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.
Stochastic Multi-Vehicle Assignment to Urban Transportation Networks
Giulio E Cantarella
;
2020
Abstract
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.File in questo prodotto:
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