Inferring Social-Demographics of Travellers based on Smart Card Data

[EN] With the wide application of the smart card technology in public transit system, traveller’s daily travel behaviours can be possibly obtained. This study devotes to investigating the pattern of individual mobility patterns and its relationship with social-demographics. We first extract travel f...

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Detalles Bibliográficos
Autores: Zhang, Yang, Cheng, Tao
Tipo de recurso: capítulo de libro
Fecha de publicación:2018
País:España
Institución:Universitat Politècnica de València (UPV)
Repositorio:RiuNet. Repositorio Institucional de la Universitat Politécnica de Valéncia
Idioma:inglés
OAI Identifier:oai:riunet.upv.es:10251/111931
Acceso en línea:https://riunet.upv.es/handle/10251/111931
Access Level:acceso abierto
Palabra clave:Web data
Internet data
Big data
QCA
PLS
SEM
Conference
Social-demographics
Smart card data
Travel variability
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spelling Inferring Social-Demographics of Travellers based on Smart Card DataZhang, YangCheng, TaoWeb dataInternet dataBig dataQCAPLSSEMConferenceSocial-demographicsSmart card dataTravel variability[EN] With the wide application of the smart card technology in public transit system, traveller’s daily travel behaviours can be possibly obtained. This study devotes to investigating the pattern of individual mobility patterns and its relationship with social-demographics. We first extract travel features from the raw smart card data, including spatial, temporal and travel mode features, which capture the travel variability of travellers. Then, travel features are fed to various supervised machine learning models to predict individual’s demographic attributes, such as age group, gender, income level and car ownership. Finally, a case study based on London’s Oyster Card data is presented and results show it is a promisingEditorial Universitat Politècnica de ValènciaRepositorio Institucional de la Universitat Politècnica de València Riunet20182018-09-07book parthttp://purl.org/coar/resource_type/c_3248VoRhttp://purl.org/coar/version/c_970fb48d4fbd8a85info:eu-repo/semantics/bookPartapplication/pdfhttps://riunet.upv.es/handle/10251/111931reponame:RiuNet. Repositorio Institucional de la Universitat Politécnica de Valénciainstname:Universitat Politècnica de València (UPV)Inglésengopen accesshttp://purl.org/coar/access_right/c_abf2Reconocimiento - No comercial - Sin obra derivada (by-nc-nd) http://creativecommons.org/licenses/by-nc-nd/4.0/info:eu-repo/semantics/openAccessoai:riunet.upv.es:10251/1119312026-06-13T07:49:27Z
dc.title.none.fl_str_mv Inferring Social-Demographics of Travellers based on Smart Card Data
title Inferring Social-Demographics of Travellers based on Smart Card Data
spellingShingle Inferring Social-Demographics of Travellers based on Smart Card Data
Zhang, Yang
Web data
Internet data
Big data
QCA
PLS
SEM
Conference
Social-demographics
Smart card data
Travel variability
title_short Inferring Social-Demographics of Travellers based on Smart Card Data
title_full Inferring Social-Demographics of Travellers based on Smart Card Data
title_fullStr Inferring Social-Demographics of Travellers based on Smart Card Data
title_full_unstemmed Inferring Social-Demographics of Travellers based on Smart Card Data
title_sort Inferring Social-Demographics of Travellers based on Smart Card Data
dc.creator.none.fl_str_mv Zhang, Yang
Cheng, Tao
author Zhang, Yang
author_facet Zhang, Yang
Cheng, Tao
author_role author
author2 Cheng, Tao
author2_role author
dc.contributor.none.fl_str_mv Repositorio Institucional de la Universitat Politècnica de València Riunet
dc.subject.none.fl_str_mv Web data
Internet data
Big data
QCA
PLS
SEM
Conference
Social-demographics
Smart card data
Travel variability
topic Web data
Internet data
Big data
QCA
PLS
SEM
Conference
Social-demographics
Smart card data
Travel variability
description [EN] With the wide application of the smart card technology in public transit system, traveller’s daily travel behaviours can be possibly obtained. This study devotes to investigating the pattern of individual mobility patterns and its relationship with social-demographics. We first extract travel features from the raw smart card data, including spatial, temporal and travel mode features, which capture the travel variability of travellers. Then, travel features are fed to various supervised machine learning models to predict individual’s demographic attributes, such as age group, gender, income level and car ownership. Finally, a case study based on London’s Oyster Card data is presented and results show it is a promising
publishDate 2018
dc.date.none.fl_str_mv 2018
2018-09-07
dc.type.none.fl_str_mv book part
http://purl.org/coar/resource_type/c_3248
VoR
http://purl.org/coar/version/c_970fb48d4fbd8a85
dc.type.openaire.fl_str_mv info:eu-repo/semantics/bookPart
format bookPart
dc.identifier.none.fl_str_mv https://riunet.upv.es/handle/10251/111931
url https://riunet.upv.es/handle/10251/111931
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
Reconocimiento - No comercial - Sin obra derivada (by-nc-nd)
http://creativecommons.org/licenses/by-nc-nd/4.0/
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
Reconocimiento - No comercial - Sin obra derivada (by-nc-nd)
http://creativecommons.org/licenses/by-nc-nd/4.0/
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
dc.publisher.none.fl_str_mv Editorial Universitat Politècnica de València
publisher.none.fl_str_mv Editorial Universitat Politècnica de València
dc.source.none.fl_str_mv reponame:RiuNet. Repositorio Institucional de la Universitat Politécnica de Valéncia
instname:Universitat Politècnica de València (UPV)
instname_str Universitat Politècnica de València (UPV)
reponame_str RiuNet. Repositorio Institucional de la Universitat Politécnica de Valéncia
collection RiuNet. Repositorio Institucional de la Universitat Politécnica de Valéncia
repository.name.fl_str_mv
repository.mail.fl_str_mv
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score 15,300719

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