Discrete choice modeling using Kernel Logistic Regression
22nd Euro Working Group on Transportation Meeting (EWGT 2019)
José Ángel Martín Baos,
Abstract
The Kernel Logistic Regression is a popular technique in machine learning. In this work this tech- nique is applied to the field of discrete choice modeling. This approach is equivalent to specifying non-parametric utilities in random utility models. A Monte Carlo simulation experiment has been carried out to compare this approach with Multinomial Logit models, comparing the goodness of fit and the capability of obtaining the specified utilities.
José Ángel Martín Baos is an Assistant Professor (PhD) at the Escuela Superior de Informática of the University of Castilla-La Mancha. He obtained his PhD in Advanced Computer Technologies from the University of Castilla-La Mancha in 2023. There, he graduated with honors on both a BSc. in computer science in the year 2018, and a MSc. in computer science in the year 2019.
Since 2016, José Ángel has been working as a researcher in the MAT (Modelos y Algoritmos en Sistemas de Transportes) research group at the University of Castilla-La Mancha. His research focuses on the use of machine learning models and artificial intelligence for solving optimization problems in the field of transportation. Specifically, he has worked on the application of machine learning models for transport demand modeling.