The key challenge in traffic flow prediction lies in modeling complex spatio-temporal dependencies effectively. While graph neural networks have shown promise, existing methods face two critical limitations: (1) static graph construction approaches fail to adapt to real-time network dynamics, and (2) prevailing spatio-temporal models neglect both interactive dependency learning and node-specific pattern variations due to spatial heterogeneity. A model based on Interactive Progressive Graph Convolution with Multi-view learning (IPGMVL) is proposed, which introduces three key innovations: First, progressive graph convolution dynamically adjusts edge weights through trend similarity learning, capturing real-time spatial evolution. Second, a multi-view interactive learning mechanism incorporates spatio-temporal heterogeneous patterns for comprehensive dependency modeling. Third, the fast parallel learning (FPL) module is used to realize the synchronous and efficient mining of spatio-temporal features through parameter streamlining, while the serial learning (SL) module expands the serial receptive field and avoid information coverage to further enhance the modeling capability of spatio-temporal dependencies. Experimental results demonstrate IPGMVL's superior performance across four benchmark datasets, establishing new state-of-the-art standards while maintaining computational efficiency. This advancement highlights the importance of dynamic graph adaptation and interactive learning in traffic prediction systems.
Traffic flow prediction remains challenging due to the complex interaction between heterogeneous temporal frequencies and irregular spatial structures. Most existing graph neural network (GNN)-based methods fail to explicitly model spatiotemporal dependencies across multiple frequency components in traffic flow. To address this limitation, a novel framework, termed Spatiotemporal Trend-Event Decomposition Graph Convolutional Network (STEDGCN), is proposed. The framework introduces a temporal signal separator that decomposes raw traffic flow sequences into low-frequency trends and high-frequency events, thereby preserving frequency-specific temporal patterns. A dual-frequency spatiotemporal encoder is designed to model the temporal and spatial characteristics of the two components. It integrates multi-head attention and causal convolution to model temporal dynamics. It also employs trend-driven and event-driven graphs to capture inter-node dependencies and spatiotemporal correlations. A fusion-gated spatiotemporal decoder is introduced to reduce channel redundancy using a gating mechanism. It enables information interaction between the trend and event branches through a query-driven attention strategy. This improves the coherence of the final prediction. Experiments on four benchmark traffic flow datasets show that the proposed model consistently outperforms state-of-the-art methods across multiple metrics. These results confirm the effectiveness of frequency-aware decoupling and dual-path fusion in complex traffic flow modeling.
Linlong Chen· Journal of King Saud Univers...· 0 citations
A Tensor-Evolving Graph with Temporal Separation Network (TEG-TSNet) for traffic flow forecasting is proposed, which constructs a unified spatial prior via graph Laplacian spectral embedding and introduces a time-conditioned structure generation paradigm.
Hongyan Wang, Linlong Chen· Journal of King Saud Univers...· 0 citations