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Stochastic Multi-Vehicle Assignment To Urban Transportation Networks
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
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
Cantarella, Giulio E. (Autor:in) / Di Febbraro, Angela (Autor:in) / Gangi, Massimo Di (Autor:in) / Giannattasio, Orlando (Autor:in)
01.06.2019
2967761 byte
Aufsatz (Konferenz)
Elektronische Ressource
Englisch
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