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

Abstract

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.

Roxborough Park Community Wildfire Evacuation Drill: Data Collection and Model Benchmarking

“…Therefore, all three indicators (95%, 99%, 100%) are directly compared and the characteristics of the entire evacuation curves are compared using functional analysis. As both models adopt pseudo-random sampling from distributions, a criterion was defined to ensure the convergence of the multiple runs is met [ 24 , 27 ]. In this case study, we assumed that convergence is met once the average total evacuation time did not variate for more than 2% over at least 10 consecutive runs.…”