Spatiotemporal learning has become a pivotal technique to enable urban intelligence. Traditional spatiotemporal models mostly focus on a specific task by assuming a same distribution between training and testing sets. However, given that urban systems are usually dynamic, multi-sourced with imbalanced data distributions, current specific task-specific models fail to generalize to new urban conditions and adapt to new domains without explicitly modeling interdependencies across various dimensions and types of urban data. To this end, we argue that there is an essential to propose a Continuous Multi-task Spatio-Temporal learning framework (CMuST) to empower collective urban intelligence, which reforms the urban spatiotemporal learning from single-domain to cooperatively multi-dimensional and multi-task learning. Specifically, CMuST proposes a new multi-dimensional spatiotemporal interaction network (MSTI) to allow cross-interactions between context and main observations as well as self-interactions within spatial and temporal aspects to be exposed, which is also the core for capturing task-level commonality and personalization. To ensure continuous task learning, a novel Rolling Adaptation training scheme (RoAda) is devised, which not only preserves task uniqueness by constructing data summarization-driven task prompts, but also harnesses correlated patterns among tasks by iterative model behavior modeling. We further establish a benchmark of three cities for multi-task spatiotemporal learning, and empirically demonstrate the superiority of CMuST via extensive evaluations on these datasets. The impressive improvements on both few-shot streaming data and new domain tasks against existing SOAT methods are achieved. Code is available at https://github.com/DILab-USTCSZ/CMuST.

翻译：时空学习已成为实现城市智能的关键技术。传统时空模型大多通过假设训练集与测试集服从相同分布来专注于特定任务。然而，鉴于城市系统通常具有动态性、多源性和数据分布不平衡的特点，当前这些特定任务模型若未显式建模不同维度与类型城市数据间的相互依赖关系，则难以泛化至新的城市条件并适应新领域。为此，我们认为有必要提出一种连续多任务时空学习框架（CMuST）以赋能集体城市智能，该框架将城市时空学习从单领域范式革新为协同的多维度多任务学习。具体而言，CMuST提出了一种新的多维度时空交互网络（MSTI），该网络不仅允许上下文与主体观测之间的跨维度交互，同时显式建模空间与时间维度内的自交互，这也是捕捉任务级共性特征与个性化特征的核心机制。为确保连续任务学习，我们设计了一种新颖的滚动自适应训练方案（RoAda），该方案不仅通过构建数据摘要驱动的任务提示来保持任务独特性，还通过迭代式模型行为建模来利用任务间的关联模式。我们进一步构建了涵盖三个城市的多任务时空学习基准数据集，并通过在这些数据集上的广泛实验验证了CMuST的优越性。相较于现有先进方法，本框架在少样本流数据及新领域任务上均取得了显著提升。代码发布于 https://github.com/DILab-USTCSZ/CMuST。