On detection of emerging anomalous traffic patterns using GPS data

Pang, LX; Chawla, S; Liu, W; Zheng, Y

On detection of emerging anomalous traffic patterns using GPS data

Pang, LX

Chawla, S Liu, W

Zheng, Y

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Publication Type:: Journal Article
Citation:: Data and Knowledge Engineering, 2013, 87 pp. 357 - 373
Issue Date:: 2013-09-01

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Field	Value	Language
dc.contributor.author	Pang, LX https://orcid.org/0000-0002-4784-9795	en_US
dc.contributor.author	Chawla, S	en_US
dc.contributor.author	Liu, W https://orcid.org/0000-0002-3003-1313	en_US
dc.contributor.author	Zheng, Y	en_US
dc.date.issued	2013-09-01	en_US
dc.identifier.citation	Data and Knowledge Engineering, 2013, 87 pp. 357 - 373	en_US
dc.identifier.issn	0169-023X	en_US
dc.identifier.uri	http://hdl.handle.net/10453/117284
dc.description.abstract	The increasing availability of large-scale trajectory data provides us great opportunity to explore them for knowledge discovery in transportation systems using advanced data mining techniques. Nowadays, large number of taxicabs in major metropolitan cities are equipped with a GPS device. Since taxis are on the road nearly 24 h a day (with drivers changing shifts), they can now act as reliable sensors to monitor the behavior of traffic. In this article, we use GPS data from taxis to monitor the emergence of unexpected behavior in the Beijing metropolitan area, which has the potential to estimate and improve traffic conditions in advance. We adapt likelihood ratio test statistic (LRT) which have previously been mostly used in epidemiological studies to describe traffic patterns. To the best of our knowledge the use of LRT in traffic domain is not only novel but results in accurate and rapid detection of anomalous behavior. Crown Copyright © 2013 Published by Elsevier B.V. All rights reserved.	en_US
dc.relation.ispartof	Data and Knowledge Engineering	en_US
dc.relation.isbasedon	10.1016/j.datak.2013.05.002	en_US
dc.subject.classification	Information Systems	en_US
dc.title	On detection of emerging anomalous traffic patterns using GPS data	en_US
dc.type	Journal Article
utslib.citation.volume	87	en_US
utslib.for	0801 Artificial Intelligence and Image Processing	en_US
utslib.for	0804 Data Format	en_US
utslib.for	0806 Information Systems	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 Computer Science
pubs.organisational-group	/University of Technology Sydney/Strength - AAI - Advanced Analytics Institute Research Centre
utslib.copyright.status	closed_access
pubs.publication-status	Published	en_US
pubs.volume	87	en_US

Abstract:

The increasing availability of large-scale trajectory data provides us great opportunity to explore them for knowledge discovery in transportation systems using advanced data mining techniques. Nowadays, large number of taxicabs in major metropolitan cities are equipped with a GPS device. Since taxis are on the road nearly 24 h a day (with drivers changing shifts), they can now act as reliable sensors to monitor the behavior of traffic. In this article, we use GPS data from taxis to monitor the emergence of unexpected behavior in the Beijing metropolitan area, which has the potential to estimate and improve traffic conditions in advance. We adapt likelihood ratio test statistic (LRT) which have previously been mostly used in epidemiological studies to describe traffic patterns. To the best of our knowledge the use of LRT in traffic domain is not only novel but results in accurate and rapid detection of anomalous behavior. Crown Copyright © 2013 Published by Elsevier B.V. All rights reserved.

Please use this identifier to cite or link to this item:

http://hdl.handle.net/10453/117284