Information metrics for improved traffic model fidelity through sensitivity analysis and data assimilation
(2016) In Transportation Research. Part B: Methodological 86. p.1-18- Abstract
- We develop theoretical and computational tools which can appraise traffic flow models and optimize their performance against current
time-series traffic data and prevailing conditions. The proposed methodology perturbs the parameter space and undertakes path-wise analysis
of the resulting time series. Most importantly the approach is valid even under non-equilibrium conditions
and is based on procuring path-space (time-series) information. More generally we propose a mathematical methodology
which quantifies traffic information loss.
In particular the method undertakes sensitivity analysis on available traffic data and optimizes the traffic
flow model based on two... (More) - We develop theoretical and computational tools which can appraise traffic flow models and optimize their performance against current
time-series traffic data and prevailing conditions. The proposed methodology perturbs the parameter space and undertakes path-wise analysis
of the resulting time series. Most importantly the approach is valid even under non-equilibrium conditions
and is based on procuring path-space (time-series) information. More generally we propose a mathematical methodology
which quantifies traffic information loss.
In particular the method undertakes sensitivity analysis on available traffic data and optimizes the traffic
flow model based on two information
theoretic tools which we develop. One of them, the relative entropy rate, can adjust and optimize model parameter values in
order to reduce the information loss. More precisely, we use the relative entropy rate as an information metric between time
series data and parametrized stochastic
dynamics describing a microscopic traffic model. On the other hand, the path-space Fisher Information Matrix, (pFIM) reduces
model complexity and can even be used to control fidelity. This is achieved by eliminating unimportant model
parameters or their combinations. This results in easier regression of parametric models with a smaller number of parameters.
The method reconstructs the Markov Chain and emulates the traffic dynamics through Monte Carlo simulations.
We use the microscopic interaction model from \cite{SK} as a representative traffic flow model to illustrate this
parameterization methodology. During the comparisons we use both synthetic and real, rush-hour, traffic data
from highway US-101 in Los Angeles, California. (Less)
Please use this url to cite or link to this publication:
https://lup.lub.lu.se/record/8560288
- author
- Sopasakis, Alexandros LU and Katsoulakis, Markos
- organization
- publishing date
- 2016
- type
- Contribution to journal
- publication status
- published
- subject
- keywords
- Traffic model parametrization, Information theoretic tools, Relative entropy rate, Fisher information matrix, Stochastic microscopic dynamics, Inverse dynamic Monte Carlo.
- in
- Transportation Research. Part B: Methodological
- volume
- 86
- pages
- 1 - 18
- publisher
- Elsevier
- external identifiers
-
- scopus:84956640670
- wos:000375505500001
- ISSN
- 0191-2615
- DOI
- 10.1016/j.trb.2016.01.003
- language
- English
- LU publication?
- yes
- id
- 2531d9a7-e4fa-4efc-ac6a-7c7aa7fe915e (old id 8560288)
- date added to LUP
- 2016-04-01 10:32:40
- date last changed
- 2022-04-27 23:09:46
@article{2531d9a7-e4fa-4efc-ac6a-7c7aa7fe915e,
abstract = {{We develop theoretical and computational tools which can appraise traffic flow models and optimize their performance against current <br/><br>
time-series traffic data and prevailing conditions. The proposed methodology perturbs the parameter space and undertakes path-wise analysis <br/><br>
of the resulting time series. Most importantly the approach is valid even under non-equilibrium conditions <br/><br>
and is based on procuring path-space (time-series) information. More generally we propose a mathematical methodology <br/><br>
which quantifies traffic information loss. <br/><br>
<br/><br>
In particular the method undertakes sensitivity analysis on available traffic data and optimizes the traffic <br/><br>
flow model based on two information <br/><br>
theoretic tools which we develop. One of them, the relative entropy rate, can adjust and optimize model parameter values in <br/><br>
order to reduce the information loss. More precisely, we use the relative entropy rate as an information metric between time <br/><br>
series data and parametrized stochastic <br/><br>
dynamics describing a microscopic traffic model. On the other hand, the path-space Fisher Information Matrix, (pFIM) reduces <br/><br>
model complexity and can even be used to control fidelity. This is achieved by eliminating unimportant model <br/><br>
parameters or their combinations. This results in easier regression of parametric models with a smaller number of parameters. <br/><br>
<br/><br>
The method reconstructs the Markov Chain and emulates the traffic dynamics through Monte Carlo simulations. <br/><br>
We use the microscopic interaction model from \cite{SK} as a representative traffic flow model to illustrate this <br/><br>
parameterization methodology. During the comparisons we use both synthetic and real, rush-hour, traffic data <br/><br>
from highway US-101 in Los Angeles, California.}},
author = {{Sopasakis, Alexandros and Katsoulakis, Markos}},
issn = {{0191-2615}},
keywords = {{Traffic model parametrization; Information theoretic tools; Relative entropy rate; Fisher information matrix; Stochastic microscopic dynamics; Inverse dynamic Monte Carlo.}},
language = {{eng}},
pages = {{1--18}},
publisher = {{Elsevier}},
series = {{Transportation Research. Part B: Methodological}},
title = {{Information metrics for improved traffic model fidelity through sensitivity analysis and data assimilation}},
url = {{http://dx.doi.org/10.1016/j.trb.2016.01.003}},
doi = {{10.1016/j.trb.2016.01.003}},
volume = {{86}},
year = {{2016}},
}