Expressway Traffic Flow Prediction Based on Data from Multiple Related Toll Stations
The strong inter-economic connection makes a spatial correlation between traffic data from multiple related toll stations between urban cluster regions,and an accurate description of this connection can improve the accuracy of expressway traffic flow prediction.However,due to many uncertainties,the correlation is difficult to be captured and quantified.To solve this problem,an ATGCN-ResGRU deep learning-based expressway traffic flow prediction method was proposed.By combining attention mechanisms,three graph convolutional networks(GCN)topological networks with high,medium,and low attention levels were constructed,and spatial learning data was obtained according to the weighted attention level of each network.The connection of multiple related toll stations was quantified and graded.At the same time,to avoid the over-smoothing problem,two gated recurrent unit(GRU)modules were connected by residuals to enhance the algorithm's ability to capture time regularity.Finally,a feature fusion layer and a fully connected layer were used to output the predicted values.This algorithm was used to predict the traffic flow at a expressway toll station in Guangdong Province,and the experimental results show that the method proposed in this paper can effectively improve the prediction accuracy.Compared with the classical models of diverse ensemble CNN-LSTM,CNN-BiLSTM,and DL-SVR,the mean absolute error(EMAE)is reduced by 7.95,4.52,and 12.88,and the root means square error(ERMSE)is reduced by 12.03,6.12,and 19.05.
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