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 l...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.展开更多
基金Supported by the National Natural Science Foundation of China(No.62162040,61966023)the Higher Educational Innovation Foundation Project of Gansu Province of China(No.2021A-028)the Science and Technology Plan of Gansu Province(No.21ZD4GA028).
文摘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.
