<p>Traffic flow prediction is a core task in intelligent transportation systems (ITS), challenged by complex spatiotemporal dynamics. Nodes in traffic networks exhibit diverse temporal patterns, including abrupt fluctuations during peak hours or sudden weather changes, while maintaining spatial correlations that evolve over time. Accurate prediction thus requires effectively capturing high-dimensional spatiotemporal dependencies. In this paper, we propose the Spatiotemporal Decoupling-Gated Transformer (STDGformer), whose key principle is decoupling for coupling. Instead of directly modeling entangled spatiotemporal features, STDGformer first decouples temporal and spatial representations using a Gated Temporal Transformer and a Gated Spatial Transformer. This allows the model to capture high- and low-frequency temporal patterns, as well as dynamic and static spatial dependencies, more effectively. Subsequently, a Cross Attention Transformer integrates the decoupled features, reconstructing their joint interactions and enabling precise modeling of multi-dimensional spatiotemporal dependencies. Extensive experiments on four real-world traffic datasets demonstrate that STDGformer consistently outperforms representative baselines, achieving superior accuracy and robustness while providing interpretable insights into temporal and spatial feature contributions. Specifically, STDGformer reduces MAE by 1.1% on PEMS04 and decreases MAPE by 1.2% on PEMS03, respectively.</p>