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


    Access

    Check access

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    Dynamic origin-destination demand estimation using automatic vehicle identification data


    Contributors:


    Publication date :

    2006-03-01


    Size :

    263590 byte




    Type of media :

    Article (Journal)


    Type of material :

    Electronic Resource


    Language :

    English



    Dynamic Origin-Destination Demand Estimation Using Automatic Vehicle Identification Data

    National Research Council (U.S.) | British Library Conference Proceedings | 2005




    Population Origin-Destination Estimation Using Automatic Vehicle Identification and Volume Data

    Dixon, M. P. / Rilett, L. R. | British Library Online Contents | 2005


    Dynamic Origin-Destination Demand Estimation Using Turning Movement Counts

    Alibabai, Hamed / Mahmassani, Hani S. | Transportation Research Record | 2008