Multi-Mode Spatial-Temporal Convolution Network for Traffic Flow Forecasting

Abstract

In order to solve the problem of poor spatial and temporal feature extraction in traffic flow prediction, a multimodal spatial-temporal graph convolution network (MMSTGCN) is proposed. The road topology map is constructed, the spatial features are extracted by graph convolution network, and the fine-grained learning of node domain is added to extract the coarse-grained and fine-grained features. The time is divided into three kinds of time slices, and the time feature is extracted by gated cycle unit (GRU). Finally, three time slice features are fused for training. Two real world datasets, PeMSD4 and PeMSD8, are used to verify the effectiveness of the method.