Exploring key spatial determinants for mobility hub placement based on micromobility ridership

Over the past decade, cities have witnessed a surge in micromobility services that offer flexible mobility options for citizens on an as-needed basis, such as for covering the first/last mile connection of their trips. Although these services have known benefits, including reduced CO2 emissions and...

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Detalles Bibliográficos
Autores: Arias Molinares, Daniela, Xu, Yihan, Büttner, Benjamin, Duran-Rodas, David
Tipo de recurso: artículo
Fecha de publicación:2023
País:España
Institución:Universidad Complutense de Madrid (UCM)
Repositorio:Docta Complutense
Idioma:inglés
OAI Identifier:oai:docta.ucm.es:20.500.14352/107131
Acceso en línea:https://hdl.handle.net/20.500.14352/107131
Access Level:acceso abierto
Palabra clave:911.3:656
Shared mobility
Mobility hubs
Allocating models
Micromobility usage
Geografía
2505 Geografía
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oai_identifier_str oai:docta.ucm.es:20.500.14352/107131
network_acronym_str ES
network_name_str España
repository_id_str
spelling Exploring key spatial determinants for mobility hub placement based on micromobility ridershipArias Molinares, DanielaXu, YihanBüttner, BenjaminDuran-Rodas, David911.3:656Shared mobilityMobility hubsAllocating modelsMicromobility usageGeografía2505 GeografíaOver the past decade, cities have witnessed a surge in micromobility services that offer flexible mobility options for citizens on an as-needed basis, such as for covering the first/last mile connection of their trips. Although these services have known benefits, including reduced CO2 emissions and less public space required for parking, there is still insufficient understanding of their common dynamics and usage, which can support decision-making in the quest for allocating new mobility infrastructure, like mobility hubs. In this paper, we propose a methodology to identify potential mobility hub locations based on the common associated spatial factors with the ridership of different micromobility services (station-based bike-sharing, dockless moped-style scooter-sharing and scooter-sharing services) in Madrid, Spain. We identify the common associated spatial factors with micromobility usage (e.g. bike stations' density, commercial land use and cycling infrastructure) and train linear models to explore which dependent variables represents better a “common ridership” of multiple micromobility services while fitting better that data. Subsequently, we test our models in a different area to identify potential hotspots for suggested locations. Findings show that considering micromobility ridership altogether using principal component analysis provides better ridership estimations in the test areas. The methodology has the potential to be replicable in other cities and guide decision-making processes for searching potential mobility hub locations.ElsevierUniversidad Complutense de Madrid20232023-06-0120232023-06-01journal articlehttp://purl.org/coar/resource_type/c_6501AOhttp://purl.org/coar/version/c_b1a7d7d4d402bcceinfo:eu-repo/semantics/articleapplication/pdfhttps://hdl.handle.net/20.500.14352/107131reponame:Docta Complutenseinstname:Universidad Complutense de Madrid (UCM)Inglésengopen accesshttp://purl.org/coar/access_right/c_abf2info:eu-repo/semantics/openAccessoai:docta.ucm.es:20.500.14352/1071312026-06-02T12:44:21Z
dc.title.none.fl_str_mv Exploring key spatial determinants for mobility hub placement based on micromobility ridership
title Exploring key spatial determinants for mobility hub placement based on micromobility ridership
spellingShingle Exploring key spatial determinants for mobility hub placement based on micromobility ridership
Arias Molinares, Daniela
911.3:656
Shared mobility
Mobility hubs
Allocating models
Micromobility usage
Geografía
2505 Geografía
title_short Exploring key spatial determinants for mobility hub placement based on micromobility ridership
title_full Exploring key spatial determinants for mobility hub placement based on micromobility ridership
title_fullStr Exploring key spatial determinants for mobility hub placement based on micromobility ridership
title_full_unstemmed Exploring key spatial determinants for mobility hub placement based on micromobility ridership
title_sort Exploring key spatial determinants for mobility hub placement based on micromobility ridership
dc.creator.none.fl_str_mv Arias Molinares, Daniela
Xu, Yihan
Büttner, Benjamin
Duran-Rodas, David
author Arias Molinares, Daniela
author_facet Arias Molinares, Daniela
Xu, Yihan
Büttner, Benjamin
Duran-Rodas, David
author_role author
author2 Xu, Yihan
Büttner, Benjamin
Duran-Rodas, David
author2_role author
author
author
dc.contributor.none.fl_str_mv Universidad Complutense de Madrid
dc.subject.none.fl_str_mv 911.3:656
Shared mobility
Mobility hubs
Allocating models
Micromobility usage
Geografía
2505 Geografía
topic 911.3:656
Shared mobility
Mobility hubs
Allocating models
Micromobility usage
Geografía
2505 Geografía
description Over the past decade, cities have witnessed a surge in micromobility services that offer flexible mobility options for citizens on an as-needed basis, such as for covering the first/last mile connection of their trips. Although these services have known benefits, including reduced CO2 emissions and less public space required for parking, there is still insufficient understanding of their common dynamics and usage, which can support decision-making in the quest for allocating new mobility infrastructure, like mobility hubs. In this paper, we propose a methodology to identify potential mobility hub locations based on the common associated spatial factors with the ridership of different micromobility services (station-based bike-sharing, dockless moped-style scooter-sharing and scooter-sharing services) in Madrid, Spain. We identify the common associated spatial factors with micromobility usage (e.g. bike stations' density, commercial land use and cycling infrastructure) and train linear models to explore which dependent variables represents better a “common ridership” of multiple micromobility services while fitting better that data. Subsequently, we test our models in a different area to identify potential hotspots for suggested locations. Findings show that considering micromobility ridership altogether using principal component analysis provides better ridership estimations in the test areas. The methodology has the potential to be replicable in other cities and guide decision-making processes for searching potential mobility hub locations.
publishDate 2023
dc.date.none.fl_str_mv 2023
2023-06-01
2023
2023-06-01
dc.type.none.fl_str_mv journal article
http://purl.org/coar/resource_type/c_6501
AO
http://purl.org/coar/version/c_b1a7d7d4d402bcce
dc.type.openaire.fl_str_mv info:eu-repo/semantics/article
format article
dc.identifier.none.fl_str_mv https://hdl.handle.net/20.500.14352/107131
url https://hdl.handle.net/20.500.14352/107131
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
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
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
dc.publisher.none.fl_str_mv Elsevier
publisher.none.fl_str_mv Elsevier
dc.source.none.fl_str_mv reponame:Docta Complutense
instname:Universidad Complutense de Madrid (UCM)
instname_str Universidad Complutense de Madrid (UCM)
reponame_str Docta Complutense
collection Docta Complutense
repository.name.fl_str_mv
repository.mail.fl_str_mv
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score 15.81155

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