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积累，越来越多的深度学习模型被提出以解决城市时空预测问题。然而，当前领域存在若干局限，包括开源数据格式多样且难以使用、少量论文公开其代码与数据，以及开源模型常基于不同框架与平台导致比较困难。亟需一个标准化框架来实现和评估这些方法。为解决这些问题，我们对城市时空预测进行了全面综述，并提出了一种名为原子文件的统一时空数据存储格式。同时，我们提出了开源库LibCity，为研究者提供可靠的实验工具和便捷的开发框架。该库复现了65种时空预测模型，收集了55个时空数据集，使研究者能够方便地进行全面实验。借助LibCity，我们开展了一系列实验验证不同模型与组件的有效性，并总结了时空预测领域未来有前景的技术发展和研究方向。通过实现公平模型比较、设计统一数据存储格式以及简化新模型开发流程，LibCity有望为时空预测领域做出重要贡献。