Variational Mode-Driven Graph Convolutional Network for Spatiotemporal Traffic Forecasting

• URL: http://arxiv.org/abs/2408.16191v3
• Date: Mon, 21 Jul 2025 06:53:30 GMT
• Title: Variational Mode-Driven Graph Convolutional Network for Spatiotemporal Traffic Forecasting
• Authors: Osama Ahmad, Lukas Wesemann, Fabian Waschkowski, Zubair Khalid,
• Abstract summary: We propose a framework that decomposes non-stationary signals into interpretable modes using variation mode decomposition (VMD) and a neural network for future state.<n>The proposed two-stage design yields significant accuracy gains while providing frequency-level interpretability with demonstrated superior performance on the LargeST dataset for both short-term and long-term forecasting tasks.
• Score: 9.24965746838578
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: This paper focuses on spatiotemporal (ST) traffic prediction using graph neural networks (GNNs). Given that ST data comprises non-stationary and complex temporal patterns, interpreting and predicting such trends is inherently challenging. Representing ST data in decomposed modes helps infer underlying behavior and assess the impact of noise on predictive performance. We propose a framework that decomposes ST data into interpretable modes using variational mode decomposition (VMD) and processes them through a neural network for future state forecasting. Unlike existing graph-based traffic forecasters that operate directly on raw or aggregated time series, the proposed hybrid approach, termed the Variational Mode Graph Convolutional Network (VMGCN), first decomposes non-stationary signals into interpretable variational modes by determining the optimal mode count via reconstruction-loss minimization and then learns both intramode and cross-mode spatiotemporal dependencies through a novel attention-augmented GCN. Additionally, we analyze the significance of each mode and the effect of bandwidth constraints on multi-horizon traffic flow predictions. The proposed two-stage design yields significant accuracy gains while providing frequency-level interpretability with demonstrated superior performance on the LargeST dataset for both short-term and long-term forecasting tasks. The implementation is publicly available on https://github.com/OsamaAhmad369/VMGCN.

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