Information metrics for improved traffic model fidelity through sensitivity analysis and data assimilation
Författare
Summary, in English
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.
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.
Avdelning/ar
Publiceringsår
2016
Språk
Engelska
Sidor
1-18
Publikation/Tidskrift/Serie
Transportation Research. Part B: Methodological
Volym
86
Dokumenttyp
Artikel i tidskrift
Förlag
Elsevier
Ämne
- Transport Systems and Logistics
- Probability Theory and Statistics
Nyckelord
- Traffic model parametrization
- Information theoretic tools
- Relative entropy rate
- Fisher information matrix
- Stochastic microscopic dynamics
- Inverse dynamic Monte Carlo.
Status
Published
ISBN/ISSN/Övrigt
- ISSN: 0191-2615