Traffic flow prediction based on frequency-domain dynamic graph and Mamba

With the rapid advancement of intelligent transportation systems (ITS), urban traffic prediction faces significant challenges in effectively modeling complex spatio–temporal dynamics while maintaining computational efficiency. Existing approaches are often limited by static graph structures and the high computational cost of self-attention mechanisms when processing long sequences. To overcome these limitations, this paper proposes a novel framework, termed spatio–temporal frequency-domain mamba network (STFD-MambaNet). Specifically, the framework integrates a frequency-domain dynamic graph learner to capture evolving traffic topologies and employs the Mamba structured state space model to efficiently extract long-range temporal dependencies with linear complexity. Furthermore, a hierarchical spatial modeling module is developed to characterize multi-scale spatial correlations. Experiments conducted on four real-world datasets demonstrate that STFD-MambaNet consistently outperforms state-of-the-art methods in both accuracy and efficiency. The results further demonstrate the effective complementarity between frequency-domain dynamic graph learning and structured state space modeling, providing a robust solution for spatio–temporal traffic forecasting.