Abstract After several decades of work by several talented researchers, estimation of the origin–destination matrix using traffic data has remained very challenging. This paper presents a set of innovative methods for estimation of the origin–destination matrix of large-scale networks, using vehicle counts on links, partial path data obtained from an automated vehicle identification system, and combinations of both data. These innovative methods are used to solve three origin–destination matrix estimation models. The first model is an extension of Spiess’s model which uses vehicle count data while the second model is an extension of Jamali’s model and it uses partial path data. The third model is a multiobjective model which utilizes combinations of vehicle counts and partial path data. The methods were tested to estimate the origin–destination matrix of a large-scale network from Mashhad City with 163 traffic zones and 2093 links, and the results were compared with the conventional gradient-based algorithm. The results show that the innovative methods performed better as compared to the gradient-based algorithm.


    Access

    Access via TIB

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    Estimation of origin–destination matrices using link counts and partial path data


    Contributors:

    Published in:

    Publication date :

    2019




    Type of media :

    Article (Journal)


    Type of material :

    Print


    Language :

    English



    Classification :

    BKL:    55.80 Verkehrswesen, Transportwesen: Allgemeines / 74.75 Verkehrsplanung, Verkehrspolitik
    Local classification TIB:    535/7000



    Estimation of origin–destination matrices using link counts and partial path data

    Rostami Nasab, Mojtaba / Shafahi, Yousef | Online Contents | 2019




    Origin-Destination Estimation Using Probe Vehicle Trajectory and Link Counts

    Xianfeng Yang / Yang Lu / Wei Hao | DOAJ | 2017

    Free access