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

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. The authors 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.

• Record URL:
  • https://doi.org/10.1016/j.trc.2019.08.010
• Record URL:
  • http://www.sciencedirect.com/science/article/pii/S0968090X18313780
• Availability:
  • Find a library where document is available. Order URL: http://worldcat.org/issn/0968090X
• Supplemental Notes:
  • © 2019 Elsevier Ltd. All rights reserved. Abstract reprinted with permission of Elsevier.
• Authors:
  • Yang, Shuguan
  • 0000-0003-0029-4945
  • Ma, Wei
  • Pi, Xidong
  • Ma, Wei
  • 0000-0001-8716-8989
• Publication Date: 2019-10

Language

• English

Media Info

• Media Type: Web
• Features: Figures; References; Tables;
• Pagination: pp 248-265
• Serial:
  • Transportation Research Part C: Emerging Technologies
  • Volume: 107
  • Issue Number: 0
  • Publisher: Elsevier
  • ISSN: 0968-090X
  • Serial URL: http://www.sciencedirect.com/science/journal/0968090X

Subject/Index Terms

• TRT Terms: Case studies; Machine learning; Mathematical prediction; Parking; Real time information; Traffic data; Traffic speed; Weather conditions
• Candidate Terms: Smart parking
• Geographic Terms: Pittsburgh (Pennsylvania)
• Subject Areas: Data and Information Technology; Highways; Operations and Traffic Management; Planning and Forecasting;

Filing Info

• Accession Number: 01717478
• Record Type: Publication
• Files: TRIS
• Created Date: Sep 19 2019 3:07PM