Traffic flow prediction method based on quick learning neural network with double optimal learning rates

Abstract

The invention relates to a traffic flow prediction method based on a quick learning neural network with double optimal learning rates. The method comprises the following steps of: normalizing m continuous traffic flow historical data which serve as the input of a prediction network; initializing weights and stretch and shift factors of a wavelet basis function by using a three-layer neural network, wherein the shift factors and transfer factors of the wavelet basis function employ a first learning rate, and network weights employ a second learning rate; providing a learning rate array, and performing network training of the double optimal learning rates; and outputting values of a current moment to first (m-1) periods, which serve as the input of a trained network, performing reverse normalization, and thus obtaining a prediction value of a traffic flow at a next moment of the current moment. The method has the advantages that the first learning rate and the second learning rate employ the optimal learning rates during network training at each time, quick network training can be realized, and high-accuracy prediction of the traffic flow is realized.

Description

technical field [0001] The invention belongs to the technical field of traffic flow prediction, and relates to a traffic flow prediction method based on double optimal learning rate fast learning neural network. Background technique [0002] Traffic flow prediction is a key technology of intelligent transportation. The accuracy and real-time performance of traffic flow prediction directly affect the quality of traffic control algorithms. After decades of development, many useful forecasting models have emerged for short-term traffic flow forecasting. For example: the moving average method, its core idea is: according to the historical traffic flow data, calculate the average value in a certain period before, and use it to predict the traffic flow of the next period or several periods. The characteristics of the moving average method can eliminate the jitter in the traffic flow data and show its long-term trend, but the moving average method is suitable for predicting the si...

AI Technical Summary

Problems solved by technology

[0004] The purpose of the present invention is to provide a traffic flow prediction method based on double optimal learning rate fast learning neural network, to solve the problem that the prediction result of the existing prediction method lags behind

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