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A spatial statistics methodology for inspector allocation against fare evasion

2026-01-24Artigo científico Acesso aberto
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datacite.subject.fosCiências Sociais::Geografia Económica e Social
datacite.subject.sdg16:Paz, Justiça e Instituições Eficazes
dc.contributor.authorFreiria, Susana
dc.contributor.authorSousa, Nuno
dc.date.accessioned2026-02-24T10:17:19Z
dc.date.available2026-02-24T10:17:19Z
dc.date.issued2026-01-24
dc.description.abstractThis article discusses public transport fare evasion from the point of view of the relations between inspection actions and detected evasion, with the aim of improving the efficacy of the former. By applying spatial statistics methods to a large dataset from Lisbon, Portugal, namely, entropy-based local bivariate relationships (LBR) and geographically weighted regression (GWR), it is shown that the two variables are associated in a widespread manner throughout the city, mostly in a linear way. Mapping out marginal gains from inspection actions then shows where they detect the most evaders, allowing transport companies to relocate their inspector teams in a more effective manner. Results for Lisbon show that gains in effectiveness of circa 50% can be obtained, mostly by moving some inspector teams from the centre of the city to the periphery during daytime. The methodology requires only inspection/detection databases, which transport companies usually have, making it a valuable, practical tool to combat fare evasion.eng
dc.identifier.citationFreiria S, Sousa N (2026). A Spatial Statistics Methodology for Inspector Allocation Against Fare Evasion. ISPRS International Journal of Geo-Information, 15(2):53
dc.identifier.doi10.3390/ijgi15020053
dc.identifier.eissn2220-9964
dc.identifier.urihttp://hdl.handle.net/10400.2/21475
dc.language.isoeng
dc.peerreviewedyes
dc.publisherMDPI
dc.relationResearch Centre for Territory, Transports and Environment
dc.relationInstitute for Systems Engineering and Computers at Coimbra
dc.relation.hasversionhttps://www.mdpi.com/2220-9964/15/2/53
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/
dc.subjectFare evasion
dc.subjectPublic transport
dc.subjectLocal bivariate relationships
dc.subjectGeographically weighted regression
dc.titleA spatial statistics methodology for inspector allocation against fare evasioneng
dc.typejournal article
dspace.entity.typePublication
oaire.awardNumberUIDP/04427/2020
oaire.awardNumberUID/Multi/00308/2013
oaire.awardTitleResearch Centre for Territory, Transports and Environment
oaire.awardTitleInstitute for Systems Engineering and Computers at Coimbra
oaire.awardURIinfo:eu-repo/grantAgreement/FCT/6817 - DCRRNI ID/UIDP%2F04427%2F2020/PT
oaire.awardURIinfo:eu-repo/grantAgreement/FCT/6817 - DCRRNI ID/UID%2FMulti%2F00308%2F2013/PT
oaire.citation.issue2
oaire.citation.startPage53
oaire.citation.titleISPRS International Journal of Geo-Information
oaire.citation.volume15
oaire.fundingStream6817 - DCRRNI ID
oaire.fundingStream6817 - DCRRNI ID
oaire.versionhttp://purl.org/coar/version/c_970fb48d4fbd8a85
person.familyNameFreiria
person.familyNameSousa
person.givenNameSusana
person.givenNameNuno
person.identifier.ciencia-idE914-E920-B2A0
person.identifier.orcid0000-0002-5736-3289
person.identifier.orcid0000-0002-2681-5035
person.identifier.ridE-8428-2015
person.identifier.ridF-5307-2014
person.identifier.scopus-author-id56152445400
person.identifier.scopus-author-id7003438443
project.funder.identifierhttp://doi.org/10.13039/501100001871
project.funder.identifierhttp://doi.org/10.13039/501100001871
project.funder.nameFundação para a Ciência e a Tecnologia
project.funder.nameFundação para a Ciência e a Tecnologia
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