%0 Journal Article
%A 张冠男
%A 张红
%A 张明虎
%A 张玺君
%A 张宪立
%T Medium and long-term traffic flow prediction
based on ACNN-Trans model
%D 2023
%R 10.19682/j.cnki.1005-8885.2023.2013
%J 中国邮电高校学报（英文）
%P 21-32
%V 30
%N 4
%X Aiming at the failure of traditional medium and long-term traffic flow forecasting methods to effectively process
long-time series data and extract the spatio-temporal characteristics between road nodes, a com-bined prediction
model attention based on convolutional neural network (CNN) and transformer ( ACNN-Trans ) network is
proposed. The spatial features between nodes on complex roads are extracted by CNN. The embedded attention
mechanism is adaptively pay attention to the results of feature extraction. The spatial features of the node traffic flow
are dug by the embedded attention mechanism. The traffic flow sequence is calculated by the transformer network.
The temporal correlation of long-term series data is captured by the relative position weight between the data.
Finally, the 30-min and 60-min traffic flow predictions are respectively modeled by the extracted temporal and
spatial features. The results show that the prediction results of the ACNN-Trans combined model are better than other models on two different real datasets. Comparing to the baseline models, the root mean square error (RMSE)
index of the prediction results is reduced by an average of 34.58%, which verifies the effectiveness of ACNN-Trans
model.
%U https://jcupt.bupt.edu.cn/CN/10.19682/j.cnki.1005-8885.2023.2013