MEEMD-DBA-based short term traffic flow prediction

• Published in: 高技术通讯（英文版） Vol. 29; no. 1; pp. 41 - 49
• Main Authors: ZHANG Xijun, HAO Jun, NIE Shengyuan, CUI Yong
• Format: Journal Article
• Language: English
• Published: College of Computer and Communication,Lanzhou University of Technology,Lanzhou 730050,P.R.China 01.03.2023
• Subjects: modified ensemble empirical mode decomposition ( MEEMD); traffic flow prediction; attention mechanism; double bidirection-al-directional gated recurrent unit ( DBiGRU)
• ISSN: 1006-6748
• DOI: 10.3772/j.issn.1006-6748.2023.01.005

Aiming at the problem that ensemble empirical mode decomposition ( EEMD ) method can not completely neutralize the added noise in the decomposition process, which leads to poor reconstruc-tion of decomposition results and low accuracy of traffic flow prediction, a traffic flow prediction model based on modified ensemble empirical mode decomposition ( MEEMD) , double-layer bidirec-tional long-short term memory ( DBiLSTM) and attention mechanism is proposed. Firstly, the intrin-sic mode functions( IMFs) and residual components( Res) are obtained by using MEEMD algorithm to decompose the original traffic data and separate the noise in the data. Secondly, the IMFs and Res are put into the DBiLSTM network for training. Finally, the attention mechanism is used to en-hance the extraction of data features, then the obtained results are reconstructed and added. The ex-perimental results show that in different scenarios, the MEEMD-DBiLSTM-attention ( MEEMD-DBA ) model can reduce the data reconstruction error effectively and improve the accuracy of the short-term traffic flow prediction.