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In the era of information explosion, spatio-temporal data mining serves as acritical part of urban management. Considering the various fields demandingattention, e.g., traffic state, human activity, and social event, predictingmultiple spatio-temporal attributes simultaneously can alleviate regulatorypressure and foster smart city construction. However, current research can nothandle the spatio-temporal multi-attribute prediction well due to the complexrelationships between diverse attributes. The key challenge lies in how toaddress the common spatio-temporal patterns while tackling their distinctions.In this paper, we propose an effective solution for spatio-temporalmulti-attribute prediction, PromptST. We devise a spatio-temporal transformerand a parameter-sharing training scheme to address the common knowledge amongdifferent spatio-temporal attributes. Then, we elaborate a spatio-temporalprompt tuning strategy to fit the specific attributes in a lightweight manner.Through the pretrain and prompt tuning phases, our PromptST is able to enhancethe specific spatio-temoral characteristic capture by prompting the backbonemodel to fit the specific target attribute while maintaining the learned commonknowledge. Extensive experiments on real-world datasets verify that ourPromptST attains state-of-the-art performance. Furthermore, we also provePromptST owns good transferability on unseen spatio-temporal attributes, whichbrings promising application potential in urban computing. The implementationcode is available to ease reproducibility.

PromptST: Prompt-Enhanced Spatio-Temporal Multi-Attribute Prediction