2026-06-23T18:32:52Zhttps://riubu.ubu.es/oai/requestoai:riubu.ubu.es:10259/68622024-05-20T08:00:07Zcom_10259.4_104com_10259_2604col_10259_6848
Martín-Baos, José Ángel
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García-Ródenas, Ricardo
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Rodriguez-Benitez, Luis
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2022-09-15T11:06:26Z
2022-09-15T11:06:26Z
2021-07
978-84-18465-12-3
http://hdl.handle.net/10259/6862
10.36443/10259/6862
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
In the last few years, Machine Learning (ML) methods have acquired great popularity due
to their success in numerous applications such as autonomous cars, image and voice
recognition systems, automatic translation systems, etc. This success has led to an increase
in the use of ML methods and the extension of their applications to areas such as transport
planning.
One of the main tasks within transport planning is the analysis of transport demand. To do
so, it is necessary to analyse the way in which users make their decisions about the trips they
make and, therefore, be able to predict the number of passengers on the transport network in
relation to respect to interventions made on the transport system. Consequently, transport
policies and plans can be evaluated according to the behaviour of the passengers. Discrete
choice models based on random utility maximization have been developed over the last four
decades and currently they have acquired a high degree of sophistication, becoming the
canonical tool for transport demand analysis. Nowadays, the use of ML methods could
provide an alternative to discrete choice models, as they offer a high level of accuracy in
their predictions. In addition, the analyst is relieved from the need of specifying the
functional expressions for the utility functions beforehand.
A Python software package called PyKernelLogit was developed to apply a ML method
called 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 of
those using a Penalized Maximum Likelihood Estimation procedure. Moreover, this tool
also provides a set of indicators for goodness of fit and the application of model validation
techniques. Finally, it allows to obtain the willingness to pay or value of time indicators
commonly used in transport planning.
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.
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Universidad de Burgos. Servicio de Publicaciones e Imagen Institucional
R-Evolucionando el transporte
http://hdl.handle.net/10259/6490
https://doi.org/10.36443/9788418465123
info:eu-repo/grantAgreement/MICIU/Plan Estatal de Investigación Científica y Técnica y de Innovación 2017-2020/FPU18%2F00802
info:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2013-2016/TRA2016-76914-C3-2-P/ES/ROBUSTEZ, EFICIENCIA Y RECUPERACION DE SISTEMAS DE TRANSPORTE PUBLICO
Big Data
Ingeniería civil
Transportes
Civil engineering
Transportation
A Python package for performing penalized maximum likelihood estimation of conditional logit models using Kernel Logistic Regression
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2024-05-20 10:00:07.155
Repositorio Institucional de la Universidad de Burgos
bubrep@ubu.es
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