Please use this identifier to cite or link to this item: http://localhost/handle/Hannan/636300
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dc.contributor.authorJianqiang Renen_US
dc.contributor.authorYangzhou Chenen_US
dc.contributor.authorLe Xinen_US
dc.contributor.authorJianjun Shien_US
dc.contributor.authorBaotong Lien_US
dc.contributor.authorYinan Liuen_US
dc.date.accessioned2020-05-20T09:57:29Z-
dc.date.available2020-05-20T09:57:29Z-
dc.date.issued2016en_US
dc.identifier.issn1751-956Xen_US
dc.identifier.issn1751-9578en_US
dc.identifier.other10.1049/iet-its.2015.0022en_US
dc.identifier.urihttp://localhost/handle/Hannan/168126en_US
dc.identifier.urihttp://localhost/handle/Hannan/636300-
dc.description.abstractNon-recurrent traffic incidents (accidents, stalled vehicles and spilled loads) often bring about traffic congestion and even secondary accidents. Detecting and positioning them quickly and accurately has important significance for early warning, timely incident-disposal and speedy congestion-evacuation. This study proposes a video-based detecting and positioning method by analysing distribution characteristics of traffic states in a road segment. Each lane in the monitored segment is divided into a cluster of cells. Traffic parameters in each cell, including flow rate, average travel speed and average space occupancy, are obtained by detecting and tracking traffic objects (vehicles and spilled loads). On the basis of the parameters, traffic states in the cells are judged via a fuzzy-identification method. For each congested cell, a feature vector is constructed by taking its state together with states of its upstream and downstream neighbouring cells in the same lane. Then, a support vector machine classifier is trained to detect incident point. If a cell is judged to be corresponding to an incident point at least for two successive time periods, an incident is detected and its position is calculated based on the identity number of the cell. Experiments prove the efficiency and practicability of the proposed method.en_US
dc.publisherIEEEen_US
dc.relation.haspart7510542.pdfen_US
dc.subjectspilled loads|traffic incident detection|traffic state video-based analysis|support vector machine classifier|speedy congestion-evacuation|traffic congestion|traffic incident positioning|early warning|traffic state distribution characteristics analysis|upstream neighbouring cells|timely incident-disposal|nonrecurrent traffic incidents|accidents|stalled vehicles|road segment|feature vector|downstream neighbouring cells|fuzzy-identification method|video-based detecting methoden_US
dc.titleDetecting and positioning of traffic incidents via video-based analysis of traffic states in a road segmenten_US
dc.typeArticleen_US
dc.journal.volume10en_US
dc.journal.issue6en_US
dc.journal.titleIET Intelligent Transport Systemsen_US
Appears in Collections:2016

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Full metadata record
DC FieldValueLanguage
dc.contributor.authorJianqiang Renen_US
dc.contributor.authorYangzhou Chenen_US
dc.contributor.authorLe Xinen_US
dc.contributor.authorJianjun Shien_US
dc.contributor.authorBaotong Lien_US
dc.contributor.authorYinan Liuen_US
dc.date.accessioned2020-05-20T09:57:29Z-
dc.date.available2020-05-20T09:57:29Z-
dc.date.issued2016en_US
dc.identifier.issn1751-956Xen_US
dc.identifier.issn1751-9578en_US
dc.identifier.other10.1049/iet-its.2015.0022en_US
dc.identifier.urihttp://localhost/handle/Hannan/168126en_US
dc.identifier.urihttp://localhost/handle/Hannan/636300-
dc.description.abstractNon-recurrent traffic incidents (accidents, stalled vehicles and spilled loads) often bring about traffic congestion and even secondary accidents. Detecting and positioning them quickly and accurately has important significance for early warning, timely incident-disposal and speedy congestion-evacuation. This study proposes a video-based detecting and positioning method by analysing distribution characteristics of traffic states in a road segment. Each lane in the monitored segment is divided into a cluster of cells. Traffic parameters in each cell, including flow rate, average travel speed and average space occupancy, are obtained by detecting and tracking traffic objects (vehicles and spilled loads). On the basis of the parameters, traffic states in the cells are judged via a fuzzy-identification method. For each congested cell, a feature vector is constructed by taking its state together with states of its upstream and downstream neighbouring cells in the same lane. Then, a support vector machine classifier is trained to detect incident point. If a cell is judged to be corresponding to an incident point at least for two successive time periods, an incident is detected and its position is calculated based on the identity number of the cell. Experiments prove the efficiency and practicability of the proposed method.en_US
dc.publisherIEEEen_US
dc.relation.haspart7510542.pdfen_US
dc.subjectspilled loads|traffic incident detection|traffic state video-based analysis|support vector machine classifier|speedy congestion-evacuation|traffic congestion|traffic incident positioning|early warning|traffic state distribution characteristics analysis|upstream neighbouring cells|timely incident-disposal|nonrecurrent traffic incidents|accidents|stalled vehicles|road segment|feature vector|downstream neighbouring cells|fuzzy-identification method|video-based detecting methoden_US
dc.titleDetecting and positioning of traffic incidents via video-based analysis of traffic states in a road segmenten_US
dc.typeArticleen_US
dc.journal.volume10en_US
dc.journal.issue6en_US
dc.journal.titleIET Intelligent Transport Systemsen_US
Appears in Collections:2016

Files in This Item:
File Description SizeFormat 
7510542.pdf973.06 kBAdobe PDFThumbnail
Preview File
Full metadata record
DC FieldValueLanguage
dc.contributor.authorJianqiang Renen_US
dc.contributor.authorYangzhou Chenen_US
dc.contributor.authorLe Xinen_US
dc.contributor.authorJianjun Shien_US
dc.contributor.authorBaotong Lien_US
dc.contributor.authorYinan Liuen_US
dc.date.accessioned2020-05-20T09:57:29Z-
dc.date.available2020-05-20T09:57:29Z-
dc.date.issued2016en_US
dc.identifier.issn1751-956Xen_US
dc.identifier.issn1751-9578en_US
dc.identifier.other10.1049/iet-its.2015.0022en_US
dc.identifier.urihttp://localhost/handle/Hannan/168126en_US
dc.identifier.urihttp://localhost/handle/Hannan/636300-
dc.description.abstractNon-recurrent traffic incidents (accidents, stalled vehicles and spilled loads) often bring about traffic congestion and even secondary accidents. Detecting and positioning them quickly and accurately has important significance for early warning, timely incident-disposal and speedy congestion-evacuation. This study proposes a video-based detecting and positioning method by analysing distribution characteristics of traffic states in a road segment. Each lane in the monitored segment is divided into a cluster of cells. Traffic parameters in each cell, including flow rate, average travel speed and average space occupancy, are obtained by detecting and tracking traffic objects (vehicles and spilled loads). On the basis of the parameters, traffic states in the cells are judged via a fuzzy-identification method. For each congested cell, a feature vector is constructed by taking its state together with states of its upstream and downstream neighbouring cells in the same lane. Then, a support vector machine classifier is trained to detect incident point. If a cell is judged to be corresponding to an incident point at least for two successive time periods, an incident is detected and its position is calculated based on the identity number of the cell. Experiments prove the efficiency and practicability of the proposed method.en_US
dc.publisherIEEEen_US
dc.relation.haspart7510542.pdfen_US
dc.subjectspilled loads|traffic incident detection|traffic state video-based analysis|support vector machine classifier|speedy congestion-evacuation|traffic congestion|traffic incident positioning|early warning|traffic state distribution characteristics analysis|upstream neighbouring cells|timely incident-disposal|nonrecurrent traffic incidents|accidents|stalled vehicles|road segment|feature vector|downstream neighbouring cells|fuzzy-identification method|video-based detecting methoden_US
dc.titleDetecting and positioning of traffic incidents via video-based analysis of traffic states in a road segmenten_US
dc.typeArticleen_US
dc.journal.volume10en_US
dc.journal.issue6en_US
dc.journal.titleIET Intelligent Transport Systemsen_US
Appears in Collections:2016

Files in This Item:
File Description SizeFormat 
7510542.pdf973.06 kBAdobe PDFThumbnail
Preview File