Urban anomaly predictions, such as traffic accident prediction and crime prediction, are of vital importance to smart city security and maintenance. Existing methods typically use deep learning to capture the intra-dependencies in spatial and temporal dimensions. However, numerous key challenges remain unsolved, for instance, sparse zero-inflated data due to urban anomalies occurring with low frequency (which can lead to poor performance on real-world datasets), and both intra- and inter-dependencies of abnormal patterns across spatial, temporal, and semantic dimensions. Moreover, a unified approach to predict multiple kinds of anomaly is left to explore. In this paper, we propose STS to jointly capture the intra- and inter-dependencies between the patterns and the influential factors in three dimensions. Further, we use a multi-task prediction module with a customized loss function to solve the zero-inflated issue. To verify the effectiveness of the model, we apply it to two urban anomaly prediction tasks, crime prediction and traffic accident risk prediction, respectively. Experiments on two application scenarios with four real-world datasets demonstrate the superiority of STS, which outperforms state-of-the-art methods in the mean absolute error and the root mean square error by 37.88% and 18.10% on zero-inflated datasets, and, 60.32% and 37.28% on non-zero datasets, respectively.

翻译：城市异常预测（如交通事故预测和犯罪预测）对智慧城市的安全与维护至关重要。现有方法通常利用深度学习捕捉空间和时间维度的内部依赖关系。然而，仍有诸多关键挑战尚未解决，例如：城市异常事件发生频率低导致的数据稀疏零膨胀问题（进而影响真实数据集上的预测性能），以及异常模式在空间、时间和语义维度上的内部与跨维度依赖关系。此外，如何实现统一框架预测多种类型异常仍有待探索。本文提出STS模型，联合捕捉三个维度上模式与影响因素间的内部与跨维度依赖关系。进一步，我们采用带有定制损失函数的多任务预测模块来解决零膨胀问题。为验证模型有效性，我们将其分别应用于犯罪预测和交通事故风险预测两项城市异常预测任务。在涵盖真实数据集的四个应用场景上的实验表明，STS具有优越性：在零膨胀数据集上，其平均绝对误差和均方根误差分别比当前最优方法降低37.88%和18.10%；在非零数据集上分别降低60.32%和37.28%。