With recent advances in sensing technologies, a myriad of spatio-temporal data has been generated and recorded in smart cities. Forecasting the evolution patterns of spatio-temporal data is an important yet demanding aspect of urban computing, which can enhance intelligent management decisions in various fields, including transportation, environment, climate, public safety, healthcare, and others. Traditional statistical and deep learning methods struggle to capture complex correlations in urban spatio-temporal data. To this end, Spatio-Temporal Graph Neural Networks (STGNN) have been proposed, achieving great promise in recent years. STGNNs enable the extraction of complex spatio-temporal dependencies by integrating graph neural networks (GNNs) and various temporal learning methods. In this manuscript, we provide a comprehensive survey on recent progress on STGNN technologies for predictive learning in urban computing. Firstly, we provide a brief introduction to the construction methods of spatio-temporal graph data and the prevalent deep-learning architectures used in STGNNs. We then sort out the primary application domains and specific predictive learning tasks based on existing literature. Afterward, we scrutinize the design of STGNNs and their combination with some advanced technologies in recent years. Finally, we conclude the limitations of existing research and suggest potential directions for future work.

翻译：随着传感技术的进步，智慧城市中产生了大量时空数据。预测时空数据的演化模式是城市计算中一个重要且具有挑战性的方面，能够增强交通、环境、气候、公共安全、医疗等各领域的智能管理决策。传统统计和深度学习方法难以捕捉城市时空数据中的复杂相关性。为此，研究者提出时空图神经网络（STGNN），近年来展现出巨大潜力。STGNN通过整合图神经网络（GNN）与各种时序学习方法，实现了对复杂时空依赖关系的提取。本文对近年来面向城市计算中预测学习的STGNN技术进展进行全面综述。首先，简要介绍时空图数据的构建方法及STGNN中常用的主流深度学习架构；其次，基于现有文献梳理主要应用领域及具体预测学习任务；随后，深入剖析STGNN的设计及近年来与先进技术的结合；最后，总结现有研究的局限性并提出未来潜在研究方向。