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

Shuguan Yang, Wei Ma, Xidong Pi, Sean Qian

Research output: Journal article publicationJournal articleAcademic researchpeer-review

38 Citations (Scopus)


A deep learning model is adopted 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 GCNN-based 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 min 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.

Original languageEnglish
Pages (from-to)248-265
Number of pages18
JournalTransportation Research Part C: Emerging Technologies
Publication statusPublished - Oct 2019
Externally publishedYes


  • Deep learning
  • Multi-source traffic data
  • Parking prediction
  • Parking transactions
  • Weather conditions

ASJC Scopus subject areas

  • Civil and Structural Engineering
  • Automotive Engineering
  • Transportation
  • Computer Science Applications

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