# STGC-GNNs: A GNN-based traffic prediction framework with a spatial-temporal Granger causality graph

arXiv: https://arxiv.org/abs/2210.16789 · HTML: https://mlsanity.com/paper/2210.16789/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- Builds on 22 of its 79 references that are in the map.
- First posted on arXiv 30 Oct 2022.

## Builds on (most cited first, 22 of 22)

- [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)
- [GMAN: A Graph Multi-Attention Network for Traffic Prediction](https://mlsanity.com/paper/1911.08415/) (2019)
- [T-GCN: A Temporal Graph ConvolutionalNetwork for Traffic Prediction](https://mlsanity.com/paper/1811.05320/) (2018)
- [Spatial-Temporal Fusion Graph Neural Networks for Traffic Flow Forecasting](https://mlsanity.com/paper/2012.09641/) (2020)
- [Spatial-Temporal Graph ODE Networks for Traffic Flow Forecasting](https://mlsanity.com/paper/2106.12931/) (2021)
- [High-Dimensional Multivariate Forecasting with Low-Rank Gaussian Copula Processes](https://mlsanity.com/paper/1910.03002/) (2019)
- [Decoupled Dynamic Spatial-Temporal Graph Neural Network for Traffic Forecasting](https://mlsanity.com/paper/2206.09112/) (2022)
- [Multi-Range Attentive Bicomponent Graph Convolutional Network for Traffic Forecasting](https://mlsanity.com/paper/1911.12093/) (2019)
- [Learning Traffic as Images: A Deep Convolutional Neural Network for Large-Scale Transportation Network Speed Prediction](https://mlsanity.com/paper/1701.04245/) (2017)
- [Granger Causality: A Review and Recent Advances](https://mlsanity.com/paper/2105.02675/) (2021)
- [A3T-GCN: Attention Temporal Graph Convolutional Network for Traffic Forecasting](https://mlsanity.com/paper/2006.11583/) (2020)
- [Traffic Flow Forecasting with Spatial-Temporal Graph Diffusion Network](https://mlsanity.com/paper/2110.04038/) (2021)
- [Tensor Completion for Weakly-dependent Data on Graph for Metro Passenger Flow Prediction](https://mlsanity.com/paper/1912.05693/) (2019)
- [AST-GCN: Attribute-Augmented Spatiotemporal Graph Convolutional Network for Traffic Forecasting](https://mlsanity.com/paper/2011.11004/) (2020)
- [Curvature Graph Neural Network](https://mlsanity.com/paper/2106.15762/) (2021)
- [Regularized Graph Structure Learning with Semantic Knowledge for Multi-variates Time-Series Forecasting](https://mlsanity.com/paper/2210.06126/) (2022)
- [Latent Space Model for Road Networks to Predict Time-Varying Traffic](https://mlsanity.com/paper/1602.04301/) (2016)
- [Augmentation-Free Graph Contrastive Learning of Invariant-Discriminative Representations](https://mlsanity.com/paper/2210.08345/) (2022)
- [KST-GCN: A Knowledge-Driven Spatial-Temporal Graph Convolutional Network for Traffic Forecasting](https://mlsanity.com/paper/2011.14992/) (2020)
- [Potential Passenger Flow Prediction: A Novel Study for Urban Transportation Development](https://mlsanity.com/paper/1912.03440/) (2019)
- [Spatio-Temporal Latent Graph Structure Learning for Traffic Forecasting](https://mlsanity.com/paper/2202.12586/) (2022)

## Built on by (most cited first, 12 of 12)

- [AllSpark: A Multimodal Spatio-Temporal General Intelligence Model with Ten Modalities via Language as a Reference Framework](https://mlsanity.com/paper/2401.00546/) (2023)
- [LSTTN: A Long-Short Term Transformer-based Spatio-temporal Neural Network for Traffic Flow Forecasting](https://mlsanity.com/paper/2403.16495/) (2024)
- [PGT-I: Scaling Spatiotemporal GNNs with Memory-Efficient Distributed Training](https://mlsanity.com/paper/2507.11683/) (2025)
- [Towards Data-centric Machine Learning on Directed Graphs: a Survey](https://mlsanity.com/paper/2412.01849/) (2024)
- [Digital Twin-Driven Pavement Health Monitoring and Maintenance Optimization Using Graph Neural Networks](https://mlsanity.com/paper/2511.02957/) (2025)
- [SeFi-CD: A Semantic First Change Detection Paradigm That Can Detect Any Change You Want](https://mlsanity.com/paper/2407.09874/) (2024)
- [Incorporating graph neural network into route choice model](https://mlsanity.com/paper/2503.02315/) (2025)
- [Urban Incident Prediction with Graph Neural Networks: Integrating Government Ratings and Crowdsourced Reports](https://mlsanity.com/paper/2506.08740/) (2025)
- [A Multi-Scale Graph Learning Framework with Temporal Consistency Constraints for Financial Fraud Detection in Transaction Networks under Non-Stationary Conditions](https://mlsanity.com/paper/2603.14592/) (2026)
- [MSCT: Addressing Time-Varying Confounding with Marginal Structural Causal Transformer for Counterfactual Post-Crash Traffic Prediction](https://mlsanity.com/paper/2407.14065/) (2024)
- [The Geography of Transportation Cybersecurity: Visitor Flows, Industry Clusters, and Spatial Dynamics](https://mlsanity.com/paper/2505.08822/) (2025)
- [Structure-Guided Spatiotemporal Attention Graph Neural Network for Traffic Flow Prediction](https://mlsanity.com/paper/2608.14177/) (2026)

## Abstract (arXiv)

The key to traffic prediction is to accurately depict the temporal dynamics of traffic flow traveling in a road network, so it is important to model the spatial dependence of the road network. The essence of spatial dependence is to accurately describe how traffic information transmission is affected by other nodes in the road network, and the GNN-based traffic prediction model, as a benchmark for traffic prediction, has become the most common method for the ability to model spatial dependence by transmitting traffic information with the message passing mechanism. However, existing methods model a local and static spatial dependence, which cannot transmit the global-dynamic traffic information (GDTi) required for long-term prediction. The challenge is the difficulty of detecting the precise transmission of GDTi due to the uncertainty of individual transport, especially for long-term transmission. In this paper, we propose a new hypothesis: GDTi behaves macroscopically as a transmitting causal relationship (TCR) underlying traffic flow, which remains stable under dynamic changing traffic flow. We further propose spatial-temporal Granger causality (STGC) to express TCR, which models global and dynamic spatial dependence. To model global transmission, we model the causal order and causal lag of TCRs global transmission by a spatial-temporal alignment algorithm. To capture dynamic spatial dependence, we approximate the stable TCR underlying dynamic traffic flow by a Granger causality test. The experimental results on three backbone models show that using STGC to model the spatial dependence has better results than the original model for 45 min and 1 h long-term prediction.