Predicting Station-Level Bike-Sharing Demands Using Graph Convolutional Neural Network

This study proposes a novel Graph Convolutional Neural Network with Data-driven Graph Filter (GCNN-DDGF) model that can learn hidden heterogeneous pairwise correlations between stations to predict station-level hourly demand in a large-scale bike-sharing network. Two architectures of the GCNN-DDGF model are explored; GCNNreg-DDGF is a regular GCNN-DDGF model which contains the convolution and feedforward blocks, and GCNNrec-DDGF additionally contains a recurrent block from the Long Short-term Memory neural net-work architecture to capture temporal dependencies in the bike-sharing demand series. Furthermore, four types of GCNN models are proposed whose adjacency matrices are based on various bike-sharing system data, including Spatial Distance matrix (SD), Demand matrix (DE), Average Trip Duration matrix (ATD), and Demand Correlation matrix (DC). These six types of GCNN models and seven other benchmark models are built and compared on a Citi Bike dataset from New York City which includes 272 stations and over 28 million transactions from 2013 to 2016. Results show that the GCNNrec-DDGF performs the best in terms of the Root Mean Square Error, the Mean Absolute Error and the coefficient of determination (R2), followed by the GCNNreg-DDGF. They outperform the other models.

• Supplemental Notes:
  • This paper was sponsored by TRB committee ANF20 Standing Committee on Bicycle Transportation.
• Corporate Authors:

  Transportation Research Board

  ,    
• Authors:
  • Lin, Lei
  • Li, Weizi
  • Peeta, Srinivas
• Conference:
  • Transportation Research Board 98th Annual Meeting
  • Location: Washington DC, United States
  • Date: 2019-1-13 to 2019-1-17
• Date: 2019

Language

• English

Media Info

• Media Type: Digital/other
• Features: References; Tables;
• Pagination: 8p

Subject/Index Terms

• TRT Terms: Bicycles; Data analysis; Demand; Mathematical models; Trip length; Vehicle sharing
• Geographic Terms: New York (New York)
• Subject Areas: Data and Information Technology; Operations and Traffic Management; Pedestrians and Bicyclists;

Filing Info

• Accession Number: 01697833
• Record Type: Publication
• Report/Paper Numbers: 19-00173
• Files: TRIS, TRB, ATRI
• Created Date: Mar 1 2019 3:51PM