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Data analytics to advance the inference of origin–destination in public transport systems: tracing network vulnerabilities and age-sensitive trip purposes

dc.contributor.authorCerqueira, Sofia
dc.contributor.authorArsenio, Elisabete
dc.contributor.authorBarateiro, José
dc.contributor.authorHenriques, Rui
dc.date.accessioned2025-07-03T09:15:14Z
dc.date.available2025-07-03T09:15:14Z
dc.date.issued2025-05-22
dc.description.abstractKnowing the passengers' final destinations, underlying motifs, and commuting habits is critical to optimise public transportation systems, guide urban planning and contribute to a more sustainable urban mobility. In entry-only Automated Fare Collection systems, the body of literature has focused on the spatial dimension by estimating alighting stops, overlooking the inference of robust alighting times. Moreover, discriminating between transfers and activities is pivotal for determining their ultimate destinations. However, current methods often struggle to adapt to the stochastic nature of passenger behaviour, further disregarding the multiplicity of routes and stops to access specific facilities and individual motivations. Further research is required to address an effective spatio-temporal and contextual inference in both challenges. With the above concerns in mind, this research uses data analytics to propose an enhanced methodology for the inference of OD matrices, with the final goal of providing a comprehensive view of OD mobility patterns across distinct age-sensitive profiles-youth, adults, and older adults. Our methodological framework integrates the following approaches: (i) alighting stop-and-time inference, (ii) ensembled model for transfer classification, (iii) indicators retrieved from statistical analysis of network vulnerabilities (e.g., number of transfers, walkability needs), frequent destinations and their underlying putative motifs against the city amenities and others points-of-interest. The reliability of alighting data (timestamp and location) inference is improved by integrating OpenStreetMap data and the past boarding data from bus and railway systems. Considering Lisbon as the target study case, we apply the methodology over smart card data collected both from metro and bus systems. A comparative analysis with state-of-the-art methods revealed that the enhanced framework for alighting and OD inference led to longer journey times for trips. Furthermore, throughout the day, the older adult group experiences longer transfer times on average compared to both the children and young adult segment and the adult segment.eng
dc.description.sponsorshipHORIZON-CL5-2022-D6-02-01 under agreement 101104163), project ILU (DSAIPA/DS/0111/2018), ATE (02/C05-i01.02/2022 under agreement PC644914747-00000023
dc.identifier.doi10.1186/s12544-025-00720-1
dc.identifier.issn1866-8887
dc.identifier.urihttp://hdl.handle.net/10400.1/27344
dc.language.isoeng
dc.peerreviewedyes
dc.publisherSpringer
dc.relationInstituto de Engenharia de Sistemas e Computadores, Investigação e Desenvolvimento em Lisboa
dc.relationSpatio-temporal Pattern Analysis of Big Data in Engineering Systems
dc.relation.ispartofEuropean Transport Research Review
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/
dc.subjectAlighting data inference
dc.subjectTransfer classifcation
dc.subjectOrigin–Destination inference
dc.subjectSpatio-temporal data analytics
dc.titleData analytics to advance the inference of origin–destination in public transport systems: tracing network vulnerabilities and age-sensitive trip purposeseng
dc.typejournal article
dspace.entity.typePublication
oaire.awardTitleInstituto de Engenharia de Sistemas e Computadores, Investigação e Desenvolvimento em Lisboa
oaire.awardTitleSpatio-temporal Pattern Analysis of Big Data in Engineering Systems
oaire.awardURIinfo:eu-repo/grantAgreement/FCT/6817 - DCRRNI ID/UIDB%2F50021%2F2020/PT
oaire.awardURIhttp://hdl.handle.net/10400.1/27343
oaire.citation.issue1
oaire.citation.titleEuropean Transport Research Review
oaire.citation.volume17
oaire.fundingStream6817 - DCRRNI ID
oaire.fundingStreamOE
oaire.versionhttp://purl.org/coar/version/c_970fb48d4fbd8a85
person.familyNameBarateiro
person.givenNameJosé
person.identifier.orcid0000-0002-4036-5528
project.funder.identifierhttp://doi.org/10.13039/501100001871
project.funder.nameFundação para a Ciência e a Tecnologia
relation.isAuthorOfPublication7f5fdaac-733a-49f1-b83b-04fa3a7dd494
relation.isAuthorOfPublication.latestForDiscovery7f5fdaac-733a-49f1-b83b-04fa3a7dd494
relation.isProjectOfPublication0b14d63a-8f78-4e31-8a86-b72e1f07871f
relation.isProjectOfPublicationcb23c391-f048-4301-8743-3df3de0f31fc
relation.isProjectOfPublication.latestForDiscovery0b14d63a-8f78-4e31-8a86-b72e1f07871f

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