DSSNet: Deep Spectral Structure Profiling Network for Traffic Flow Prediction

Traffic flow prediction is a fundamental task in intelligent transportation systems. Existing methods primarily rely on temporal dependency modeling and thus face two critical limitations: struggling to effectively capture diverse periodicity; overlooking the relationship between spectral structures in the traffic flow and spatiotemporal traffic behaviors. To address these limitations, we propose DSSNet (Deep Spectral Structure Profiling Network), an adaptive spectral profiling framework. DSSNet follows the paradigm of "spectral structure profiling" and then "dual-domain modeling". Initially, a Spectral Structure Learning module captures the dominant frequency bands of the spectrum to enhance regional representations while simultaneously serving as discriminative spatiotemporal identity between regions. Based on these identities, we construct a global Resonance Graph to uncover cross-region dependencies and introduce a Dual-Domain Knowledge Integration module to aggregate structural knowledge across both time and frequency domains. Extensive experiments on multiple benchmark datasets demonstrate that DSSNet achieves state-of-the-art performance, while highlighting the potential of frequency domain modeling for understanding spatiotemporal traffic behaviors.