Neural-embedded discrete choice models

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).

Name thesis:degree_name

Doctoral

Department dc:contributor.department

Massachusetts Institute of Technology. Department of Civil and Environmental Engineering

Grantor dc:publisher

Massachusetts Institute of Technology

Year dc:date.issued

2019

Author dc:creator

• Han, Yafei.

Advisor dc:contributor.advisor

• P. Christopher Zegras, Francisco C. Pereira and Moshe E. Ben-Akiva.

• Civil and Environmental Engineering.

Statement dc: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.

Licence dc:rights.uri

• http://dspace.mit.edu/handle/1721.1/7582

Language dc:language.iso

eng

Handle dc:identifier.uri

https://hdl.handle.net/1721.1/124207

OAI identifier oai:identifier

oai:dspace.mit.edu:1721.1/124207