# Bayesian Spatio-Temporal Graph Convolutional Network for Traffic Forecasting

arXiv: https://arxiv.org/abs/2010.07498 · HTML: https://mlsanity.com/paper/2010.07498/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 15 Oct 2020.

## Built on by (most cited first, 5 of 5)

- [Graph Neural Network for Traffic Forecasting: A Survey](https://mlsanity.com/paper/2101.11174/) (2021)
- [Spatio-Temporal Graph Neural Networks for Predictive Learning in Urban Computing: A Survey](https://mlsanity.com/paper/2303.14483/) (2023)
- [Network-level Safety Metrics for Overall Traffic Safety Assessment: A Case Study](https://mlsanity.com/paper/2201.13229/) (2022)
- [Probabilistic modeling of lake surface water temperature using a Bayesian spatio-temporal graph convolutional neural network](https://mlsanity.com/paper/2109.13235/) (2021)
- [Unveiling Stochasticity: Universal Multi-modal Probabilistic Modeling for Traffic Forecasting](https://mlsanity.com/paper/2604.16084/) (2026)

## Abstract (arXiv)

In traffic forecasting, graph convolutional networks (GCNs), which model traffic flows as spatio-temporal graphs, have achieved remarkable performance. However, existing GCN-based methods heuristically define the graph structure as the physical topology of the road network, ignoring potential dependence of the graph structure over traffic data. And the defined graph structure is deterministic, which lacks investigation of uncertainty. In this paper, we propose a Bayesian Spatio-Temporal Graph Convolutional Network (BSTGCN) for traffic prediction. The graph structure in our network is learned from the physical topology of the road network and traffic data in an end-to-end manner, which discovers a more accurate description of the relationship among traffic flows. Moreover, a parametric generative model is proposed to represent the graph structure, which enhances the generalization capability of GCNs. We verify the effectiveness of our method on two real-world datasets, and the experimental results demonstrate that BSTGCN attains superior performance compared with state-of-the-art methods.