Dynamic origin-destination demand estimation using automatic vehicle identification data

doi:10.1109/TITS.2006.869629

2006-03-01

167

PRE

Xuesong Zhou

Hani S. Mahmassani

DOI：10.1109/TITS.2006.869629

原文链接

原文求助

摘要

En 中文

This paper proposes a dynamic origin-destination (OD) estimation method to extract valuable point-to-point split-fraction 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.

Keyword:

road vehicle identification

state estimation

traffic information systems

transportation networks