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

2025-06-30

AI Summary

To address challenges in traffic speed forecasting—including difficult spatiotemporal feature fusion, non-stationarity of historical data, and severe anomaly interference—this paper proposes the Graph Fusion Enhancement Network (GFEN). GFEN introduces a trainable topology–data joint fusion mechanism, integrates *k*-order differencing to jointly model non-stationarity and anomaly robustness, and synergistically captures multi-scale spatiotemporal dependencies via attention-guided graph neural networks. The architecture adaptively smooths anomalies and dynamically suppresses noise, significantly improving model robustness and convergence efficiency. Extensive experiments on multiple real-world traffic datasets demonstrate that GFEN achieves an average 6.3% improvement in prediction accuracy over state-of-the-art methods and converges approximately twice as fast as comparable hybrid models, thereby enhancing both accuracy and stability of traffic forecasting systems.

Technology Category

• Machine Learning: Graph-based Machine Learning
• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
• Knowledge Representation and Reasoning: Geometric, Spatial, and Temporal Reasoning

Application Category

• Graph Algorithms and Modeling for the Web: Graph neural networks and deep learning approaches for Web-related graphs
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Search and Retrieval-Augmented AI: Efficiency and scalability of Web search engines

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.

Problem

• Integrating spatial-temporal features in traffic prediction
• Adapting to non-stationary and anomalous traffic data
• Improving accuracy and convergence in speed prediction

Innovation

• Topological spatiotemporal graph fusion technique
• Hybrid k-th order difference and attention framework
• Trainable multi-scale spatiotemporal feature modeling