In the era of information explosion, spatio-temporal data mining serves as a
critical part of urban management. Considering the various fields demanding
attention, e.g., traffic state, human activity, and social event, predicting
multiple spatio-temporal attributes simultaneously can alleviate regulatory
pressure and foster smart city construction. However, current research can not
handle the spatio-temporal multi-attribute prediction well due to the complex
relationships between diverse attributes. The key challenge lies in how to
address the common spatio-temporal patterns while tackling their distinctions.
In this paper, we propose an effective solution for spatio-temporal
multi-attribute prediction, PromptST. We devise a spatio-temporal transformer
and a parameter-sharing training scheme to address the common knowledge among
different spatio-temporal attributes. Then, we elaborate a spatio-temporal
prompt tuning strategy to fit the specific attributes in a lightweight manner.
Through the pretrain and prompt tuning phases, our PromptST is able to enhance
the specific spatio-temoral characteristic capture by prompting the backbone
model to fit the specific target attribute while maintaining the learned common
knowledge. Extensive experiments on real-world datasets verify that our
PromptST attains state-of-the-art performance. Furthermore, we also prove
PromptST owns good transferability on unseen spatio-temporal attributes, which
brings promising application potential in urban computing. The implementation
code is available to ease reproducibility.

在信息爆炸时代，时空数据挖掘作为城市管理的关键部分，通过同时预测多个时空属性，可以减轻监管压力，推动智能城市建设。本文提出了一种针对时空多属性预测的有效解决方案，PromptST，通过时空变换器和参数共享训练机制来处理不同时空属性之间的共同知识，以及一种轻量级时空指令调优策略来适应特定属性。通过预训练和指令调优阶段，PromptST 能够增强对特定时空特征的捕捉能力，并保持已学习的共同知识。在真实数据集上进行的广泛实验证明，PromptST 达到了最先进的性能。此外，我们还证明 PromptST 在未见过的时空属性上具有很高的可迁移性，为城市计算带来了有前景的应用潜力。实现代码可用于方便的复现。