Dynamic origin-destination demand estimation using automatic vehicle identification data

Xuesong Zhou*, Hani S. Mahmassani

*Corresponding author for this work

Research output: Contribution to journalArticle

122 Scopus citations

Abstract

This paper proposes a dynamic origin-destination (OD) estimation method to extract valuable point-to-point splitfraction information from automatic vehicle identification (AVI) counts without estimating market-penetration rates and identification rates of AVI tags. A nonlinear ordinary least-squares estimation model is presented to combine AVI counts, link counts, and historical demand information into a multiobjective optimization framework. A joint estimation formulation and a one-sided linear-penalty formulation are further developed to take into account possible identification and representativeness errors, and the resulting optimization problems are solved by using an iterative bilevel estimation procedure. Based on a synthetic data set, this study shows the effectiveness of the proposed estimation models under different market-penetration rates and identification rates.

Original languageEnglish (US)
Pages (from-to)105-114
Number of pages10
JournalIEEE Transactions on Intelligent Transportation Systems
Volume7
Issue number1
DOIs
StatePublished - Mar 1 2006

Keywords

  • Road vehicle identification
  • State estimation
  • Traffic information systems
  • Transportation networks

ASJC Scopus subject areas

  • Automotive Engineering
  • Mechanical Engineering
  • Computer Science Applications

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