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Guiding evacuees to Improve fire building evacuation efficiency: hazard and congestion models to support decision making by a context-aware recommender system

datacite.subject.fosCiências Naturais::Ciências da Computação e da Informação
datacite.subject.fosEngenharia e Tecnologia::Engenharia Eletrotécnica, Eletrónica e Informática
dc.contributor.authorCoelho, António
dc.contributor.authorNeto, Joaquim
dc.contributor.authorMorais, A. Jorge
dc.contributor.author Gonçalves, Ramiro Ramos Moreira
dc.date.accessioned2026-02-26T15:36:02Z
dc.date.available2026-02-26T15:36:02Z
dc.date.issued2023-12-06
dc.description.abstractFires in large buildings can have tragic consequences, including the loss of human lives. Despite the advancements in building construction and fire safety technologies, the unpredictable nature of fires, particularly in large buildings, remains an enormous challenge. Acknowledging the paramount importance of prioritising human safety, the academic community has been focusing consistently on enhancing the efficiency of building evacuation. While previous studies have integrated evacuation simulation models, aiding in aspects such as the design of evacuation routes and emergency signalling, modelling human behaviour during a fire emergency remains challenging due to cognitive complexities. Moreover, behavioural differences from country to country add another layer of complexity, hindering the creation of a universal behaviour model. Instead of centring on modelling the occupant behaviour, this paper proposes an innovative approach aimed at enhancing the occupants’ behaviour predictability by providing real-time information to the occupants regarding the most suitable evacuation routes. The proposed models use a building’s environmental conditions to generate contextual information, aiding in developing solutions to make the occupants’ behaviour more predictable by providing them with real-time information on the most appropriate and efficient evacuation routes at each moment, guiding the occupants to safety during a fire emergency. The models were incorporated into a context-aware recommender system for testing purposes. The simulation results indicate that such a system, coupled with hazard and congestion models, positively influences the occupants’ behaviour, fostering faster adaptation to the environmental conditions and ultimately enhancing the efficiency of building evacuations.eng
dc.identifier.authenticusidP-00Z-E8S
dc.identifier.citationNeto, J.; Morais, A.J.; Gonçalves, R.; Coelho, A.L. Guiding Evacuees to Improve Fire Building Evacuation Efficiency: Hazard and Congestion Models to Support Decision Making by a Context-Aware Recommender System. Buildings 2023, 13, 3038. https://doi.org/ 10.3390/buildings13123038
dc.identifier.doi10.3390/buildings13123038
dc.identifier.eid2-s2.0-85180654368
dc.identifier.issn2075-5309
dc.identifier.urihttp://hdl.handle.net/10400.2/21544
dc.identifier.wos001131470600001
dc.language.isoeng
dc.peerreviewedyes
dc.publisherMDPI
dc.relation.hasversionhttps://www.mdpi.com/2075-5309/13/12/3038
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/
dc.subjectFire building evacuation
dc.subjectHuman behaviour
dc.subjectInternet of things
dc.subjectBuilding evacuation efficiency
dc.subjectMulti-agent recommender system
dc.subjectContext-aware recommender system
dc.titleGuiding evacuees to Improve fire building evacuation efficiency: hazard and congestion models to support decision making by a context-aware recommender systemeng
dc.typejournal article
dspace.entity.typePublication
oaire.citation.endPage22
oaire.citation.issue12
oaire.citation.startPage1
oaire.citation.titleBuildings
oaire.citation.volume13
oaire.versionhttp://purl.org/coar/version/c_970fb48d4fbd8a85
person.familyNameCoelho
person.familyNameNeto
person.familyNameMorais
person.familyName Gonçalves
person.givenNameAntónio
person.givenNameJoaquim
person.givenNameA. Jorge
person.givenNameRamiro Ramos Moreira
person.identifierR-000-217
person.identifier2488644
person.identifierD-1723-2009
person.identifier.ciencia-idBA1E-EDE1-477E
person.identifier.ciencia-id0A10-2459-C7E6
person.identifier.ciencia-idF314-1D77-536E
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person.identifier.orcid0000-0001-7949-2877
person.identifier.orcid0000-0003-1228-1236
person.identifier.orcid0000-0003-2224-1609
person.identifier.orcid0000-0001-8698-866X
person.identifier.ridG-2216-2011
person.identifier.ridHKO-1960-2023
person.identifier.scopus-author-id57188679352
person.identifier.scopus-author-id56326334100
person.identifier.scopus-author-id57194584599
relation.isAuthorOfPublication6e665843-0e0a-4277-a3a7-ef0799750226
relation.isAuthorOfPublication809f9cc1-ef9c-4d9f-8bb9-a1cb572a1c78
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relation.isAuthorOfPublication6c1e2a9f-a782-4a28-bbb5-e254369f5793
relation.isAuthorOfPublication.latestForDiscovery809f9cc1-ef9c-4d9f-8bb9-a1cb572a1c78

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