A novel hybrid time-varying graph neural network for traffic flow forecasting

Real-time and accurate traffic flow prediction is the foundation for ensuring the efficient operation of intelligent transportation systems.In existing traffic flow prediction methods based on graph neural networks (GNNs), pre-defined graphs were usually used to describe the spatial correlations of different traffic nodes in urban road networks. However, the ability of pre-defined graphs used to describe spatial correlation was limited by prior knowledge and graph generation methods. Although time-varying graphs based on data-driven learning can partially overcome the drawbacks of pre-defined graphs, the learning ability of existing adaptive graphs was limited. For example, time-varying graphs cannot adequately capture the inherent spatial correlations in traffic flow data.In order to solve these problems, we have proposed a hybrid time-varying graph neural network (HTVGNN) for traffic flow prediction.