Title: A deep learning approach to real-time parking occupancy prediction in spatio-termporal networks incorporating multiple spatio-temporal data sources,
Abstract: A deep learning model is proposed 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 12.0\%
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.