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Introducing uncertainty into evacuation modeling via dynamic traffic assignment with probabilistic demand and capacity constraints
Descriptive
TypeOfResource
Text
TitleInfo
(ID = T-1)
Title
Introducing uncertainty into evacuation modeling via dynamic traffic assignment with probabilistic demand and capacity constraints
Identifier
ETD_2921
Identifier
(type = hdl)
http://hdl.rutgers.edu/1782.1/rucore10001600001.ETD.000056867
Language
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eng
Genre
(authority = marcgt)
theses
Subject
(ID = SBJ-1);
(authority = RUETD)
Topic
Civil and Environmental Engineering
Subject
(ID = SBJ-2);
(authority = ETD-LCSH)
Topic
Emergency management
Subject
(ID = SBJ-3);
(authority = ETD-LCSH)
Topic
Evacuation of civilians
Subject
(ID = SBJ-4);
(authority = ETD-LCSH)
Topic
Transportation--Planning
Abstract
(type = abstract)
Emergency evacuations are low-probability-high-consequence events that have attracted the attention of researchers since 1960s. An evacuation process can be triggered by various natural (hurricane, flood, tsunami etc.) and man-made (industrial accidents, terrorist attack etc.) events. Regardless of the threat, the nature of the evacuation process involves a very high utilization of the transportation network and searching for plans/strategies to move large number of people to a safe place in the shortest possible time. Researchers from different disciplines approach to the evacuation problem from different perspectives. Two major components of any evacuation event are estimation of the evacuation demand and traffic analysis to make planning inferences about the evacuation performance measures such as clearance time. Although related studies and real-life practices show a significant uncertainty regarding the evacuation demand due to the unpredictability of human behavior and changing roadway as a result of disaster impacts, the state-of-the-practice does not consider this type of randomness. This dissertation aims to address this important gap by proposing a dynamic traffic assignment formulation with probabilistic constraints that takes into account uncertainties in demand and roadway capacities. The proposed model uses a cell transmission model based system optimal dynamic traffic assignment formulation. The demand and roadway capacities are assumed to follow a discrete random distribution and the p-level efficient points approach [115] is employed to solve the proposed model. Two numerical examples regarding the use of the model are provided. The numerical examples also discuss the implications using individual chance constraints vs. joint chance constraints which provide different interpretations for the reliability of the results. Overall, the proposed formulation generates evacuation time performance measures that can be interpreted within reliability measures rather than single deterministic point estimates that would not be necessarily observed during a real life test, mainly due to high level of uncertainty created by human behavior and capacity impacts of the disaster.
PhysicalDescription
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electronic resource
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x, 118 p. : ill.
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Note
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Ph.D.
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Includes bibliographical references
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(type = vita)
Includes vita
Note
(type = statement of responsibility)
by Mustafa Anil Yazici
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Yazici
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Mustafa Anil
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1979-
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author
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Mustafa Yazici
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Ozbay
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Kaan
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chair
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Kaan Ozbay
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(type = family)
Boile
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Maria
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internal member
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Advisory Committee
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Maria Boile
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(type = family)
Nassif
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Hani
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internal member
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Advisory Committee
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Hani Nassif
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(type = family)
Chatman
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(type = given)
Daniel G.
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outside member
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Advisory Committee
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Daniel G. Chatman
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Rutgers University
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degree grantor
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Graduate School - New Brunswick
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school
OriginInfo
DateCreated
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2010
DateOther
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2010-10
Place
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(type = code)
xx
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TitleInfo
Title
Rutgers University Electronic Theses and Dissertations
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ETD
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Title
Graduate School - New Brunswick Electronic Theses and Dissertations
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rucore19991600001
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NjNbRU
Identifier
(type = doi)
doi:10.7282/T3Q52PBV
Genre
(authority = ExL-Esploro)
ETD doctoral
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Rights
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(ID = rulibRdec0006)
The author owns the copyright to this work.
Copyright
Status
Copyright protected
Availability
Status
Open
Reason
Permission or license
RightsHolder
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(type = personal)
Name
FamilyName
Yazici
GivenName
Mustafa
Role
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Permission or license
DateTime
2010-09-28 15:07:21
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Mustafa Yazici
Affiliation
Rutgers University. Graduate School - New Brunswick
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Author Agreement License
Detail
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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ETD
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application/pdf
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application/x-tar
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1802240
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