A comparative study of machine learning, deep neural networks and random utility maximization models for travel mode choice modelling

Traditionally, Random Utility Maximization (RUM) models have been widely applied to travel mode choice modelling. Currently, Machine Learning (ML) models are being applied as an alternative to RUM models, since they provide better results in terms of prediction capability and they can manage large v...

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Autores: García García, José Carlos, García Ródenas, Ricardo, López Gómez, Julio Alberto, Martín Baos, José Ángel
Tipo de recurso: artículo
Fecha de publicación:2022
País:España
Institución:Universidad de Castilla-La Mancha
Repositorio:RUIdeRA. Repositorio Institucional de la UCLM
OAI Identifier:oai:ruidera.uclm.es:10578/32968
Acceso en línea:https://www.sciencedirect.com/science/article/pii/S2352146522001740
https://hdl.handle.net/10578/32968
Access Level:acceso abierto
Palabra clave:Random Utility Maximization models
Deep Neural Networks
Machine Learning models
Travel behaviour
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spelling A comparative study of machine learning, deep neural networks and random utility maximization models for travel mode choice modellingGarcía García, José CarlosGarcía Ródenas, RicardoLópez Gómez, Julio AlbertoMartín Baos, José ÁngelRandom Utility Maximization modelsDeep Neural NetworksMachine Learning modelsTravel behaviourTraditionally, Random Utility Maximization (RUM) models have been widely applied to travel mode choice modelling. Currently, Machine Learning (ML) models are being applied as an alternative to RUM models, since they provide better results in terms of prediction capability and they can manage large volumes of data. In this paper, a comprehensive comparison between classic RUM models and ML models, including single and ensemble classifiers as well as Deep Neural Networks (DNNs), is provided in order to assess systematically the performance of different models over two different datasets which have different sizes and nature of data. Numerical experiments show Random Forest (RF) is the best classifier in terms of accuracy index and the computational cost to train the model.Elsevier202420242022info:eu-repo/semantics/articleapplication/pdfapplication/pdfhttps://www.sciencedirect.com/science/article/pii/S2352146522001740https://hdl.handle.net/10578/32968reponame:RUIdeRA. Repositorio Institucional de la UCLMinstname:Universidad de Castilla-La ManchaInglésinfo:eu-repo/semantics/openAccessoai:ruidera.uclm.es:10578/329682026-05-27T07:36:41Z
dc.title.none.fl_str_mv A comparative study of machine learning, deep neural networks and random utility maximization models for travel mode choice modelling
title A comparative study of machine learning, deep neural networks and random utility maximization models for travel mode choice modelling
spellingShingle A comparative study of machine learning, deep neural networks and random utility maximization models for travel mode choice modelling
García García, José Carlos
Random Utility Maximization models
Deep Neural Networks
Machine Learning models
Travel behaviour
title_short A comparative study of machine learning, deep neural networks and random utility maximization models for travel mode choice modelling
title_full A comparative study of machine learning, deep neural networks and random utility maximization models for travel mode choice modelling
title_fullStr A comparative study of machine learning, deep neural networks and random utility maximization models for travel mode choice modelling
title_full_unstemmed A comparative study of machine learning, deep neural networks and random utility maximization models for travel mode choice modelling
title_sort A comparative study of machine learning, deep neural networks and random utility maximization models for travel mode choice modelling
dc.creator.none.fl_str_mv García García, José Carlos
García Ródenas, Ricardo
López Gómez, Julio Alberto
Martín Baos, José Ángel
author García García, José Carlos
author_facet García García, José Carlos
García Ródenas, Ricardo
López Gómez, Julio Alberto
Martín Baos, José Ángel
author_role author
author2 García Ródenas, Ricardo
López Gómez, Julio Alberto
Martín Baos, José Ángel
author2_role author
author
author
dc.subject.none.fl_str_mv Random Utility Maximization models
Deep Neural Networks
Machine Learning models
Travel behaviour
topic Random Utility Maximization models
Deep Neural Networks
Machine Learning models
Travel behaviour
description Traditionally, Random Utility Maximization (RUM) models have been widely applied to travel mode choice modelling. Currently, Machine Learning (ML) models are being applied as an alternative to RUM models, since they provide better results in terms of prediction capability and they can manage large volumes of data. In this paper, a comprehensive comparison between classic RUM models and ML models, including single and ensemble classifiers as well as Deep Neural Networks (DNNs), is provided in order to assess systematically the performance of different models over two different datasets which have different sizes and nature of data. Numerical experiments show Random Forest (RF) is the best classifier in terms of accuracy index and the computational cost to train the model.
publishDate 2022
dc.date.none.fl_str_mv 2022
2024
2024
dc.type.none.fl_str_mv info:eu-repo/semantics/article
format article
dc.identifier.none.fl_str_mv https://www.sciencedirect.com/science/article/pii/S2352146522001740
https://hdl.handle.net/10578/32968
url https://www.sciencedirect.com/science/article/pii/S2352146522001740
https://hdl.handle.net/10578/32968
dc.language.none.fl_str_mv Inglés
language_invalid_str_mv Inglés
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
application/pdf
dc.publisher.none.fl_str_mv Elsevier
publisher.none.fl_str_mv Elsevier
dc.source.none.fl_str_mv reponame:RUIdeRA. Repositorio Institucional de la UCLM
instname:Universidad de Castilla-La Mancha
instname_str Universidad de Castilla-La Mancha
reponame_str RUIdeRA. Repositorio Institucional de la UCLM
collection RUIdeRA. Repositorio Institucional de la UCLM
repository.name.fl_str_mv
repository.mail.fl_str_mv
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score 15,300719

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