TreeCN: Time Series Prediction With the Tree Convolutional Network for Traffic Prediction

The complexity of traffic scenarios, the spatial-temporal feature correlations pose higher challenges for traffic prediction research. Traffic spatial-temporal model is an essential method in this research field, primarily focusing on capturing the spatial-temporal features among nodes and their neighboring nodes. However, existing methods lack comprehensive consideration of directional and hierarchical features among traffic nodes. They are mostly applicable to scenarios with random uniform distribution of nodes, but not suitable for more complex small-scale aggregation distribution scenarios. Therefore, this study proposes the Tree Convolutional Network (TreeCN), a tree-based structure. The data design and model design of TreeCN focus on capturing the directional and hierarchical features among nodes. The directional and hierarchical relationships among nodes are represented by the plane tree matrix and constructed as the spatial tree matrix. The TreeCN, with a full convolution network, performs a bottom-up convolution structure on the tree matrix to complete the task of node feature capturing. In this study, TreeCN is thoroughly compared with statistical, machine learning, and deep learning methods in traffic time series prediction. The experimental results show that TreeCN not only performs well in scenarios with random uniform distribution but also exhibits outstanding effect in more complex small-scale aggregation distribution. Moreover, TreeCN adheres to the design principles of Graph Convolutional Networks (GCN) in capturing the spatial features of traffic nodes and can further capture directional and hierarchical features among them. This is expected to make TreeCN a new method to handle complex traffic scenarios and improve prediction accuracy.