2026-09-19T04:39:04Zhttps://dspace.mit.edu/server/oai/requestoai:dspace.mit.edu:1721.1/626562022-01-13T07:54:29Zcom_1721.1_7582com_1721.1_7581col_1721.1_131023
Deb K. Roy.
Jitapunkul, Thananat
Massachusetts Institute of Technology. Dept. of Electrical Engineering and Computer Science.
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
2011-05-09T15:15:22Z
2011-05-09T15:15:22Z
2010
2010
http://hdl.handle.net/1721.1/62656
713705823
Thesis (M. Eng.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2010.
Cataloged from PDF version of thesis.
Includes bibliographical references (p. 105-107).
Parameter search in an agent-based model of pedestrian movement in retail environments is part of a research effort by data-driven architecture in the Cognitive Machine Group at the MIT Media Lab. The approach pursued in this thesis is agent-based modeling, with an ultimate goal to use generative behaviors in agents to study effects of architectural and managerial decisions on retail environments. In this thesis, I designed and implemented an agent training module as a part of a software system which simulates and learns patterns of human pedestrian movement in retail environments. This thesis covers two different components: (1) the implementation of a hill-climbing training module and (2) a pedestrian path comparison metric. To measure the module's performance, the system is tested against video sequences collected from the actual retail environment.
by Thananat Jitapunkul.
M.Eng.
107 p.
eng
Massachusetts Institute of Technology
M.I.T. theses are protected by
copyright. They may be viewed from this source for any purpose, but
reproduction or distribution in any format is prohibited without written
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http://dspace.mit.edu/handle/1721.1/7582
Electrical Engineering and Computer Science.
Parameter search in an agent-based model of pedestrian movement in retail environments
Thesis
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<Abstract>Parameter search in an agent-based model of pedestrian movement in retail environments is part of a research effort by data-driven architecture in the Cognitive Machine Group at the MIT Media Lab. The approach pursued in this thesis is agent-based modeling, with an ultimate goal to use generative behaviors in agents to study effects of architectural and managerial decisions on retail environments. In this thesis, I designed and implemented an agent training module as a part of a software system which simulates and learns patterns of human pedestrian movement in retail environments. This thesis covers two different components: (1) the implementation of a hill-climbing training module and (2) a pedestrian path comparison metric. To measure the module's performance, the system is tested against video sequences collected from the actual retail environment.</Abstract>
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