Methodology for efficient real time OD demand estimation on large scale networks

In previous work, the authors have explored the idea of dimensionality reduction and approximation of OD (origin and destination) demand based on principal component analysis (PCA). In particular, they have shown how they can apply PCA to linearly transform the high dimensional OD matrices into the lower dimensional space without significant loss of accuracy. Next, they have defined a new transformed set of variables (demand principal components) that is used to represent the OD demand in lower dimensional space. These new variables are defined as state variable in a novel reduced state space model for real time estimation of OD demand. In this paper, the authors review previous work and continue this line of research. Based on the previous results, they demonstrate the quality improvement of OD estimates using this new formulation and a so-called, 'colored' Kalman filter approach for OD estimation, in which correlated measurement noise is accounted. Moreover, they provide a thorough analysis of the model performance and computational efficiency using real data from a large network, and method for obtaining a reduced set of state variables.

  • Supplemental Notes:
    • This paper was sponsored by TRB committee ADB30(3) Route Choice and Spatio-Temporal Behavior. Alternate title: Methodology for Efficient Real-Time O-D Demand Estimation on Large-Scale Networks
  • Corporate Authors:

    Transportation Research Board

    500 Fifth Street, NW
    Washington, DC  United States  20001
  • Authors:
    • Djukic, Tamara
    • van Lint, Hans
    • Hoogendoorn, Serge Paul
  • Conference:
  • Date: 2014

Language

  • English

Media Info

  • Media Type: Digital/other
  • Features: Figures; Maps; References; Tables;
  • Pagination: 16p
  • Monograph Title: TRB 93rd Annual Meeting Compendium of Papers

Subject/Index Terms

Filing Info

  • Accession Number: 01519999
  • Record Type: Publication
  • Report/Paper Numbers: 14-3864
  • Files: TRIS, TRB, ATRI
  • Created Date: Jan 27 2014 3:20PM