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Conference Paper: Modeling of traffic data characteristics by Dirichlet Process Mixtures

TitleModeling of traffic data characteristics by Dirichlet Process Mixtures
Authors
KeywordsDirichlet process mixtures
Outlier detection
Traffic flow analysis
Issue Date2012
PublisherIEEE. The Journal's web site is located at http://ieeexplore.ieee.org/xpl/conhome.jsp?punumber=1001095
Citation
The 8th IEEE International Conference on Automation Science and Engineering (CASE 2012), Seoul, Korea, 20-24 August 2012. In Conference Proceedings, 2012, p. 224-229 How to Cite?
AbstractThis paper presents a statistical method for modeling large volume of traffic data by Dirichlet Process Mixtures (DPM). Traffic signals are in general defined by their spatial-temporal characteristics, of which some can be common or similar across a set of signals, while a minority of these signals may have characteristics inconsistent with the majority. These are termed outliers. Outlier detection aims to segment and eliminate them in order to improve signal quality. It is accepted that the problem of outlier detection is non-trivial. As traffic signals generally share a high degree of spatial-temporal similarities within the signal and between different types of traffic signals, traditional modeling approaches are ineffective in distinguishing these similarities and discerning their differences. In regard to modeling the traffic data characteristics by DPM, this paper conveys three contributions. First, a new generic statistical model for traffic data is proposed based on DPM. Second, this model achieves an outlier detection rate of 96.74% based on a database of 764,027 vehicles. Third, the proposed model is scalable to the entire road network. © 2012 IEEE.
DescriptionConference Theme: Green Automation Toward a Sustainable Society
Persistent Identifierhttp://hdl.handle.net/10722/189653
ISBN

 

DC FieldValueLanguage
dc.contributor.authorNgan, YTen_US
dc.contributor.authorYung, NHCen_US
dc.contributor.authorYeh, AGOen_US
dc.date.accessioned2013-09-17T14:52:14Z-
dc.date.available2013-09-17T14:52:14Z-
dc.date.issued2012en_US
dc.identifier.citationThe 8th IEEE International Conference on Automation Science and Engineering (CASE 2012), Seoul, Korea, 20-24 August 2012. In Conference Proceedings, 2012, p. 224-229en_US
dc.identifier.isbn978-1-4673-0430-6-
dc.identifier.urihttp://hdl.handle.net/10722/189653-
dc.descriptionConference Theme: Green Automation Toward a Sustainable Society-
dc.description.abstractThis paper presents a statistical method for modeling large volume of traffic data by Dirichlet Process Mixtures (DPM). Traffic signals are in general defined by their spatial-temporal characteristics, of which some can be common or similar across a set of signals, while a minority of these signals may have characteristics inconsistent with the majority. These are termed outliers. Outlier detection aims to segment and eliminate them in order to improve signal quality. It is accepted that the problem of outlier detection is non-trivial. As traffic signals generally share a high degree of spatial-temporal similarities within the signal and between different types of traffic signals, traditional modeling approaches are ineffective in distinguishing these similarities and discerning their differences. In regard to modeling the traffic data characteristics by DPM, this paper conveys three contributions. First, a new generic statistical model for traffic data is proposed based on DPM. Second, this model achieves an outlier detection rate of 96.74% based on a database of 764,027 vehicles. Third, the proposed model is scalable to the entire road network. © 2012 IEEE.-
dc.languageengen_US
dc.publisherIEEE. The Journal's web site is located at http://ieeexplore.ieee.org/xpl/conhome.jsp?punumber=1001095en_US
dc.relation.ispartofIEEE International Conference on Automation Science and Engineering Proceedingsen_US
dc.rightsCreative Commons: Attribution 3.0 Hong Kong Licenseen_US
dc.rights©2012 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.en_US
dc.subjectDirichlet process mixtures-
dc.subjectOutlier detection-
dc.subjectTraffic flow analysis-
dc.titleModeling of traffic data characteristics by Dirichlet Process Mixturesen_US
dc.typeConference_Paperen_US
dc.identifier.emailNgan, YT: ytngan@hku.hken_US
dc.identifier.emailYung, NHC: nyung@eee.hku.hken_US
dc.identifier.emailYeh, AGO: hdxugoy@hkucc.hku.hken_US
dc.identifier.authorityYung, NHC=rp00226en_US
dc.identifier.authorityYeh, AGO=rp01033en_US
dc.description.naturepublished_or_final_version-
dc.identifier.doi10.1109/CoASE.2012.6386311-
dc.identifier.scopuseid_2-s2.0-84872512130-
dc.identifier.hkuros221177en_US
dc.identifier.spage224en_US
dc.identifier.epage229en_US
dc.publisher.placeUnited Statesen_US
dc.customcontrol.immutablesml 140110-

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