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Hypothetical design space defined by two design variables
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During the whole evacuation modelling process, there are uncertainties in every task. To date, scarce literature is available on the assessment of those uncertainties and evacuation model users generally do not present this information consistently (if omitted at all) in evacuation modelling studies. Considering the classification made in other mod...
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... (for instance, laboratory tests, computational simulations etc.) are commonly performed in order to discover something about a particular process or system. Following this thinking, an experiment can be defined as a test or series of tests in which purposeful changes are made to the input variables of a process (or system) so that it is possible to observe and identify the reasons for changes that may be observed in the output response 27 . Furthermore, when an experiment, or a set of experiments, is defined (i.e., designed) a methodological process is then established. This “process” is named Design of Experiments, commonly known as DoE. These techniques are well- known within statistics and largely applied in many fields, particularly in the Operational Research area 27–29 . Within the context of this paper, in order to address the issue of input definition, specifically for defining the input parameters, the use of alternative numerical techniques such as Design of Experiments (DoE) techniques is here proposed. Nowadays, there are a large number of DoE techniques. This paper is not focused on the discussion of DoE techniques in depth as there is a vast specialized literature in this field. These techniques are based on algorithms which define the nature of the combinations between the design variables (i.e., design points ). In other terms, the DoE technique will establish the number of design points needed and the location of these design points within the design space. Fundamentally, the difference between the DoE techniques is based on how their algorithms work. It is possible to understand how important is to minimize the uncertainties associated with the input parameters within evacuation modelling simulations, as previous studies have shown 15,30 . For instance, assuming that the evacuation time estimated by an evacuation model is the output response which is influenced by different factors or better called here as design variables (such as exit locations, floor area, geometric layout, etc.), the adequate representation of their relationship is crucial for an accurate estimation. This is better explained below. For a hypothetical scenario where the designer wants to calculate the evacuation time (which can be named as “Response”) using an evacuation model and considering the impact of two design variables (for instance, the number of exits and the number of occupants, which can be named as “Design variable 1” and “Design variable 2” respectively), it is possible to draw the design space for this using a Cartesian coordinate system, see Figure 4. Figure 4 shows the Response located in the Z axis and the Design variables located in the X and Y axes. The design space is then defined by all the possible combinations between Design variable 1 and Design variable 2. These combinations are the design points. Clearly, it is reasonable to consider that the performance of evacuation simulations for all these design points might not be feasible. And this is the reason why DoE technique can be used: for representing the design space optimally through the selection of strategically well located design points which will cover the design space. If these design points do not cover properly the design space, the uncertainty associated with the inputs will increase. For example, if an alternative inadequate type of technique is used, the design points picked up by it might not provide a good coverage of the design space. The grey region shown in Figure 4 illustrates this issue. It shows clearly that this selection was inappropriate and will not represent realistically the design space. The immediate consequence of this will be that the evacuation modelling will fail in estimating an accurate evacuation time since its estimation will be based on a partial sub-set of points which do not cover the entire design space. In summary, each design point in an n -dimensional design space is a result of the combination of design variables, where n is the total number of design variables. The particular arrangement of points in the design space is known as an experimental design or Design of Experiments (DoE). Indeed, DoE techniques are then used for providing better arrangements than those suggested when using other types of techniques. This paper aims at discussing the advantages of using DoE techniques within the evacuation modelling context. This paper is not focusing on investigating the DoE techniques in detail, but rather to present them as alternative technique to random techniques such as Monte Carlo for reducing the likelihood of uncertainties associated in evacuation modelling, particularly those related with the scenarios and inputs. Previous studies have shown the effectiveness of applying DoE techniques into the evacuation modelling, generating more accurate results than other more common approaches, such as Monte Carlo and the following list is based on these investigations 15,31 ...
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... (for instance, laboratory tests, computational simulations etc.) are commonly performed in order to discover something about a particular process or system. Following this thinking, an experiment can be defined as a test or series of tests in which purposeful changes are made to the input variables of a process (or system) so that it is possible to observe and identify the reasons for changes that may be observed in the output response 27 . Furthermore, when an experiment, or a set of experiments, is defined (i.e., designed) a methodological process is then established. This “process” is named Design of Experiments, commonly known as DoE. These techniques are well- known within statistics and largely applied in many fields, particularly in the Operational Research area 27–29 . Within the context of this paper, in order to address the issue of input definition, specifically for defining the input parameters, the use of alternative numerical techniques such as Design of Experiments (DoE) techniques is here proposed. Nowadays, there are a large number of DoE techniques. This paper is not focused on the discussion of DoE techniques in depth as there is a vast specialized literature in this field. These techniques are based on algorithms which define the nature of the combinations between the design variables (i.e., design points ). In other terms, the DoE technique will establish the number of design points needed and the location of these design points within the design space. Fundamentally, the difference between the DoE techniques is based on how their algorithms work. It is possible to understand how important is to minimize the uncertainties associated with the input parameters within evacuation modelling simulations, as previous studies have shown 15,30 . For instance, assuming that the evacuation time estimated by an evacuation model is the output response which is influenced by different factors or better called here as design variables (such as exit locations, floor area, geometric layout, etc.), the adequate representation of their relationship is crucial for an accurate estimation. This is better explained below. For a hypothetical scenario where the designer wants to calculate the evacuation time (which can be named as “Response”) using an evacuation model and considering the impact of two design variables (for instance, the number of exits and the number of occupants, which can be named as “Design variable 1” and “Design variable 2” respectively), it is possible to draw the design space for this using a Cartesian coordinate system, see Figure 4. Figure 4 shows the Response located in the Z axis and the Design variables located in the X and Y axes. The design space is then defined by all the possible combinations between Design variable 1 and Design variable 2. These combinations are the design points. Clearly, it is reasonable to consider that the performance of evacuation simulations for all these design points might not be feasible. And this is the reason why DoE technique can be used: for representing the design space optimally through the selection of strategically well located design points which will cover the design space. If these design points do not cover properly the design space, the uncertainty associated with the inputs will increase. For example, if an alternative inadequate type of technique is used, the design points picked up by it might not provide a good coverage of the design space. The grey region shown in Figure 4 illustrates this issue. It shows clearly that this selection was inappropriate and will not represent realistically the design space. The immediate consequence of this will be that the evacuation modelling will fail in estimating an accurate evacuation time since its estimation will be based on a partial sub-set of points which do not cover the entire design space. In summary, each design point in an n -dimensional design space is a result of the combination of design variables, where n is the total number of design variables. The particular arrangement of points in the design space is known as an experimental design or Design of Experiments (DoE). Indeed, DoE techniques are then used for providing better arrangements than those suggested when using other types of techniques. This paper aims at discussing the advantages of using DoE techniques within the evacuation modelling context. This paper is not focusing on investigating the DoE techniques in detail, but rather to present them as alternative technique to random techniques such as Monte Carlo for reducing the likelihood of uncertainties associated in evacuation modelling, particularly those related with the scenarios and inputs. Previous studies have shown the effectiveness of applying DoE techniques into the evacuation modelling, generating more accurate results than other more common approaches, such as Monte Carlo and the following list is based on these investigations 15,31 ...
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... (for instance, laboratory tests, computational simulations etc.) are commonly performed in order to discover something about a particular process or system. Following this thinking, an experiment can be defined as a test or series of tests in which purposeful changes are made to the input variables of a process (or system) so that it is possible to observe and identify the reasons for changes that may be observed in the output response 27 . Furthermore, when an experiment, or a set of experiments, is defined (i.e., designed) a methodological process is then established. This “process” is named Design of Experiments, commonly known as DoE. These techniques are well- known within statistics and largely applied in many fields, particularly in the Operational Research area 27–29 . Within the context of this paper, in order to address the issue of input definition, specifically for defining the input parameters, the use of alternative numerical techniques such as Design of Experiments (DoE) techniques is here proposed. Nowadays, there are a large number of DoE techniques. This paper is not focused on the discussion of DoE techniques in depth as there is a vast specialized literature in this field. These techniques are based on algorithms which define the nature of the combinations between the design variables (i.e., design points ). In other terms, the DoE technique will establish the number of design points needed and the location of these design points within the design space. Fundamentally, the difference between the DoE techniques is based on how their algorithms work. It is possible to understand how important is to minimize the uncertainties associated with the input parameters within evacuation modelling simulations, as previous studies have shown 15,30 . For instance, assuming that the evacuation time estimated by an evacuation model is the output response which is influenced by different factors or better called here as design variables (such as exit locations, floor area, geometric layout, etc.), the adequate representation of their relationship is crucial for an accurate estimation. This is better explained below. For a hypothetical scenario where the designer wants to calculate the evacuation time (which can be named as “Response”) using an evacuation model and considering the impact of two design variables (for instance, the number of exits and the number of occupants, which can be named as “Design variable 1” and “Design variable 2” respectively), it is possible to draw the design space for this using a Cartesian coordinate system, see Figure 4. Figure 4 shows the Response located in the Z axis and the Design variables located in the X and Y axes. The design space is then defined by all the possible combinations between Design variable 1 and Design variable 2. These combinations are the design points. Clearly, it is reasonable to consider that the performance of evacuation simulations for all these design points might not be feasible. And this is the reason why DoE technique can be used: for representing the design space optimally through the selection of strategically well located design points which will cover the design space. If these design points do not cover properly the design space, the uncertainty associated with the inputs will increase. For example, if an alternative inadequate type of technique is used, the design points picked up by it might not provide a good coverage of the design space. The grey region shown in Figure 4 illustrates this issue. It shows clearly that this selection was inappropriate and will not represent realistically the design space. The immediate consequence of this will be that the evacuation modelling will fail in estimating an accurate evacuation time since its estimation will be based on a partial sub-set of points which do not cover the entire design space. In summary, each design point in an n -dimensional design space is a result of the combination of design variables, where n is the total number of design variables. The particular arrangement of points in the design space is known as an experimental design or Design of Experiments (DoE). Indeed, DoE techniques are then used for providing better arrangements than those suggested when using other types of techniques. This paper aims at discussing the advantages of using DoE techniques within the evacuation modelling context. This paper is not focusing on investigating the DoE techniques in detail, but rather to present them as alternative technique to random techniques such as Monte Carlo for reducing the likelihood of uncertainties associated in evacuation modelling, particularly those related with the scenarios and inputs. Previous studies have shown the effectiveness of applying DoE techniques into the evacuation modelling, generating more accurate results than other more common approaches, such as Monte Carlo and the following list is based on these investigations 15,31 ...
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... Within this context, it is correct to say that the evacuation models available on the market mostly use random sampling techniques to generate model inputs from user-defined or model developer-defined distributions 14 . In some cases, random variables might also be included in the model itself and the user might not have access to the exact formulation in use (for instance in closed source models) nor the possibility to modify them. These modelling assumptions lead to the generation of multiple results from the same input, such as series of evacuation times or occupant- evacuation time curves. To date, the judgement on the evaluation of the final values representative of these series of model predictions is left to the user and there is no standard method to be used for the analysis of the results. In this context, Ronchi et al 5 recommended the use of functional analysis for the assessment of the convergence of model results. Functional analysis is a branch of mathematics which represents curves as vectors and makes use of simple geometrical operations on the curves. This technique is already employed in the field of fire and evacuation research for the evaluation of experimental data and model validation 32,33 , but its use is not currently largely implemented in evacuation models. This paper suggests to embed the method proposed by Ronchi et al 5 for the study of behavioural uncertainty within evacuation models. The method is based on the concept that simple vector operators (Euclidean Relative distance, Euclidean Projection Coefficient and Secant Cosine) can be used to assess the convergence of occupant-evacuation time curves towards the average curve. These operators allow indeed to evaluating the differences between consecutive aggregated average curves in terms of both their distance and shape. The method can be used by applying simple user-defined acceptance criteria regarding the tolerated behavioural uncertainty 5 or it can be coupled with inferential statistics 4 . The present paper does not have the scope to present in detail the application of functional analysis for the study of the convergence of evacuation model results (which instead can be found in Ronchi et al 5 and Lovreglio et al 4 ) but it recommends its inclusion in the bigger picture of the analysis of uncertainties. In fact, the inclusion of this method within evacuation models and its coupling with different sampling techniques would permit a complete evaluation of those techniques since it will allow an evaluation of the speed of convergence towards the average, with a possible subsequent saving of computational time and assessment of a better coverage of the design space with a lower number of iterated simulations. This paper discussed the uncertainties found within the evacuation modelling context. This discussion focused firstly on the importance of identifying and classifying these uncertainties along the three main steps of evacuation modelling: 1) definition of the inputs; 2) the evacuation modelling and 3) the analysis of the outputs. Considering also the current scarce literature available on the assessment of these uncertainties, this paper presented different solutions to address some of the evacuation modelling uncertainties. These solutions were the use of Design of Experiments (DoE) techniques for assessing uncertainties associated with scenarios and inputs as well as the use of functional analysis for assessing behavioural uncertainties. The use of DoE techniques are expected to minimize the uncertainties associated with the input parameters, as previous studies have shown 15,30 . Based on this, the use of DoE techniques into the context of evacuation modelling is believed to be a valid alternative tool for cases where random sampling techniques are generally adopted, e.g., the Monte Carlo approach. 4 In being a stochastic simulation tool itself, Monte Carlo is based on uncertainty. Despite its large use in many applications (including evacuation modelling), the Monte Carlo approach, also known as “Monte Carlo simulation method(s)” and random sampling in general are not necessarily the best approach to use. The potential issues of this method are associated with its randomness, i.e., it picks the design points randomly throughout the design space. In brief terms, the way Monte Carlo is applied can be simply summarized: the values of the design variables are calculated by mapping the result of a random number generator in the range defined by the minimum and maximum values of the design variables. For instance, the random number generator used to construct the random design can make use of a uniform statistical distribution to create new random points. This means that every design point in the design space has an equal probability of being chosen as the next random point. The Monte Carlo technique is very often used to generate the design points for this purpose 35,36 . In addition to what has been discussed via Figure 4, there are other core-aspects which can help the designer and/or modeller to assess if Monte Carlo shall be an adequate approach when performing evacuation modelling, namely: (i) is the sampling technique in use appropriate? and (ii) is the sample enough to ensure accuracy in the results? Lastly, the fundamental issue is: one of the main attributes that classifies Monte Carlo in being a “high quality” approach is randomness tests for checking if it is producing values which are not biased. This characteristic can be useful for many fields, however not necessarily for the evacuation modelling context, where a more important issue is to assure that the numbers produced are covering satisfactorily the design space defined by the design variables. It is also relevant to mention that, for example, if an adequate statistical testing (e.g., inferential statistics) is used to assure that the design space is properly covered and furthermore the design points randomly drawn from a distribution are representative of the distribution in use, Monte Carlo can be, for this case, useful indeed. It is expected that this paper can bring some additional light to the relevant issues associated with uncertainties in evacuation modelling. It is also expected that the discussion promoted in this paper can support further research to be developed on this subject. Within this context, for instance, the development and proposal of a general framework for identifying, defining and measuring uncertainties based on the functional analysis can be pursued. Dr. Rodrigo Machado Tavares would like to thank CH2M and especially Dr. Mike Deevy for the support in publishing this paper. Dr. Rodrigo Machado Tavares is also very grateful to Ms. Lyz Jennings for her support when writing this paper and also to Mr. Eric D. Grohl for his outstanding inspiring work. 1. Tavares, R. M. Evacuation Processes Versus Evacuation Models: ‘Quo Vadimus’? Fire Technol. 45, 419–430 (2008). 2. International Standards Organization. Fire Safety Engineering – Assessment, verification and validation of calculation methods. ISO 16730. (2008). 3. Oberkampf, W. L., DeLand, S. M., Rutherford, B. M., Diegert, K. V. & Alvin, K. F. Error and uncertainty in modeling and simulation. Reliab. Eng. Syst. Saf. 75, 333–357 (2002). 4. Lovreglio, R., Ronchi, E. & Borri, D. The validation of evacuation simulation models through the analysis of behavioural uncertainty. Reliab. Eng. Syst. Saf. 131, 166–174 (2014). 5. Ronchi, E., Reneke, P. A. & Peacock, R. D. A Method for the Analysis of Behavioural Uncertainty in Evacuation Modelling. Fire Technol. 50, 1545–1571 (2014). 6. Warmink, J. J., Janssen, J. A. E. B., Booij, M. J. & Krol, M. S. Identification and classification of uncertainties in the application of environmental models. Environ. Model. Softw. 25, 1518–1527 (2010). 7. Walker, W. E. et al. Defining Uncertainty: A Conceptual Basis for Uncertainty Management in Model-Based Decision Support. Integr. Assess. 4, 5–17 (2003). 8. Xie, Q., Lu, S., Kong, D. & Wang, J. The effect of uncertain parameters on evacuation time in commercial buildings. J. Fire Sci. 30, 55–67 (2012). 9. Hamins, A. & McGrattan, K. Verification and Validation of Selected Fire Models for Nuclear Power Plant Applications . (National Institute of Standards and Technology, 2007). 10. Oberkampf, W. L. & Blottner, F. G. Issues in Computational Fluid Dynamics Code Verification and Validation. AIAA J. 36, 687–695 (1998). 11. Roache, P. J. Quantification of uncertainty in computational fluid dynamics. Annu. Rev. Fluid Mech. 29, 123–160 (1997). 12. McGrattan, K., Peacock, R. & Overholt, K. Validation of Fire Models Applied to Nuclear Power Plant Safety. Fire Technol. (2014). doi:10.1007/s10694-014-0436-z 13. Gwynne, S. M. V., Hulse, L. M. & Kinsey, M. J. Guidance for the Model Developer on Representing Human Behavior in Egress Models. Fire Technol. (2015). doi:10.1007/s10694-015- 0501-2 14. Ronchi, E. & Kinsey, M. Evacuation models of the future: Insights from an online survey on user’s experiences and needs. In 145–155 (Capote, J. et al, 2011). 15. Tavares, R. M. & Galea, E. R. Evacuation modelling analysis within the operational research context: A combined approach for improving enclosure designs. Build. Environ. 44, 1005–1016 (2009). 16. Taylor, B. N. & Kuyatt, C. E. Guidelines for Evaluating and Expressing the Uncertainty of NIST Measurement Results . (National Institute of Standards and Technology, 1994). 17. Gwynne, S. M. V., Kuligowski, E., Spearpoint, M. & Ronchi, E. Bounding defaults in egress models. Fire Mater. (2013). doi:10.1002/fam.2212 18. Bukowski, R., Peacock, R. & Jones, W. W. Sensitivity Examination of the airEXODUS1 Aircraft Evacuation Simulation Model. in 16, 1–14 (1998). 19. Lord, J., Meacham, B., Moore, A., Fahy, R. & Proulx, G. Guide for evaluating the predictive capabilities of computer egress models NIST GCR 06-886. (2005). at <> 20. Ronchi, E., Gwynne, S. & Purser, D. A. The impact ...
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... a hypothetical scenario where the designer wants to calculate the evacuation time (which can be named as "Response") using an evacuation model and considering the impact of two design variables (for instance, the number of exits and the number of occupants, which can be named as "Design variable 1" and "Design variable 2" respectively), it is possible to draw the design space for this using a Cartesian coordinate system, see Figure 4. Figure 4 shows the Response located in the Z axis and the Design variables located in the X and Y axes. ...
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... a hypothetical scenario where the designer wants to calculate the evacuation time (which can be named as "Response") using an evacuation model and considering the impact of two design variables (for instance, the number of exits and the number of occupants, which can be named as "Design variable 1" and "Design variable 2" respectively), it is possible to draw the design space for this using a Cartesian coordinate system, see Figure 4. Figure 4 shows the Response located in the Z axis and the Design variables located in the X and Y axes. The design space is then defined by all the possible combinations between Design variable 1 and Design variable 2. These combinations are the design points. ...
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... example, if an alternative inadequate type of technique is used, the design points picked up by it might not provide a good coverage of the design space. The grey region shown in Figure 4 illustrates this issue. It shows clearly that this selection was inappropriate and will not represent realistically the design space. ...
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... Monte Carlo technique is very often used to generate the design points for this purpose 35,36 . In addition to what has been discussed via Figure 4, there are other core-aspects which can help the designer and/or modeller to assess if Monte Carlo shall be an adequate approach when performing evacuation modelling, namely: (i) is the sampling technique in use appropriate? and (ii) is the sample enough to ensure accuracy in the results? ...
Citations
... Therefore, distributions are often used within evacuation models to reflect this variability [1]. The uncertainty associated with the representation of variability of human behaviour within an evacuation model is often referred to as 'behavioural uncertainty' [18,21]. Unlike other types of uncertainty which are considered within evacuation modelling, behavioural uncertainty reflects the current understanding of human behaviour in fire. ...
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.
... The simulation strategy was based on the fire evacuation process shown in Figure 2 simulating human behaviour [41] under various egress path constraints to represent occupants receiving different evacuation instructions. The instructions (egress path constraints) varied from floor to floor. ...
... The ultimate goal was to determine the optimum set of instructions that resulted in the fastest and safest evacuation. According to a study [41], the differences between reality and the evacuation simulation can be framed as arising from uncertainty. In this regard, the study [41] defined uncertainty as a "potential deficiency in any activity or phase of the modelling process as consequence of the lack of knowledge or understanding." ...
... According to a study [41], the differences between reality and the evacuation simulation can be framed as arising from uncertainty. In this regard, the study [41] defined uncertainty as a "potential deficiency in any activity or phase of the modelling process as consequence of the lack of knowledge or understanding." The objective of the modelling procedure ( Figure 2) was to model the evacuation process as accurately as possible. ...
As the possibility of safe escape is one of the most crucial aspects of a building’s fire safety features, understanding of human behaviour under fire conditions is important for a successful evacuation. Although most of today’s buildings are equipped with fire safety systems, a fire can still occur at anytime and anywhere in a building and have devastating consequences. In the last decade, researchers and practitioners have used information technology to assist with fire safety design and emergency management. Building Information Modelling (BIM) is an exemplar process whose underpinning digital technology has been helpful for fire safety design, simulation, and analysis, but there is a lack of research on how BIM-based models combined with agent-based simulations can help improve evacuation via effective navigation and wayfinding in high-rise residential buildings. Customising evacuation instructions based on BIM, simulation results and occupant location, and delivery of these bespoke instructions to occupants’ smartphones during a fire emergency is relatively novel and research is needed to realise the potential of this approach. Therefore, this study investigates how customised evacuation instructions delivered to each occupant in a high-rise residential building could result in a faster evacuation during a fire incident. The research adopted a case study building and used Pathfinder (agent-based evacuation simulation software) to simulate evacuation from this eleven-floor high-rise residential building in Cairo, Egypt. Constraining evacuees (simulated agents in Pathfinder) to take particular exit routes was used as a proxy for delivering customised evacuation instructions to actual evacuees. Simulation results show that, in general, allowing the use of lifts for the benefit of disabled occupants could lead to their misuse by able-bodied occupants; evacuees would attempt to use the first visible point of exit regardless of how crowded it is. With optimally customised instructions, the evacuation time was, on average, 17.6 min (almost 50%) shorter than when the occupant’s choice of egress route was simulated based on standard path planning factors such as route length, nearby crowds and visible hazards. With evacuation instructions sent via smartphones, occupants could exit more rapidly via alternative routes. Such bespoke instructions were shown to reduce the adverse effects of crowdedness and uneven distribution of occupants along vertical and horizontal evacuation routes on evacuation time.
... Finally, it implicitly accounts for the uncertainty ISSN: 2185-8322 DOI10.5595/001c.18160 associated with the input data and the simulation running outputs, while most of the other evacuation models do not necessarily integrate uncertainty into their reasoning (Ronchi et al., 2013;Tavares & Ronchi, 2015). ...
Generating well-informed and reliable predictions for disaster evacuation is a large challenge. Crisis and disaster management policymakers have to deal with poor data quality, a limited understanding of households’ behaviour dynamics, and uncertainty regarding the effects of the various actions/measures in place. Agent-based simulation models are frequently used to support decisions when planning disaster evacuation procedures. However, one of the most important aspects of this issue, which is social influence, is not often considered. Most of existing evacuation models largely overlook the importance of the households’ behaviours and social influences, which leads to oversimplified models. Moreover, it is almost impossible to find models in the literature that focus on the extrinsic decision-making factors of some evacuees, such as compromised lifelines, in the case of catastrophic events. In contrast to the existing evacuation models, this paper suggests a probabilistic agent-based model that relies on theloss of different lifelines as factors affecting evacuees’ decision-making in addition to some intrinsic factors that are used to characterise the propensity of households to evacuate and explicitly allow for social contagion as well as uncertainties to be considered. This model, in which all the variables are considered uncertain and Monte Carlo Simulations are run to estimate the confidence range of the predictions, is tailored to estimate the potential number of inhabitants that have not been evacuated in high-rise buildings in the face of critical infrastructure failures induced by a slow-onset flood and/or the actions taken during the related crisis, considering different uncertainties that may affect the reliability of the prediction. The model has been specifically designed to predict the dynamics of households’ self-evacuations in fourteen residential high-rise buildings located in a flood-prone area in Paris. This paper describes the suggested model and also reports the results of an illustrative case study in which three scenarios are simulated to demonstrate the applicability of the model, to test its effectiveness and to explore the uncertainty regarding some modelling assumptions using sensitivity analysis.
... An additional key aspect to consider when evaluating the predictive capabilities of evacuation models is the assessment of the uncertainties associated with the results produced. As most evacuation models make use of a probabilistic approach [80], it is crucial to assess the convergence of results [81]. This is often linked to the so-called behavioural uncertainty, i.e. the uncertainty linked with the use of pseudo-random sampling from distributions causing that the same input configuration can provide different results [28,82]. ...
Evacuation models can adopt different approaches for the simulation of human behaviour in fire. This paper provides an overview of the most commonly used modelling methods to represent the evacuation process in a fire scenario. This is presented through a structure matching the engineering time-line model of evacuation. The evacuation model development process is discussed considering both data-driven empirical correlations as well as theory-based modelling approaches. Examples of alternative methods to the currently used evacuation modelling assumptions are also presented. These methods have been chosen to provide examples of cases in which revisions of well-established assumptions may be needed. This review mainly focuses on buildings and pedestrian evacuation scenarios. Nevertheless, many concepts presented are potentially applicable to traffic evacuation. Particular attention is given to the representation of the impact of smoke on the evacuation process, as this is an important issue for fire safety engineering. Finally, a discussion on existing methods and procedures for the verification and validation of evacuation models is presented and the need for their standardization is advocated.
... pre-evacuation times, walking speeds). This method was found to be suitable for probabilistic evacuation modelling [29] and it allows to evaluate the impact of repeated simulations on results [30]. ...
This paper introduces an integrated approach for evacuation assessment of nuclear physics research facilities exposed to fire risk. The approach combines the use of a simplified egress modelling method and advanced agent-based simulations of evacuation. An integrated multi-model approach is proposed here given the varying level of complexity concerning evacuation safety in underground physics facilities. This paper introduces a simplified probabilistic egress model based on existing hand calculations for 1D smoke spread modelling and it suggests a procedure for its combined use with advanced agent-based evacuation simulations. This includes the use of the outputs of the simplified egress model in underground smoke-filled portions of underground nuclear research facilities (e.g. tunnel arcs) as an input for complex agent-based evacuation simulations in the underground access shafts. An exemplary application of the integrated approach is presented for the simulation of a set of hypothetical fire risk scenarios in the Future Circular Collider (FCC) at CERN. This approach is deemed to facilitate fire evacuation safety assessment in underground physics research facilities by optimizing the simulation of relevant fire risk scenarios. A discussion on the advantages and implications of the use of an integrated approach in comparison with other safety assessment methods is presented.
... The computational cost may manifest itself in the number of simulations required to reach convergence but may also be related to the computational effort required to do the sampling. There are a range of sampling methods of varying complexity that could be implemented by computational tools but several concerns have been raised in the literature [31,32] regarding which sampling technique is appropriate to confirm the precision of the results. However, the authors of this paper are not aware of any studies investigating the impact of different sampling methods on the output of evacuation simulation tools. ...
Simulating human behaviour in fire is often one of the main challenges in designing complex buildings, structures or sites for the life safety of occupants. In fact, evacuation simulations represent a fundamental input to assess fire safety performance using a risk analysis approach. The variability in evacuee behaviours (e.g. pre-evacuation delays and uncongested walking speed) can be probabilistically simulated in egress models using distribution functions. The application of probabilistic simulations requires the input distributions to be sampled. This paper describes a series of eight repeated trial evacuations that were carried out using a classroom-based scenario. The paper then investigates how four different sampling methods (namely Simple Random, Stratified, Inversed Stratified and Halton) affect the ability of a computational egress tool to reach convergence when determining the total time for occupants to leave the room. The analysis found that the Stratified and the Inverse Stratified sampling approaches require the least number of simulation runs to converge while the Halton sampling approach needs the greatest number of simulation runs. Moreover, the results indicate that the Halton sampling generates the highest variance for the simulated total evacuation time and thus is more effective at examining scenarios that utilise the extreme ends of the distribution functions.
... Thus, the limited amount of available data in combination with the difficulties to combine this data may lead to an unwanted propagation of uncertainties in life safety analyses including evacuation in smoke. This has also been acknowledged in the past, both by developers of evacuation simulation software and fire safety designers [31]. The consequence is that this uncertainty has been often treated with crude and conservative assumptions regarding people walking speed in smoke for different visibility levels [32]. ...
Limited guidance is available to engineers on how people's walking speed in smoke can and should be represented in the fire safety design process of underground transportation systems, such as road and rail tunnels. To address this issue, the behaviour and movement of people in case of evacuation due to a fire in underground transportation systems has been investigated. In this paper, the relationship between walking speed and visibility conditions has been analysed by performing a systematic review of current experimental research conducted in the field. This includes data-sets collected in Sweden, Japan, UK, Norway, Finland, Canada, and The Netherlands. A design recommendation on how to represent walking speed in both smoke-free and smoke-filled environments is presented. Uncertainty in data is thoroughly discussed and addressed in the recommendation. Three different methods to represent walking speed during the design of an underground transportation system are suggested. The selection of the method depends on the required treatment of uncertainty in the design. The developed representation substantially differs from existing methods used in fire engineering design to represent walking speed in smoke since it describes walking speed as a function of visibility, rather than the extinction coefficient. This permits comparison of data-sets collected in relationship to the presence of reflecting or emitting lights. Finally, suggestions on future research to be conducted in order to reduce the current uncertainties are provided.
... For example, this can be used to assess if a deterministic modelling approach that only captures average dynamics without any variability is appropriate given observed variability in data [26]. Other examples for this work assess how many replicate simulations of models that include variability have to be performed to ensure the convergence of average dynamics and variability in dynamics [27][28][29]. However, as this work is not directly concerned with statistical modelling, we will not discuss it further here. ...
Pedestrian dynamics is concerned with understanding the movement patterns that arise in places where more than one person walks. Relating theoretical models to data is a crucial goal of research in this field. Statistical model fitting and model selection are a suitable approach to this problem and here we review the concepts and literature related to this methodology in the context of pedestrian dynamics. The central tenet of statistical modelling is to describe the relationship between different variables by using probability distributions. Rather than providing a critique of existing methodology or a "how to" guide for such an established research technique, our review aims to highlight broad concepts, different uses, best practices, challenges and opportunities with a focussed view on theoretical models for pedestrian behaviour. This contribution is aimed at researchers in pedestrian dynamics who want to carefully analyse data, relate a theoretical model to data, or compare the relative quality of several theoretical models. The survey of the literature we present provides many methodological starting points and we suggest that the particular challenges to statistical modelling in pedestrian dynamics make this an inherently interesting field of research.
... 4. The method to treat uncertainties and represent pedestrian movement, either deterministic or stochastic [67]. This last aspect would be used to assume correlations which may be based on fixed constant movement speeds/relationships or using pseudorandom sampling from distributions [68]. ...
The understanding of pedestrian movement in smoke-filled environments is of significant importance in fire safety engineering applications. This chapter presents an overview of the main concepts concerning pedestrian movement in smoke, with a particular emphasis on the adverse effects that it can have on pedestrian evacuation. Several factors are discussed, including fire, pedestrian and environmental factors. Factors associated with the presence of fire relate to the impact of reduced visibility conditions, the presence of asphyxiant/irritant gases and cognitive and emotional influences are also explored. Pedestrian factors include walking speed and pedestrian movement abilities, visual acuity and physical exertion. Environmental factors include geometric complexity, the interaction with way-finding and signage systems, inclination of floor/ground or inclines (similar to stairs), stairs and surface materials. An overview of the current capabilities of pedestrian and evacuation models used in fire safety engineering applications is also presented along with recommendations for future areas of research in the domain of pedestrian movement in smoke.
The use of egress simulation models in performance-based analysis relies on the confidence in the input and output data. These data strongly depend on a large number of parameters. The study presented here focuses on the statistical aspects of these data, especially the population behavioral parameters which are the most likely to be scattered. The study proposes a method to analyze the statistical aspects of an egress simulation model. The method is based on statistical estimations of the distribution quantiles of the output parameters and can be applied to stochastic simulations results. It provides quantitative informations on the key output parameters dispersion, such as Required Safe Egress Time (RSET). It also gives a justification to the required number of simulations and input parameters precision to ensure a relevant output precision level. It gives access to quantitative criteria to compare experimental and numerical data. This method will be applied to analyze case studies simulated with BuildingExodus.
