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dc.contributor.authorGonzález Villa, Javier 
dc.contributor.authorCuesta Jiménez, Arturo 
dc.contributor.authorSpagnolo, Marco
dc.contributor.authorZaotti, Marisa
dc.contributor.authorSummers, Luke
dc.contributor.authorElms, Alexander
dc.contributor.authorDhaya, Anay
dc.contributor.authorJedlicka, Karel
dc.contributor.authorMartolos, Jan
dc.contributor.authorCetinkaya, Deniz
dc.contributor.otherUniversidad de Cantabriaes_ES
dc.date.accessioned2024-09-24T17:39:12Z
dc.date.available2024-09-24T17:39:12Z
dc.date.issued2024-07
dc.identifier.issn1380-7501
dc.identifier.issn1573-7721
dc.identifier.urihttps://hdl.handle.net/10902/33961
dc.description.abstractCounter-terrorism measures and preparedness play a critical role in securing mass gather ings, soft targets, and critical infrastructures within urban environments. This paper intro duces a comprehensive Decision Support System developed as part of the S4AllCitites project, designed to seamlessly integrate with existing legacy systems in Smart Cities. The system encompasses urban pedestrian and vehicular evacuation, incorporating predictive models to anticipate the progression of incendiary and mass shooting attacks, alongside a probabilistic model for threat assessment in the case of improvised explosive devices. A notable achievement of this research is the successful implementation and deployment of the system in operational environments through pilot studies. It empowers public and private security operators with real time decision support capabilities during both preven tion and intervention stages of potential attacks. The decision support information provided encompasses various aspects, including optimal evacuation strategies, estimated egress times, pedestrian movement profles, probability assessments, and the potential impact of diferent terrorist threats in terms of casualties. Additionally, the system ofers real-time insights into the status of the trafc network under normal and unusual conditions, ena bling efcient emergency management throughout its progression. This includes the ability to identify optimal intervention routes and assess the impact of anomalous trafc resulting from evacuationses_ES
dc.description.sponsorshipOpen Access funding provided thanks to the CRUE-CSIC agreement with Springer Nature. The research leading to these results received funding from European Union’s H2020 research and innovation programme under Grant Agreement No. 883522.es_ES
dc.format.extent24 p.es_ES
dc.language.isoenges_ES
dc.publisherSpringer Naturees_ES
dc.rightsAttribution 4.0 Internationales_ES
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/*
dc.sourceMultimedia Tools and Applications, 2024, 83(22), 61971-61994es_ES
dc.subject.otherDecision Support Systemes_ES
dc.subject.otherEvacuationes_ES
dc.subject.otherFire and Smokees_ES
dc.subject.otherSecurity and Safetyes_ES
dc.subject.otherSimulationes_ES
dc.subject.otherTerrorismes_ES
dc.subject.otherThreatses_ES
dc.subject.otherTraffices_ES
dc.titleDecision-support system for safety and security assessment and management in smart citieses_ES
dc.typeinfo:eu-repo/semantics/articlees_ES
dc.relation.publisherVersionhttps://doi.org/10.1007/s11042-023-16020-6es_ES
dc.rights.accessRightsopenAccesses_ES
dc.relation.projectIDinfo:eu-repo/grantAgreement/EC/H2020/883522/eu/Smart Spaces Safety and Security for All Cities/S4AllCities/es_ES
dc.identifier.DOI10.1007/s11042-023-16020-6
dc.type.versionpublishedVersiones_ES


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Attribution 4.0 InternationalExcepto si se señala otra cosa, la licencia del ítem se describe como Attribution 4.0 International