As deep learning technology advances and more urban spatial-temporal data accumulates, an increasing number of deep learning models are being proposed to solve urban spatial-temporal prediction problems. However, there are limitations in the existing field, including open-source data being in various formats and difficult to use, few papers making their code and data openly available, and open-source models often using different frameworks and platforms, making comparisons challenging. A standardized framework is urgently needed to implement and evaluate these methods. To address these issues, we provide a comprehensive review of urban spatial-temporal prediction and propose a unified storage format for spatial-temporal data called atomic files. We also propose LibCity, an open-source library that offers researchers a credible experimental tool and a convenient development framework. In this library, we have reproduced 65 spatial-temporal prediction models and collected 55 spatial-temporal datasets, allowing researchers to conduct comprehensive experiments conveniently. Using LibCity, we conducted a series of experiments to validate the effectiveness of different models and components, and we summarized promising future technology developments and research directions for spatial-temporal prediction. By enabling fair model comparisons, designing a unified data storage format, and simplifying the process of developing new models, LibCity is poised to make significant contributions to the spatial-temporal prediction field.

翻译：随着深度学习技术的进步和城市时空数据的不断积累，越来越多的深度学习模型被提出用于解决城市时空预测问题。然而，现有领域存在局限性，包括开源数据格式多样且难以使用、少数论文公开其代码和数据、以及开源模型常采用不同框架和平台导致比较困难。亟需一个标准化框架来实施和评估这些方法。为解决这些问题，我们对城市时空预测进行了全面综述，并提出了一种称为原子文件的时空数据统一存储格式。我们还提出了LibCity，一个开源库，为研究人员提供可靠的实验工具和便捷的开发框架。在该库中，我们复现了65个时空预测模型并收集了55个时空数据集，使研究人员能够方便地进行全面实验。利用LibCity，我们开展了一系列实验以验证不同模型和组件的有效性，并总结了未来时空预测领域有前景的技术发展和研究方向。通过实现公平的模型比较、设计统一的数据存储格式和简化新模型的开发流程，LibCity有望为时空预测领域做出重要贡献。