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