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Stochastic noise approach to traffic flow modeling

Sopasakis, Alexandros LU (2004) In Physica A: Statistical Mechanics and its Applications 342(3-4). p.741-754
Abstract
Traffic flow states are described as resulting from a stochastically driven system. Vehicles advance based on the energy profile of their surrounding traffic.



We create a stochastic process generated from an ergodicity satisfying Markov chain whose system dynamics sample from the Gibbs distribution. Specifically, we employ Arrhenius microscopic dynamics in order to also capture non-equilibrium behavior and monitor the states favored by the system through its time evolution.



Monte Carlo simulations of this traffic system provide information and statistics regarding free-flow, “synchronized” traffic, jam wave formation or dissipation, “stop and go” regimes and a variety of interesting such traffic... (More)
Traffic flow states are described as resulting from a stochastically driven system. Vehicles advance based on the energy profile of their surrounding traffic.



We create a stochastic process generated from an ergodicity satisfying Markov chain whose system dynamics sample from the Gibbs distribution. Specifically, we employ Arrhenius microscopic dynamics in order to also capture non-equilibrium behavior and monitor the states favored by the system through its time evolution.



Monte Carlo simulations of this traffic system provide information and statistics regarding free-flow, “synchronized” traffic, jam wave formation or dissipation, “stop and go” regimes and a variety of interesting such traffic behavior, summarized in, among others, the fundamental diagram. Generalizations to the current model and a number of ideas for further studies are proposed. (Less)
Please use this url to cite or link to this publication:
author
publishing date
type
Contribution to journal
publication status
published
subject
keywords
Traffic flow, Stochastic Arrhenius microscopic dynamics, Monte Carlo simulations
in
Physica A: Statistical Mechanics and its Applications
volume
342
issue
3-4
pages
741 - 754
publisher
Elsevier
external identifiers
  • scopus:4544221140
ISSN
0378-4371
DOI
10.1016/j.physa.2004.05.040
language
English
LU publication?
no
id
fa6b365e-8d1a-49bd-a5bc-60ace3060c1e (old id 2201952)
date added to LUP
2012-01-03 11:28:44
date last changed
2017-10-01 03:45:14
@article{fa6b365e-8d1a-49bd-a5bc-60ace3060c1e,
  abstract     = {Traffic flow states are described as resulting from a stochastically driven system. Vehicles advance based on the energy profile of their surrounding traffic.<br/><br>
<br/><br>
We create a stochastic process generated from an ergodicity satisfying Markov chain whose system dynamics sample from the Gibbs distribution. Specifically, we employ Arrhenius microscopic dynamics in order to also capture non-equilibrium behavior and monitor the states favored by the system through its time evolution.<br/><br>
<br/><br>
Monte Carlo simulations of this traffic system provide information and statistics regarding free-flow, “synchronized” traffic, jam wave formation or dissipation, “stop and go” regimes and a variety of interesting such traffic behavior, summarized in, among others, the fundamental diagram. Generalizations to the current model and a number of ideas for further studies are proposed.},
  author       = {Sopasakis, Alexandros},
  issn         = {0378-4371},
  keyword      = {Traffic flow,Stochastic Arrhenius microscopic dynamics,Monte Carlo simulations},
  language     = {eng},
  number       = {3-4},
  pages        = {741--754},
  publisher    = {Elsevier},
  series       = {Physica A: Statistical Mechanics and its Applications},
  title        = {Stochastic noise approach to traffic flow modeling},
  url          = {http://dx.doi.org/10.1016/j.physa.2004.05.040},
  volume       = {342},
  year         = {2004},
}