Traffic Volume Forecasting Based on Wavelet Transform and Neural Networks
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.
KeywordsRoot Mean Square Error Discrete Wavelet Transform Wavelet Transform Traffic Volume Mean Absolute Percentage Error
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