A Spatio-Temporal Tree and Gauss Convolutional Network for Traffic Flow Forecasting

Traffic flow forecasting plays a crucial role in Intelligent Transportation Systems (ITS) for the development and operation of modern transportation networks. Current methods primarily rely on Graph Convolutional Neural Networks (GNN) and Recurrent Neural Networks (RNN) to predict traffic flow. However, these methods face challenges in effectively capturing hierarchical and directional information within the traffic network while quantitatively balancing the relationships between current, previous, and future time data. To address these issues, this paper introduces a novel approach called Spatio-Temporal Tree and Gauss Convolutional Network (ST-TGCN) for traffic flow forecasting. The model utilizes a tree structure to construct a planar tree matrix for extracting spatial features and employs gaussian temporal convolution to extract temporal features of traffic flow. Experimental results demonstrate that ST-TGCN outperforms baseline methods, indicating its superior predictive capabilities.