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The day activity schedule approach to travel demand analysis

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dc.contributor.advisor Moshe Ben-Akiva. en_US
dc.contributor.author Bowman, John L. (John Lawrence) en_US
dc.contributor.other Massachusetts Institute of Technology. Dept. of Civil and Environmental Engineering. en_US
dc.date.accessioned 2005-05-19T14:23:28Z
dc.date.available 2005-05-19T14:23:28Z
dc.date.issued 1998 en_US
dc.identifier.uri http://hdl.handle.net/1721.1/16731
dc.description Thesis (Ph.D.)--Massachusetts Institute of Technology, Dept. of Civil and Environmental Engineering, 1998. en_US
dc.description Includes bibliographical references (p. 181-184) and index. en_US
dc.description This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections. en_US
dc.description.abstract This study develops a model of a person's day activity schedule that can be used to forecast urban travel demand. It is motivated by the notion that travel outcomes are part of an activity scheduling decision, and uses discrete choice models to address the basic modeling problem-capturing decision interactions among the many choice dimensions of the immense activity schedule choice set. An integrated system of choice models represents a person's day activity schedule as an activity pattern and a set of tours. A pattern model identifies purposes, priorities and structure of the day's activities and travel. Conditional tour models describe timing, location and access mode of on-tour activities. The system captures trade-offs people consider, when faced with space and time constraints, among patterns that can include at-home and on-tour activities, multiple tours and trip chaining. It captures sensitivity of pattern choice to activity and travel conditions through a measure of expected tour utility arising from the tour models. When travel and activity conditions change, the relative attractiveness of patterns changes because expected tour utility changes differently for different patterns. An empirical implementation of the model system for Portland, Oregon, establishes the feasibility of specifying, estimating and using it for forecasting. Estimation results match a priori expectations of lifestyle effects on activity selection, including those of (a) household structure and role, such as for females with children, (b) capabilities, such as income, and (c) activity commitments, such as usual work levels. en_US
dc.description.abstract (cont.) They also confirm the significance of activity and travel accessibility in pattern choice. Application of the model with road pricing and other policies demonstrates its lifestyle effects and how it captures pattern shifting-with accompanying travel changes-that goes undetected by more narrowly focused trip-based and tour-based systems. Although the model has not yet been validated in before-and-after prediction studies, this study gives strong evidence of its behavioral soundness, current practicality, potential to generate cost-effective predictions superior to those of the best existing systems, and potential for enhanced implementations as computing technology advances. en_US
dc.description.statementofresponsibility by John L. Bowman. en_US
dc.format.extent 185 p. en_US
dc.format.extent 657870 bytes
dc.format.extent 657625 bytes
dc.format.mimetype application/pdf
dc.format.mimetype application/pdf
dc.language.iso eng en_US
dc.publisher Massachusetts Institute of Technology en_US
dc.rights 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 permission. See provided URL for inquiries about permission. en_US
dc.rights.uri http://dspace.mit.edu/handle/1721.1/7582
dc.subject Civil and Environmental Engineering. en_US
dc.subject.lcc HE336.T7 B69 en_US
dc.subject.lcsh Traffic estimation en_US
dc.subject.lcsh Traffic surveys en_US
dc.subject.lcsh Commuting en_US
dc.title The day activity schedule approach to travel demand analysis en_US
dc.type Thesis en_US
dc.description.degree Ph.D. en_US
dc.contributor.department Massachusetts Institute of Technology. Dept. of Civil and Environmental Engineering. en_US
dc.identifier.oclc 44704881 en_US


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