Dynamic graph convolution recurrent neural network for traffic flow prediction

Abstract

To forecast the condition of traffic networks in the future, it is crucial to model the spatial and temporal correlation of traffic series. The majority of current research has been on creating complicated graph neural networks that can capture common patterns using preconfigured graphs. In this paper, we claim that predefined graphs may be avoided and that adaptive graphs can be used to capture spatial correlations between traffic series and improve the performance of graph neural networks. In order to capture the temporal relationships of sequences, we also aggregated gated recurrent neural networks. Then, we encode the relative time position of the sequence in order to fully extract the characteristics of the traffic sequence. Finally, we add the values of the previous day and the same day of the previous week as a reference in the final prediction to improve the accuracy of our prediction. Experimental results on two sets of real-word traffic data (PeMSD4 and PeMSD8) demonstrate that our method is better than the existing methods.