Graph convolutional network approach applied to predict hourly bike-sharing demands considering spatial, temporal, and global effects

Solving the supply–demand imbalance is the most crucial issue for stable implementation of a public bike-sharing system. This gap can be reduced by increasing the accuracy of demand prediction by considering spatial and temporal properties of bike demand. However, only a few attempts have been made to account for both features simultaneously. Therefore, the authors propose a prediction framework based on graph convolutional networks. The authors' framework reflects not only spatial dependencies among stations, but also various temporal patterns over different periods. Additionally, the authors consider the influence of global variables, such as weather and weekday/weekend to reflect non-station-level changes. The authors compare their framework to other baseline models using the data from Seoul’s bike-sharing system. Results show that the authors' approach has better performance than existing prediction models.

Language

  • English

Media Info

  • Media Type: Web
  • Features: Figures; References; Tables;
  • Pagination: e0220782
  • Serial:
  • Publication flags:

    Open Access (libre)

Subject/Index Terms

Filing Info

  • Accession Number: 01720412
  • Record Type: Publication
  • Files: TRIS
  • Created Date: Oct 28 2019 10:27AM