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Stochastic modeling and simulation of traffic flow: asymmetric single exclusion process with Arrhenius look-ahead dynamics

Sopasakis, Alexandros LU and Katsoulakis, Markos (2006) In SIAM Journal on Applied Mathematics 66(3). p.921-944
Abstract
A novel traffic flow model based on stochastic microscopic dynamics is introduced and analyzed. Vehicles advance based on the energy profile of their surrounding traffic implementing the "look-ahead" rule and following an underlying asymmetric exclusion process with Arrhenius spin-exchange dynamics. Monte Carlo simulations produce numerical solutions of the microscopic traffic model. Fluctuations play an important role in profiling observationally documented but, at the simulation level, elusive traffic phenomena. Furthermore, based on scaling and limit arguments we obtain a macroscopic description of this microscopic dynamics formulation which up to leading term of the expansions takes the form of integrodifferential Burgers or... (More)
A novel traffic flow model based on stochastic microscopic dynamics is introduced and analyzed. Vehicles advance based on the energy profile of their surrounding traffic implementing the "look-ahead" rule and following an underlying asymmetric exclusion process with Arrhenius spin-exchange dynamics. Monte Carlo simulations produce numerical solutions of the microscopic traffic model. Fluctuations play an important role in profiling observationally documented but, at the simulation level, elusive traffic phenomena. Furthermore, based on scaling and limit arguments we obtain a macroscopic description of this microscopic dynamics formulation which up to leading term of the expansions takes the form of integrodifferential Burgers or higher-order dispersive partial differential equations. We outline connections and comparisons of the hierarchical models presented here (microscopic, macroscopic) with other well-known traffic flow models. (Less)
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author
publishing date
type
Contribution to journal
publication status
published
subject
keywords
traffic flow, Monte Carlo simulations, look-ahead stochastic Arrhenius microscopic dynamics
in
SIAM Journal on Applied Mathematics
volume
66
issue
3
pages
921 - 944
publisher
SIAM Publications
external identifiers
  • scopus:33745642938
ISSN
0036-1399
DOI
10.1137/040617790
language
English
LU publication?
no
id
7afffef4-eb88-43a7-844b-8610d84fbbab (old id 2201826)
date added to LUP
2011-12-30 19:52:28
date last changed
2019-09-11 01:30:35
@article{7afffef4-eb88-43a7-844b-8610d84fbbab,
  abstract     = {A novel traffic flow model based on stochastic microscopic dynamics is introduced and analyzed. Vehicles advance based on the energy profile of their surrounding traffic implementing the "look-ahead" rule and following an underlying asymmetric exclusion process with Arrhenius spin-exchange dynamics. Monte Carlo simulations produce numerical solutions of the microscopic traffic model. Fluctuations play an important role in profiling observationally documented but, at the simulation level, elusive traffic phenomena. Furthermore, based on scaling and limit arguments we obtain a macroscopic description of this microscopic dynamics formulation which up to leading term of the expansions takes the form of integrodifferential Burgers or higher-order dispersive partial differential equations. We outline connections and comparisons of the hierarchical models presented here (microscopic, macroscopic) with other well-known traffic flow models.},
  author       = {Sopasakis, Alexandros and Katsoulakis, Markos},
  issn         = {0036-1399},
  keyword      = {traffic flow,Monte Carlo simulations,look-ahead stochastic Arrhenius microscopic dynamics},
  language     = {eng},
  number       = {3},
  pages        = {921--944},
  publisher    = {SIAM Publications},
  series       = {SIAM Journal on Applied Mathematics},
  title        = {Stochastic modeling and simulation of traffic flow: asymmetric single exclusion process with Arrhenius look-ahead dynamics},
  url          = {http://dx.doi.org/10.1137/040617790},
  volume       = {66},
  year         = {2006},
}