An Evacuation Validation Data Set for Large Passenger Ships

Galea, Edwin R.; Deere, Steven; Brown, Robert; Filippidis, L.

doi:10.1007/978-3-319-02447-9_7

Cited by 16 publications

(15 citation statements)

References 4 publications

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“…This suggests that the evacuation model is doing a good job of predicting the overall assembly process. Furthermore, the level of agreement with the CS data-set appears to be significantly better than that of the RP1 data-set [5].…”

Section: Comparing Model Predictions With Trial Resultsmentioning

confidence: 81%

“…From these trials five passenger response time data-sets were collected and two full-scale validation data-sets. The response time data-sets have been presented in another publication [4] and the first of the validation data-sets has been presented in another paper [5]. In this paper we present the second and more comprehensive of the two Safeguard Validation Data-Sets (SGVDS) [6].…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

A Validation Data-Set and Suggested Validation Protocol for Ship Evacuation Models

Galea¹,

Deere²,

Brown³

et al. 2014

Fire Saf. Sci.

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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.

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Section: Comparing Model Predictions With Trial Resultsmentioning

confidence: 81%

Section: Introductionmentioning

confidence: 99%

A Validation Data-Set and Suggested Validation Protocol for Ship Evacuation Models

Galea¹,

Deere²,

Brown³

et al. 2014

Fire Saf. Sci.

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“…From the previous examples, the parameter of most interest is a representative TET. However, an analysis based solely on the TET may miss important details of the evacuation [16][17][18][19][20]29], e.g., a set of egress trials could have similar representative TETs, but the individual evacuation dynamics could be very different. A more detailed analysis of the evacuation can be performed by additionally examining the exit time of certain percentages of the agents [18,20].…”

Section: Introductionmentioning

confidence: 99%

“…A more detailed analysis of the evacuation can be performed by additionally examining the exit time of certain percentages of the agents [18,20]. However, an even greater level of detail can be achieved by examining the entire agent egress curve (an ordered vector of the individual agent's exit time) via FAMs [16,17,19,29]. Furthermore, egress curves are a typical output from many evacuation models.…”

Section: Introductionmentioning

confidence: 99%

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Determining Confidence Intervals, and Convergence, for Parameters in Stochastic Evacuation Models

Grandison

2020

Fire Technol

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An issue when using stochastic egress models is how many simulations are required to accurately represent the modelled scenario? Engineers are mostly interested in a representative Total Evacuation Time (TET). However, the convergence of the TET may not ensure that the full range of evacuation dynamics has been adequately represented. The average total egress curve (AC) has been suggested as an improved measure. Unfortunately, defining a confidence interval (CI) for the AC is problematic. CIs can robustly quantify the precision of many statistics and have been used to define convergence in egress modelling and other research fields. This paper presents a novel application of bootstrapping, functional analysis measures (FAMs), and a bisection algorithm, to derive three FAM-based CIs representing the precision of the AC. These CIs were tested using a theoretical model to demonstrate the consistency of the coverage probability, the actual percentage of CIs that contain the theoretical parameter, with the nominal 95% confidence level (NCL). For two of the FAMbased CIs, it was found that the coverage probability was between 94.2% and 95.6% for all tested sample sizes between 10 and 4000 simulations. The third FAM-based CI's coverage probability was always greater than the NCL and was a conservative estimate, but this presented no problems in practice. A FAM-based CI may suggest if there is more or less variability in an earlier phase of the evacuation. A convergence scheme based on statistical precision, CI widths, is proposed and verified. The method can be extended to other statistics.

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Evacuation Simulation of a Passenger Ship Fire Based on a Modified Cellular Automaton

Liu

Zhang

Shi³

et al. 2023

Advcd Theory and Sims

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This study proposes a modified cellular automaton to simulate passenger ship fire evacuation. A fire dynamics simulator (FDS) is used to simulate the firing process for the passenger ship. The generated carbon monoxide (CO) concentration, temperature, and visibility are converted into equivalent lengths and input to the equivalent field of the modified cellular automaton. The fire field is applied to keep the evacuees away from the fire. And game theory is adopted to solve the conflict in the choice of route for evacuees. With or without fire on the passenger ship, three different evacuees' densities are used to study the influence of different proportions of evacuees who are familiar with exits during the evacuation time. The positions of exits are also studied. The results show that when the fire release rate increases from 3000 to 8000 kW s −2 for the same evacuation scenario, the earliest time that evacuees are in danger is 50% earlier. The impact of fire on evacuees is greater than that of evacuees' density. In the case of two close exits or exits close to the bulkheads, the congestion near exits is obvious and has the greatest effect on the evacuation time of the high evacuees' density.

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An Evacuation Validation Data Set for Large Passenger Ships

Cited by 16 publications

References 4 publications

A Validation Data-Set and Suggested Validation Protocol for Ship Evacuation Models

A Validation Data-Set and Suggested Validation Protocol for Ship Evacuation Models

Determining Confidence Intervals, and Convergence, for Parameters in Stochastic Evacuation Models

Evacuation Simulation of a Passenger Ship Fire Based on a Modified Cellular Automaton

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