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As deep learning technology advances and more urban spatial-temporal dataaccumulates, an increasing number of deep learning models are being proposed tosolve urban spatial-temporal prediction problems. However, there arelimitations in the existing field, including open-source data being in variousformats and difficult to use, few papers making their code and data openlyavailable, and open-source models often using different frameworks andplatforms, making comparisons challenging. A standardized framework is urgentlyneeded to implement and evaluate these methods. To address these issues, wepropose LibCity, an open-source library that offers researchers a credibleexperimental tool and a convenient development framework. In this library, wehave reproduced 65 spatial-temporal prediction models and collected 55spatial-temporal datasets, allowing researchers to conduct comprehensiveexperiments conveniently. By enabling fair model comparisons, designing aunified data storage format, and simplifying the process of developing newmodels, LibCity is poised to make significant contributions to thespatial-temporal prediction field.

LibCity: A Unified Library Towards Efficient and Comprehensive Urban Spatial-Temporal Prediction