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dc.contributor.advisorJinhua Zhao, Haris N. Koutsopoulos and Rabi G. Mishalani.en_US
dc.contributor.authorBasu, Abhishek Arunasisen_US
dc.contributor.otherMassachusetts Institute of Technology. Department of Civil and Environmental Engineering.en_US
dc.date.accessioned2018-11-28T15:43:32Z
dc.date.available2018-11-28T15:43:32Z
dc.date.copyright2018en_US
dc.date.issued2018en_US
dc.identifier.urihttp://hdl.handle.net/1721.1/119331
dc.descriptionThesis: S.M. in Transportation, Massachusetts Institute of Technology, Department of Civil and Environmental Engineering, 2018.en_US
dc.descriptionCataloged from PDF version of thesis.en_US
dc.descriptionIncludes bibliographical references (pages 187-191).en_US
dc.description.abstractTo ensure customer satisfaction, a transit agency must strive to understand and cater to its users' needs. The goal of this research is to develop a framework that could help the transit agency to better understand its users and their behaviors. Segmentation of the market for transit users is the first step, since it allows for the understanding of heterogeneity in their characteristics and their varying requirements, at a granular level as opposed to an aggregate one. In this study, we create a framework, which uses smart card data, to identify customer segments. The framework developed in this study includes a segmentation scheme that creates segments based on the spatial and temporal characteristics of the travel behavior of customers. Data from Hong Kong's MTR system were used to demonstrate the practical application of the developed segmentation methodology. In doing so, a thorough analysis was conducted to interpret the specifics of the identified segments. The segmentation scheme created in this study is capable of catering to meaningful applications that could serve both the agency and the users of the transit system. A few applications explored in the context of this study include the use of the customer segmentation framework for the provision of personalized information. It was demonstrated how targeted information could be provided to users who may likely be affected by a particular service disruption event. In addition, the segmentation framework was used to understand the impact of changes in the network, through a before-and-after analysis where the impact on customer travel patterns due to the provision of service on the newly opened South Island Line is adopted as a case study. Lastly, a predictive transit smart card attrition model was developed by using the features created for the purpose of segmentation. The framework for segmentation developed in this study was found to be useful for multiple applications. Furthermore, the framework is flexible and, therefore, could be generalized for use to address other applications and across other agencies.en_US
dc.description.statementofresponsibilityby Abhishek Arunasis Basu.en_US
dc.format.extent191 pagesen_US
dc.language.isoengen_US
dc.publisherMassachusetts Institute of Technologyen_US
dc.rightsMIT theses are protected by copyright. They may be viewed, downloaded, or printed from this source but further reproduction or distribution in any format is prohibited without written permission.en_US
dc.rights.urihttp://dspace.mit.edu/handle/1721.1/7582en_US
dc.subjectCivil and Environmental Engineering.en_US
dc.titleData-driven customer segmentation and personalized information provision in public transiten_US
dc.typeThesisen_US
dc.description.degreeS.M. in Transportationen_US
dc.contributor.departmentMassachusetts Institute of Technology. Department of Civil and Environmental Engineering
dc.identifier.oclc1065523083en_US


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