ESTIMATION OF TIME-VARYING ORIGIN-DESTINATION FLOWS FROM COUNTS : A NEURAL NETWORK APPROACH

This paper presents a dynamic model based, on the error backpropagation learning principle in neural network theory, for estimating origin-destination flows from the road entering and exiting counts in a transportation network. Two experiments are conducted to evaluate the performance of the model. Results show that the backpropagation based model is capable of tracking the time variations of the origin-destination flows with a high stability.

  • Availability:
  • Supplemental Notes:
    • Publication Date: May-June 1998
  • Corporate Authors:

    University of Maryland, College Park

    Department of Civil and Environmental Engineering
    College Park, MD  United States  20742
  • Authors:
    • Yang, Hai
    • Akiyama, T
    • Sasaki, T
  • Publication Date: 1998

Language

  • English

Media Info

Subject/Index Terms

Filing Info

  • Accession Number: 00776535
  • Record Type: Publication
  • Source Agency: UC Berkeley Transportation Library
  • Files: PATH
  • Created Date: Nov 17 1999 12:00AM