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Enhancing Predictive Models for On-Street Parking Occupancy: Integrating Adaptive GCN and GRU with Household Categories and POI Factors

Author

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  • Xiaohang Zhao

    (Department of Construction Management, Dalian University of Technology, Dalian 116024, China
    Department of Architecture and Civil Engineering, City University of Hong Kong, Kowloon, Hong Kong 999077, China)

  • Mingyuan Zhang

    (Department of Construction Management, Dalian University of Technology, Dalian 116024, China)

Abstract

Accurate predictions of parking occupancy are vital for navigation and autonomous transport systems. This research introduces a deep learning mode, AGCRU, which integrates Adaptive Graph Convolutional Networks (GCNs) with Gated Recurrent Units (GRUs) for predicting on-street parking occupancy. By leveraging real-world data from Melbourne, the proposed model utilizes on-street parking sensors to capture both temporal and spatial dynamics of parking behaviors. The AGCRU model is enhanced with the inclusion of Points of Interest (POIs) and housing data to refine its predictive accuracy based on spatial relationships and parking habits. Notably, the model demonstrates a mean absolute error (MAE) of 0.0156 at 15 min, 0.0330 at 30 min, and 0.0558 at 60 min; root mean square error (RMSE) values are 0.0244, 0.0665, and 0.1003 for these intervals, respectively. The mean absolute percentage error (MAPE) for these intervals is 1.5561%, 3.3071%, and 5.5810%. These metrics, considerably lower than those from traditional and competing models, indicate the high efficiency and accuracy of the AGCRU model in an urban setting. This demonstrates the model as a tool for enhancing urban parking management and planning strategies.

Suggested Citation

  • Xiaohang Zhao & Mingyuan Zhang, 2024. "Enhancing Predictive Models for On-Street Parking Occupancy: Integrating Adaptive GCN and GRU with Household Categories and POI Factors," Mathematics, MDPI, vol. 12(18), pages 1-23, September.
  • Handle: RePEc:gam:jmathe:v:12:y:2024:i:18:p:2823-:d:1476236
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    References listed on IDEAS

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