Short-term traffic flow prediction of expressway based on CEEMD-GRU combination model

Abstract

In order to improve the accuracy of short-term traffic flow prediction,a short-term traffic flow prediction method of expressway based on the combined model of complementary ensemble empirical mode decomposition (CEEMD) and gated recurrent unit (GRU) was proposed.Firstly,the unstable original traffic flow time series data were decomposed into relatively stable multiple modal components by complementary ensemble empirical mode decomposition algorithm.Then,a GRU model was established for each decomposed modal component sequence for one-step prediction.Finally,the predicted value of each component was superimposed to obtain the final prediction result,and the measured traffic flow data of north-south elevated expressway in Shanghai was used to verify and analyze the model.The experimental results show that the prediction effect of CEEMD-GRU combination model is superior to GRU neural network model,EMD-GRU combination model and EEMD-GRU combination model,and the average prediction accuracy is improved by [BF]33.4%[BFQ],[BF]25.6%[BFQ] and [BF]18.3%[BFQ],respectively.CEEMD-GRU combination model can effectively extract the characteristic components of traffic flow data and improve the prediction accuracy,which provides scientific decision-making basis for traffic control management.[HQ]

Keywords

• road transportation management; urban expressway; short-term traffic flow prediction; complementary ensemble empirical mode decomposition; gated recurrent unit neural network