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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    Title :

    Stochastic Multi-Vehicle Assignment To Urban Transportation Networks




    Publication date :

    2019-06-01


    Size :

    2967761 byte




    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



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