Disentangling Urban Flow: A Dynamic ST-GNN Approach Based on Multi-View Contrastive Learning

Timely and accurate prediction of urban traffic flow is crucial in the development of smart cities. In spite of the significant stride in spatial-temporal modeling, there are the following challenges. Firstly, traffic flow is influenced by the interplay between long-term trends and sudden events. Secondly, traffic flow exhibits distribution heterogeneity across different regions and temporal dynamics over various time periods. To address these challenges, we introduce a novel method, called Dynamic Spatio-Temporal Graph Neural Network with Decoupled Contrasts (D2STCNet). Specifically, our model based on an efficient spatio-temporal encoder with a dynamic graph structure. In the data upstream, we leverage a decoupled input head to process data independently, disentangling complex traffic flow. Following this, we incorporate self-supervised learning to model Heterogeneity within the data. Through comprehensive experiments conducted on four real-world datasets, we demonstrate that our model boasts superior performance while taking efficiency into account. Our code is available on GitHub<sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup><sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup>https://github.com/21ess/D2STCNet.