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A validation data-set and suggested validation protocol for ship evacuation models

A validation data-set and suggested validation protocol for ship evacuation models

Galea, Edwin R. ORCID: 0000-0002-0001-6665, Deere, Steven J., Brown, Robert and Filippidis, Lazaros (2014) A validation data-set and suggested validation protocol for ship evacuation models. Fire Safety Science, 11. pp. 1115-1128. ISSN 1817-4299 (doi:https://doi.org/10.3801/IAFSS.FSS.11-1115)

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Abstract

An evacuation model validation data-set collected as part of the EU FP7 project SAFEGUARD is presented. The data was collected from a cruise ship operated by Royal Caribbean International (CS). The trial was a semi-unannounced assembly trial conducted at sea and involved some 2500 passengers. The trial took place at an unspecified time however, passengers were aware that on their voyage an assembly exercise would take place. The validation data-set consists of passenger; response times, starting locations, end locations and arrival times in the assembly stations. The validation data were collected using a novel data acquisition system consisting of ship-mounted beacons, each emitting unique Infra-Red (IR) signals and IR data logging tags worn by each passenger. The results from blind simulations using maritimeEXODUS for the assembly trial are presented and compared with the measured data. Three objective measures are proposed to assess the goodness of fit between the predicted model data and the measured data.

Item Type: Article
Additional Information: Proceedings of the 11th International Symposium
Uncontrolled Keywords: Transportation; Validation; Modelling; Egress; Human factors; Human behavior
Faculty / Department / Research Group: Faculty of Architecture, Computing & Humanities
Faculty of Architecture, Computing & Humanities > Centre for Numerical Modelling & Process Analysis (CNMPA) > Fire Safety Engineering Group (FSEG)
Last Modified: 08 Nov 2016 11:22
Selected for GREAT 2016: None
Selected for GREAT 2017: None
Selected for GREAT 2018: None
Selected for GREAT 2019: None
URI: http://gala.gre.ac.uk/id/eprint/14401

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