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JACIII Vol.13 No.6 pp. 713-725
doi: 10.20965/jaciii.2009.p0713
(2009)

Paper:

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Traffic Flow Prediction with Genetic Network Programming (GNP)

Huiyu Zhou, Shingo Mabu, Wei Wei, Kaoru Shimada,
and Kotaro Hirasawa

Graduate School of Information, Production and Systems, Waseda University, Hibikino 2-7, Wakamatsu-ku, Kitakyushu, Fukuoka 808-0135, Japan,

Received:
December 8, 2008
Accepted:
April 3, 2009
Published:
November 20, 2009
Creative Commons License
Keywords:
traffic, prediction, GNP
Abstract
In this paper, a method for traffic flow prediction has been proposed to obtain prediction rules from the past traffic data using Genetic Network Programming (GNP). GNP is an evolutionary approach which can evolve itself and find the optimal solutions. It has been clarified that GNP works well especially in dynamic environments since GNP is consisted of directed graph structures, creates quite compact programs and has an implicit memory function. In this paper, GNP is applied to create a traffic flow prediction model. And we proposed the spatial adjacency model for the prediction and two kinds of models for N-step prediction. Additionally, the adaptive penalty functions are adopted for the fitness function in order to alleviate the infeasible solutions containing loops in the training process. Furthermore, the sharing function is also used to avoid the premature convergence.
Cite this article as:
H. Zhou, S. Mabu, W. Wei, K. Shimada, and K. Hirasawa, “Traffic Flow Prediction with Genetic Network Programming (GNP),” J. Adv. Comput. Intell. Intell. Inform., Vol.13 No.6, pp. 713-725, 2009.
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