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November 13, 2025PLoS ONEOpen Access

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

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Authors

ZZZhang ZhouyuanWXWang XinTXTan, Xu

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Overview

Analysis reveals superior forecasting accuracy in urban road networks using temporal convolutional networks and graph-based approaches.

Key Points

  • Traffic flow forecasting improves with a framework that couples temporal learning and adaptive spatial modules, enhancing accuracy.
  • Comprehensive experiments on real-world datasets show that DSSA-TCN outperforms existing methods in forecasting efficiency and reliability.
  • Approach involves a unique integration of temporal convolutional networks and graph-based approaches for enhanced spatial reasoning.
  • Significance lies in providing a new paradigm for traffic prediction, highlighting the benefits of adaptive sparsity in urban road networks.

Cite This Study

Zhouyuan et al. (2025) studied this question.

synapsesocial.com/papers/692523b2c0ce034ddc35484fhttps://doi.org/10.1371/journal.pone.0336787
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Also Consider

Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context:

  1. 1Adaptive Diffused Spatiotemporal Graph Convolution for Traffic Flow Forecasting2026
  2. 2Research on traffic flow prediction of dynamic graph convolution network based on spatio-temporal attention mechanism2026
  3. 3Transformer-Based Spatiotemporal Graph Diffusion Convolution Network for Traffic Flow Forecasting2024 · 16 citations
  4. 4Spatiotemporal Adaptive Hybrid Graph Convolutional Networks for Traffic Flow Forecasting2025 · 2 citations
  5. 5Traffic Speed Prediction Based on Dynamic Spatial-Temporal Attention Graph Convolutional Network2024