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Information Technology Journal

Year: 2011 | Volume: 10 | Issue: 11 | Page No.: 2105-2111
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Authors

Yuesheng Gu

Yancui Li

Jiucheng Xu

Yanpei Liu

Keywords

Research Article

Novel Model Based on Wavelet Transform and GA-fuzzy Neural Network Applied to Short Time Traffic Flow Prediction

Yuesheng Gu, Yancui Li, Jiucheng Xu and Yanpei Liu
Precise prediction of short time traffic flow is the key point to realize reasonable traffic control and induce. The intelligent Artificial Neural Network (ANN) can provide effective forecasting performance. However, the prediction precision is influenced greatly by the structure of the ANN. Inadequate design of the ANN prediction model may lead to a low prediction rate. In addition, due to the nonlinear and stochastic of the data, it is often difficult to predict the traffic flow precisely. Hence, a new hybrid intelligent forecasting approach base on the integration of Wavelet Transform (WT), Genetic Algorithm (GA) optimization and Fuzzy Neural Network (FNN) is proposed for the short time traffic flow prediction in this study. The advantages of the proposed method are that the WT can process the nonlinear and stochastic characteristics of the original data and GA-FNN offer optimized ANN model to avoid the influence of the improper ANN structure. By doing so, the forecasting rate can be improved much higher than traditional ways. Three hundred and sixty samples of the practical traffic flow data were collected to validate the proposed prediction model. The analysis results showed that the proposed method can extract the underlying rules of the testing data and improve the prediction accuracy by 15% or better when compared with only ANN approach. Thus, the new WT-GA-FNN traffic flow prediction model can provide practical use.
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