A Joint Topology-Data Fusion Graph Network for Robust Traffic Speed Prediction with Data Anomalism
This work addresses traffic prediction for Intelligent Transportation Systems, offering a novel method that is incremental in its hybrid approach to data smoothing and anomaly mitigation.
The paper tackled the problem of accurate traffic speed prediction by addressing the complexity of traffic dynamics and issues with non-stationary and anomalous data, resulting in a proposed framework that improves prediction accuracy by approximately 6.3% and achieves convergence rates nearly twice as fast as recent hybrid models.
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.