Traffic Flow Prediction Based on Two-Stage Attention Mechanism Recurrent Neural Network
With the development of deep learning,the accuracy of traffic flow forecasting is increasing.This article starts from the perspective of time series forecasting traffic flow,and is based on a two-stage attention mechanism recurrent neural network model(DA-RNN)to solve the current traffic flow.It is difficult to capture the correlation between time data series in the time series forecasting of traffic,which leads to the problem of inaccurate prediction and solves the problem of overfitting in the experiment.This paper conducts experiments based on PEMS04 data and compares the prediction results with the prediction results of LSTM and GRU models.It shows that this time series prediction model has good performance and can provide an effective basis for traffic man-agement and control.
deep learningrecurrent neural networkattention mechanismencoder-decoder
国家科技期刊平台
NSTL
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