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Traffic Volume Forecasting Based on Wavelet Transform and Neural Networks

  • Shuyan Chen
  • Wei Wang
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3973)

Abstract

This paper focuses on traffic volume forecasting that is an essential component of any responsive traffic control or route guidance system. A new approach for traffic volume prediction is proposed based on wavelet transform and neural networks. First, apply multi-resolution analysis to the original traffic volume time series to obtain a trend series and a hierarchy of detail series. Then apply neural networks to each obtained time series. Next sum all the forecasting values to get the final prediction of traffic volume. This hybrid method is implemented within a Matlab environment. The feasibility of the developed method as well as its validity to predict traffic volume has been demonstrated on real data gathered in Suzhou city. Moreover, a comparison between the hybrid method and conventional neural networks is conducted. The results show the proposed hybrid model outperformed the neural networks.

Keywords

Root Mean Square Error Discrete Wavelet Transform Wavelet Transform Traffic Volume Mean Absolute Percentage Error 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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References

  1. 1.
    Skander, S.: On the Use of the Wavelet Decomposition for Time Series Prediction. Neurocomputing 48(1-4), 267–277 (2002)MATHCrossRefGoogle Scholar
  2. 2.
    Yao, S.J., Song, Y.H., Zhang, L.Z., Cheng, X.Y.: Wavelet Transform and Networks for Short-term Electrical Load Forecasting. Energy Conversion and Management 41(18), 1975–1988 (2000)CrossRefGoogle Scholar
  3. 3.
    Teng, H.L., Qi, Y.: Application of Wavelet Technique to Freeway Incident Detection. Transportation Research C 11(3-4), 289–308 (2003)CrossRefGoogle Scholar
  4. 4.
    Chen, S.Y., Wang, W., Qu, G.f.: Combining Wavelet Transform and Markov Model to Forecast Traffic Volume. In: The Third International Conference on Machine Learning and Cybernetics (ICMLC 2004), vol. 5, pp. 2815–2818 (2004)Google Scholar
  5. 5.
    Dong, Z., Guo, X., Zheng, J., et al.: Calculation of Noise Resistance by Use of the Discrete Wavelets Transform. Electrochemistry Communications 3, 561–565 (2001)CrossRefGoogle Scholar
  6. 6.
    Aballe, M.B., Botana, F.J.: Using Wavelets Transform in the Analysis of Electrochemical Noise Data. Electrochimica Acta 44, 4805–4816 (1999)CrossRefGoogle Scholar
  7. 7.
    Yin, H., Wong, S., Xu, J., Wong, C.: Urban Traffic Volume Prediction Using a Fuzzy-neural Approach. Transportation Research C 10, 85–98 (2002)CrossRefGoogle Scholar
  8. 8.
    Chen, H., Grant-Muller, S.: Use of Sequential Learning for Short-term Traffic Volume Forecasting. Transportation Research Part C 9, 319–336 (2001)CrossRefGoogle Scholar
  9. 9.
    Tom, M.M.: Machine Learning, 1st edn. China Machine Press, Beijing (2003)Google Scholar
  10. 10.
    Srinivasan, D., Jin, X., Cheu, R.L.: Evaluation of Adaptive Neural Network Models for Freeway Incident Detection. IEEE Transactions on Intelligent Transportation Systems 5(1), 1–11 (2004)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Shuyan Chen
    • 1
    • 2
  • Wei Wang
    • 2
  1. 1.College of TransportationSoutheast UniversityNanjingChina
  2. 2.Department of Electronic informationNanjing Normal UniversityNanjingChina

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