Spatio-Temporal Heterogeneous Graph-Based Convolutional Networks for Traffic Flow Forecasting

Abstract

Traffic flow forecasting plays a crucial role in the construction of intelligent transportation. The aims of this paper are to fully exploit the spatial correlation between nodes in a traffic network and to compensate for the inability of graph-based deep learning methods to model multiple relationship types, resulting in inadequate extraction of spatially correlated information about the traffic network. In this paper, we propose a deep spatio-temporal recurrent evolution network based on the graph convolution network (STREGCN) for heterogeneous graphs. Specifically, we transform the traffic network into a multi-relational heterogeneous graph to improve the information representation of the graph. This allows our model to capture multiple types of spatially relevant information. In the temporal dimension, we use one-dimensional causal convolution based on the gated linear unit to extract the temporal correlation information of the traffic flow. In addition, we designed the output of the spatio-temporal convolution module to obtain the final traffic flow predictions after a fully connected layer. Experiments on real datasets illustrate the effectiveness of the proposed STREGCN model and show the importance of representing information through heterogeneous graphs for the task of traffic flow prediction.