DSSA-TCN: Exploiting adaptive sparse attention and diffusion graph convolutions in temporal convolutional networks for traffic flow forecasting

Accurate traffic flow forecasting is essential for intelligent transportation systems, yet the nonlinear and dynamically evolving spatio-temporal dependencies in urban road networks make reliable prediction challenging. Existing graph-based and attention-based approaches have improved performance but often decouple spatial and temporal learning, which leads to redundant computation and weak directional interpretability. To address these limitations, we propose DSSA-TCN, a unified framework that establishes an alternating spatio-temporal coupling mechanism, where each temporal convolutional block is tightly integrated with an adaptive spatial module that combines sparse attention with diffusion-based graph convolution. Within this mechanism, adaptive sparse attention dynamically selects the most informative neighbors to reduce spatial complexity, and bidirectional diffusion convolution enforces physically consistent directional and multi-hop propagation over the road topology. Temporal patterns are modeled with gated dilated convolutions to preserve parallelism and stability. Comprehensive experiments on six real-world datasets demonstrate that DSSA-TCN achieves superior forecasting accuracy and computational efficiency while providing interpretable spatial reasoning. These results indicate that layer-wise coupling of adaptive sparsity and diffusion within a causal temporal backbone offers a scalable and physically grounded paradigm for spatio-temporal traffic prediction.