RT info:eu-repo/semantics/conferenceObject
T1 A Python package for performing penalized maximum likelihood estimation of conditional logit models using Kernel Logistic Regression
A1 Martín-Baos, José Ángel
A1 García-Ródenas, Ricardo
A1 Rodriguez-Benitez, Luis
K1 Big Data
K1 Ingeniería civil
K1 Civil engineering
K1 Transporte
K1 Transportation
AB In the last few years, Machine Learning (ML) methods have acquired great popularity dueto their success in numerous applications such as autonomous cars, image and voicerecognition systems, automatic translation systems, etc. This success has led to an increasein the use of ML methods and the extension of their applications to areas such as transportplanning.One of the main tasks within transport planning is the analysis of transport demand. To doso, it is necessary to analyse the way in which users make their decisions about the trips theymake and, therefore, be able to predict the number of passengers on the transport network inrelation to respect to interventions made on the transport system. Consequently, transportpolicies and plans can be evaluated according to the behaviour of the passengers. Discretechoice models based on random utility maximization have been developed over the last fourdecades and currently they have acquired a high degree of sophistication, becoming thecanonical tool for transport demand analysis. Nowadays, the use of ML methods couldprovide an alternative to discrete choice models, as they offer a high level of accuracy intheir predictions. In addition, the analyst is relieved from the need of specifying thefunctional expressions for the utility functions beforehand.A Python software package called PyKernelLogit was developed to apply a ML methodcalled Kernel Logistic Regression (KLR) to the problem of predicting the transport demand.This package allows the user to specify a set of models using KLR and the estimation ofthose using a Penalized Maximum Likelihood Estimation procedure. Moreover, this toolalso provides a set of indicators for goodness of fit and the application of model validationtechniques. Finally, it allows to obtain the willingness to pay or value of time indicatorscommonly used in transport planning.
PB Universidad de Burgos. Servicio de Publicaciones e Imagen Institucional
SN 978-84-18465-12-3
YR 2021
FD 2021-07
LK http://hdl.handle.net/10259/6862
UL http://hdl.handle.net/10259/6862
LA eng
NO Trabajo presentado en: R-Evolucionando el transporte, XIV Congreso de Ingeniería del Transporte (CIT 2021), realizado en modalidad online los días 6, 7 y 8 de julio de 2021, organizado por la Universidad de Burgos
NO The research of Martín-Baos has been supported by the FPU Predoctoral Program of the Spanish Ministry of Science, Innovation and Universities (Grant Ref. FPU18/00802); and the research of Martín-Baos and García-Ródenas was supported by Project TRA2016- 76914-C3-2-P of the Spanish Ministry of Science and Innovation, co-funded by the European Regional Development Fund.
DS Repositorio Institucional de la Universidad de Burgos
RD 19-abr-2024