STH-SynNet:Spatio-Temporal Heterogeneity-aware Synergistic Network for Traffic Prediction

High-precision traffic flow prediction plays a crucial role in optimizing intelligent transportation systems and enhancing urban operational efficiency. However, existing methods still face limitations in modeling spatial heterogeneity and multi-scale temporal dependencies within traffic networks, which restrict the expressiveness and prediction accuracy of the models. To address these challenges, this paper proposes a Spatio- Temporal Heterogeneity-aware Synergistic Network for traffic prediction, termed STH-SynNet. Built upon an encoder-decoder architecture, the framework integrates an enhanced gating unit-ST-MS-Spectral GRU-within its core module. This unit incorporates dynamic graph modeling, time-frequency decoupling, and attention-enhanced mechanisms to improve the model's capacity to capture structural diversity and complex temporal dynamics. By employing multi-scale convolutional receptive fields with a dynamic graph generation mechanism, the model adapts to heterogeneous interactions among nodes. Additionally, a frequency masking mechanism based on Fourier Transform is introduced to disentangle periodic patterns from short-term fluctuations, while a lightweight attention-enhanced path dynamically selects and models critical historical states. Experiments on three real-world datasets demonstrate sig-nificant improvements in predictive accuracy, validating the effectiveness and robustness of the proposed method in complex traffic forecasting tasks.