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A Dual-Heterogeneity Temporal-Spatial Network (DHTS-Net) is proposed to explicitly decouple and dynamically model heterogeneous patterns across temporal and spatial dimensions and achieves competitive prediction performance across multiple real-world traffic flow datasets.
Experimental results demonstrate IPGMVL's superior performance across four benchmark datasets, establishing new state-of-the-art standards while maintaining computational efficiency, highlighting the importance of dynamic graph adaptation and interactive learning in traffic prediction systems.
A novel framework that decomposes raw traffic flow sequences into low-frequency trends and high-frequency events, thereby preserving frequency-specific temporal patterns, and confirms the effectiveness of frequency-aware decoupling and dual-path fusion in complex traffic flow modeling.
A Tensor-Evolving Graph with Temporal Separation Network (TEG-TSNet) for traffic flow forecasting is proposed, which constructs a unified spatial prior via graph Laplacian spectral embedding and introduces a time-conditioned structure generation paradigm.
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