This paper focuses on multi-vehicle stochastic assignment to an urban transportation network, where paths likely overlap; route choice behavior is modeled through a Probit model, whose application requires Montecarlo techniques. Main aim is to compare two different pseudo-random generators, Mersenne-Twister and Sobol, and four step size strategies for solution algorithms based on the Method of Successive Averages.


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    Titel :

    Stochastic Multi-Vehicle Assignment To Urban Transportation Networks


    Beteiligte:


    Erscheinungsdatum :

    2019-06-01


    Format / Umfang :

    2967761 byte




    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



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