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Prediction for Bicycle Demand using Spatial-Temporal Graph Models

  • Journal of Internet of Things and Convergence
  • Abbr : JKIOTS
  • 2023, 9(6), pp.111-117
  • DOI : 10.20465/KIOTS.2023.9.6.111
  • Publisher : The Korea Internet of Things Society
  • Research Area : Engineering > Computer Science > Internet Information Processing
  • Received : October 23, 2023
  • Accepted : December 21, 2023
  • Published : December 29, 2023

Park Jang Woo 1

1국립순천대학교

Accredited

ABSTRACT

There is a lot of research on using a combination of graph neural networks and recurrent neural networks as a way to account for both temporal and spatial dependencies. In particular, graph neural networks are an emerging area of research. Seoul's bicycle rental service (aka Daereungi) has rental stations all over the city of Seoul, and the rental information at each station is a time series that is faithfully recorded. The rental information of each rental station has temporal characteristics that show periodicity over time, and regional characteristics are also thought to have important effects on the rental status. Regional correlations can be well understood using graph neural networks. In this study, we reconstructed the time series data of Seoul's bicycle rental service into a graph and developed a rental prediction model that combines a graph neural network and a recurrent neural network. We considered temporal characteristics such as periodicity over time, regional characteristics, and the degree importance of each rental station.

Citation status

* References for papers published after 2023 are currently being built.