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dc.contributor.advisorJinhua Zhao and Haris N. Koutsopoulos.en_US
dc.contributor.authorHalvorsen, Anne (Anne Fire)en_US
dc.contributor.otherMassachusetts Institute of Technology. Department of Civil and Environmental Engineering.en_US
dc.date.accessioned2015-10-30T18:34:17Z
dc.date.available2015-10-30T18:34:17Z
dc.date.copyright2015en_US
dc.date.issued2015en_US
dc.identifier.urihttp://hdl.handle.net/1721.1/99543
dc.descriptionThesis: S.M., Massachusetts Institute of Technology, Department of Civil and Environmental Engineering, 2015.en_US
dc.descriptionThis electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.en_US
dc.descriptionCataloged from student-submitted PDF version of thesis.en_US
dc.descriptionIncludes bibliographical references (pages 169-174).en_US
dc.description.abstractIncreases in ridership are outpacing capacity expansions in a number of transit systems. By shifting their focus to demand management, agencies can instead influence how customers use the system, getting more out of the capacity they already have. However, while demand management is well researched for personal vehicle use, its applications for public transportation are still emerging. This thesis explores the strategies transit agencies can use to reduce overcrowding, with a particular focus of how automatically collected fare data can support the design and evaluation of these measures. A framework for developing demand management policies is introduced to help guide agencies through this process. It includes establishing motivations for the program, aspects to consider in its design, as well as dimensions and metrics to evaluate its impacts. Additional considerations for updating a policy are also discussed, as are the possible data sources and methods for supporting analysis. This framework was applied to a fare incentive strategy implemented at Hong Kong's MTR system. In addition to establishing existing congestion patterns, a customer classification analysis was performed to understand the typical travel patterns among MTR users. These results were used to evaluate the promotion at three levels of customer aggregation: all users, user groups, and a panel of high frequency travelers. The incentive was found to have small but non-negligible impacts on morning travel, particularly at the beginning of the peak hour and among users with commuter-like behavior. Through a change point analysis, it was possible to identify the panel members that responded to the promotion and quantify factors that influenced their decision using a discrete choice model. The findings of these analyses are used to recommend potential improvements to MTR's current scheme.en_US
dc.description.statementofresponsibilityby Anne Halvorsen.en_US
dc.format.extentpagesen_US
dc.language.isoengen_US
dc.publisherMassachusetts Institute of Technologyen_US
dc.rightsM.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.urihttp://dspace.mit.edu/handle/1721.1/7582en_US
dc.subjectCivil and Environmental Engineering.en_US
dc.titleImproving transit demand management with Smart Card data : general framework and applicationsen_US
dc.typeThesisen_US
dc.description.degreeS.M.en_US
dc.contributor.departmentMassachusetts Institute of Technology. Department of Civil and Environmental Engineering
dc.identifier.oclc925478787en_US


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