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Double randomization method for estimating the moments of solution to vehicular traffic problems with random parameters. / Burmistrov, Aleksandr; Korotchenko, Mariya.

In: Russian Journal of Numerical Analysis and Mathematical Modelling, Vol. 35, No. 3, 01.06.2020, p. 143-152.

Research output: Contribution to journal › Article › peer-review

Harvard

Burmistrov, A & Korotchenko, M 2020, 'Double randomization method for estimating the moments of solution to vehicular traffic problems with random parameters', Russian Journal of Numerical Analysis and Mathematical Modelling, vol. 35, no. 3, pp. 143-152. https://doi.org/10.1515/rnam-2020-0011

APA

Burmistrov, A., & Korotchenko, M. (2020). Double randomization method for estimating the moments of solution to vehicular traffic problems with random parameters. Russian Journal of Numerical Analysis and Mathematical Modelling, 35(3), 143-152. https://doi.org/10.1515/rnam-2020-0011

Vancouver

Burmistrov A, Korotchenko M. Double randomization method for estimating the moments of solution to vehicular traffic problems with random parameters. Russian Journal of Numerical Analysis and Mathematical Modelling. 2020 Jun 1;35(3):143-152. doi: 10.1515/rnam-2020-0011

Author

Burmistrov, Aleksandr ; Korotchenko, Mariya. / Double randomization method for estimating the moments of solution to vehicular traffic problems with random parameters. In: Russian Journal of Numerical Analysis and Mathematical Modelling. 2020 ; Vol. 35, No. 3. pp. 143-152.

BibTeX

@article{d4004ca605784e68a413e9e3ceac750f,
title = "Double randomization method for estimating the moments of solution to vehicular traffic problems with random parameters",
abstract = "In this paper we consider a Boltzmann type equation arising in the kinetic vehicle traffic flow model with an acceleration variable. The latter model is improved within the framework of the previously developed approach by introducing a set of random parameters. This enables us to take into account different types of interacting vehicles, as well as various parameters describing skills and behavior of particular drivers. We develop new Monte Carlo algorithms to evaluate probabilistic moments of linear functionals of the solution to the considered equation. ",
keywords = "kinetic model, Markov chain modelling, Monte Carlo method, multi-particle system evolution, vehicular traffic flow (VTF), ACCELERATION, SPEED, MODEL, ALGORITHMS, FLOW, Marlcov chain modelling",
author = "Aleksandr Burmistrov and Mariya Korotchenko",
note = "The work was supported by the budget project 0315-2019-0002 for ICM&MG SB RAS and was also partly supported by the Russian Foundation for Basic Research (project No. 18-01-00356).",
year = "2020",
month = jun,
day = "1",
doi = "10.1515/rnam-2020-0011",
language = "English",
volume = "35",
pages = "143--152",
journal = "Russian Journal of Numerical Analysis and Mathematical Modelling",
issn = "0927-6467",
publisher = "Walter de Gruyter GmbH",
number = "3",

}

RIS

TY - JOUR

T1 - Double randomization method for estimating the moments of solution to vehicular traffic problems with random parameters

AU - Burmistrov, Aleksandr

AU - Korotchenko, Mariya

N1 - The work was supported by the budget project 0315-2019-0002 for ICM&MG SB RAS and was also partly supported by the Russian Foundation for Basic Research (project No. 18-01-00356).

PY - 2020/6/1

Y1 - 2020/6/1

N2 - In this paper we consider a Boltzmann type equation arising in the kinetic vehicle traffic flow model with an acceleration variable. The latter model is improved within the framework of the previously developed approach by introducing a set of random parameters. This enables us to take into account different types of interacting vehicles, as well as various parameters describing skills and behavior of particular drivers. We develop new Monte Carlo algorithms to evaluate probabilistic moments of linear functionals of the solution to the considered equation.

AB - In this paper we consider a Boltzmann type equation arising in the kinetic vehicle traffic flow model with an acceleration variable. The latter model is improved within the framework of the previously developed approach by introducing a set of random parameters. This enables us to take into account different types of interacting vehicles, as well as various parameters describing skills and behavior of particular drivers. We develop new Monte Carlo algorithms to evaluate probabilistic moments of linear functionals of the solution to the considered equation.

KW - kinetic model

KW - Markov chain modelling

KW - Monte Carlo method

KW - multi-particle system evolution

KW - vehicular traffic flow (VTF)

KW - ACCELERATION

KW - SPEED

KW - MODEL

KW - ALGORITHMS

KW - FLOW

KW - Marlcov chain modelling

UR - http://www.scopus.com/inward/record.url?scp=85090835173&partnerID=8YFLogxK

U2 - 10.1515/rnam-2020-0011

DO - 10.1515/rnam-2020-0011

M3 - Article

AN - SCOPUS:85090835173

VL - 35

SP - 143

EP - 152

JO - Russian Journal of Numerical Analysis and Mathematical Modelling

JF - Russian Journal of Numerical Analysis and Mathematical Modelling

SN - 0927-6467

IS - 3

ER -

ID: 25309643