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Mathematical Problems in Engineering
Volume 2016 (2016), Article ID 5128528, 13 pages
Research Article

Fuzzy Prediction for Traffic Flow Based on Delta Test

Beijing Key Lab of Traffic Engineering, Beijing University of Technology, 100 Pingleyuan, Chaoyang District, Beijing 100124, China

Received 15 March 2016; Revised 9 July 2016; Accepted 4 August 2016

Academic Editor: Alberto Borboni

Copyright © 2016 Yang Wang et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


This paper presents a novel approach to one-step-forward prediction of traffic flow based on fuzzy reasoning. The successful construction of a competent fuzzy inference system of Sugeno type largely relies on proper choice of input dimension and accurate estimation of structure parameters and rules. The first issue is addressed with a proposed method, based on -test, which can simultaneously determine input dimension and reduce noise level. In response to the second issue, two clustering techniques, based on nearest-neighbor clustering and Gaussian mixture models, are successively employed to determine the antecedent parameters and rules, and the estimation for the consequent parameters is achieved by the least square estimation technique. A number of experiments have been performed on the one-week data of traffic flow to evaluate the proposed approach in terms of denosing, prediction performances, overfitting, and so forth. The experimental results have demonstrated that the proposed prediction approach is effective in removing noise and constructing a competent and compact fuzzy inference system without significant overfitting.