Rethinking spatial-temporal contrastive learning for Urban traffic flow forecasting: multi-level augmentation framework
Abstract Graph neural networks integrating contrastive learning have attracted growing attention in urban traffic flow forecasting. However, most existing graph contrastive learning methods do not perform well in capturing local–global spatial dependencies or designing contrastive learning schemes f...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Springer
2024-12-01
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| Series: | Complex & Intelligent Systems |
| Subjects: | |
| Online Access: | https://doi.org/10.1007/s40747-024-01754-z |