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- Publisher Website: 10.1109/CoASE.2012.6386311
- Scopus: eid_2-s2.0-84872512130
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Conference Paper: Modeling of traffic data characteristics by Dirichlet Process Mixtures
Title | Modeling of traffic data characteristics by Dirichlet Process Mixtures |
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Authors | |
Keywords | Dirichlet process mixtures Outlier detection Traffic flow analysis |
Issue Date | 2012 |
Publisher | IEEE. 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? |
Abstract | This 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. |
Description | Conference Theme: Green Automation Toward a Sustainable Society |
Persistent Identifier | http://hdl.handle.net/10722/189653 |
ISBN |
DC Field | Value | Language |
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dc.contributor.author | Ngan, YT | en_US |
dc.contributor.author | Yung, NHC | en_US |
dc.contributor.author | Yeh, AGO | en_US |
dc.date.accessioned | 2013-09-17T14:52:14Z | - |
dc.date.available | 2013-09-17T14:52:14Z | - |
dc.date.issued | 2012 | en_US |
dc.identifier.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 | en_US |
dc.identifier.isbn | 978-1-4673-0430-6 | - |
dc.identifier.uri | http://hdl.handle.net/10722/189653 | - |
dc.description | Conference Theme: Green Automation Toward a Sustainable Society | - |
dc.description.abstract | This 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.language | eng | en_US |
dc.publisher | IEEE. The Journal's web site is located at http://ieeexplore.ieee.org/xpl/conhome.jsp?punumber=1001095 | en_US |
dc.relation.ispartof | IEEE International Conference on Automation Science and Engineering Proceedings | en_US |
dc.subject | Dirichlet process mixtures | - |
dc.subject | Outlier detection | - |
dc.subject | Traffic flow analysis | - |
dc.title | Modeling of traffic data characteristics by Dirichlet Process Mixtures | en_US |
dc.type | Conference_Paper | en_US |
dc.identifier.email | Ngan, YT: ytngan@hku.hk | en_US |
dc.identifier.email | Yung, NHC: nyung@eee.hku.hk | en_US |
dc.identifier.email | Yeh, AGO: hdxugoy@hkucc.hku.hk | en_US |
dc.identifier.authority | Yung, NHC=rp00226 | en_US |
dc.identifier.authority | Yeh, AGO=rp01033 | en_US |
dc.description.nature | link_to_subscribed_fulltext | - |
dc.identifier.doi | 10.1109/CoASE.2012.6386311 | - |
dc.identifier.scopus | eid_2-s2.0-84872512130 | - |
dc.identifier.hkuros | 221177 | en_US |
dc.identifier.spage | 224 | en_US |
dc.identifier.epage | 229 | en_US |
dc.publisher.place | United States | en_US |
dc.customcontrol.immutable | sml 140110 | - |