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Dynamic origin-destination demand estimation using automatic vehicle identification data

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2 Author(s)
Xuesong Zhou ; Dept. of Civil & Environ. Eng., Univ. of Maryland, College Park, MD, USA ; H. S. Mahmassani

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

Published in:

IEEE Transactions on Intelligent Transportation Systems  (Volume:7 ,  Issue: 1 )