A genetic-algorithm-based neural network approach for short-term traffic flow forecasting

Authors:
Mingzhe Liu;Ruili Wang;Jiansheng Wu;Ray Kemp
Affiliations:
Institute of Information Sciences and Technology, Massey University, Palmerston North, New Zealand;Institute of Information Sciences and Technology, Massey University, Palmerston North, New Zealand;Department of Mathematics and Computer, Liuzhou Teachers College, Guangxi, China;Institute of Information Sciences and Technology, Massey University, Palmerston North, New Zealand
Venue:
ISNN'05 Proceedings of the Second international conference on Advances in Neural Networks - Volume Part III
Year:
2005

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Abstract

In this paper, a Genetic-Algorithm-based Artificial Neural Network (GAANN) model for short-term traffic flow forecasting is proposed. GAANN can integrate capabilities of approximation of Artificial Neural Networks (ANN) and of global optimization of Genetic Algorithms (GA) so that the hybrid model can enhance capability of generalization and prediction accuracy, theoretically. With this model, both the number of hidden nodes and connection weights matrix in ANN are optimized using genetic operation. The real data sets are applied to the introduced method and the results are discussed and compared with the traditional Back Propagation (BP) neural network, showing the feasibility and validity of the proposed approach.