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

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  • Jinxing Shen
  • Wenquan Li

Abstract

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.

Suggested Citation

  • Jinxing Shen & Wenquan Li, 2013. "Sensitivity Analysis of Wavelet Neural Network Model for Short‐Term Traffic Volume Prediction," Journal of Applied Mathematics, John Wiley & Sons, vol. 2013(1).
    • Handle: RePEc:wly:jnljam:v:2013:y:2013:i:1:n:953548
    DOI: 10.1155/2013/953548
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    References listed on IDEAS

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    1. Ngoduy, D., 2008. "Applicable filtering framework for online multiclass freeway network estimation," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 387(2), pages 599-616.
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    Cited by:

    1. Emilio Jiménez-Macías & Angel Sánchez-Roca & Hipólito Carvajal-Fals & Julio Blanco-Fernández & Eduardo Martínez-Cámara, 2014. "Wavelets Application in Prediction of Friction Stir Welding Parameters of Alloy Joints from Vibroacoustic ANN‐Based Model," Abstract and Applied Analysis, John Wiley & Sons, vol. 2014(1).
    2. Liuli Ou & Lei Liu & Shuai Dong & Yongji Wang, 2014. "Robust Stability Clearance of Flight Control Law Based on Global Sensitivity Analysis," Journal of Applied Mathematics, John Wiley & Sons, vol. 2014(1).

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