STGAtt: A Spatial-Temporal Unified Graph Attention Network for Traffic Flow Forecasting
Fuente:
arXiv
Saved in:
| Main Authors: | , , , , , |
|---|---|
| Format: | Preprint |
| Published: |
2025
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866912549612552192 |
|---|---|
| author | Liang, Zhuding Cui, Jianxun Zeng, Qingshuang Liu, Feng Filipovic, Nenad Geroski, Tijana |
| author_facet | Liang, Zhuding Cui, Jianxun Zeng, Qingshuang Liu, Feng Filipovic, Nenad Geroski, Tijana |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2508_16685 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | STGAtt: A Spatial-Temporal Unified Graph Attention Network for Traffic Flow Forecasting Liang, Zhuding Cui, Jianxun Zeng, Qingshuang Liu, Feng Filipovic, Nenad Geroski, Tijana Machine Learning Artificial Intelligence 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. |
| title | STGAtt: A Spatial-Temporal Unified Graph Attention Network for Traffic Flow Forecasting |
| topic | Machine Learning Artificial Intelligence |
| url | https://arxiv.org/abs/2508.16685 |