AdpSTGCN: Adaptive spatial–temporal graph convolutional network for traffic forecasting

Abstract

Traffic flow forecasting plays a crucial role in applications such as intelligent transportation systems. Despite significant research in this field, the current methods have limitations that hinder the realization of highly accurate predictions. Existing GCN-based approaches typically rely on a definite graph structure derived from a physical topology or learned from node features, which is insufficient for building intricate spatial relationships among nodes. To address this challenge, we propose an adaptive spatial–temporal graph convolutional network for traffic forecasting. Our approach exploits a multi-head attention mechanism to construct multi-view feature graphs. We then introduce an adaptive graph convolution method to dynamically aggregate and propagate information from both the topology graph and multi-view feature graphs, which are capable of capturing complex spatial correlations across diverse proximity ranges. Furthermore, we designed a cascaded structural framework that combines temporal information with node features using gated dilated causal convolution to ensure the integrated modeling of spatial–temporal dynamics in traffic flow. Experiments on real-world datasets demonstrate that our proposed method outperforms the current mainstream methods, achieving better performance in traffic flow forecasting. The code is available at https://github.com/dhxdla/AdpSTGCN.git.