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

• URL: http://arxiv.org/abs/2304.14343v7
• Date: Thu, 7 Mar 2024 16:41:10 GMT
• Title: LibCity: A Unified Library Towards Efficient and Comprehensive Urban Spatial-Temporal Prediction
• Authors: Jiawei Jiang, Chengkai Han, Wenjun Jiang, Wayne Xin Zhao, Jingyuan Wang
• Abstract summary: There are limitations in the existing field, including open-source data being in various formats and difficult to use. We propose LibCity, an open-source library that offers researchers a credible experimental tool and a convenient development framework.
• Score: 74.08181247675095
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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