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TSFusion: Multi-Grained Temporal-Spatial Graph Learning for Stable Traffic Flow Forecasting
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DOI：10.1049/cmu2.70150.png)
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Abstract
En 中文
Time-evolving traffic flow forecasting is 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 have 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 overfitting on the pre-defined geographical correlations, and thus hinder the model's robustness in the complex traffic environment. To tackle this issue, in this work, we proposed TSFusion, 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 station 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 (more than 11.5% for MAE) other strong baselines on various real-world traffic networks.
Keywords:
graph theory
intelligent transportation systems
learning (artificial intelligence)
Journal
IF:
1.6
Papers:
130
Citations:
2.9K
