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LibCity: An Open Library for Traffic Prediction

  • Peng Cheng Laboratory
  • Beihang University
  • Gaoling School of Artificial Intelligence

科研成果: 书/报告/会议事项章节会议稿件同行评审

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.

源语言英语
主期刊名29th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems, SIGSPATIAL 2021
编辑Xiaofeng Meng, Fusheng Wang, Chang-Tien Lu, Yan Huang, Shashi Shekhar, Xing Xie
出版商Association for Computing Machinery
145-148
页数4
ISBN(电子版)9781450386647
DOI
出版状态已出版 - 2 11月 2021
活动29th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems, SIGSPATIAL 2021 - Virtual, Online, 中国
期限: 2 11月 20215 11月 2021

出版系列

姓名GIS: Proceedings of the ACM International Symposium on Advances in Geographic Information Systems

会议

会议29th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems, SIGSPATIAL 2021
国家/地区中国
Virtual, Online
时期2/11/215/11/21

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