Online discrete choice models : applications in smart mobility

Discrete 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.

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

• Danaf, Mazen(Mazen Salah)

Advisor dc:contributor.advisor

• Moshe E. Ben-Akiva and Bilge Atasoy.

• 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/1721.1/123227

OAI identifier oai:identifier

oai:dspace.mit.edu:1721.1/123227