Clustering of transactional traffic data to analyse mobility patterns

Detalhes bibliográficos
Autor(a) principal: Pagel, Felix Julian
Data de Publicação: 2021
Tipo de documento: Dissertação
Idioma: eng
Título da fonte: Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
Texto Completo: http://hdl.handle.net/10362/140141
Resumo: This work develops a framework to analyse the development of mobility patterns over time. Based on flow data of individual vehicles on Portuguese motorways the proposed methodology defines a set of features that describe each individual’s movement characteristics. K-means was identified as most suitable algorithm to cluster the set of features allowing for a meaningful interpretation of mobility patterns. The analysis showed a conflict of objectives between cluster quality, and the interpretability of the defined features. Therefore, for an optimal outcome of the analysis the number of clusters should be manually aligned with the goal of the analysis.
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spelling Clustering of transactional traffic data to analyse mobility patternsData scienceBusiness analyticsTraffic clusteringTraffic flow modelingMobility pattern analysisDomínio/Área Científica::Ciências Sociais::Economia e GestãoThis work develops a framework to analyse the development of mobility patterns over time. Based on flow data of individual vehicles on Portuguese motorways the proposed methodology defines a set of features that describe each individual’s movement characteristics. K-means was identified as most suitable algorithm to cluster the set of features allowing for a meaningful interpretation of mobility patterns. The analysis showed a conflict of objectives between cluster quality, and the interpretability of the defined features. Therefore, for an optimal outcome of the analysis the number of clusters should be manually aligned with the goal of the analysis.Xufre, PatríciaRUNPagel, Felix Julian2022-01-202021-12-172025-12-17T00:00:00Z2022-01-20T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/masterThesisapplication/pdfhttp://hdl.handle.net/10362/140141TID:202972135enginfo:eu-repo/semantics/embargoedAccessreponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãoinstacron:RCAAP2024-03-11T05:17:12Zoai:run.unl.pt:10362/140141Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-20T03:49:34.754906Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãofalse
dc.title.none.fl_str_mv Clustering of transactional traffic data to analyse mobility patterns
title Clustering of transactional traffic data to analyse mobility patterns
spellingShingle Clustering of transactional traffic data to analyse mobility patterns
Pagel, Felix Julian
Data science
Business analytics
Traffic clustering
Traffic flow modeling
Mobility pattern analysis
Domínio/Área Científica::Ciências Sociais::Economia e Gestão
title_short Clustering of transactional traffic data to analyse mobility patterns
title_full Clustering of transactional traffic data to analyse mobility patterns
title_fullStr Clustering of transactional traffic data to analyse mobility patterns
title_full_unstemmed Clustering of transactional traffic data to analyse mobility patterns
title_sort Clustering of transactional traffic data to analyse mobility patterns
author Pagel, Felix Julian
author_facet Pagel, Felix Julian
author_role author
dc.contributor.none.fl_str_mv Xufre, Patrícia
RUN
dc.contributor.author.fl_str_mv Pagel, Felix Julian
dc.subject.por.fl_str_mv Data science
Business analytics
Traffic clustering
Traffic flow modeling
Mobility pattern analysis
Domínio/Área Científica::Ciências Sociais::Economia e Gestão
topic Data science
Business analytics
Traffic clustering
Traffic flow modeling
Mobility pattern analysis
Domínio/Área Científica::Ciências Sociais::Economia e Gestão
description This work develops a framework to analyse the development of mobility patterns over time. Based on flow data of individual vehicles on Portuguese motorways the proposed methodology defines a set of features that describe each individual’s movement characteristics. K-means was identified as most suitable algorithm to cluster the set of features allowing for a meaningful interpretation of mobility patterns. The analysis showed a conflict of objectives between cluster quality, and the interpretability of the defined features. Therefore, for an optimal outcome of the analysis the number of clusters should be manually aligned with the goal of the analysis.
publishDate 2021
dc.date.none.fl_str_mv 2021-12-17
2022-01-20
2022-01-20T00:00:00Z
2025-12-17T00:00:00Z
dc.type.status.fl_str_mv info:eu-repo/semantics/publishedVersion
dc.type.driver.fl_str_mv info:eu-repo/semantics/masterThesis
format masterThesis
status_str publishedVersion
dc.identifier.uri.fl_str_mv http://hdl.handle.net/10362/140141
TID:202972135
url http://hdl.handle.net/10362/140141
identifier_str_mv TID:202972135
dc.language.iso.fl_str_mv eng
language eng
dc.rights.driver.fl_str_mv info:eu-repo/semantics/embargoedAccess
eu_rights_str_mv embargoedAccess
dc.format.none.fl_str_mv application/pdf
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instacron:RCAAP
instname_str Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação
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reponame_str Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
collection Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
repository.name.fl_str_mv Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação
repository.mail.fl_str_mv
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