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Journal of Applied Mathematics
Volume 2013, Article ID 953548, 10 pages
Research Article

Sensitivity Analysis of Wavelet Neural Network Model for Short-Term Traffic Volume Prediction

Transportation College, Southeast University, Nanjing, Jiangsu 210096, China

Received 23 August 2013; Revised 12 December 2013; Accepted 13 December 2013

Academic Editor: Han H. Choi

Copyright © 2013 Jinxing Shen and Wenquan Li. 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.


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.