Application of EEMD + BiGRU combination model in short-term traffic flow prediction

Aiming at the problems of high random fluctuation of urban traffic flow and high noise in data, which leads to the decline of prediction accuracy, a combined traffic flow prediction model based on EEMD (ensemble empirical mode decomposition) and BiGRU (bidirectional gated recurrent unit) was proposed, which can effectively improve the accuracy of short-term traffic flow prediction. EEMD algorithm was used to decompose the original data, and the noise energy map was drawn according to the IMF (intrinsic mode function) component to remove the noise in the component. The denoised IMF components were trained as the input of BiGRU network. And the results of training were reconstructed and added to obtain the final prediction result. The experimental results show that, compared with the EMD + LSTM model, LSTM model and EEMD + LSTM model proposed in references, the mean absolute percentage errors are improved by 42.36%, 61.82% and 30.95% when the IMF components containing noise are not abandoned during reconstruction; after abandoning the IMF component containing noise, the mean absolute percentage error is improved by 56.62% compared with the reconstruction of all IMF components.