Efficient Prompt Learning for Traffic Forecasting

• URL: http://arxiv.org/abs/2605.08273v1
• Date: Fri, 08 May 2026 03:45:51 GMT
• Title: Efficient Prompt Learning for Traffic Forecasting
• Abstract summary: We introduce a lightweight and model-agnostic prompt tuning framework for adapting pre-trained-temporal GNNs, named SimpleST.<n>It reduces the overhead and complexity of adaptation, enabling efficient utilization of pre-trained models for out-distribution generalization.
• Score: 78.86991846411793
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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