UniFlow：用于统一城市时空流预测的基础模型
UniFlow: A Foundation Model for Unified Urban Spatio-Temporal Flow Prediction
AI总结：
本文提出统一城市时空流预测的基础模型UniFlow，通过多视图时空分块机制统一网格与图数据，结合时空transformer架构与ST-MRA记忆检索增强技术，在数据有限场景下显著提升了预测性能。
摘要：
城市时空流预测，包括交通流和人流，对于优化城市基础设施以及管理交通和应急响应至关重要。传统方法依赖于为基于网格的数据（将城市表示为均匀单元）或基于图的数据（将城市建模为节点和边的网络）量身定制的独立模型。在本文中，我们构建了UniFlow，这是一个用于通用城市流预测的基础模型，统一了基于网格和基于图的数据。我们首先设计了一种多视图时空分块机制，将不同数据标准化为一致的序列格式，然后引入时空transformer架构来捕捉复杂的相关性和动态。为了利用不同数据类型之间共享的时空模式并促进有效的交叉学习，我们提出了时空记忆检索增强（ST-MRA）。通过创建结构化记忆模块来存储共享的时空模式，ST-MRA通过自适应记忆检索来增强预测。大量实验表明，UniFlow在基于网格和基于图的流预测中均优于现有模型，在数据可用性有限的场景中表现尤为出色，展示了其卓越的性能和广泛的适用性。数据集和代码实现已在https://github.com/YuanYuan98/UniFlow上发布。
英文摘要：
Urban spatio-temporal flow prediction, encompassing traffic flows and crowd flows, is crucial for optimizing city infrastructure and managing traffic and emergency responses. Traditional approaches have relied on separate models tailored to either grid-based data, representing cities as uniform cells, or graph-based data, modeling cities as networks of nodes and edges. In this paper, we build UniFlow, a foundational model for general urban flow prediction that unifies both grid-based and graphbased data. We first design a multi-view spatio-temporal patching mechanism to standardize different data into a consistent sequential format and then introduce a spatio-temporal transformer architecture to capture complex correlations and dynamics. To leverage shared spatio-temporal patterns across different data types and facilitate effective cross-learning, we propose SpatioTemporal Memory Retrieval Augmentation (ST-MRA). By creating structured memory modules to store shared spatio-temporal patterns, ST-MRA enhances predictions through adaptive memory retrieval. Extensive experiments demonstrate that UniFlow outperforms existing models in both grid-based and graph-based flow prediction, excelling particularly in scenarios with limited data availability, showcasing its superior performance and broad applicability. The datasets and code implementation have been released on https://github.com/YuanYuan98/UniFlow.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
