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A deep learning model is applied for predicting block-level parking occupancyin real time. The model leverages Graph-Convolutional Neural Networks (GCNN) toextract the spatial relations of traffic flow in large-scale networks, andutilizes Recurrent Neural Networks (RNN) with Long-Short Term Memory (LSTM) tocapture the temporal features. In addition, the model is capable of takingmultiple heterogeneously structured traffic data sources as input, such asparking meter transactions, traffic speed, and weather conditions. The modelperformance is evaluated through a case study in Pittsburgh downtown area. Theproposed model outperforms other baseline methods including multi-layer LSTMand Lasso with an average testing MAPE of 10.6\% when predicting block-levelparking occupancies 30 minutes in advance. The case study also shows that, ingenerally, the prediction model works better for business areas than forrecreational locations. We found that incorporating traffic speed and weatherinformation can significantly improve the prediction performance. Weather datais particularly useful for improving predicting accuracy in recreational areas.

A deep learning approach to real-time parking occupancy prediction in spatio-temporal networks incorporating multiple spatio-temporal data sources