关键词: 图卷积网络, 门控循环单元, GraphSAGE算法, 图注意力机制, 城区路网

Abstract:

To solve the problem that the spatial characteristics of urban road network are difficult to be fully extracted in the process of traffic flow prediction, which leads to the low accuracy of the prediction results, a short-time traffic flow prediction model combining Graph Convolutional Network（GCN） and Gated Recurrent Unit（GRU） is proposed. Based on the advantages of GCN in topological data processing, the spatial arrangement structure of urban road network is transformed into topological relation modeling, and the spatial characteristics of road networks are extracted effectively by solving the problem of topological relation. The GCN model is improved by using GraphSAGE algorithm, and spatial features are aggregated by adding aggregation operator and Graph Attention mechanism（GAT）, and the output containing spatial features is used as the input of GRU model to extract time features. Compared with the model without GCN, the result shows that the prediction accuracy of this model is about 8% higher and the mean square error is about 0.010?37 smaller, indicating that the proposed model has relatively high stability and prediction accuracy and can provide an important traffic guidance basis for large urban road networks.

Key words: Graph Convolutional Network（GCN）, Gated Recurrent Unit（GRU）, GraphSAGE algorithm, Graph Attention mechanism（GAT）, urban road network