Stochastic noise approach to traffic flow modeling
(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:
https://lup.lub.lu.se/record/2201952
- author
- Sopasakis, Alexandros LU
- publishing date
- 2004
- 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
- 2016-04-01 12:00:10
- date last changed
- 2022-04-13 04:37:43
@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}},
keywords = {{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}},
doi = {{10.1016/j.physa.2004.05.040}},
volume = {{342}},
year = {{2004}},
}