Traffic congestion is becoming more and more frequent with the increase of city vehicles. There are still some problems in data processing and real-time traffic state identification for the intelligent judgment of road congestion. Based on this, a multi-class support vector machine method for pattern recognition was proposed, which was an improvement of the traditional support vector machine. Firstly, the road situation was divided into three kinds: “traffic”, “congestion” and “traffic paralysis” by using pattern recognition technology, and the road traffic situation was divided into “traffic” and “congestion” by using support vector machine, on the basis of this, the quadratic discriminant of “congestion” and “traffic paralysis” were carried out to “congestion” state, so that the intelligent judgment of three kinds of traffic state was met. Then combined with the actual road sections and real-time monitoring of road data, the simulation experiment of the pattern recognition was carried out to show that the pattern recognition method can effectively divide and analyze the road traffic situation, and realize the function of intelligent judgment, which could promote the intelligent management of the road, improve the urban road planning and improve the service quality of the traffic system.
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The study was supported by “The National Natural Science Foundation of China (Grant No. 61303029)”.
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