| ZHANG Xijun(张玺君),HAO Jun,NIE Shengyuan,CUI Yong.[J].高技术通讯(英文),2023,29(1):41~49 |
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| MEEMD-DBA-based short term traffic flow prediction |
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| DOI：10. 3772/ j. issn. 1006-6748. 2023. 01. 005 |
| 中文关键词: |
| 英文关键词: modified ensemble empirical mode decomposition (MEEMD), double bidirectional-directional gated recurrent unit (DBiGRU), attention mechanism, traffic flow prediction |
| 基金项目: |
| Author Name | Affiliation | | ZHANG Xijun(张玺君) | (College of Computer and Communication, Lanzhou University of Technology, Lanzhou 730050, P.R.China) | | HAO Jun | (College of Computer and Communication, Lanzhou University of Technology, Lanzhou 730050, P.R.China) | | NIE Shengyuan | (College of Computer and Communication, Lanzhou University of Technology, Lanzhou 730050, P.R.China) | | CUI Yong | (College of Computer and Communication, Lanzhou University of Technology, Lanzhou 730050, P.R.China) |
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| 中文摘要: |
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| 英文摘要: |
| 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 reconstruction 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 bidirectional long-short term memory (DBiLSTM) and attention mechanism is proposed. Firstly, the intrinsic 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 enhance the extraction of data features, then the obtained results are reconstructed and added. The experimental 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. |
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