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Search, information, learning, and k...

Zhang, Lei.

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Search, information, learning, and knowledge in travel decision-making: A positive approach for travel behavior and demand analysis.

紀錄類型:	書目-語言資料,印刷品 : Monograph/item
正題名/作者:	Search, information, learning, and knowledge in travel decision-making: A positive approach for travel behavior and demand analysis./
作者:	Zhang, Lei.
面頁冊數:	241 p.
附註:	Adviser: David M. Levinson.
Contained By:	Dissertation Abstracts International67-07B.
標題:	Engineering, Civil. -
電子資源:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3225789
ISBN:	9780542794322

Search, information, learning, and knowledge in travel decision-making: A positive approach for travel behavior and demand analysis.
Zhang, Lei.

Search, information, learning, and knowledge in travel decision-making: A positive approach for travel behavior and demand analysis. - 241 p.

Adviser: David M. Levinson.

Thesis (Ph.D.)--University of Minnesota, 2006.

The behavioral model developed is also applied to two typical planning and policy analysis scenarios, so that some practical conclusions can be drawn. (Abstract shortened by UMI.)

ISBN: 9780542794322Subjects--Topical Terms:

783781
Engineering, Civil.

Search, information, learning, and knowledge in travel decision-making: A positive approach for travel behavior and demand analysis.
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245 1 0 $a Search, information, learning, and knowledge in travel decision-making: A positive approach for travel behavior and demand analysis.
300 $a 241 p.
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500 $a Source: Dissertation Abstracts International, Volume: 67-07, Section: B, page: 3978.
502 $a Thesis (Ph.D.)--University of Minnesota, 2006.
520 $a The behavioral model developed is also applied to two typical planning and policy analysis scenarios, so that some practical conclusions can be drawn. (Abstract shortened by UMI.)
520 $a Travel demand models provide a foundation for transportation systems analysis, transportation planning, and policy studies. They are based on theories of travel behavior that describe individuals' travel decision-making processes, which are complex, constrained, multidimensional, and dynamic. The normative theorization of travel behavior, seen in a long thread of research that assumes perfection information and rationality, does not meet the emerging needs of developing advanced travel demand models both for its lack of behavioral realism in complex decision situations and for computational difficulties as choice dimensions increase.
520 $a This dissertation addresses this fundamental issue and aims to develop a coherent positive approach for travel behavior and demand analysis. First, a positive theory of travel behavior is developed, which avoids assumptions of complete information and perfect rationality. Instead, the proposed SILK theory defines and illustrates subjective factors related to the travel decision-making process, including spatial knowledge, belief, information acquisition, learning, perception, and heuristics, and explains how travel behavior is formed and adjusted as the result of interactions among these factors. In addition, the SILK theory combines quantified spatial knowledge, behavioral search rules, and Bayesian learning principles to provide a quantitative framework for developing positive travel demand models.
520 $a This research also identifies modeling methods and data needs for the proposed positive approach. A behavioral route choice model is specified, estimated, and validated with positive principles. Modeling methods employed include knowledge representation and acquisition for empirically deriving individual search and decision rules, and agent-based simulation for linking individual behavior to system-level demand. Two empirical studies collect process data, defined as observations of the decision-making process, for the model development using survey and experimental techniques. This modeling work demonstrates that the SILK theoretical framework is able to produce fully operational models of travel decisions. It also shows what and how process data can be collected for constructing positive travel demand models.
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