  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.
