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<p>Accurate traffic flow forecasting serves as a cornerstone for intelligent transportation systems, enabling proactive accident prevention and metropolitan mobility optimization. However, existing approaches face fundamental limitations in modeling the spatiotemporal heterogeneity of traffic dynamics, particularly in simultaneously addressing (1) the decaying significance of temporal dependencies across input sequences and prediction horizons, (2) multi-scale spatial interactions spanning local congestion patterns and global functional correlations, and (3) inter-sample temporal variance in evolving traffic states. To address these limitations, this paper proposes MVA-DCNet (Multi-View Attention Dilated Convolutional Network), a novel deep learning architecture incorporating a multidimensional temporal analysis framework that systematically examines temporal influence mechanisms through three complementary perspectives: inter-sample variance, intra-sequence temporal importance, and output sequence temporal propagation. The proposed model systematically addresses temporal data heterogeneity through three innovative mechanisms: variance-aware data augmentation, adaptive temporal attention, and decaying loss weighting. For enhanced spatial correlation modeling, we develop a dilated convolutional architecture with enhanced receptive field coverage and multi-scale spatial pattern recognition capabilities. Empirical validation on two urban traffic datasets demonstrates superior efficacy in capturing complex spatiotemporal evolution patterns, achieving relative reductions of 12.7% and 9.3% in Root Mean Square Error (RMSE) respectively compared with state-of-the-art benchmarks.</p>

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Enhancing traffic flow prediction through multi-view attention mechanism and dilated convolutional networks

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年12月15日

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  • Wei Li,
  • Hao Wei,
  • Xin Liu,
  • Jialin Liu,
  • Dazhi Zhan,
  • Xiao Han,
  • Wei Tao

摘要

Accurate traffic flow forecasting serves as a cornerstone for intelligent transportation systems, enabling proactive accident prevention and metropolitan mobility optimization. However, existing approaches face fundamental limitations in modeling the spatiotemporal heterogeneity of traffic dynamics, particularly in simultaneously addressing (1) the decaying significance of temporal dependencies across input sequences and prediction horizons, (2) multi-scale spatial interactions spanning local congestion patterns and global functional correlations, and (3) inter-sample temporal variance in evolving traffic states. To address these limitations, this paper proposes MVA-DCNet (Multi-View Attention Dilated Convolutional Network), a novel deep learning architecture incorporating a multidimensional temporal analysis framework that systematically examines temporal influence mechanisms through three complementary perspectives: inter-sample variance, intra-sequence temporal importance, and output sequence temporal propagation. The proposed model systematically addresses temporal data heterogeneity through three innovative mechanisms: variance-aware data augmentation, adaptive temporal attention, and decaying loss weighting. For enhanced spatial correlation modeling, we develop a dilated convolutional architecture with enhanced receptive field coverage and multi-scale spatial pattern recognition capabilities. Empirical validation on two urban traffic datasets demonstrates superior efficacy in capturing complex spatiotemporal evolution patterns, achieving relative reductions of 12.7% and 9.3% in Root Mean Square Error (RMSE) respectively compared with state-of-the-art benchmarks.