The objective of traffic prediction is to accurately forecast and analyze the dynamics of transportation patterns, considering both space and time. However, the presence of distribution shift poses a significant challenge in this field, as existing models struggle to generalize well when faced with test data that significantly differs from the training distribution. To tackle this issue, this paper introduces a simple and universal spatio-temporal prompt-tuning framework-FlashST, which adapts pre-trained models to the specific characteristics of diverse downstream datasets, improving generalization in diverse traffic prediction scenarios. Specifically, the FlashST framework employs a lightweight spatio-temporal prompt network for in-context learning, capturing spatio-temporal invariant knowledge and facilitating effective adaptation to diverse scenarios. Additionally, we incorporate a distribution mapping mechanism to align the data distributions of pre-training and downstream data, facilitating effective knowledge transfer in spatio-temporal forecasting. Empirical evaluations demonstrate the effectiveness of our FlashST across different spatio-temporal prediction tasks using diverse urban datasets. Code is available at https://github.com/HKUDS/FlashST.

翻译：交通预测的目标是准确预测和分析交通模式的时空动态。然而，分布偏移的存在给该领域带来了重大挑战，因为当测试数据与训练分布存在显著差异时，现有模型难以良好地泛化。为解决此问题，本文提出了一种简单通用的时空提示调优框架——FlashST，该框架使预训练模型能够适应多样化下游数据集的特定特征，从而提升其在多种交通预测场景中的泛化能力。具体而言，FlashST框架采用一个轻量级的时空提示网络进行上下文学习，以捕获时空不变知识，并促进对不同场景的有效适应。此外，我们引入了一种分布映射机制，以对齐预训练数据与下游数据的分布，从而促进时空预测中有效的知识迁移。实证评估表明，我们的FlashST框架在使用多种城市数据集的不同时空预测任务中均表现出有效性。代码可在 https://github.com/HKUDS/FlashST 获取。