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dc.contributor.advisorMoshe E. Ben-Akiva and Bilge Atasoy.en_US
dc.contributor.authorDanaf, Mazen(Mazen Salah)en_US
dc.contributor.otherMassachusetts Institute of Technology. Department of Civil and Environmental Engineering.en_US
dc.date.accessioned2019-12-13T18:53:06Z
dc.date.available2019-12-13T18:53:06Z
dc.date.copyright2019en_US
dc.date.issued2019en_US
dc.identifier.urihttps://hdl.handle.net/1721.1/123227
dc.descriptionThesis: Ph. D. in Transportation, Massachusetts Institute of Technology, Department of Civil and Environmental Engineering, 2019en_US
dc.descriptionCataloged from PDF version of thesis.en_US
dc.descriptionIncludes bibliographical references (pages 100-108).en_US
dc.description.abstractDiscrete choice models have been widely applied in different fields to better understand behavior and forecast market shares. Because of their ability to capture taste heterogeneity, logit mixture models have gained increasing interest among researchers and practitioners. However, since the estimation of these models is computationally expensive, their applications have been limited to offline contexts. On the other hand, online applications (such as recommender systems) require users' preferences to be updated frequently and dynamically. The objective of this dissertation is to develop a methodology for estimating discrete choice models online, while accounting for inter- and intra-consumer heterogeneity. An offline-online framework is proposed to update individual-specific parameters after each choice using Bayesian estimation.en_US
dc.description.abstractThe online estimator is computationally efficient, as it uses the data of the individual making the choice only in updating his/her individual preferences. Periodically, data from multiple individuals are pooled, and population parameters are updated offline. Online estimation allows for new and innovative applications of discrete choice models such as personalized recommendations, dynamic personalized pricing, and real-time individual forecasting. This methodology subsumes the utility-based advantages of discrete choice models and the personalization capabilities of common recommendation techniques by making use of all the available data including user-specific, item specific, and contextual variables. In order to enhance online learning, two extensions are proposed to the logit mixture model with inter- and intra-consumer heterogeneity.en_US
dc.description.abstractIn the first extension, socio-demographic variables and contextual variables are used to model systematic inter- and intra-consumer taste heterogeneity respectively. In the second extension, a latent class model is used to allow for more flexibility in modeling the inter- and intra-consumer mixing distributions. Finally, the online estimation methodology is applied to Tripod, an app-based travel advisor that aims to incentivize and shift travelers' behavior towards more sustainable alternatives. Stated preferences data are collected in the Greater Boston Area and used to estimate the population parameters, which are then used by the app in online estimation. Using the collected data, a large number of synthetic users is simulated, and the recommendation system is tested over several days, and under different scenarios. The results show that the average hit-rate generally increases over time as we learn individual preferences and population parameters.en_US
dc.description.sponsorship"funding from the Advanced Research Projects Agency-Energy (ARPA-E), Ford, the Civil and Environmental Engineering Department at MIT, and the MIT-Singapore Alliance for Research and Technology (SMART)"--Page 5en_US
dc.description.statementofresponsibilityby Mazen Danaf.en_US
dc.format.extent118 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.titleOnline discrete choice models : applications in smart mobilityen_US
dc.typeThesisen_US
dc.description.degreePh. D. in Transportationen_US
dc.contributor.departmentMassachusetts Institute of Technology. Department of Civil and Environmental Engineeringen_US
dc.identifier.oclc1129589503en_US
dc.description.collectionPh.D.inTransportation Massachusetts Institute of Technology, Department of Civil and Environmental Engineeringen_US
dspace.imported2019-12-13T18:53:06Zen_US
mit.thesis.degreeDoctoralen_US
mit.thesis.departmentCivEngen_US


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