A Joint Topology-Data Fusion Graph Network for Robust Traffic Speed Prediction with Data Anomalism

• URL: http://arxiv.org/abs/2507.00085v1
• Date: Mon, 30 Jun 2025 06:33:47 GMT
• Title: A Joint Topology-Data Fusion Graph Network for Robust Traffic Speed Prediction with Data Anomalism
• Authors: Ruiyuan Jiang, Dongyao Jia, Eng Gee Lim, Pengfei Fan, Yuli Zhang, Shangbo Wang,
• Abstract summary: We propose Graph Fusion Enhanced Network (GFEN), an innovative framework for network-level traffic speed prediction.<n>GFEN extracts spatial temporal correlations from both data distribution and network topology using trainable methods.<n>Experiments demonstrate that GFEN surpasses state-of-the-art methods by approximately 6.3% in prediction accuracy and convergence rates nearly twice as fast as recent hybrid models.
• Score: 12.43932698231744
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Accurate traffic prediction is essential for Intelligent Transportation Systems (ITS), yet current methods struggle with the inherent complexity and non-linearity of traffic dynamics, making it difficult to integrate spatial and temporal characteristics. Furthermore, existing approaches use static techniques to address non-stationary and anomalous historical data, which limits adaptability and undermines data smoothing. To overcome these challenges, we propose the Graph Fusion Enhanced Network (GFEN), an innovative framework for network-level traffic speed prediction. GFEN introduces a novel topological spatiotemporal graph fusion technique that meticulously extracts and merges spatial and temporal correlations from both data distribution and network topology using trainable methods, enabling the modeling of multi-scale spatiotemporal features. Additionally, GFEN employs a hybrid methodology combining a k-th order difference-based mathematical framework with an attention-based deep learning structure to adaptively smooth historical observations and dynamically mitigate data anomalies and non-stationarity. Extensive experiments demonstrate that GFEN surpasses state-of-the-art methods by approximately 6.3% in prediction accuracy and exhibits convergence rates nearly twice as fast as recent hybrid models, confirming its superior performance and potential to significantly enhance traffic prediction system efficiency.

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