Estimating Multi-class Dynamic Origin-destination Demand Through a Forward-backward Algorithm on Computational Graphs

Transportation networks are unprecedentedly complex with heterogeneous vehicular flow. Conventionally, vehicle classes are considered by vehicle classifications (such as standard passenger cars and trucks). However, vehicle flow heterogeneity stems from many other aspects in general, e.g., ride-sourcing vehicles versus personal vehicles, human driven vehicles versus connected and automated vehicles. Provided with some observations of vehicular flow for each class in a large-scale transportation network, how to estimate the multi-class spatio-temporal vehicular flow, in terms of time-varying Origin-Destination (OD) demand and path/link flow, remains a big challenge. This paper presents a solution framework for multi-class dynamic OD demand estimation (MCDODE) in large-scale networks. The proposed framework is built on a computational graph with tensor representations of spatio-temporal flow and all intermediate features involved in the MCDODE formulation. A forward-backward algorithm is proposed to efficiently solve the MCDODE formulation on computational graphs. In addition, we propose a novel concept of tree-based cumulative curves to estimate the gradient of OD demand. A Growing Tree algorithm is developed to construct tree-based cumulative curves. The proposed framework is examined on a small network as well as a real-world large-scale network. The experiment results indicate that the proposed framework is compelling, satisfactory and computationally plausible.

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  • Supplemental Notes:
    • This document was sponsored by the U.S. Department of Transportation, University Transportation Centers Program. Cover title: Mesoscopic car-truck flow modeling and simulation: theory and applications.
  • Corporate Authors:

    Mobility21

    Carnegie Mellon University
    Pittsburgh, PA  United States 

    Office of the Assistant Secretary for Research and Technology

    University Transportation Centers Program
    Department of Transportation
    Washington, DC  United States  20590
  • Authors:
    • Ma, Wei
    • Pi, Xidong
    • Qian, Zhen (Sean)
  • Publication Date: 2019-3-9

Language

  • English

Media Info

  • Media Type: Digital/other
  • Edition: Final Research Report
  • Features: Figures; References; Tables;
  • Pagination: 32p

Subject/Index Terms

Filing Info

  • Accession Number: 01708237
  • Record Type: Publication
  • Contract Numbers: 69A3551747111
  • Files: UTC, NTL, TRIS, ATRI, USDOT
  • Created Date: Jun 3 2019 12:59PM