Multi-Grained Temporal-Spatial Graph Learning for Stable Traffic Flow Forecasting
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| Main Authors: | , , , , , , , |
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| Format: | Preprint |
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2025
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| _version_ | 1866915422907924480 |
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| author | Lin, Zhenan Lai, Yuni Lo, Wai Lun Hsung, Richard Tai-Chiu Tsang, Harris Sik-Ho Xue, Xiaoyu Zhou, Kai Zhu, Yulin |
| author_facet | Lin, Zhenan Lai, Yuni Lo, Wai Lun Hsung, Richard Tai-Chiu Tsang, Harris Sik-Ho Xue, Xiaoyu Zhou, Kai Zhu, Yulin |
| contents | Time-evolving traffic flow forecasting are playing a vital role in intelligent transportation systems and smart cities. However, the dynamic traffic flow forecasting is a highly nonlinear problem with complex temporal-spatial dependencies. Although the existing methods has provided great contributions to mine the temporal-spatial patterns in the complex traffic networks, they fail to encode the globally temporal-spatial patterns and are prone to overfit on the pre-defined geographical correlations, and thus hinder the model's robustness on the complex traffic environment. To tackle this issue, in this work, we proposed a multi-grained temporal-spatial graph learning framework to adaptively augment the globally temporal-spatial patterns obtained from a crafted graph transformer encoder with the local patterns from the graph convolution by a crafted gated fusion unit with residual connection techniques. Under these circumstances, our proposed model can mine the hidden global temporal-spatial relations between each monitor stations and balance the relative importance of local and global temporal-spatial patterns. Experiment results demonstrate the strong representation capability of our proposed method and our model consistently outperforms other strong baselines on various real-world traffic networks. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2508_00884 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Multi-Grained Temporal-Spatial Graph Learning for Stable Traffic Flow Forecasting Lin, Zhenan Lai, Yuni Lo, Wai Lun Hsung, Richard Tai-Chiu Tsang, Harris Sik-Ho Xue, Xiaoyu Zhou, Kai Zhu, Yulin Machine Learning Artificial Intelligence Time-evolving traffic flow forecasting are playing a vital role in intelligent transportation systems and smart cities. However, the dynamic traffic flow forecasting is a highly nonlinear problem with complex temporal-spatial dependencies. Although the existing methods has provided great contributions to mine the temporal-spatial patterns in the complex traffic networks, they fail to encode the globally temporal-spatial patterns and are prone to overfit on the pre-defined geographical correlations, and thus hinder the model's robustness on the complex traffic environment. To tackle this issue, in this work, we proposed a multi-grained temporal-spatial graph learning framework to adaptively augment the globally temporal-spatial patterns obtained from a crafted graph transformer encoder with the local patterns from the graph convolution by a crafted gated fusion unit with residual connection techniques. Under these circumstances, our proposed model can mine the hidden global temporal-spatial relations between each monitor stations and balance the relative importance of local and global temporal-spatial patterns. Experiment results demonstrate the strong representation capability of our proposed method and our model consistently outperforms other strong baselines on various real-world traffic networks. |
| title | Multi-Grained Temporal-Spatial Graph Learning for Stable Traffic Flow Forecasting |
| topic | Machine Learning Artificial Intelligence |
| url | https://arxiv.org/abs/2508.00884 |