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A demonstration of distribution-based calibrationIoulia MARKOU, Vasileia PAPATHANASOPOULOU, Constantinos ANTONIOUNational Technical University of Athens, Greece
MT-ITS 2015 3-5. June 2015, Budapest
Outline Motivation Overview Methodology Experimental set-up Application and results Conclusion and future research prospects
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Capturing heterogeneity
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Calibration essential for traffic simulation models
Heterogeneity in driving behavior => lots of useful
information
Point/aggregate measures miss a lot of information
Not a single distribution, but distributions of subgroups
Overview
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Capture the behavior of drivers in relation with the preceding vehicle in the same lane Multi-agent models with differential equations, each of which captures a different state
Car – Following Models
Overview
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A comprehensive methodology that will allow quick and efficient calibration of models parameters is important The Simultaneous Perturbation Stochastic Approximation (SPSA) could be a fairly promising algorithm
Optimization approach
Overview
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SPSA Algorithm
Overview
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Calibration using Distributions
Most of the proposed calibration approaches choose to calibrate a few
selected parameters for simplicity
In emergency situations, it is important to depart from point values and
restrict the necessary assumptions by dealing with distributions
The proposed approach assumes as input a set of measured
distributions
The data need to be appropriately preprocessed
Distributions have been used in some off-line calibration studies
Methodology
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Calibration using Distributions
Point values of surveillance data have been used in this
research
A distribution of values for each parameter has been
defined.
SPSA identifies the optimal combination of parameters for
each observation
Methodology
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Determination of calibration parameters
Collection of historical measurements
Selection of calibration algorithm
Choice of Loss Function
Start of Calibration
Experimental set-up
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Car-following model of TransModeler Traffic Simulation Software
Experimental set-up
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Experimental set-up
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City of Naples, Italy
October 2002(Punzo et al., 2005, Papathanasopoulou and Antoniou, 2012)
Application
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Results
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No. of necessary iteration sets for SPSA termination
No. of iteration sets
No.
of r
ecor
ds
Results
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Acceleration Deceleration
Results
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Acceleration Deceleration
Results
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Driver 1
Driver 2
Driver 3
Results
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Driver 2mean = -1,16
Driver 3mean = -1,48
Driver 1mean = -1,51
Conclusion
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Developed distributions of car-following model parameters
Captured heterogeneous driver behavior
Utilized state-of-the-art efficient optimization algorithms
(SPSA)
Future research prospects
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Application of the methodology in more complex situations Incorporation of the distribution of model parameter values
into a simulation model The phenomena observed in the present application should
be explained through further extensive experiments Vehicle dynamics and the correlation between different
parameter values should also be taken into account
A demonstration of distribution-based calibration
I ou l i a MARKOUiou l i a@cent ra l . n tua .gr
National Technical University of Athens, Greece
MT-ITS 2015 3-5. June 2015, Budapest
Vasi leia PAPATHANASOPOULOUvpapath@central.ntua.
gr
Cons tant inos Anton iouanton iou@cent ra l . n tua .gr