  The escalation in urban private car ownership has worsened the urban parking
predicament, necessitating effective parking availability prediction for urban
planning and management. However, the existing prediction methods suffer from
low prediction accuracy with the lack of spatial-temporal correlation features
related to parking volume, and neglect of flow patterns and correlations
between similar parking lots within certain areas. To address these challenges,
this study proposes a parking availability prediction framework integrating
spatial-temporal deep learning with multi-source data fusion, encompassing
traffic demand data from multiple sources (e.g., metro, bus, taxi services),
and parking lot data. The framework is based on the Transformer as the
spatial-temporal deep learning model and leverages K-means clustering to
establish parking cluster zones, extracting and integrating traffic demand
characteristics from various transportation modes (i.e., metro, bus, online
ride-hailing, and taxi) connected to parking lots. Real-world empirical data
was used to verify the effectiveness of the proposed method compared with
different machine learning, deep learning, and traditional statistical models
for predicting parking availability. Experimental results reveal that, with the
proposed pipeline, the developed Transformer model outperforms other models in
terms of various metrics, e.g., Mean Squared Error (MSE), Mean Absolute Error
(MAE), and Mean Absolute Percentage Error (MAPE). By fusing multi-source
demanding data with spatial-temporal deep learning techniques, this approach
offers the potential to develop parking availability prediction systems that
furnish more accurate and timely information to both drivers and urban
planners, thereby fostering more efficient and sustainable urban mobility.
