Journal of South China University of Technology (Natural Science Edition) ›› 2015, Vol. 43 ›› Issue (8): 135-143.doi: 10.3969/j.issn.1000-565X.2015.08.020

• Traffic & Transportation Engineering • Previous Articles     Next Articles

An Improved Method to Analyze Car-Following Behavior Based on Fuzzy Inference System#br#

Qiu Xiao-ping1,2,3  Sun Ｒuo-xiao1  Yu Dan1  Yang Da1,2   

  1. 1． School of Transportation and Logistics，Southwest Jiaotong University，Chengdu 610031，Sichuan，China; 2． Comprehensive Intelligent Transportation National and Local joint Engineering Laboratory，Southwest Jiaotong University，Chengdu 610031，Sichuan，China; 3． Comprehensive Transportation Key Laboratory of Sichuan Province，Southwest Jiaotong University，Chengdu 610031，Sichuan，China
  • Received:2015-01-14 Revised:2015-04-23 Online:2015-08-25 Published:2015-07-01
  • Contact: 杨达(1985-)，男，博士，讲师，主要从事智能交通研究． E-mail:yangd8@gmail.com
  • About author: 邱小平(1976-)，男，博士，教授，主要从事交通运输规划与管理研究． E-mail: qxp@home.swjtu.edu.cn
  • Supported by:
    Supported by the National Natural Science Foundation of China(51408509)and the Foundation of Science and
    Technology Department of Sichuan Province(2013GZX0167，2014ZR0091)

PDF

68

Abstract:  In order to establish the car-following fuzzy inference system (FIS) accurately，the fuzzy clustering
analysis method is adopted to divide input and output variables into various fuzzy sets，and the corresponding Gaussian membership function is determined． Then，the Takagi-Sugeno inference method is introduced to conductthe car-following fuzzy inference and the defuzzification． Finally，the fuzzy inference system and the Gippscar-following model are calibrated and evaluated by using NGSIM data． The results show that the improved method to establish the car-following fuzzy inference systemcan truly reflect the characteristics of the data itself and the driver’s psychological and physiological features，and that the established fuzzy inference system achieves a much higher simulation accuracy of error indexes in comparison with the Gipps model．

Key words:  car following, fuzzy inference system, fuzzy clustering, Gaussian membership function, Takagi-Su-geno inference method

CLC Number: