Deep neural networks for choice analysis

As deep neural networks (DNNs) outperform classical discrete choice models (DCMs) in many empirical studies, one pressing question is how to reconcile them in the context of choice analysis. So far researchers mainly compare their prediction accuracy, treating them as completely different modeling methods. However, DNNs and classical choice models are closely related and even complementary. This dissertation seeks to lay out a new foundation of using DNNs for choice analysis. It consists of three essays, which respectively tackle the issues of economic interpretation, architectural design, and robustness of DNNs by using classical utility theories. Essay 1 demonstrates that DNNs can provide economic information as complete as the classical DCMs.

Name thesis:degree_name

Doctoral

Department dc:contributor.department

Massachusetts Institute of Technology. Department of Urban Studies and Planning

Grantor dc:publisher

Massachusetts Institute of Technology

Year dc:date.issued

2020

Author dc:creator

• Wang, Shenhao.

Advisor dc:contributor.advisor

• Jinhua Zhao.

• Urban Studies and Planning.

Statement dc:rights

• MIT theses may be protected by copyright. Please reuse MIT thesis content according to the MIT Libraries Permissions Policy, which is available through the URL provided.

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/1721.1/129894

OAI identifier oai:identifier

oai:dspace.mit.edu:1721.1/129894