Dynamic Spatio-Temporal Attention-Based Graph Neural Network Using Ordinary Differential Equation and Multi-Scale Semantics for Traffic Prediction

In the fields of traffic flow prediction and other intelligent forecasting applications, the technology of multivariate time series forecasting using graph neural networks (GNNs) is receiving growing attention. Although various GNN models based on dynamic graph structures have introduced promising approaches, they still face challenges in adequately capturing spatio-temporal information for traffic forecasting. To address this issue, this paper proposes a novel dynamic spatio-temporal attention-based GNN model using ordinary differential equations (ODEs), termed the dynamic graph ordinary differential equation (DGODE) model. Building upon the prior graph ordinary differential equation (GODE) framework, DGODE employs ODEs to mitigate the over-smoothing problem in GNNs and further incorporates dynamic graph structures to enhance the graph information extraction capabilities of GODE. These enhancements enable DGODE to optimize the integration of ODEs within GNNs, alleviate overfitting, and deepen the mining of spatio-temporal dependencies. The related experimental results demonstrate that, compared with existing baseline models, the proposed DGODE model achieves superior performance in traffic flow prediction with a reduced prediction error rate.