Multi-View actionable patterns for managing traffic bottleneck

Cao, L; Xu, B; Yue, X; Chen, Y

Multi-View actionable patterns for managing traffic bottleneck

Cao, L

Xu, B Yue, X Chen, Y

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Publication Type:: Conference Proceeding
Citation:: AAAI Workshop - Technical Report, 2015, WS-15-05 pp. 64 - 70
Issue Date:: 2015-01-01

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Field	Value	Language
dc.contributor.author	Cao, L https://orcid.org/0000-0003-1562-9429	en_US
dc.contributor.author	Xu, B	en_US
dc.contributor.author	Yue, X	en_US
dc.contributor.author	Chen, Y	en_US
dc.date.issued	2015-01-01	en_US
dc.identifier.citation	AAAI Workshop - Technical Report, 2015, WS-15-05 pp. 64 - 70	en_US
dc.identifier.isbn	9781577357162	en_US
dc.identifier.uri	http://hdl.handle.net/10453/121780
dc.description.abstract	Discovering congestion patterns from table-formed traffic reports is critical for traffic bottleneck analysis. However, patterns mined by existing algorithms often do not satisfy user requirements and are not actionable for traffic management. Traffic officers may not pursue the most frequent patterns but expect mining outcomes showing the dependence between congestion and various kinds of road properties for traffic planning. Such multi-view analysis requires to integrate user preferences of data attributes into pattern mining process. To tackle this problem, we propose a multi-view attributes reduction model for discovering the patterns of user interests, in which user views are interpreted as preferred attributes and formulated by attribute orders. Based on the pattern discovery model, a workflow is built for traffic bottleneck analysis, which consists of data preprocessing, preference representation and congestion pattern mining. Our approach is validated on the reports of road conditions from Shanghai, which shows that the resultant multi-view findings are effective for analyzing congestion causes and traffic management.	en_US
dc.relation.ispartof	AAAI Workshop - Technical Report	en_US
dc.title	Multi-View actionable patterns for managing traffic bottleneck	en_US
dc.type	Conference Proceeding
utslib.citation.volume	WS-15-05	en_US
utslib.for	0801 Artificial Intelligence and Image Processing	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/Strength - AAI - Advanced Analytics Institute Research Centre
utslib.copyright.status	open_access
pubs.publication-status	Published	en_US
pubs.volume	WS-15-05	en_US

Abstract:

Discovering congestion patterns from table-formed traffic reports is critical for traffic bottleneck analysis. However, patterns mined by existing algorithms often do not satisfy user requirements and are not actionable for traffic management. Traffic officers may not pursue the most frequent patterns but expect mining outcomes showing the dependence between congestion and various kinds of road properties for traffic planning. Such multi-view analysis requires to integrate user preferences of data attributes into pattern mining process. To tackle this problem, we propose a multi-view attributes reduction model for discovering the patterns of user interests, in which user views are interpreted as preferred attributes and formulated by attribute orders. Based on the pattern discovery model, a workflow is built for traffic bottleneck analysis, which consists of data preprocessing, preference representation and congestion pattern mining. Our approach is validated on the reports of road conditions from Shanghai, which shows that the resultant multi-view findings are effective for analyzing congestion causes and traffic management.

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http://hdl.handle.net/10453/121780