# Variational Mode-Driven Graph Convolutional Network for Spatiotemporal Traffic Forecasting

arXiv: https://arxiv.org/abs/2408.16191 · HTML: https://mlsanity.com/paper/2408.16191/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 19 of its 63 references that are in the map.
- First posted on arXiv 29 Aug 2024.

## Builds on (most cited first, 19 of 19)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Convolutional Neural Networks on Graphs with Fast Localized Spectral Filtering](https://mlsanity.com/paper/1606.09375/) (2016)
- [Deformable Convolutional Networks](https://mlsanity.com/paper/1703.06211/) (2017)
- [Spatio-Temporal Graph Convolutional Networks: A Deep Learning Framework for Traffic Forecasting](https://mlsanity.com/paper/1709.04875/) (2017)
- [Graph WaveNet for Deep Spatial-Temporal Graph Modeling](https://mlsanity.com/paper/1906.00121/) (2019)
- [Adaptive Graph Convolutional Recurrent Network for Traffic Forecasting](https://mlsanity.com/paper/2007.02842/) (2020)
- [GMAN: A Graph Multi-Attention Network for Traffic Prediction](https://mlsanity.com/paper/1911.08415/) (2019)
- [Spatial-Temporal Graph ODE Networks for Traffic Flow Forecasting](https://mlsanity.com/paper/2106.12931/) (2021)
- [Dynamic Graph Convolutional Recurrent Network for Traffic Prediction: Benchmark and Solution](https://mlsanity.com/paper/2104.14917/) (2021)
- [Traffic Graph Convolutional Recurrent Neural Network: A Deep Learning Framework for Network-Scale Traffic Learning and Forecasting](https://mlsanity.com/paper/1802.07007/) (2018)
- [Decoupled Dynamic Spatial-Temporal Graph Neural Network for Traffic Forecasting](https://mlsanity.com/paper/2206.09112/) (2022)
- [LargeST: A Benchmark Dataset for Large-Scale Traffic Forecasting](https://mlsanity.com/paper/2306.08259/) (2023)
- [Deep Reinforcement Learning for Intelligent Transportation Systems: A Survey](https://mlsanity.com/paper/2005.00935/) (2020)
- [Multi-Range Attentive Bicomponent Graph Convolutional Network for Traffic Forecasting](https://mlsanity.com/paper/1911.12093/) (2019)
- [Towards Explainable Traffic Flow Prediction with Large Language Models](https://mlsanity.com/paper/2404.02937/) (2024)
- [RPMixer: Shaking Up Time Series Forecasting with Random Projections for Large Spatial-Temporal Data](https://mlsanity.com/paper/2402.10487/) (2024)
- [Graph Neural Rough Differential Equations for Traffic Forecasting](https://mlsanity.com/paper/2303.10909/) (2023)
- [Urban Regional Function Guided Traffic Flow Prediction](https://mlsanity.com/paper/2303.09789/) (2023)
- [Triplet Attention Transformer for Spatiotemporal Predictive Learning](https://mlsanity.com/paper/2310.18698/) (2023)

## Built on by (most cited first, 2 of 2)

- [Robust and Noise-resilient Long-Term Prediction of Spatiotemporal Data Using Variational Mode Graph Neural Networks with 3D Attention](https://mlsanity.com/paper/2504.06660/) (2025)
- [Robust Spatiotemporal Forecasting Using Adaptive Deep-Unfolded Variational Mode Decomposition](https://mlsanity.com/paper/2509.00703/) (2025)

## Abstract (arXiv)

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.