STMGF: An Effective Spatial-Temporal Multi-Granularity Framework for Traffic Forecasting
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arXiv
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| Main Authors: | , , , , , |
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| Format: | Preprint |
| Published: |
2024
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| _version_ | 1866914744903925760 |
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| author | Zhao, Zhengyang Yuan, Haitao Jiang, Nan Chen, Minxiao Liu, Ning Li, Zengxiang |
| author_facet | Zhao, Zhengyang Yuan, Haitao Jiang, Nan Chen, Minxiao Liu, Ning Li, Zengxiang |
| contents | Accurate Traffic Prediction is a challenging task in intelligent transportation due to the spatial-temporal aspects of road networks. The traffic of a road network can be affected by long-distance or long-term dependencies where existing methods fall short in modeling them. In this paper, we introduce a novel framework known as Spatial-Temporal Multi-Granularity Framework (STMGF) to enhance the capture of long-distance and long-term information of the road networks. STMGF makes full use of different granularity information of road networks and models the long-distance and long-term information by gathering information in a hierarchical interactive way. Further, it leverages the inherent periodicity in traffic sequences to refine prediction results by matching with recent traffic data. We conduct experiments on two real-world datasets, and the results demonstrate that STMGF outperforms all baseline models and achieves state-of-the-art performance. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2404_05774 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | STMGF: An Effective Spatial-Temporal Multi-Granularity Framework for Traffic Forecasting Zhao, Zhengyang Yuan, Haitao Jiang, Nan Chen, Minxiao Liu, Ning Li, Zengxiang Machine Learning Artificial Intelligence Accurate Traffic Prediction is a challenging task in intelligent transportation due to the spatial-temporal aspects of road networks. The traffic of a road network can be affected by long-distance or long-term dependencies where existing methods fall short in modeling them. In this paper, we introduce a novel framework known as Spatial-Temporal Multi-Granularity Framework (STMGF) to enhance the capture of long-distance and long-term information of the road networks. STMGF makes full use of different granularity information of road networks and models the long-distance and long-term information by gathering information in a hierarchical interactive way. Further, it leverages the inherent periodicity in traffic sequences to refine prediction results by matching with recent traffic data. We conduct experiments on two real-world datasets, and the results demonstrate that STMGF outperforms all baseline models and achieves state-of-the-art performance. |
| title | STMGF: An Effective Spatial-Temporal Multi-Granularity Framework for Traffic Forecasting |
| topic | Machine Learning Artificial Intelligence |
| url | https://arxiv.org/abs/2404.05774 |