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
Dynamic origin-destination demand estimation using automatic vehicle identification data
IEEE Transactions on Intelligent Transportation Systems ; 7 , 1 ; 105-114
2006-03-01
263590 byte
Article (Journal)
Electronic Resource
English
Dynamic Origin-Destination Demand Estimation Using Automatic Vehicle Identification Data
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