Pearclustering: a novel clustering algorithm with an application to bike mobility

Bike Sharing Systems (BSS) have become a key solution for urban mobility, reducing traffic-related CO2 emissions. However, managing BSS poses challenges that require data-driven solutions, particularly for understanding their global behavior and forecasting their evolution. These dynamics arise from...

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Detalhes bibliográficos
Autores: Márquez Saldaña, Francisco, Aranda Corral, Gonzalo Antonio, Borrego Díaz, Joaquín
Formato: artículo
Fecha de publicación:2025
País:España
Recursos:Universidad de Huelva (UHU)
Repositorio:Arias Montano. Repositorio Institucional de la Universidad de Huelva
Idioma:inglés
OAI Identifier:oai:ariasmontano.uhu.es:10272/27408
Acesso em linha:https://hdl.handle.net/10272/27408
Access Level:acceso abierto
Palavra-chave:Clustering analysis
Machine-learning in mobility
Bike sharing platforms
Artificial intelligence in engineering
3327 Tecnología de Los Sistemas de Transporte
1203.12 Bancos de Datos
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oai_identifier_str oai:ariasmontano.uhu.es:10272/27408
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spelling Pearclustering: a novel clustering algorithm with an application to bike mobilityMárquez Saldaña, FranciscoAranda Corral, Gonzalo AntonioBorrego Díaz, JoaquínClustering analysisMachine-learning in mobilityBike sharing platformsArtificial intelligence in engineering3327 Tecnología de Los Sistemas de Transporte1203.12 Bancos de DatosBike Sharing Systems (BSS) have become a key solution for urban mobility, reducing traffic-related CO2 emissions. However, managing BSS poses challenges that require data-driven solutions, particularly for understanding their global behavior and forecasting their evolution. These dynamics arise from the interaction among users, companies, dock stations, and city policies, influenced by sociological and infrastructure-based factors. This paper proposes a novel clustering methodology to analyze BSS data across multiple cities. By clustering station-day tuples instead of aggregating statistics, our approach captures seasonal patterns, special events, and weekday/weekend differences. Using Pearson Correlation as a distance metric, it remains robust across different station sizes and system scales. Trained on three European BSS and evaluated across six cities from 4 different countries, our model uncovers meaningful patterns such as work, residential, and leisure areas, as well as seasonal changes even in systems not used in the training process. These insights enhance BSS management, expansion, and decision-making, with applications in monitoring, anomaly detection, and demand prediction.Springer20252025-01-0120252025-01-01journal articlehttp://purl.org/coar/resource_type/c_6501VoRhttp://purl.org/coar/version/c_970fb48d4fbd8a85info:eu-repo/semantics/articleapplication/pdfhttps://hdl.handle.net/10272/27408reponame:Arias Montano. Repositorio Institucional de la Universidad de Huelvainstname:Universidad de Huelva (UHU)Inglésengopen accesshttp://purl.org/coar/access_right/c_abf2Attribution 4.0 Internationalhttp://creativecommons.org/licenses/by/4.0/info:eu-repo/semantics/openAccessoai:ariasmontano.uhu.es:10272/274082026-06-02T14:58:11Z
dc.title.none.fl_str_mv Pearclustering: a novel clustering algorithm with an application to bike mobility
title Pearclustering: a novel clustering algorithm with an application to bike mobility
spellingShingle Pearclustering: a novel clustering algorithm with an application to bike mobility
Márquez Saldaña, Francisco
Clustering analysis
Machine-learning in mobility
Bike sharing platforms
Artificial intelligence in engineering
3327 Tecnología de Los Sistemas de Transporte
1203.12 Bancos de Datos
title_short Pearclustering: a novel clustering algorithm with an application to bike mobility
title_full Pearclustering: a novel clustering algorithm with an application to bike mobility
title_fullStr Pearclustering: a novel clustering algorithm with an application to bike mobility
title_full_unstemmed Pearclustering: a novel clustering algorithm with an application to bike mobility
title_sort Pearclustering: a novel clustering algorithm with an application to bike mobility
dc.creator.none.fl_str_mv Márquez Saldaña, Francisco
Aranda Corral, Gonzalo Antonio
Borrego Díaz, Joaquín
author Márquez Saldaña, Francisco
author_facet Márquez Saldaña, Francisco
Aranda Corral, Gonzalo Antonio
Borrego Díaz, Joaquín
author_role author
author2 Aranda Corral, Gonzalo Antonio
Borrego Díaz, Joaquín
author2_role author
author
dc.contributor.none.fl_str_mv
dc.subject.none.fl_str_mv Clustering analysis
Machine-learning in mobility
Bike sharing platforms
Artificial intelligence in engineering
3327 Tecnología de Los Sistemas de Transporte
1203.12 Bancos de Datos
topic Clustering analysis
Machine-learning in mobility
Bike sharing platforms
Artificial intelligence in engineering
3327 Tecnología de Los Sistemas de Transporte
1203.12 Bancos de Datos
description Bike Sharing Systems (BSS) have become a key solution for urban mobility, reducing traffic-related CO2 emissions. However, managing BSS poses challenges that require data-driven solutions, particularly for understanding their global behavior and forecasting their evolution. These dynamics arise from the interaction among users, companies, dock stations, and city policies, influenced by sociological and infrastructure-based factors. This paper proposes a novel clustering methodology to analyze BSS data across multiple cities. By clustering station-day tuples instead of aggregating statistics, our approach captures seasonal patterns, special events, and weekday/weekend differences. Using Pearson Correlation as a distance metric, it remains robust across different station sizes and system scales. Trained on three European BSS and evaluated across six cities from 4 different countries, our model uncovers meaningful patterns such as work, residential, and leisure areas, as well as seasonal changes even in systems not used in the training process. These insights enhance BSS management, expansion, and decision-making, with applications in monitoring, anomaly detection, and demand prediction.
publishDate 2025
dc.date.none.fl_str_mv 2025
2025-01-01
2025
2025-01-01
dc.type.none.fl_str_mv journal article
http://purl.org/coar/resource_type/c_6501
VoR
http://purl.org/coar/version/c_970fb48d4fbd8a85
dc.type.openaire.fl_str_mv info:eu-repo/semantics/article
format article
dc.identifier.none.fl_str_mv https://hdl.handle.net/10272/27408
url https://hdl.handle.net/10272/27408
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 4.0 International
http://creativecommons.org/licenses/by/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
Attribution 4.0 International
http://creativecommons.org/licenses/by/4.0/
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
dc.publisher.none.fl_str_mv Springer
publisher.none.fl_str_mv Springer
dc.source.none.fl_str_mv reponame:Arias Montano. Repositorio Institucional de la Universidad de Huelva
instname:Universidad de Huelva (UHU)
instname_str Universidad de Huelva (UHU)
reponame_str Arias Montano. Repositorio Institucional de la Universidad de Huelva
collection Arias Montano. Repositorio Institucional de la Universidad de Huelva
repository.name.fl_str_mv
repository.mail.fl_str_mv
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score 15,812455

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