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More Efficient Learning in Traffic G...

Lane, Marion Charles.

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More Efficient Learning in Traffic Grids Viewed as Complex Adaptive Systems Using Agent Based Modeling.

Record Type:	Electronic resources : Monograph/item
Title/Author:	More Efficient Learning in Traffic Grids Viewed as Complex Adaptive Systems Using Agent Based Modeling./
Author:	Lane, Marion Charles.
Published:	Ann Arbor : ProQuest Dissertations & Theses, : 2017,
Description:	70 p.
Notes:	Source: Dissertations Abstracts International, Volume: 79-02, Section: B.
Contained By:	Dissertations Abstracts International79-02B.
Subject:	Information Technology. -
Online resource:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=10284919
ISBN:	9780355041729

More Efficient Learning in Traffic Grids Viewed as Complex Adaptive Systems Using Agent Based Modeling.
Lane, Marion Charles.

More Efficient Learning in Traffic Grids Viewed as Complex Adaptive Systems Using Agent Based Modeling. - Ann Arbor : ProQuest Dissertations & Theses, 2017 - 70 p.

Source: Dissertations Abstracts International, Volume: 79-02, Section: B.

Thesis (Ph.D.)--The University of North Carolina at Charlotte, 2017.

This item must not be sold to any third party vendors.

Equation based modeling (EBM) is the most common form of scientific modeling. However, the creation of an appropriate EBM for a large system is likely to be complicated and computationally expensive. In contrast, consider the possibilities if even a large system is treated as a complex adaptive system (CAS), applying the basic tenets of a CAS to a model using the concepts of agent based modeling (ABM). ABM requires only the definition of the model environment, the identification of key agents, and a minimum number of key behaviors of those agents. It was a contention of this study that a CAS/ABM model would be easier to design, implement, execute, and extend than an EBM model. It was also a contention that the results would still be predictive and useful. The ABM herein was based on the Uptown Charlotte, North Carolina, traffic grid using traffic volumes based on actual Charlotte traffic counts. It operated with autonomous traffic signals as an adaptive CAS.

ISBN: 9780355041729Subjects--Topical Terms:

1030799
Information Technology.

More Efficient Learning in Traffic Grids Viewed as Complex Adaptive Systems Using Agent Based Modeling.
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500 $a Source: Dissertations Abstracts International, Volume: 79-02, Section: B.
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500 $a Advisor: Hadzikadic, Mirsad.
502 $a Thesis (Ph.D.)--The University of North Carolina at Charlotte, 2017.
506 $a This item must not be sold to any third party vendors.
520 $a Equation based modeling (EBM) is the most common form of scientific modeling. However, the creation of an appropriate EBM for a large system is likely to be complicated and computationally expensive. In contrast, consider the possibilities if even a large system is treated as a complex adaptive system (CAS), applying the basic tenets of a CAS to a model using the concepts of agent based modeling (ABM). ABM requires only the definition of the model environment, the identification of key agents, and a minimum number of key behaviors of those agents. It was a contention of this study that a CAS/ABM model would be easier to design, implement, execute, and extend than an EBM model. It was also a contention that the results would still be predictive and useful. The ABM herein was based on the Uptown Charlotte, North Carolina, traffic grid using traffic volumes based on actual Charlotte traffic counts. It operated with autonomous traffic signals as an adaptive CAS.
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