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Mathematical Problems in Engineering
Volume 2015, Article ID 201686, 10 pages
http://dx.doi.org/10.1155/2015/201686
Research Article

Research on Combinational Forecast Models for the Traffic Flow

1School of Computer Science and Information Technology, Northeast Normal University, Changchun 130117, China
2Academy of Fine Art, Northeast Normal University, Changchun 130117, China
3Key Laboratory of Intelligent Information Processing of Jilin Universities, Northeast Normal University, Changchun 130117, China

Received 13 February 2015; Accepted 22 April 2015

Academic Editor: Chih-Cheng Hung

Copyright © 2015 Zhiheng Yu 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.

Abstract

In order to improve the prediction accuracy of the traffic flow, this paper proposes two combinational forecast models based on GM, ARIMA, and GRNN. Firstly, the paper proposes the concept of associate-forecast and the weight distribution method based on reciprocal absolute percentage error and then uses GM(1,1), ARIMA, and GRNN to establish a combinational model of highway traffic flow according to the fixed weight coefficients. Then the paper proposes the use of neural networks to determine variable weight coefficients and establishes Elman combinational forecast model based on GM(1,1), ARIMA, and GRNN, which achieves the integration of these three individuals. Lastly, these two combinational models are applied to highway traffic flow on Chongzun of China and the experimental results verify their effectiveness compared with GM(1,1), ARIMA, and GRNN.