Urban spatio-temporal prediction is crucial for informed decision-making, such as traffic management, resource optimization, and emergence response. Despite remarkable breakthroughs in pretrained natural language models that enable one model to handle diverse tasks, a universal solution for spatio-temporal prediction remains challenging Existing prediction approaches are typically tailored for specific spatio-temporal scenarios, requiring task-specific model designs and extensive domain-specific training data. In this study, we introduce UniST, a universal model designed for general urban spatio-temporal prediction across a wide range of scenarios. Inspired by large language models, UniST achieves success through: (i) utilizing diverse spatio-temporal data from different scenarios, (ii) effective pre-training to capture complex spatio-temporal dynamics, (iii) knowledge-guided prompts to enhance generalization capabilities. These designs together unlock the potential of building a universal model for various scenarios Extensive experiments on more than 20 spatio-temporal scenarios demonstrate UniST's efficacy in advancing state-of-the-art performance, especially in few-shot and zero-shot prediction. The datasets and code implementation are released on https://github.com/tsinghua-fib-lab/UniST.

翻译：城市时空预测对于交通管理、资源优化和应急响应等科学决策至关重要。尽管预训练自然语言模型取得了显著突破，使得单一模型能够处理多样化任务，但针对时空预测的通用解决方案仍具挑战性。现有预测方法通常针对特定时空场景定制，需要任务特定的模型设计和大量领域专用训练数据。本研究提出UniST，一种为广泛场景下的通用城市时空预测设计的统一模型。受大语言模型启发，UniST通过以下机制实现突破：(i) 利用多源异构时空数据进行跨场景训练，(ii) 通过高效预训练捕捉复杂时空动态特征，(iii) 采用知识引导的提示机制增强泛化能力。这些设计共同释放了构建跨场景通用模型的潜力。在超过20种时空场景上的大规模实验表明，UniST在推进预测性能前沿方面成效显著，尤其在少样本与零样本预测场景中表现突出。完整数据集与代码实现已发布于https://github.com/tsinghua-fib-lab/UniST。