Research on the intelligent judgment of traffic congestion in intelligent traffic based on pattern recognition technology
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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.
KeywordsPattern recognition technology Traffic congestion Intelligent judgment
The study was supported by “The National Natural Science Foundation of China (Grant No. 61303029)”.
- 5.Zhang, W., Tan, G., Shi, H.M., et al.: A distributed threshold algorithm for vehicle classification based on binary proximity sensors and intelligent neuron classifier. J. Inf. Sci. Eng. 26(3), 769–783 (2010)Google Scholar
- 6.Lee, D.S., Wang, T.H., Wang, D.J., et al.: Performance evaluation of UHF RFID technologies for real-time bus recognition in the Taipei bus station. Sensors 13(6), 7797–7812 (2013)Google Scholar
- 9.Yang, Z.S., Zhang, M.L.: A model for road traffic state analysis based on fuzzy comprehensive evaluation. J. Highw. Transp. Res. Dev. 27(9), 122–123 (2010)Google Scholar
- 17.Sato, H., Xing, J., Tanaka, S., et al.: Examining the effect of connecting auxiliary lanes on mitigation of expressway traffic congestion. Int. J. Intell. Transp. Syst. Res. 9(2), 55–63 (2011)Google Scholar
- 18.Gandhi, P., Chandrana, A., Karia, T.: Intelligent traffic congestion reduction system using proximity sensors. Int. J. Comput. Appl. 101(8), 34–36 (2014)Google Scholar