吉林大学学报(工学版) ›› 2009, Vol. 39 ›› Issue (增刊2): 131-0134.

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Urban road traffic status classification based on fuzzy support vector machines

LI Qing-quan 1, 2, GAO De-quan 1, 2, YANG Bi-sheng 1, 2   

  1. 1.State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan 430079, China|2.Transportation Research Center, Wuhan University, Wuhan 430079, China
  • Received:2009-03-13 Online:2009-09-30 Published:2009-09-30
  • Contact: LI Qing-quan E-mail:qqli@whu.edu.cn

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Abstract:

This paper uses fuzzy support vector machines (FSVM) algorithm to classify urban road traffic status. Fuzzy membership handles linguistic expression bias of human perception and uncertainty in status parameter partition scale. Meanwhile, integrated SVM learning ability can solve the limitation that pure fuzzy classification method can′t train sample data. The one against one approach in FSVM is applied for multiclass classification. Finally, a microcosmic simulation work is performed for an experiment data example, and the results indicates that FSVM method can reduce influence of noise sample data and provide better classification accuracy.

Key words: engineering of communication and transportation, traffic status, classification, fuzzy sets, fuzzy support vector machines

CLC Number: 

  • U491
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