Please use this identifier to cite or link to this item: http://localhost/handle/Hannan/636300
Title: Detecting and positioning of traffic incidents via video-based analysis of traffic states in a road segment
Authors: Jianqiang Ren;Yangzhou Chen;Le Xin;Jianjun Shi;Baotong Li;Yinan Liu
subject: spilled 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 method
Year: 2016
Publisher: IEEE
Abstract: Non-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.
URI: http://localhost/handle/Hannan/168126
http://localhost/handle/Hannan/636300
ISSN: 1751-956X
1751-9578
volume: 10
issue: 6
Appears in Collections:2016

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Title: Detecting and positioning of traffic incidents via video-based analysis of traffic states in a road segment
Authors: Jianqiang Ren;Yangzhou Chen;Le Xin;Jianjun Shi;Baotong Li;Yinan Liu
subject: spilled 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 method
Year: 2016
Publisher: IEEE
Abstract: Non-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.
URI: http://localhost/handle/Hannan/168126
http://localhost/handle/Hannan/636300
ISSN: 1751-956X
1751-9578
volume: 10
issue: 6
Appears in Collections:2016

Files in This Item:
File Description SizeFormat 
7510542.pdf973.06 kBAdobe PDFThumbnail
Preview File
Title: Detecting and positioning of traffic incidents via video-based analysis of traffic states in a road segment
Authors: Jianqiang Ren;Yangzhou Chen;Le Xin;Jianjun Shi;Baotong Li;Yinan Liu
subject: spilled 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 method
Year: 2016
Publisher: IEEE
Abstract: Non-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.
URI: http://localhost/handle/Hannan/168126
http://localhost/handle/Hannan/636300
ISSN: 1751-956X
1751-9578
volume: 10
issue: 6
Appears in Collections:2016

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