计算机科学
标准化
实施
领域(数学)
可扩展性
集合(抽象数据类型)
软件工程
数据挖掘
数据科学
程序设计语言
数学
操作系统
纯数学
作者
Jingyuan Wang,Jiawei Jiang,Wenjun Jiang,Chao Li,Wayne Xin Zhao
标识
DOI:10.1145/3474717.3483923
摘要
With the increase of traffic prediction models, there has become an urgent need to develop a standardized framework to implement and evaluate these methods. This paper presents LibCity, a unified, comprehensive, and extensible library for traffic prediction, which provides researchers with a credible experimental tool and a convenient development framework. In this library, we reproduce 42 traffic prediction models and collect 29 spatial-temporal datasets, which allows researchers to conduct comprehensive experiments in a convenient way. To accelerate the development of new models, we design unified model interfaces based on unified data formats, which effectively encapsulate the details of the implementation. To verify the effectiveness of our implementations, we also report the reproducibility comparison results of LibCity, and set up a performance leaderboard for the four kinds of traffic prediction tasks. Our library will contribute to the standardization and reproducibility in the field of traffic prediction. The open source link of LibCity is https://github.com/LibCity/Bigscity-LibCity.
科研通智能强力驱动
Strongly Powered by AbleSci AI