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Calibration of traffic simulation models using Simultaneous Perturbation Stochastic Approximation (SPSA) method extended through Bayesian sampling methodology
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Calibration of traffic simulation models using Simultaneous Perturbation Stochastic Approximation (SPSA) method extended through Bayesian sampling methodology
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Lee
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Jung-Beom
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Jung-Beom Lee
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Kaan
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Advisory Committee
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Kaan Ozbay
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Hani
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Advisory Committee
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Hani H. Nassif
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Boile
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Maria
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Advisory Committee
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Maria P. Boile
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Brail
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Richard
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Advisory Committee
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Richard K. Brail
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Rutgers University
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Graduate School - New Brunswick
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theses
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2008
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2008-10
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English
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electronic
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xiii, 157 pages
Abstract
The main goal of this dissertation is to propose a new methodology for the calibration of traffic simulation models. Simulation is useful in representing complex real-world systems, and many alternatives can be compared via different system designs. However, to evaluate road conditions accurately, the selection of model parameters to be calibrated and the calibration methodology are very important aspects of the overall simulation modeling process.
One of the key elements of this dissertation is the application of the Simultaneous Perturbation Stochastic Approximation (SPSA) algorithm (Spall (1992))--one of the well-known stochastic approximation (SA) algorithms, to determine optimal model parameters. The SPSA algorithm has an inherent advantage that can be exploited in both stochastic gradient and gradient-free settings; it can also be applied to solve optimization problems that have a large number of variables.
One of the main distinctions between this study and previous studies is with regards to calibration while considering a wide range of all likely demand conditions. Previous studies on calibration have focused on minimizing a deterministic objective function, which is the sum of the relative error between the observed data and the simulation output from a certain time period in a typical day. Even though this approach can be considered a calibration that uses data obtained at one point in time, this type of calibration approach cannot capture a realistic distribution of all possible traffic conditions. Thus, a more general calibration methodology needs to be implemented--one that enables use with any traffic condition. In this dissertation, we propose the Bayesian sampling approach, in conjunction with the application of the SPSA stochastic optimization method, which enables the modeler to enhance the theoretic application to consider statistical data distribution. Thus, this proposed new and advanced methodology makes it possible to overcome the limitations of previous calibration studies.
Testing the methodology for larger networks, as well as for other microscopic traffic simulation tools such as CORSIM or VISSIM, are future research tasks. In the future, other simulation parameters and more extensive data sets can be used to test the strengths and weaknesses of the proposed calibration methodology.
One of the key elements of this dissertation is the application of the Simultaneous Perturbation Stochastic Approximation (SPSA) algorithm (Spall (1992))--one of the well-known stochastic approximation (SA) algorithms, to determine optimal model parameters. The SPSA algorithm has an inherent advantage that can be exploited in both stochastic gradient and gradient-free settings; it can also be applied to solve optimization problems that have a large number of variables.
One of the main distinctions between this study and previous studies is with regards to calibration while considering a wide range of all likely demand conditions. Previous studies on calibration have focused on minimizing a deterministic objective function, which is the sum of the relative error between the observed data and the simulation output from a certain time period in a typical day. Even though this approach can be considered a calibration that uses data obtained at one point in time, this type of calibration approach cannot capture a realistic distribution of all possible traffic conditions. Thus, a more general calibration methodology needs to be implemented--one that enables use with any traffic condition. In this dissertation, we propose the Bayesian sampling approach, in conjunction with the application of the SPSA stochastic optimization method, which enables the modeler to enhance the theoretic application to consider statistical data distribution. Thus, this proposed new and advanced methodology makes it possible to overcome the limitations of previous calibration studies.
Testing the methodology for larger networks, as well as for other microscopic traffic simulation tools such as CORSIM or VISSIM, are future research tasks. In the future, other simulation parameters and more extensive data sets can be used to test the strengths and weaknesses of the proposed calibration methodology.
Note
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Ph.D.
Note
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Includes bibliographical references (p. 144-148).
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Civil and Environmental Engineering
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Traffic engineering--Mathematical models
Subject
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Traffic flow--Simulation methods
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Graduate School - New Brunswick Electronic Theses and Dissertations
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rucore19991600001
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doi:10.7282/T3668DHR
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ETD doctoral
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Jung-Beom Lee
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Rutgers University. Graduate School - New Brunswick
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I hereby grant to the Rutgers University Libraries and to my school the non-exclusive right to archive, reproduce and distribute my thesis or dissertation, in whole or in part, and/or my abstract, in whole or in part, in and from an electronic format, subject to the release date subsequently stipulated in this submittal form and approved by my school. I represent and stipulate that the thesis or dissertation and its abstract are my original work, that they do not infringe or violate any rights of others, and that I make these grants as the sole owner of the rights to my thesis or dissertation and its abstract. I represent that I have obtained written permissions, when necessary, from the owner(s) of each third party copyrighted matter to be included in my thesis or dissertation and will supply copies of such upon request by my school. I acknowledge that RU ETD and my school will not distribute my thesis or dissertation or its abstract if, in their reasonable judgment, they believe all such rights have not been secured. I acknowledge that I retain ownership rights to the copyright of my work. I also retain the right to use all or part of this thesis or dissertation in future works, such as articles or books.
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