Integrated spatio-temporal modeling with hybrid graph convolutions and the graph fourier neural operator for traffic prediction

Accurate long-term traffic forecasting is pivotal for resilient intelligent transportation systems (ITS), enabling proactive congestion mitigation, energy optimization, and enhanced urban mobility. However, existing methods struggle to capture the intricate interplay of spatial and temporal dependencies in non-Euclidean road networks. Classical autoregressive approaches fail to model nonlinear dynamics, while deep learning techniques—such as RNN-based graph models, attention-driven Transformers, and state-space architectures—often decouple spatial and temporal learning, rely on computationally expensive mechanisms, and exhibit limited scalability and training instability in long-horizon settings. Although recent advances in spatio-temporal fusion and adaptive graph learning partially address multi-scale interactions, they remain constrained by efficiency and the lack of unified global temporal modeling. To overcome these limitations, we propose HG-GFNO (Hybrid Static–Adaptive Graph Convolutions and Graph Fourier Neural Operator), a unified and parameter-efficient framework that combines hybrid graph convolutions for localized and dynamic spatial encoding with a novel GFNO that extends spectral operators to graph domains for linear-complexity long-range temporal modeling. Extensive experiments on four benchmark datasets (PEMS03, PEMS04, PEMS07, and PEMS08) demonstrate that HG-GFNO consistently outperforms state-of-the-art baselines by up to 10.9% in RMSE and 11.9% in MAE across forecasting horizons, while using fewer parameters and exhibiting superior stability. These results position HG-GFNO as a scalable and practical solution for real-world intelligent transportation systems in smart city environments.