Knowledge fusion enhanced graph neural network for traffic flow prediction

Traffic flow prediction is a very important and challenging task in intelligent transportation systems. There has been a lot of related research work on this issue, especially the application of graph convolutional networks has achieved quite good results. However, the existing methods usually only consider the temporal and spatial dependence in traffic data, and cannot fully explore the implicit semantic relationship from traffic knowledge. To solve this problem, we model the transportation system as topological graphs containing different types of knowledge such as network structure, regional functionality, and traffic flow patterns. We propose a Knowledge Fusion Enhanced Graph Neural Network (KFGNN) module based on multiple graph convolutional networks. Specifically, topological graphs are represented by relation matrices obtained by calculating traffic semantic similarity, and are used as the input of the Graph Convolutional Network(GCN) layer to capture the semantic dependence. The KFGNN module finally fuses these features to obtain a complex semantic representation of the traffic flow. Finally, knowledge fusion enhanced models (KE-TGCN, KE-STGCN and KE-GWN) are proposed to verify the effectiveness and versatility of this module. Experimental results on real-world datasets show that knowledge-enhanced models have higher prediction performance compared with classic GCN-based models.