DC FieldValueLanguage
dc.contributor.authorLi, Weilian-
dc.contributor.authorHaunert, Jan-Henrik-
dc.contributor.authorForsch, Axel-
dc.contributor.authorZhu, Jun-
dc.contributor.authorZhu, Qing-
dc.contributor.authorDehbi, Youness-
dc.date.accessioned2024-11-14T12:38:49Z-
dc.date.available2024-11-14T12:38:49Z-
dc.date.issued2024-
dc.identifier.issn1365-8816en_US
dc.identifier.urihttps://repos.hcu-hamburg.de/handle/hcu/1076-
dc.description.abstractAttractive cycling routes can effectively promote active mobility, thus reducing the twin pressures of the population boom and the greenhouse effect. However, the existing approaches for cycling route recommendation primarily concentrate on identifying the most efficient routes while ignoring the urban spatial context, which is essential to meet the user’s particular preferences. This article proposes a novel method for informed sampling and recommending cycling routes leveraging crowd-sourced trajectories with weighted-latent Dirichlet allocation (WLDA). Precisely, spatial context mapping, incorporating a weighting mechanism into LDA, latent topics mining, and cycling route recommendation based on informed sampling are introduced. We collected 1,016 cycling trajectories around Cologne, Germany, for experimental analysis. The experimental results show that the three latent topics within the trajectories, leisure, city, and green tours, are clearly presented in the line density analysis. The insightful recommendation for unfamiliar cyclists could also be actively sampled upon the WLDA model. These findings suggest that our approach could shift the route recommendation paradigm from GIS analysis to a semantic mining perspective, yielding highly interpretable results and offering novel research avenues for applying machine learning in route planning.en
dc.language.isoenen_US
dc.publisherTaylor & Francisen_US
dc.relation.ispartofInternational Journal of Geographical Information Scienceen_US
dc.subjectCycling route recommendationen
dc.subjectweighted-latent Dirichlet allocationen
dc.subjectcrowd-sourced trajectoriesen
dc.subjectspatial context mappingen
dc.subjectnatural language processingen
dc.subject.ddc710: Landschaftsgestaltung, Raumplanungen_US
dc.titleInformed sampling and recommendation of cycling routes: leveraging crowd-sourced trajectories with weighted-latent Dirichlet allocationen
dc.typeArticleen_US
dc.type.diniarticle-
dc.type.driverarticle-
dc.rights.cchttps://creativecommons.org/licenses/by/4.0/en_US
dc.type.casraiJournal Article-
dcterms.DCMITypeText-
tuhh.identifier.urnurn:nbn:de:gbv:1373-repos-13746-
tuhh.oai.showtrueen_US
tuhh.publisher.doi10.1080/13658816.2024.2391428-
tuhh.publication.instituteComputational Methodsen_US
tuhh.type.opus(wissenschaftlicher) Artikel-
tuhh.container.issue12en_US
tuhh.container.volume38en_US
tuhh.container.startpage2492en_US
tuhh.container.endpage2513en_US
tuhh.type.rdmfalse-
openaire.rightsinfo:eu-repo/semantics/openAccessen_US
item.cerifentitytypePublications-
item.creatorOrcidLi, Weilian-
item.creatorOrcidHaunert, Jan-Henrik-
item.creatorOrcidForsch, Axel-
item.creatorOrcidZhu, Jun-
item.creatorOrcidZhu, Qing-
item.creatorOrcidDehbi, Youness-
item.openairecristypehttp://purl.org/coar/resource_type/c_6501-
item.fulltextWith Fulltext-
item.openairetypeArticle-
item.grantfulltextopen-
item.creatorGNDLi, Weilian-
item.creatorGNDHaunert, Jan-Henrik-
item.creatorGNDForsch, Axel-
item.creatorGNDZhu, Jun-
item.creatorGNDZhu, Qing-
item.creatorGNDDehbi, Youness-
item.languageiso639-1en-
crisitem.author.deptHydrographie und Geodäsie-
crisitem.author.deptComputational Methods-
crisitem.author.orcid0000-0003-0133-4099-
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