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A bike-sharing demand prediction model based on Spatio-Temporal Graph Convolutional Networks.

Chaoran Zhou1, Jiahao Hu1, Xin Zhang1

  • 1School of Computer Science and Technology, Changchun University of Science and Technology, Changchun, Jilin, China.

Peerj. Computer Science
|December 9, 2024
PubMed
Summary

This study introduces a Spatio-Temporal Bike-sharing Demand Prediction (ST-BDP) model to optimize bike placement. The model accurately forecasts demand, improving bike-sharing efficiency and urban mobility.

Keywords:
Demand graphDemand predictionGraph convolutional network (GCN)Spatio-temporal characteristic

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Area of Science:

  • Urban planning and transportation science
  • Data science and artificial intelligence
  • Environmental sustainability

Background:

  • Station-based bike-sharing systems often lead to underutilized bikes due to suboptimal placement strategies.
  • Existing models may not fully account for complex spatial and temporal factors influencing bike demand.
  • Eco-friendly transport modes like shared bikes are crucial for urban congestion and emissions reduction.

Purpose of the Study:

  • To develop an advanced model for predicting spatial user demand for shared bikes between stations.
  • To enhance the efficiency and resource allocation of bike-sharing systems.
  • To provide data-driven insights for optimizing bike-sharing policies and operations.

Main Methods:

  • Introduction of the Spatio-Temporal Bike-sharing Demand Prediction (ST-BDP) model.
  • Leveraging multi-source data including weather and temporal information.
  • Utilizing Spatio-Temporal Graph Convolutional Networks (STGCN) with attention mechanisms and sequential convolutional networks.

Main Results:

  • The ST-BDP model demonstrated superior performance on real-world datasets.
  • Achieved excellent accuracy metrics: MAE = 1.62, MAPE = 15.82%, SMAPE = 16.14%, RMSE = 2.36.
  • Outperformed existing baseline techniques in demand prediction accuracy.

Conclusions:

  • The ST-BDP model offers a significant advancement in predicting bike-sharing demand.
  • Accurate demand forecasting can guide more effective bike-sharing system management and policy development.
  • The model's precision supports improved urban mobility and resource utilization in shared transportation.