A demonstration of distribution-based calibration Ioulia MARKOU, Vasileia PAPATHANASOPOULOU,...

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

MT-ITS 2015 3-5. JUNE 2015, BUDAPEST 2

Capturing heterogeneity


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


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


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


SPSA Algorithm

Overview


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


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


Determination of calibration parameters

Collection of historical measurements

Selection of calibration algorithm

Choice of Loss Function

Start of Calibration

Experimental set-up


Car-following model of TransModeler Traffic Simulation Software

Experimental set-up


Experimental set-up


City of Naples, Italy

October 2002(Punzo et al., 2005, Papathanasopoulou and Antoniou, 2012)

Application


Results


No. of necessary iteration sets for SPSA termination

No. of iteration sets

No.

of r

ecor

ds

Results


Acceleration Deceleration

Results


Driver 1

Driver 2

Driver 3

Results


Driver 2mean = -1,16



Conclusion


Developed distributions of car-following model parameters

Captured heterogeneous driver behavior

Utilized state-of-the-art efficient optimization algorithms

(SPSA)

Future research prospects


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 [email protected].

gr

Cons tant inos Anton iouanton iou@cent ra l . n tua .gr

A demonstration of distribution-based calibration Ioulia MARKOU, Vasileia PAPATHANASOPOULOU,...

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