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Effect of Weather Conditions and Transactions Records on Work Accidents in the Retail Sector – A Case Study

2024Conference paper Restricted access
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dc.contributor.authorBorges, Lucas D.
dc.contributor.authorSena, Inês
dc.contributor.authorMarcelino, Vitor Fernandes
dc.contributor.authorSilva, Felipe G.
dc.contributor.authorFernandes, Florbela P.
dc.contributor.authorPacheco, Maria F.
dc.contributor.authorVaz, Clara B.
dc.contributor.authorLima, José
dc.contributor.authorPereira, Ana I.
dc.date.accessioned2024-10-08T08:46:19Z
dc.date.available2024-10-08T08:46:19Z
dc.date.issued2024
dc.description.abstractWeather change plays an important role in work-related accidents, it impairs people’s cognitive abilities, increasing the risk of injuries and accidents. Furthermore, weather conditions can cause an increase or decrease in daily sales in the retail sector by influencing individual behaviors. The increase in transactions, in turn, leads employees to fatigue and overload, which can also increase the risk of injuries and accidents. This work aims to conduct a case study in a company in the retail sector to verify whether the transactions records in stores and the weather conditions of each district in mainland Portugal impact the occurrence of work accidents, as well as to perform predictive analysis of the occurrence or non-occurrence of work accidents in each district using these data and comparing different machine learning techniques. The correlation analysis of the occurrence or non-occurrence of work accidents with weather conditions and some transactions pointed out the nonexistence of correlation between the data. Evaluating the precision and the confusion matrix of the predictive models, the study indicates a predisposition of the models to predict the non-occurrence of work accidents to the detriment of the ability to predict the occurrence of work accidents.pt_PT
dc.description.sponsorshipThe authors are grateful to the Foundation for Science and Technology (FCT, Portugal) for financial support through national funds FCT/MCTES (PIDDAC) to CeDRI (UIDB/05757/2020 and UIDP/05757/2020) and SusTEC (LA/ P/0007/2021). This work has been supported by NORTE-01-0247-FEDER-072598 iSafety: Intelligent system for occupational safety and well-being in the retail sector.pt_PT
dc.description.versioninfo:eu-repo/semantics/publishedVersionpt_PT
dc.identifier.citationBorges, Lucas D.; Sena, Inês; Marcelino, Vitor; Silva, Felipe G.; Fernandes, Florbela P.; Pacheco, Maria F.; Vaz, Clara B.; Lima, José; Pereira, Ana I. (2024). Effect of Weather Conditions and Transactions Records on Work Accidents in the Retail Sector – A Case Study. In 3rd International Conference on Optimization, Learning Algorithms and Applications (OL2A 2023). Cham: Springer Nature, Vol. 1, p. 34–48. ISBN 978-3-031-53024-1.pt_PT
dc.identifier.doi10.1007/978-3-031-53025-8_3pt_PT
dc.identifier.isbn978-3-031-53024-1
dc.identifier.isbn978-3-031-53025-8
dc.identifier.urihttp://hdl.handle.net/10198/30341
dc.language.isoengpt_PT
dc.peerreviewedyespt_PT
dc.publisherSpringer Naturept_PT
dc.relationResearch Centre in Digitalization and Intelligent Robotics
dc.relationResearch Centre in Digitalization and Intelligent Robotics
dc.relationAssociate Laboratory for Sustainability and Tecnology in Mountain Regions
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/pt_PT
dc.subjectPredictive Analysispt_PT
dc.subjectCorrelation Analysispt_PT
dc.subjectWeather Conditionspt_PT
dc.subjectTransactions Recordspt_PT
dc.titleEffect of Weather Conditions and Transactions Records on Work Accidents in the Retail Sector – A Case Studypt_PT
dc.typeconference paper
dspace.entity.typePublication
oaire.awardTitleResearch Centre in Digitalization and Intelligent Robotics
oaire.awardTitleResearch Centre in Digitalization and Intelligent Robotics
oaire.awardTitleAssociate Laboratory for Sustainability and Tecnology in Mountain Regions
oaire.awardURIinfo:eu-repo/grantAgreement/FCT/6817 - DCRRNI ID/UIDB%2F05757%2F2020/PT
oaire.awardURIinfo:eu-repo/grantAgreement/FCT/6817 - DCRRNI ID/UIDP%2F05757%2F2020/PT
oaire.awardURIinfo:eu-repo/grantAgreement/FCT/6817 - DCRRNI ID/LA%2FP%2F0007%2F2020/PT
oaire.citation.endPage48pt_PT
oaire.citation.startPage34pt_PT
oaire.citation.title3rd International Conference on Optimization, Learning Algorithms and Applications (OL2A 2023)pt_PT
oaire.fundingStream6817 - DCRRNI ID
oaire.fundingStream6817 - DCRRNI ID
oaire.fundingStream6817 - DCRRNI ID
person.familyNameSena
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person.familyNameLima
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person.givenNameInês
person.givenNameFlorbela P.
person.givenNameMaria F.
person.givenNameClara B.
person.givenNameJosé
person.givenNameAna I.
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person.identifier.orcid0000-0001-7902-1207
person.identifier.orcid0000-0003-3803-2043
person.identifier.ridF-1519-2016
person.identifier.ridL-3370-2014
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person.identifier.scopus-author-id57222722951
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person.identifier.scopus-author-id15071961600
project.funder.identifierhttp://doi.org/10.13039/501100001871
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
project.funder.nameFundação para a Ciência e a Tecnologia
rcaap.rightsrestrictedAccesspt_PT
rcaap.typeconferenceObjectpt_PT
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