Stochastic description of traffic flow
(2008) In Journal of Statistical Physics 133(6). p.1083-1105- Abstract
- We propose a traffic model based on microscopic stochastic dynamics. We built a Markov chain equipped with an Arrhenius interaction law. The resulting stochastic process is comprised of both spin-flip and spin-exchange dynamics which models vehicles exiting, entering and interacting in a two-dimensional lattice environment corresponding to a multi-lane highway. The process is further equipped with a novel look-ahead type, anisotropic interaction potential which allows drivers/vehicles to ascertain local fluctuations and advance to new cells forward or sideways. The resulting vehicular traffic model is simulated via kinetic Monte Carlo and examined under both, typical and extreme traffic flow scenarios. The model is shown to correctly... (More)
- We propose a traffic model based on microscopic stochastic dynamics. We built a Markov chain equipped with an Arrhenius interaction law. The resulting stochastic process is comprised of both spin-flip and spin-exchange dynamics which models vehicles exiting, entering and interacting in a two-dimensional lattice environment corresponding to a multi-lane highway. The process is further equipped with a novel look-ahead type, anisotropic interaction potential which allows drivers/vehicles to ascertain local fluctuations and advance to new cells forward or sideways. The resulting vehicular traffic model is simulated via kinetic Monte Carlo and examined under both, typical and extreme traffic flow scenarios. The model is shown to correctly predict both qualitative as well as quantitative traffic observables for any highway geometry. Furthermore it also captures interesting multi-scale phenomena in traffic flows after a simulated accident which lead to oscillatory, dissipating, traffic waves with different periods per lane. (Less)
Please use this url to cite or link to this publication:
https://lup.lub.lu.se/record/2201603
- author
- Alperovich, Timur and Sopasakis, Alexandros LU
- publishing date
- 2008
- type
- Contribution to journal
- publication status
- published
- subject
- keywords
- Multi-lane traffic flow - Anisotropic look-ahead rule - Markov Chain - Spin-flip/spin-exchange dynamics - Arrhenius potential - Kinetic Monte Carlo
- in
- Journal of Statistical Physics
- volume
- 133
- issue
- 6
- pages
- 1083 - 1105
- publisher
- Springer
- external identifiers
-
- scopus:57849103196
- ISSN
- 1572-9613
- DOI
- 10.1007/s10955-008-9652-6
- language
- English
- LU publication?
- no
- id
- 5c20025a-89b4-4d7b-9799-73786b87da7d (old id 2201603)
- date added to LUP
- 2016-04-01 12:08:05
- date last changed
- 2022-04-29 01:07:03
@article{5c20025a-89b4-4d7b-9799-73786b87da7d,
abstract = {{We propose a traffic model based on microscopic stochastic dynamics. We built a Markov chain equipped with an Arrhenius interaction law. The resulting stochastic process is comprised of both spin-flip and spin-exchange dynamics which models vehicles exiting, entering and interacting in a two-dimensional lattice environment corresponding to a multi-lane highway. The process is further equipped with a novel look-ahead type, anisotropic interaction potential which allows drivers/vehicles to ascertain local fluctuations and advance to new cells forward or sideways. The resulting vehicular traffic model is simulated via kinetic Monte Carlo and examined under both, typical and extreme traffic flow scenarios. The model is shown to correctly predict both qualitative as well as quantitative traffic observables for any highway geometry. Furthermore it also captures interesting multi-scale phenomena in traffic flows after a simulated accident which lead to oscillatory, dissipating, traffic waves with different periods per lane.}},
author = {{Alperovich, Timur and Sopasakis, Alexandros}},
issn = {{1572-9613}},
keywords = {{Multi-lane traffic flow - Anisotropic look-ahead rule - Markov Chain - Spin-flip/spin-exchange dynamics - Arrhenius potential - Kinetic Monte Carlo}},
language = {{eng}},
number = {{6}},
pages = {{1083--1105}},
publisher = {{Springer}},
series = {{Journal of Statistical Physics}},
title = {{Stochastic description of traffic flow}},
url = {{http://dx.doi.org/10.1007/s10955-008-9652-6}},
doi = {{10.1007/s10955-008-9652-6}},
volume = {{133}},
year = {{2008}},
}