Prediction for Bicycle Demand using Spatial-Temporal Graph Models (시-공간 그래프 모델을 이용한 자전거 대여 예측)

Park Jang Woo (박장우)

doi:10.20465/KIOTS.2023.9.6.111

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.

KEYWORDS

Graph Neural Network, Recurrent Neural Network, Spatial-Temporal Model

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

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Journal of Internet of Things and Convergence 2023 KCI Impact Factor : 0.89

Prediction for Bicycle Demand using Spatial-Temporal Graph Models

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

Journal of Internet of Things and Convergence 2023 KCI Impact Factor : 0.89

Prediction for Bicycle Demand using Spatial-Temporal Graph Models

ABSTRACT

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