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Evacuation models commonly employ pseudorandom sampling from distributions to represent the variability of human behaviour in the evacuation process, otherwise referred to as ‘behavioural uncertainty’. This paper presents a method based on functional analysis and inferential statistics to study the convergence of probabilistic evacuation model results to inform deciding how many repeat simulation runs are required for a given scenario. Compared to existing approaches which typically focus on measuring variance in evacuation times, the proposed method utilises multifactor variance to assess the convergence of a range of different evacuation model outputs, referred to as factors. The factors include crowd density, flowrates, occupant locations, exit usage, and queuing times. These factors were selected as they represent a range of means to assess variance in evacuation dynamics between repeat simulation runs and can be found in most evacuation models. The application of the method (along with a tool developed for its implementation) is demonstrated through two case studies. The first case study consists of an analysis of convergence in evacuation simulation results for a building including 1855 occupants. The second case study is a simple verification test aimed at demonstrating the capabilities of the method. Results from the case studies suggest that multifactor variance assessment provides a more holistic assessment of the variance in evacuation dynamics and results provided by an evacuation model compared to existing methods which adopt single factor analysis. This provides increased confidence in determining an appropriate number of repeat simulation runs to ensure key evacuation dynamics and results which may be influenced by pseudorandom sampling are represented.

Multifactor Variance Assessment for Determining the Number of Repeat Simulation Runs in Evacuation Modelling