{"id":{"repo_id":"mit","oai_identifier":"oai:dspace.mit.edu:1721.1/124207"},"canonical_url":"https://search.dev.ndltd.org/etd/mit/oai:dspace.mit.edu:1721.1/124207","repository":{"repo_id":"mit","name":"MIT","base_url":"https://dspace.mit.edu/oai/request"},"display":{"title":"Neural-embedded discrete choice models","abstract":"This dissertation is motivated by the possible value of integrating theory-based discrete choice models (DCM) and data-driven neural networks. How to benefit from the strengths of both is the overarching question. I propose hybrid structures and strategies to flexibly represent taste heterogeneity, reduce potential biases, and improve predictability while keeping model interpretability. Also, I utilize neural networks' training machinery to speed up and scale up the estimation of Latent Class Choice Models (LCCMs). First, I embed neural networks in DCMs to enable flexible representations of taste heterogeneity and enhance prediction accuracy. I propose two neural-embedded choice models: TasteNet-MNL and nonlinear-LCCM. Both models provide a flexible specification of taste as a function of individual characteristics. TasteNet-MNL extends the Multinomial Logit Model (MNL).","abstract_html":"This dissertation is motivated by the possible value of integrating theory-based discrete choice models (DCM) and data-driven neural networks. How to benefit from the strengths of both is the overarching question. I propose hybrid structures and strategies to flexibly represent taste heterogeneity, reduce potential biases, and improve predictability while keeping model interpretability. Also, I utilize neural networks' training machinery to speed up and scale up the estimation of Latent Class Choice Models (LCCMs). First, I embed neural networks in DCMs to enable flexible representations of taste heterogeneity and enhance prediction accuracy. I propose two neural-embedded choice models: TasteNet-MNL and nonlinear-LCCM. Both models provide a flexible specification of taste as a function of individual characteristics. TasteNet-MNL extends the Multinomial Logit Model (MNL).","abstract_has_math":false,"creators":["Han, Yafei."],"institution":"Massachusetts Institute of Technology","degree_name":"Doctoral","degree_level":null,"degree_discipline":null,"degree_department":"Massachusetts Institute of Technology. Department of Civil and Environmental Engineering","school":null,"contributors":[],"advisors":["P. Christopher Zegras, Francisco C. Pereira and Moshe E. Ben-Akiva."],"committee_chairs":[],"committee_members":[],"year":2019,"date_issued":"2019","date_published":"2019","updated_at":"2026-07-22T22:21:43Z","subjects":["Civil and Environmental Engineering."],"languages":["eng"],"rights":["MIT 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."],"rights_urls":["http://dspace.mit.edu/handle/1721.1/7582"],"identifier_entries":[]},"links":{"outbound_url":"https://hdl.handle.net/1721.1/124207","outbound_label":"Handle","outbound_source":"dc:identifier.uri"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor.advisor","label":"Advisor","values":["P. Christopher Zegras, Francisco C. Pereira and Moshe E. Ben-Akiva."]},{"key":"dc:contributor.department","label":"Department","values":["Massachusetts Institute of Technology. Department of Civil and Environmental Engineering","CivEng"]},{"key":"dc:contributor.other","label":"Dc Contributor Other","values":["Massachusetts Institute of Technology. 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They may be viewed, downloaded, or printed from this source but further reproduction or distribution in any format is prohibited without written permission."]},{"key":"dc:rights.uri","label":"Rights URI","values":["http://dspace.mit.edu/handle/1721.1/7582"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier.uri","label":"Identifier URI","values":["https://hdl.handle.net/1721.1/124207"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description","label":"Description","values":["This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.","Thesis: Ph. D., Massachusetts Institute of Technology, Department of Civil and Environmental Engineering, 2019","Cataloged from student-submitted PDF version of thesis.","Includes bibliographical references (pages 131-139)."]},{"key":"dc:description.abstract","label":"Abstract","values":["This dissertation is motivated by the possible value of integrating theory-based discrete choice models (DCM) and data-driven neural networks. How to benefit from the strengths of both is the overarching question. I propose hybrid structures and strategies to flexibly represent taste heterogeneity, reduce potential biases, and improve predictability while keeping model interpretability. Also, I utilize neural networks' training machinery to speed up and scale up the estimation of Latent Class Choice Models (LCCMs). First, I embed neural networks in DCMs to enable flexible representations of taste heterogeneity and enhance prediction accuracy. I propose two neural-embedded choice models: TasteNet-MNL and nonlinear-LCCM. Both models provide a flexible specification of taste as a function of individual characteristics. TasteNet-MNL extends the Multinomial Logit Model (MNL).","A feed-forward neural network (TasteNet) is utilized to predict taste parameters as a nonlinear function of individual characteristics. Taste parameters generated by TasteNet are further fed into a parametric logit model to formulate choice probabilities. I demonstrate the effectiveness of this integrated model in capturing nonlinearity in tastes without a priori knowledge. Using synthetic data, TasteNet-MNL is able to recover the underlying utility specification and predict more accurately than some misspecified MNLs and continuous mixed logit models. TasteNet-MNL also provides interpretations close to the ground truth. In an application to a public dataset (Swissmetro), TasteNet-MNL achieves the best out-of-sample prediction accuracy and discovers a broader spectrum of taste variation than the benchmark MNLs with linear utility specifications. Nonlinear-LCCM enriches the class membership model of a typical LCCM.","I represent an LCCM by a neural network and add hidden layers with nonlinear transformations to its class membership model. The nonlinearity introduced by the neural network provides a flexible approximation of the mixing distribution for both systematic and random taste heterogeneity. I apply this method to model Swissmetro mode choice. The nonlinear-LCCM outperforms an LCCM with a linear class membership model with respect to the out-of-sample prediction accuracy. Nonlinear-LCCM also provides interpretable taste parameters for each latent class."]},{"key":"dc:description.degree","label":"Dc Description Degree","values":["Ph. D."]},{"key":"dc:title","label":"Title","values":["Neural-embedded discrete choice models"]}]}],"canonical_facts":{"dc:contributor.advisor":["P. Christopher Zegras, Francisco C. Pereira and Moshe E. Ben-Akiva."],"dc:contributor.department":["Massachusetts Institute of Technology. Department of Civil and Environmental Engineering","CivEng"],"dc:contributor.other":["Massachusetts Institute of Technology. Department of Civil and Environmental Engineering."],"dc:creator":["Han, Yafei."],"dc:date.accessioned":["2020-03-23T20:45:28Z"],"dc:date.available":["2020-03-23T20:45:28Z"],"dc:date.issued":["2019"],"dc:description":["This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.","Thesis: Ph. 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Both models provide a flexible specification of taste as a function of individual characteristics. TasteNet-MNL extends the Multinomial Logit Model (MNL).","A feed-forward neural network (TasteNet) is utilized to predict taste parameters as a nonlinear function of individual characteristics. Taste parameters generated by TasteNet are further fed into a parametric logit model to formulate choice probabilities. I demonstrate the effectiveness of this integrated model in capturing nonlinearity in tastes without a priori knowledge. Using synthetic data, TasteNet-MNL is able to recover the underlying utility specification and predict more accurately than some misspecified MNLs and continuous mixed logit models. TasteNet-MNL also provides interpretations close to the ground truth. In an application to a public dataset (Swissmetro), TasteNet-MNL achieves the best out-of-sample prediction accuracy and discovers a broader spectrum of taste variation than the benchmark MNLs with linear utility specifications. Nonlinear-LCCM enriches the class membership model of a typical LCCM.","I represent an LCCM by a neural network and add hidden layers with nonlinear transformations to its class membership model. The nonlinearity introduced by the neural network provides a flexible approximation of the mixing distribution for both systematic and random taste heterogeneity. I apply this method to model Swissmetro mode choice. The nonlinear-LCCM outperforms an LCCM with a linear class membership model with respect to the out-of-sample prediction accuracy. Nonlinear-LCCM also provides interpretable taste parameters for each latent class."],"dc:description.degree":["Ph. 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