Urban grid prediction can be applied to many classic spatial-temporal prediction tasks such as air quality prediction, crowd density prediction, and traffic flow prediction, which is of great importance to smart city building. In light of its practical values, many methods have been developed for it and have achieved promising results. Despite their successes, two main challenges remain open: a) how to well capture the global dependencies and b) how to effectively model the multi-scale spatial-temporal correlations? To address these two challenges, we propose a novel method—DeepMeshCity, with a carefully-designed Self-Attention Citywide Grid Learner (SA-CGL) block comprising of a self-attention unit and a Citywide Grid Learner (CGL) unit. The self-attention block aims to capture the global spatial dependencies, and the CGL unit is responsible for learning the spatial-temporal correlations. In particular, a multi-scale memory unit is proposed to traverse all stacked SA-CGL blocks along a zigzag path to capture the multi-scale spatial-temporal correlations. In addition, we propose to initialize the single-scale memory units and the multi-scale memory units by using the corresponding ones in the previous fragment stack, so as to speed up the model training. We evaluate the performance of our proposed model by comparing with several state-of-the-art methods on four real-world datasets for two urban grid prediction applications. The experimental results verify the superiority of DeepMeshCity over the existing ones. The code is available at https://github.com/ILoveStudying/DeepMeshCity.

城市网格预测可以应用于空气质量预测、人群密度预测、交通流量预测等许多经典时空预测任务，对智慧城市建设具有重要意义。鉴于其实用价值，人们开发了许多方法并取得了可喜的结果。尽管取得了成功，但仍然存在两个主要挑战：a）如何很好地捕获全局依赖性；b）如何有效地建模多尺度时空相关性？为了解决这两个挑战，我们提出了一种新方法——深网城市，具有精心设计的自注意力城市范围网格学习器（SA-CGL）由自注意力单元和城市网格学习器（CGL） 单元。自注意力块旨在捕获全局空间依赖性，并且CGL单元负责学习时空相关性。特别地，提出了一种多尺度存储单元来遍历所有堆叠的SA-CGL沿着锯齿形路径的块来捕获多尺度的时空相关性。此外，我们建议使用先前片段堆栈中的相应单元来初始化单尺度记忆单元和多尺度记忆单元，以加快模型训练速度。我们通过与两个城市网格预测应用的四个现实世界数据集上的几种最先进的方法进行比较来评估我们提出的模型的性能。实验结果验证了DeepMeshCity相对于现有的优越性。代码可在 https://github.com/ILoveStuying/DeepMeshCity 获取。