Prediction of Expressway Traffic Flow Based on EEMD-Att-GCN-LSTM
The current predictions of expressway traffic flow primarily rely on time series modeling,without incorporating dynamic spatial correlation into the modeling process.For the purpose of efficiently and accurately predicting expressway traffic flow,a combined prediction model based on attention mechanism(ATT),ensemble empirical mode decomposition(EEMD),graph convolutional neural network(GCN),and long short-term memory(LSTM)is proposed.Firstly,EEMD is em-ployed to denoise the traffic flow data,thereby reducing the non-stationarity inherent in expressway traffic flow.Subsequently,a spatio-temporal attention mechanism is utilized to dynamically capture the spatio-temporal correlations.Finally,the outputs of temporal and spatial features are weighted and fused as inputs into a fully connected layer for comprehensive analysis.The performance evaluation metrics of this model includes mean absolute error(MAE),mean absolute percentage error(MAPE),and root mean square error(RMSE).It exhibits practicality in medium and long-term traffic flow prediction.
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