Spatio-Temporal Heterogeneous Tree Convolutional Networks for Traffic Flow Prediction

Traffic flow prediction is a vital pillar supporting the development and operation of modern transportation networks and is a significant research area within Intelligent Transportation Systems (ITS). The complexity of traffic scenarios and the heterogeneity of traffic data present greater challenges to traffic prediction research. Currently, most traffic flow prediction methods rely on Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs), with Graph Convolutional Neural Networks (GCNs) being widely used. However, both homogeneous and heterogeneous GCNs have limitations in fully capturing the hierarchical and directional information of networks. To address this, we propose the Spatio-Temporal Heterogeneous Tree Convolutional Networks (STHTCN), a novel model for predicting future traffic flow. STHTCN utilizes a heterogeneously-structured tree to form spatial tree matrices, enabling the extraction of spatial characteristics and achieve the fusion of spatiotemporal features. Experimental results on two real-world traffic datasets from California demonstrate that the proposed STHTCN model reduces comprehensive error by an average of 14.43% and 10.38% compared to the heterogeneous graph convolutional baseline methods and exhibits excellent predictive capabilities.