Spatio-temporal traffic flow forecasting based on second-order continuous graph neural network

Abstract Spatio-temporal forecasting has wide applications across various domains, particularly in intelligent transportation systems, where it plays a crucial role. Traffic flow prediction, a typical spatio-temporal forecasting task, involves complex dependencies across both time and space dimensions. Current research predominantly relies on graph neural networks (GNNs) for modeling. However, deep GNN architectures often face the issue of over-smoothing. To address this challenge, recent studies have explored integrating residual connections or neural ordinary differential equations (ODEs) with GNNs. Nonetheless, existing graph ODE methods have limitations in initializing latent feature representations for time series data and capturing higher order spatio-temporal dependencies. Additionally, they struggle to extract multi-scale temporal dependencies. In this paper, we propose a framework called the Multiple Second-order Continuous Graph Neural Network. The framework utilizes a second-order continuous GNN, and experiments on four real-world datasets demonstrate that it outperforms mainstream baseline models, thereby confirming the effectiveness of the proposed method.