Journal of South China University of Technology (Natural Science Edition) ›› 2016, Vol. 44 ›› Issue (12): 53-60.doi: 10.3969/j.issn.1000-565X.2016.12.008

• Traffic & Transportation Engineering • Previous Articles     Next Articles

Division of Traffic Control Periods Based on Improved FCM Clustering

YU De-xin1,2 TIAN Xiu-juan1 YANG Zhao-sheng1,2   

  1. 1.School of Transportation,Jilin University,Changchun 130022,Jilin,China; 2.Jilin Province Key Laboratory of Road Traffic,Changchun 130022,Jilin,China
  • Received:2016-01-23 Revised:2016-06-12 Online:2016-12-25 Published:2016-11-01
  • Contact: 于德新(1972-),男,教授,博士生导师,主要从事智能交通系统、交通控制理论与技术研究. E-mail:1255277858@qq.com
  • About author:于德新(1972-),男,教授,博士生导师,主要从事智能交通系统、交通控制理论与技术研究.
  • Supported by:
    Supported by the National Key Technology Research and Development Program of the Ministry of Science and Technology of China(2014BAG03B03)

Abstract:

In this paper,the traditional fuzzy c-means clustering (FCM) algorithm is improved,and a method to divide the traffic signal control periods is proposed based on the improved FCM algorithm.In the method,first,a cardinal number of fuzzy clustering membership degree is introduced to automatically select the cluster number.Then,the hybrid simulated annealing genetic algorithm is employed to optimize the initial clustering center.Final- ly,the traffic control periods are divided according to the actual traffic flow data,and the performance of the schemes is evaluated by using the simulation software.The results show that,as compared with the traditional FCM algorithm,the proposed method can divide the traffic control periods more effectively and reflect the actual traffic characteristics more accurately,and it achieves a global optimal solution.In addition,in comparison with the origi- nal signal control scheme,although both the scheme based on the FCM algorithm and the proposed scheme can re- duce the average vehicle delay,the proposed scheme has a more obvious effect.

Key words: traffic control, time-of-day control, period division, FCM clustering, simulated annealing genetic al- gorithm

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