BLRGCN: A dynamic traffic flow prediction model based on spatiotemporal graph convolutional network

Abstract

Because of the complex topology of urban road and the dynamic changes of traffic information over time, how to accurately and efficiently predict traffic flow has become a difficult problem. In order to capture spatiotemporal dependence at the same time, in this paper we propose BLRGCN, a dynamic traffic flow prediction model based on spatiotemporal graph convolutional network. This model improves the performance of bidirectional long short-term memory network (Bi-LSTM) by using residual network (ResNet), which can directly connect the input short to the output of nonlinear layer, and then combines the improved Bi-LSTM with graph convolutional neural network (GCN). BLRGCN model uses graphs to construct the network information of urban roads. In the graph, nodes express roads and edges express relationship between roads. The attributes of nodes describe traffic information on roads. We use GCN to extract the topological features of road network to capture spatial dependence. Besides, the BLRGCN model uses Bi-LSTM to obtain the information of the whole input sequence and extract the dynamic change characteristics of traffic data on the road to capture the temporal dependence. Compared with other methods on two traffic datasets, BLRGCN model has improved RMSE index by 0.53%-37.03% and 23.69%-43.83% respectively. The MAE index was improved by 34.89%-37.78% and 7.80%-51.88%, the accuracy index was improved by 0.19%-82.53% and 4.55%- 10.45%, the R2 index was improved by 0.24%-25.65% and 16.67%-29.94%, and the var index is improved by 0.25%-24.24% and 16.99%-30.39% respectively, which indicates that BLRGCN model has good prediction performance.