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2013 Sensitivity Analysis of Wavelet Neural Network Model for Short-Term Traffic Volume Prediction
Jinxing Shen, Wenquan Li
J. Appl. Math. 2013: 1-10 (2013). DOI: 10.1155/2013/953548


In order to achieve a more accurate and robust traffic volume prediction model, the sensitivity of wavelet neural network model (WNNM) is analyzed in this study. Based on real loop detector data which is provided by traffic police detachment of Maanshan, WNNM is discussed with different numbers of input neurons, different number of hidden neurons, and traffic volume for different time intervals. The test results show that the performance of WNNM depends heavily on network parameters and time interval of traffic volume. In addition, the WNNM with 4 input neurons and 6 hidden neurons is the optimal predictor with more accuracy, stability, and adaptability. At the same time, a much better prediction record will be achieved with the time interval of traffic volume are 15 minutes. In addition, the optimized WNNM is compared with the widely used back-propagation neural network (BPNN). The comparison results indicated that WNNM produce much lower values of MAE, MAPE, and VAPE than BPNN, which proves that WNNM performs better on short-term traffic volume prediction.


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Jinxing Shen. Wenquan Li. "Sensitivity Analysis of Wavelet Neural Network Model for Short-Term Traffic Volume Prediction." J. Appl. Math. 2013 1 - 10, 2013.


Published: 2013
First available in Project Euclid: 14 March 2014

MathSciNet: MR3147911
Digital Object Identifier: 10.1155/2013/953548

Rights: Copyright © 2013 Hindawi


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