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Information metrics for improved traffic model fidelity through sensitivity analysis and data assimilation

Sopasakis, Alexandros LU and Katsoulakis, Markos (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:
author
organization
publishing date
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
2016-03-02 17:10:49
date last changed
2017-06-04 03:14:59
@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},
  keyword      = {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},
  volume       = {86},
  year         = {2016},
}