DSSA-TCN: Exploiting adaptive sparse attention and diffusion graph convolutions in temporal convolutional networks for traffic flow forecasting
Zhouyuan Zhang, Xin Wang, Xu Tan, Jiatian Pi
TL;DR
This paper introduces a new traffic forecasting model that combines spatial and temporal learning for better accuracy and efficiency.
Contribution
DSSA-TCN introduces a unified spatio-temporal coupling mechanism with adaptive sparse attention and diffusion graph convolutions.
Findings
DSSA-TCN achieves superior forecasting accuracy on six real-world datasets.
The model offers computational efficiency and interpretable spatial reasoning.
Layer-wise coupling of adaptive sparsity and diffusion improves spatio-temporal prediction.
Abstract
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…
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Taxonomy
TopicsTraffic Prediction and Management Techniques · Traffic control and management · Transportation Planning and Optimization