Two-layer Dynamic Graph Convolutional Recurrent Neural Network for Traffic Flow Prediction

Traffic flow prediction can improve transportation efficiency, which is an important part of intelligent transportation systems. In recent years, the prediction method based on graph convolutional recurrent neural network has been widely used in traffic flow prediction. However, in real application scenarios, the spatial dependence of graph signals will change with time, and the filter using a fixed graph displacement operator cannot accurately predict traffic flow at the current moment. To improve the accuracy of traffic flow prediction, a two-layer graph convolutional recurrent neural network based on the dynamic graph displacement operator is proposed. The framework of our proposal is to use the first layer of static graph convolutional recurrent neural network to generate the sequence wave vector of the graph displacement operator. The sequence wave vector is passed through the deconvolutional neural network to obtain the sequence dynamic graph displacement operator, and then the second layer dynamic graph convolutional recurrent neural network is used to predict the traffic flow at the next moment. The model is evaluated on the METR-LA and PEMS-BAY datasets. Experimental results demonstrate that our model significantly outperforms other baseline models.