Graph Neural Network for Robust Public Transit Demand Prediction

Understanding and forecasting mobility patterns and travel demand are fundamental and critical to efficient transport infrastructure planning and service operation. However, most existing studies focused on deterministic demand estimation/prediction/analytics. Differently, this study provides confidence interval based demand forecasting, which can help transport planning and operation authorities to better accommodate demand uncertainty/variability. The proposed Origin-Destination (OD) demand prediction approach well captures and utilizes the correlations among spatial and temporal information. In particular, the proposed Probabilistic Graph Convolution Model (PGCM) consists of two components: (i) a prediction module based on Graph Convolution Network and combined with the gated mechanism to predict OD demand by utilizing spatio-temporal relations; (ii) a Bayesian-based approximation module to measure the confidence interval of demand prediction by evaluating the graph-based model uncertainty. The authors use a large-scale real-world public transit dataset from the Greater Sydney area to test and evaluate the proposed approach. The experimental results demonstrate that the proposed method is capable of capturing the spatial-temporal correlations for more robust demand prediction against several established tools in the literature.ƒ

• Record URL:
  • http://dx.doi.org/10.1109/TITS.2020.3041234
• Record URL:
  • https://ieeexplore.ieee.org/document/9310711
• Availability:
  • Find a library where document is available. Order URL: http://worldcat.org/oclc/41297384
• Supplemental Notes:
  • Copyright © 2022, IEEE.
• Authors:
  • Li, Can
  • Bai, Lei
  • Liu, Wei
  • Yao, Lina
  • Waller, S Travis
• Publication Date: 2022-5

Language

• English

Media Info

• Media Type: Web
• Features: References;
• Pagination: pp 4086-4098
• Serial:
  • IEEE Transactions on Intelligent Transportation Systems
  • Volume: 23
  • Issue Number: 5
  • Publisher: Institute of Electrical and Electronics Engineers (IEEE)
  • ISSN: 1524-9050
  • Serial URL: http://ieeexplore.ieee.org/xpl/RecentIssue.jsp?punumber=6979

Subject/Index Terms

• TRT Terms: Confidence intervals; Data files; Origin and destination; Predictive models; Public transit; Transportation planning; Travel demand
• Geographic Terms: Sydney (Australia)
• Subject Areas: Data and Information Technology; Planning and Forecasting; Public Transportation;

Filing Info

• Accession Number: 01850343
• Record Type: Publication
• Files: TRIS
• Created Date: Jun 27 2022 5:19PM