# STGAtt: A Spatial-Temporal Unified Graph Attention Network for Traffic Flow Forecasting

arXiv: https://arxiv.org/abs/2508.16685 · HTML: https://mlsanity.com/paper/2508.16685/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 15 of its 50 references that are in the map.
- First posted on arXiv 21 Aug 2025.

## Builds on (most cited first, 15 of 15)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Swin Transformer: Hierarchical Vision Transformer using Shifted Windows](https://mlsanity.com/paper/2103.14030/) (2021)
- [Sequence to Sequence Learning with Neural Networks](https://mlsanity.com/paper/1409.3215/) (2014)
- [Informer: Beyond Efficient Transformer for Long Sequence Time-Series Forecasting](https://mlsanity.com/paper/2012.07436/) (2020)
- [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 Transformer Networks for Traffic Flow Forecasting](https://mlsanity.com/paper/2001.02908/) (2020)
- [Multi-Range Attentive Bicomponent Graph Convolutional Network for Traffic Forecasting](https://mlsanity.com/paper/1911.12093/) (2019)
- [KST-GCN: A Knowledge-Driven Spatial-Temporal Graph Convolutional Network for Traffic Forecasting](https://mlsanity.com/paper/2011.14992/) (2020)
- [Adaptive Graph Spatial-Temporal Transformer Network for Traffic Flow Forecasting](https://mlsanity.com/paper/2207.05064/) (2022)
- [STS-CCL: Spatial-Temporal Synchronous Contextual Contrastive Learning for Urban Traffic Forecasting](https://mlsanity.com/paper/2307.02507/) (2023)
- [Enhancing Traffic Flow Prediction using Outlier-Weighted AutoEncoders: Handling Real-Time Changes](https://mlsanity.com/paper/2312.16596/) (2023)

## Abstract (arXiv)

Accurate and timely traffic flow forecasting is crucial for intelligent transportation systems. This paper presents a novel deep learning model, the Spatial-Temporal Unified Graph Attention Network (STGAtt). By leveraging a unified graph representation and an attention mechanism, STGAtt effectively captures complex spatial-temporal dependencies. Unlike methods relying on separate spatial and temporal dependency modeling modules, STGAtt directly models correlations within a Spatial-Temporal Unified Graph, dynamically weighing connections across both dimensions. To further enhance its capabilities, STGAtt partitions traffic flow observation signal into neighborhood subsets and employs a novel exchanging mechanism, enabling effective capture of both short-range and long-range correlations. Extensive experiments on the PEMS-BAY and SHMetro datasets demonstrate STGAtt's superior performance compared to state-of-the-art baselines across various prediction horizons. Visualization of attention weights confirms STGAtt's ability to adapt to dynamic traffic patterns and capture long-range dependencies, highlighting its potential for real-world traffic flow forecasting applications.