integrated mobility modelling.

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http://hdl.handle.net/10453/131248