LibCity: A Unified Library Towards Efficient and Comprehensive Urban Spatial-Temporal Prediction
This provides a unified framework for researchers in urban spatial-temporal prediction, addressing issues of data format inconsistency and model comparability, though it is incremental as it builds on existing models and datasets.
The authors tackled the lack of standardization in urban spatial-temporal prediction by developing LibCity, an open-source library that reproduces 65 models and collects 55 datasets to enable fair comparisons and simplify development.
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.