JACIII Vol.20 No.7 pp. 1181-1191
doi: 10.20965/jaciii.2016.p1181


Pruned Fast Learning Fuzzy Approach for Data-Driven Traffic Flow Prediction

Chengdong Li*, Yisheng Lv**, Jianqiang Yi**, and Guiqing Zhang*

*School of Information and Electrical Engineering, Shandong Jianzhu University
Jinan 250101, China
**Institute of Automation, Chinese Academy of Sciences

Beijing 100190, China

March 2, 2016
October 26, 2016
Online released:
December 20, 2016
December 20, 2016
traffic flow prediction, data-driven method, fuzz system, extreme learning method, fuzzy rule pruning

Traffic flow prediction plays an important role in intelligent transportation systems. With the rapid growth of traffic flow data, fast and accurate traffic flow prediction methods are now required. In this paper, we propose a novel fast learning data-driven fuzzy approach for the traffic flow prediction problem. In the proposed approach, to achieve fast learning, an extreme learning machine is utilized to optimize the consequent parameters of the fuzzy rules. Further, a fuzzy rule pruning strategy that involves measuring the firing levels of the fuzzy rules is presented to obtain reduced fuzzy inference systems. To evaluate the performance of the proposed approach, it was experimentally applied to traffic flow prediction and its results compared with those of widely used methods. The experimental results verify that the proposed approach can achieve satisfactory performance. The comparisons show that the proposed approach can obtain better (sometimes similar) performances, but with a simpler structure, fewer parameters, and much faster learning speed than the other methods.

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Last updated on May. 26, 2017