Image Measurement of Traffic Flow Parameters and its Traffic Congestion Evaluation

Abstract:

Current evaluation methods on urban traffic congestion are mostly based on traffic flow information. However, the measurement of traffic flow remains to be controversial and difficult for the community. This paper points out an algorithm to acquire traffic parameters and studies the evaluation methods based on it. By extracting multi-color-feature information from image and vehicle shape match algorithm based on fuzzy rules, this method can efficiently distinguish vehicles from each other thus to calculate the traffic state parameters according to the results of this method. Then it can build congestion evaluation model with vehicle delay rate as the critical parameter. The experiment indicates that this method can acquire the accurate real-time road parameters and also proves it is valid to apply this method in urban traffic congestion evaluation in different situations.

• Periodical: Applied Mechanics and Materials (Volume 538)
• Pages: 455-459
• DOI: https://doi.org/10.4028/www.scientific.net/AMM.538.455
• Online since: April 2014
• Authors: Dong Yao Jia*, Po Hu
• Keywords: Feature Fusion, Image Measurement, Traffic Congestion, Vehicle Delay Rate

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