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Efficient prompt learning for traffic forecasting
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DOI：10.1007/s00778-026-00983-7.png)
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Abstract
En 中文
Accurate traffic prediction is essential for optimizing transportation systems, enhancing resource allocation, and improving overall urban administration. Spatio-temporal graph neural networks (GNNs) have achieved state-of-the-art performance and have been widely used in various spatio-temporal prediction scenarios. However, these prediction methods often exhibit low generalization ability, struggling with distribution shifts caused by spatio-temporal dynamics. To address this challenge, we propose an approach to enhance the generalization and adaptation of spatio-temporal GNNs through efficient prompting. Specifically, we introduce a lightweight and model-agnostic prompt tuning framework for spatio-temporal GNNs, named SimpleST. It facilitates adapting pre-trained spatio-temporal GNNs to novel distributions while keeping the model parameters fixed. This prompt mechanism reduces the overhead and complexity of adaptation, enabling efficient utilization of pre-trained models for out-of-distribution generalization. Extensive experiments conducted on five real-world urban spatio-temporal datasets demonstrate the superiority of our approach in terms of prediction accuracy and computational efficiency.
Keywords:
Efficient prompt learning
Traffic forecasting
Spatio-temporal GNNs
Journal
IF:
3.8
Papers:
81
Citations:
2.4K
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Cited Papers
Connecting the Dots: Multivariate Time Series Forecasting with Graph Neural Networks.png)
Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining
OAAI
Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data MiningIF0
OAAIShareSaveAST-GCN: Attribute-Augmented Spatiotemporal Graph Convolutional Network for Traffic ForecastingIEEE ACCESSOAAI
IEEE ACCESSIF3.6
OAAIShareSaveSelf-supervised Trajectory Representation Learning with Temporal Regularities and Travel SemanticsOAAI
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