Understanding Origin-Destination Ride Demand with Interpretable and Scalable Nonnegative Tensor Decomposition

This paper focuses on the estimation and compression of ride demand from origin-destination (OD) trip event data. By representing the OD event data as a three-way tensor (origin, destination, and time), the authors model the data as a Poisson process with an intensity tensor that can be decomposed according to a Tucker decomposition. The authors establish and justify a specific form of nonnegative Tucker-like tensor decomposition that represents OD demand via K latent origin spatial factors and K latent destination spatial factors. The authors then provide a computational and memory efficient algorithm for performing this decomposition and demonstrate its use for real-time compression and estimation of OD ride demand. Two case studies based on New York City (NYC) taxi and Washington DC (DC) taxi were implemented. Results from the case studies demonstrate the applicability of the proposed method in data compression and short-term forecast for ride demand. Furthermore, the authors found that the learned latent spatial factors are interpretable and localized to specific areas for both NYC and DC cases. Hence, this method can be used to understand OD trip data through latent spatial factors and be used to identify spatio-temporal patterns for OD trip and travel demand generation mechanism in general.

• Record URL:
  • https://doi.org/10.1287/trsc.2022.0101
• Availability:
  • Find a library where document is available. Order URL: http://worldcat.org/oclc/1767714
• Supplemental Notes:
  • Abstracts reprinted with permission of INFORMS (Institute for Operations Research and the Management Sciences, http://www.informs.org).
• Authors:
  • Li, Xiaoyue
  • Sun, Ran
  • Sharpnack, James
  • Fan, Yueyue
• Publication Date: 2023-11

Language

• English

Media Info

• Media Type: Web
• Features: References;
• Pagination: 1473-1495
• Serial:
  • Transportation Science
  • Volume: 57
  • Issue Number: 6
  • Publisher: Institute for Operations Research and the Management Sciences (INFORMS)
  • ISSN: 0041-1655
  • Serial URL: http://transci.journal.informs.org/

Subject/Index Terms

• TRT Terms: Origin and destination; Taxicabs; Travel demand
• Geographic Terms: New York (New York); Washington (District of Columbia)
• Subject Areas: Highways; Operations and Traffic Management; Passenger Transportation; Planning and Forecasting;

Filing Info

• Accession Number: 01902143
• Record Type: Publication
• Files: TRIS
• Created Date: Dec 15 2023 8:44AM