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 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. 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以及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，极少有论文公开其代码和数据，以及开源模型通常使用不同的框架和平台，导致模型比较困难。亟需一个标准化的框架来实现和评估这些方法。为了解决这些问题，我们提出了LibCity，一个开源库，为研究人员提供可信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的实验。通过实现公平的模型比较、设计统一的数据存储格式、以及简化新模型的开发过程，LibCity有望为时空预测领域做出重要贡献。