Analysis on predict model of railway passenger travel factors judgment with soft-computing methods

Purpose: With the development of the transportation, more traveling factors acting on the railway passengers change greatly with the passengers’ choice. With the help of the modern information computing technology, the factors were integrated to realize quantitative analyze according to the travel p...

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Detalles Bibliográficos
Autores: Yan, Xi, Li, Jing
Tipo de recurso: artículo
Fecha de publicación:2014
País:España
Institución:Universitat Politècnica de Catalunya (UPC)
Repositorio:UPCommons. Portal del coneixement obert de la UPC
Idioma:inglés
OAI Identifier:oai:upcommons.upc.edu:2099/14485
Acceso en línea:https://hdl.handle.net/2099/14485
Access Level:acceso abierto
Palabra clave:Railroads
Business logistics -- Mathematical models
Railway Passenger
Travel Choice
Genetic Algorithm
BP Neural Network
Comparative
Ferrocarrils
Logística (Indústria) -- Models matemàtics
Àrees temàtiques de la UPC::Economia i organització d'empreses::Direcció d'operacions::Modelització de transports i logística
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oai_identifier_str oai:upcommons.upc.edu:2099/14485
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repository_id_str
spelling Analysis on predict model of railway passenger travel factors judgment with soft-computing methodsYan, XiLi, JingRailroadsBusiness logistics -- Mathematical modelsRailway PassengerTravel ChoiceGenetic AlgorithmBP Neural NetworkComparativeFerrocarrilsLogística (Indústria) -- Models matemàticsÀrees temàtiques de la UPC::Economia i organització d'empreses::Direcció d'operacions::Modelització de transports i logísticaPurpose: With the development of the transportation, more traveling factors acting on the railway passengers change greatly with the passengers’ choice. With the help of the modern information computing technology, the factors were integrated to realize quantitative analyze according to the travel purpose and travel cost. Design/methodology/approach: The detailed comparative study was implemented with comparing the two soft-computing methods: genetic algorithm, BP neural network. The two methods with different idea were also studied in this model to discuss the key parameter setting and its applicable range. Findings: During the study, the data about the railway passengers is difficult to analyzed detailed because of the inaccurate information. There are still many factors to affect the choice of passengers. Research limitations/implications: The model-designing thought and its computing procession were also certificated with programming and data illustration according to thorough analysis. The comparative analysis was also proved effective and applicable to predict the railway passengers’ travel choice through the empirical study with soft-computing supporting. Practical implications: The techniques of predicting and parameters’ choice were conducted with algorithm-operation supporting. Originality/value: The detail form comparative study in this paper could be provided for researchers and managers and be applied in the practice according the actual demand.Peer ReviewedOmniaScience20142014-04-0120142014-04-09journal articlehttp://purl.org/coar/resource_type/c_6501NAhttp://purl.org/coar/version/c_be7fb7dd8ff6fe43info:eu-repo/semantics/articleapplication/pdfhttps://hdl.handle.net/2099/14485reponame:UPCommons. Portal del coneixement obert de la UPCinstname:Universitat Politècnica de Catalunya (UPC)Inglésengopen accesshttp://purl.org/coar/access_right/c_abf2Attribution-NonCommercial 3.0 Spainhttp://creativecommons.org/licenses/by-nc/3.0/es/info:eu-repo/semantics/openAccessoai:upcommons.upc.edu:2099/144852026-05-27T15:37:01Z
dc.title.none.fl_str_mv Analysis on predict model of railway passenger travel factors judgment with soft-computing methods
title Analysis on predict model of railway passenger travel factors judgment with soft-computing methods
spellingShingle Analysis on predict model of railway passenger travel factors judgment with soft-computing methods
Yan, Xi
Railroads
Business logistics -- Mathematical models
Railway Passenger
Travel Choice
Genetic Algorithm
BP Neural Network
Comparative
Ferrocarrils
Logística (Indústria) -- Models matemàtics
Àrees temàtiques de la UPC::Economia i organització d'empreses::Direcció d'operacions::Modelització de transports i logística
title_short Analysis on predict model of railway passenger travel factors judgment with soft-computing methods
title_full Analysis on predict model of railway passenger travel factors judgment with soft-computing methods
title_fullStr Analysis on predict model of railway passenger travel factors judgment with soft-computing methods
title_full_unstemmed Analysis on predict model of railway passenger travel factors judgment with soft-computing methods
title_sort Analysis on predict model of railway passenger travel factors judgment with soft-computing methods
dc.creator.none.fl_str_mv Yan, Xi
Li, Jing
author Yan, Xi
author_facet Yan, Xi
Li, Jing
author_role author
author2 Li, Jing
author2_role author
dc.subject.none.fl_str_mv Railroads
Business logistics -- Mathematical models
Railway Passenger
Travel Choice
Genetic Algorithm
BP Neural Network
Comparative
Ferrocarrils
Logística (Indústria) -- Models matemàtics
Àrees temàtiques de la UPC::Economia i organització d'empreses::Direcció d'operacions::Modelització de transports i logística
topic Railroads
Business logistics -- Mathematical models
Railway Passenger
Travel Choice
Genetic Algorithm
BP Neural Network
Comparative
Ferrocarrils
Logística (Indústria) -- Models matemàtics
Àrees temàtiques de la UPC::Economia i organització d'empreses::Direcció d'operacions::Modelització de transports i logística
description Purpose: With the development of the transportation, more traveling factors acting on the railway passengers change greatly with the passengers’ choice. With the help of the modern information computing technology, the factors were integrated to realize quantitative analyze according to the travel purpose and travel cost. Design/methodology/approach: The detailed comparative study was implemented with comparing the two soft-computing methods: genetic algorithm, BP neural network. The two methods with different idea were also studied in this model to discuss the key parameter setting and its applicable range. Findings: During the study, the data about the railway passengers is difficult to analyzed detailed because of the inaccurate information. There are still many factors to affect the choice of passengers. Research limitations/implications: The model-designing thought and its computing procession were also certificated with programming and data illustration according to thorough analysis. The comparative analysis was also proved effective and applicable to predict the railway passengers’ travel choice through the empirical study with soft-computing supporting. Practical implications: The techniques of predicting and parameters’ choice were conducted with algorithm-operation supporting. Originality/value: The detail form comparative study in this paper could be provided for researchers and managers and be applied in the practice according the actual demand.
publishDate 2014
dc.date.none.fl_str_mv 2014
2014-04-01
2014
2014-04-09
dc.type.none.fl_str_mv journal article
http://purl.org/coar/resource_type/c_6501
NA
http://purl.org/coar/version/c_be7fb7dd8ff6fe43
dc.type.openaire.fl_str_mv info:eu-repo/semantics/article
format article
dc.identifier.none.fl_str_mv https://hdl.handle.net/2099/14485
url https://hdl.handle.net/2099/14485
dc.language.none.fl_str_mv Inglés
eng
language_invalid_str_mv Inglés
language eng
dc.rights.none.fl_str_mv open access
http://purl.org/coar/access_right/c_abf2
Attribution-NonCommercial 3.0 Spain
http://creativecommons.org/licenses/by-nc/3.0/es/
dc.rights.openaire.fl_str_mv info:eu-repo/semantics/openAccess
rights_invalid_str_mv open access
http://purl.org/coar/access_right/c_abf2
Attribution-NonCommercial 3.0 Spain
http://creativecommons.org/licenses/by-nc/3.0/es/
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
dc.publisher.none.fl_str_mv OmniaScience
publisher.none.fl_str_mv OmniaScience
dc.source.none.fl_str_mv reponame:UPCommons. Portal del coneixement obert de la UPC
instname:Universitat Politècnica de Catalunya (UPC)
instname_str Universitat Politècnica de Catalunya (UPC)
reponame_str UPCommons. Portal del coneixement obert de la UPC
collection UPCommons. Portal del coneixement obert de la UPC
repository.name.fl_str_mv
repository.mail.fl_str_mv
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score 15.300719

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