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

TL;DR

This paper introduces GFEN, a novel graph neural network framework that fuses topological and data-driven features to improve traffic speed prediction accuracy and robustness against data anomalies.

Contribution

GFEN's innovative topological spatiotemporal fusion and hybrid smoothing approach enhance traffic prediction by effectively modeling multi-scale features and handling data non-stationarity.

Findings

GFEN outperforms state-of-the-art methods by 6.3% in accuracy.

GFEN converges nearly twice as fast as recent hybrid models.

GFEN effectively mitigates data anomalies and non-stationarity.

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.