Spatiotemporal-decoupled interactive learning for traffic flow prediction

Accurate traffic flow prediction is a core capability of intelligent transportation systems, supporting trip planning, network dispatch, and management decisions. Most existing approaches overlook interactive learning of spatiotemporal dependencies and struggle to accommodate pattern diversity arising from spatial heterogeneity and multi-scale temporal variation. To address these limitations, this paper proposes Spatiotemporal-Decoupled Interactive Learning (STDIL), a framework comprising a spatiotemporal decoupling module and an interactive learning module. The former reconstructs sequences along spatial and temporal dimensions to yield more discriminative contextual representations. The latter dynamically reconstructs the graph structure in a data-driven manner to capture spatiotemporal correlations from global and local perspectives, thereby leveraging both neighborhood information and long-range dependencies. Experiments on four real-world urban traffic flow datasets show that STDIL attains significantly higher accuracy than existing methods across all prediction horizons. These results demonstrate STDIL's effectiveness in handling spatiotemporal heterogeneity and dynamic dependencies, as well as its adaptability to diverse traffic scenarios.