Spatial-Temporal Dynamic Graph Convolutional Network With Interactive Learning for Traffic Forecasting

Accurate traffic forecasting is essential in urban traffic management, route planning, and flow detection. Recent advances in spatial-temporal models have markedly improved the modeling of intricate spatial-temporal correlations for traffic forecasting. Unfortunately, most previous studies have encountered challenges in effectively modeling spatial-temporal correlations across various perceptual perspectives and have neglected the interactive learning between spatial and temporal correlations. Additionally, constrained by spatial heterogeneity, most studies fail to consider distinct spatial-temporal patterns of each node. To overcome these limitations, we propose a Spatial-Temporal Interactive Dynamic Graph Convolutional Network (STIDGCN) for traffic forecasting. Specifically, we propose an interactive learning framework composed of spatial and temporal modules for downsampling traffic data. This framework aims to capture spatial and temporal correlations by adopting a perception perspective from the global to the local level and facilitating their mutual utilization with positive feedback. In the spatial module, we design a dynamic graph convolutional network based on graph construction methods. The network is designed to leverage a traffic pattern bank considering spatial-temporal heterogeneity as a query to reconstruct a data-driven dynamic graph structure. The reconstructed graph structure can reveal dynamic associations between nodes in the traffic network. Extensive experiments on eight real-world traffic datasets demonstrate that STIDGCN outperforms the state-of-the-art baseline while balancing computational costs. The source codes are available at https://github.com/LiuAoyu1998/STIDGCN.