A New Measure for Traffic Data Collection and Processing
ICICTA '09 Proceedings of the 2009 Second International Conference on Intelligent Computation Technology and Automation - Volume 03
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In analyzing the nonlinearity characteristics and strong interference of traffic flow parameters, a new approach has been proposed for the prediction of traffic flow parameters. First, multi-scale analysis is used to decompose the sequences of traffic flow parameters into the low and high frequency ones and restore them according to the reconstruct principle of wavelet coefficients. Then artificial neural network is used in multi-scale forecast of these coefficients, with gene algorithm for optimization. Finally, some real detected traffic data are used to testify the precision of the model. The results show that the model can produce more accurate predictions than with traditional artificial neural network model.