DSpace at VNU: Integration of Smoke Effect and Blind Evacuation Strategy (SEBES) within fire evacuation simulation

Simulation Modelling Practice and Theory 36 (2013) 44–59 Contents lists available at SciVerse ScienceDirect Simulation Modelling Practice and Theory journal homepage: www.elsevier.com/locate/simpat Integration of Smoke Effect and Blind Evacuation Strategy (SEBES) within fire evacuation simulation Manh Hung Nguyen a,b,⇑, Tuong Vinh Ho a, Jean-Daniel Zucker a,c a IRD, UMI 209, UMMISCO, IFI/MSI, Vietnam National University of Hanoi, Viet Nam Posts and Telecommunications Institute of Technology (PTIT), Hanoi, Viet Nam c UPMC Univ Paris 06, UMI 209, UMMISCO, F-75005 Paris, France b a r t i c l e i n f o Article history: Received August 2012 Received in revised form 16 February 2013 Accepted April 2013 Available online June 2013 Keywords: Multi-agents system Fire evacuation simulation Evacuation strategy Blind evacuation Simulation modelling a b s t r a c t Many fire evacuation models have been proposed in recent years to better simulate such as an emergency situation However most of them not respect a recommendation of fire evacuation experts regarding the fact that evacuees should follow the boundaries of obstacles or wall to find the exits when their visibility is limited by smoke This paper presents an agent-based evacuation model with Smoke Effect and Blind Evacuation Strategy (SEBES) which respects that recommendation by integrating a model of smoke diffusion and its effect on the evacuee’s visibility, speed, and evacuation strategy The implementation of this model enables us to optimise the evacuation strategies taking into account the level of visibility The obtained simulation results on a realistic model of the Metro supermarket of Hanoi confirm the important impact of smoke effect and blind evacuation strategy on the number of casualties Ó 2013 Elsevier B.V All rights reserved Introduction Fire is increasingly a cause of casualties in modern life For instance, the Myojo 56 building fire in Tokyo (Japan) on September 1st 2001 has killed 44 people and 291 people killed in Mesa Redonda shopping center fire in Lima (Peru) on December 29th 2001 There were also 11 people who died in a fire at the detention center of Amsterdam Schiphol Airport (Netherlands) on October 27th 2005 The Moscow (Russian) hospital fire killed 46 people on December 9th 2006 The Santika Club fire in Bangkok (Thailand) killed 66 people on January 1st 2009 The ABC daycare center fire killed 47 people in Hermosillo (Mexico) on June 5th 2009 The 2010 Dhaka fire was a fire in the city of Dhaka (Bangladesh) on 3rd June 2010 that killed at least 117 people And this list could infinitely grow up The huge loss in these fires leads to at least two important questions: (1) Were people trained to practice the best strategy to fire evacuate? and (2) Were the building designed with the best inside configuration regarding to fire evacuate? These two questions show common issues: how can we assess which strategy is best among the fire evacuation strategies? More specifically, given a particular building which strategy is the best one? The real answer does not exist unless we could experiment in the real environment! One truthful approach is to rely on simulation environment modelling as close as the real world fire evacuation conditions Once a fire evacuation simulation model is proposed, it has to comply with at least two modelling points of view First, from the point of view of fire evacuation experts, the model should take into account the smoke diffusion and its effect on the evacuation, the observable range, the evacuation speed, and the toxic poisoning level of evacuees In particular, it should ⇑ Corresponding author at: Posts and Telecommunications Institute of Technology (PTIT), Hanoi, Viet Nam E-mail addresses: nmhufng@yahoo.com (M.H Nguyen), ho.tuong.vinh@auf.org (T.V Ho), jean-daniel.zucker@ird.fr (J.-D Zucker) 1569-190X/$ - see front matter Ó 2013 Elsevier B.V All rights reserved http://dx.doi.org/10.1016/j.simpat.2013.04.001 M.H Nguyen et al / Simulation Modelling Practice and Theory 36 (2013) 44–59 45 respect the recommendation of these experts by modelling the movement of evacuees in a limited visibility condition with the strategy of tracking the wall or obstacles Moreover, the proposed model should accommodate to the model different evacuation strategies in order to see which is the best suitable strategy for a given building Second, from the point of view of building architecture designers, it should enable to apply with several realistic building evacuation plans (with GIS data) to see which is the best evacuation plan for a given building Our objective in this paper is to propose a model for fire evacuation simulation based on agents This model does not only simulate the effects of fire/smoke on the abilities to move, to observe of evacuees, but also takes into account the given advise of fire evacuation experts, called Smoke Effect and Blind Evacuation Strategy (SEBES) We thus developed this model as a tool which could help fire evacuation training experts to visually demonstrate what evacuation strategy is better in a given environment Our contribution is thus three-folds:  First, a proposal of a new agent-based model for fire evacuation simulation is given This model respects the recommendation of experts in fire evacuation by taking into account their recommendation that evacuees should follow the boundaries of obstacles or wall to find the exits when their visibility is limited due to the smoke  Second, an implementation of the proposed model based on an agent based integrated GIS support platform (GAMA [1]) supporting the development of an useful tool for two groups of users: – The first group is experts in fire evacuation They could use this tool as a visual demonstration to illustrate what strategy is the best for evacuees to evacuate by applying all considered strategies into the model and run it, then compare the output parameters to see which is the best among them This could lead their evacuation training courses to be more intuitive and convince For instances, in the case study of the Metro supermarket of Hanoi, we compare three strategies of fire evacuation: following the evacuation signs, following the crowd, and following the own’s path when evacuees could observe still, and following the boundaries of obstacles and/or wall when their visibility is limited The simulation results show that following the evacuation signs is the best strategy in that situation – The second group is building architects, constructors, interior designers, etc They could use this tool to choose the best internal configuration of a given building regarding the effect in fire evacuation by applying their different designs into this model and run it, then compare the output parameters to see which is the best configuration This paper is organised as follows: Section presents some related works in the field of crowd evacuation modelling and simulation Section presents our agent-based model including a Smoke Effect and Blind Evacuation Strategy (SEBES) module for fire evacuation simulation Section presents the application of our model to a real case study, including two types of scenario: scenarios comparing three bind evacuation strategies, and scenarios comparing three other evacuation strategies in normal condition Finally Section presents a discussion of the simulation results and some conclusions as well as a discussion about future research Related works Recently, there has been an increasing number of models proposed for fire evacuation modelling in buildings Table summaries a partial collection of recent proposed agent models for fire evacuation We consider models at two levels:  At the level of modelling, we consider the modelling of agent types involving a fire evacuation: the evacuees (eV – column), the group or crowd of evacuees (g/c – column), the fire (fi – column), the alarm or voice system (a/v – column), and the smoke (sm – column)  At the level of optimisation, we consider the optimisation on the building design and the evacuation plan design (de – column), the optimisation on evacuation strategies in normal condition (visible evacuation strategy – v.e column), and that in limited visibility condition (blind evacuation strategy – b.e column) More detail, let us analyse at the level of modelling Evacuee and fire are two objects modelled in most of the listed models There is only a small number of models modelling the smoke [6,7,12,39] In these smoke models, the authors took into account the fact that smoke affects the visibility and speed of evacuee Furthermore, no model does respect a recommendation of fire evacuation experts on the fact that evacuees should follow the boundaries of obstacles or wall to find the exits when their visibility is limited by smoke At the level of optimisation, there are many models built to choose the best floor designs or evacuation plan for a given building [3,5,6,10,29,30,35,40] There are also some models optimised evacuation strategies in normal (visible) condition [25,31,34] But there is no model to optimise evacuation strategies in limited visibility condition We not aim to build a better model of existing over of all aspects but to focus on smoke modelling and taking into account expert recommendations; Our model will model many kinds of agent: evacuee, fire, alarm, smoke, etc in which the behaviour of evacuee is modelled based on a recommendation of fire evacuation experts on the fact that evacuees should follow the boundaries of obstacles or wall to find the exits when their visibility is limited by smoke This enables us to optimise on many aspects: optimise the evacuation plans, optimise the evacuation strategies in both conditions: visible and invisible 46 M.H Nguyen et al / Simulation Modelling Practice and Theory 36 (2013) 44–59 Table Summary of recent proposed models (eV = evacuee, g/c = group or crowd, fi = fire, a/v = alarm or voice, sm = smoke, de = design, v.e = visible evacuation, b.e = blind evacuation) Models Alavizadeh et al [2] Averill and Song [3] Garca-Cabrera et al [4] Chaturvedi et al [5] Daito and Tanida [6] Filippoupolitis et al [7,8] Gianni et al [9] Hanea et al [10] Helbing et al [11] Hu et al [12] Huang et al [13] Lin et al [14] Luo et al [15] Korhonen and Hostikka [16] Kuligowski et al [17,18] Okaya and Takahashi [19,20] Pan et al [21] Patvichaichod et al [22,23] Qiu and Hu [24] Rahman et al [25] Ren et al [26] Ruppel et al [27,28] Sagun et al [29] Said et al [30] Schneider and Konnecke [31] Shen and Chien [32] Shendarkar et al [33] Suryotrisongko and Ishida [34] Tang and Ren [35] Tingyong et al [36] Tsai et al [37] Wang et al [38] Weifeng and Hai [39] Yi and Shi [40] Our model Modelling Optimisation eV g/c fi U U U U U U U U U U U U U U U U U U U U U U U U U U U U U U U U U U U U U U U U U U U U U U U U U U U U U U U U U U U U U U U U U U U U U U U U U U U U U U U U U a/v sm de v.e b.e U U U U U U U U U U U U U U U U U U U U U SEBES: an agent-based simulation model This section presents our agent-based model including a Smoke Effect and Blind Evacuation Strategy (SEBES) module for fire evacuation simulation: Section 3.1 presents the general architecture of the model; Section 3.2 presents the modelling of evacuee agents; Section 3.3 presents the modelling of fire agents; Section 3.4 presents the modelling of smoke agents; Section 3.5 presents the modelling of fire alarm agents; and Section 3.6 presents the modelling of sign and plan agents 3.1 Architecture of the model The architecture of our simulator could be seen at three levels as depicted in Fig At the platform level, the model is developed on the simulation platform GAMA [1] GAMA provides a simulation development environment for building spatially explicit agent-based simulations It enables: (i) to use arbitrarily complex GIS data as environments for the agents; (ii) to run simulations composed of vast numbers of agents; (iii) to conduct automated controlled experiments on various scenarios, with a systematic, guided or ‘‘intelligent’’ exploration of the space of parameters of models; and (iv) to let users interact with the agents in the course of the simulations The second level is the simulator which relies on a multiagent system This is the core of our approach which includes the following types of agent:  Evacuee agent: representing an evacuee This agent could see the fire/smoke, hear the alarm, and evacuate to one of the emergency exits by avoiding the obstacles and other evacuees  Alarm agent: representing a fire alarm This agent could detect fire/smoke in its detection range and ring in a ringing duration of time  Fire agent: representing fire The fire agent could propagate within the building space M.H Nguyen et al / Simulation Modelling Practice and Theory 36 (2013) 44–59 47 Fig The three levels architecture of the model SEBES  Smoke agent: representing smoke The smoke agent is created from fire agents It could propagate inside the building space and therefore increase the smoke intensity at a give position by time  Sign and plan agent: representing of evacuation signs and plan This is a non-movable agent This provides the information about the direction to emergency exits The modelling of these agents will be presented in the next sections The third level is the visualisation level This level supports displaying the realistic status of the simulation as well as the values of the output parameters The details of the classes of the model are depicted in Fig 2: all agent classes inherit from the species agent which is the highest in the hierarchy of agent in the GAMA language At the level of agent skills, the Evacuee agents are able to move, so they have a Moving skill Other agents have Situated skill Fig The different classes of the model SEBES: the diffusion of smoke is modelled by the method propagate() 48 M.H Nguyen et al / Simulation Modelling Practice and Theory 36 (2013) 44–59 3.2 Evacuee agents This agent represents an evacuee, he has the following attributes:  Observable range: the space around an evacuee that can be observed and perceived by the evacuee An evacuee agent could only observe evacuation signs and/or other evacuees within this range  Toxic level: the level of toxicity poisoning an evacuee This is initially as zero, and then increased due to the effect of smoke An agent is considered as to be died if his toxic level reaches 100%  Fire exposure level: this represents the sensitive level in the fire of the evacuee The higher this value is, the more the evacuee agent is affected by fire/smoke  Speed: speed of an evacuee in evacuation This speed is changed according to the effect of visibility, and the toxicity level  Passed position list: The time stamped list of positions that an evacuee agent has had during evacuation The evacuee agent’s behaviours are presented in Fig 3a: in a normal condition, the evacuee agent normally moves inside the building He starts to evacuate if and only if either he sees a fire/smoke or he hears the fire alarm’s ringing His evacuation movement is finished when he gets out of the building During his evacuation, he moves following the evacuation movement principle His observable range is reduced and the toxic level is increased by time due to the intensity of fire/smoke These principles are presented in Sections 3.2.1 and 3.2.3 3.2.1 Evacuee movement principle The fire evacuation experts of Hanoi Fire Evacuation Association have suggested us to respect the fire evacuation guidelines when the evacuee meets obstacles: the evacuee should move along the border of the obstacle until the door (target) or there no more obstacle in front of the evacuee We take into account this principle of evacuee movement We use priority direction approach for modelling of agent’s movement In this approach, agent chooses the direction having the highest priority to move Other directions will then be prioritised relatively to the one having the highest priority There are two movement strategies: directions or directions (as depicted in Fig 4) We use the directions strategy in all simulations The more the direction is near the highest priority direction, the more the direction has high priority At each step, the agent considers the highest priority direction to move If it is not possible, the agent will consider the next lower priority direction, and so on A candidate direction is not considered if only if: either it is on an obstacle, or it leads to a position which is in the recent passed positions list of the agent In order to avoid the infinite loop of agent movement in the case having obstacles on the agent direction, we use a recent passed positions list which contains the n last positions of agent Agent thus considers the next position to move which is not in its recent passed positions list We not save all the passed positions of agent because the dynamic environments: some kind of obstacles, such as fire, can dynamically change There may be a fire at the position x at the moment t1, but may be no Fig Behaviour modelling for agents M.H Nguyen et al / Simulation Modelling Practice and Theory 36 (2013) 44–59 49 Fig The two movement strategies based on or directions more fire at x at the moment t2 > t1 So we limit the size of the list to give agent a possibility to return to the positions which it passed in long time An agent determines its own recent movement tendency by considering m last positions (m < n, n is the size of recent passed positions list) Therefore, the priority direction is the arc from the m-latest position to the current position of agent Fig illustrates the movement principle of an agent when there is an obstacle on its evacuation way At the time t = t0, the agent has not meet the obstacle yet, so it continues to move to its target Next step, t = t0 + 1, the agent meets the obstacle, it finds its recent movement tendency which is still direct to target because the two latest positions are on the same line (m = 2) But the 1st, 2nd and 3rd direction are impossible (in the obstacle, case of directions), so the 4th and 5th direction are possible Assume that the agent turns right At the time t = t0 + 2, the priority direction is the arc from the position at t = t0 to the one at t = t0 + In this direction, the first four priorities are not possible, the 5th is possible (the blue arc), and so on At the time t = t0 + 4, there is no more obstacles in front of the agent, so its priority direction is the direct line to its target 3.2.2 Blind movement principle In a blind situation, an evacuee uses the same recent movement tendency principle to move, except that he does not know exactly where is the target Therefore, his movement is based on following rules (Fig 6):  Blind movement rule 1: if there is not any obstacles or walls near him, the evacuee moves ahead (straight, right-straight, or left-straight)  Blind movement rule 2: if there is some obstacles or walls in front of him, the evacuee changes its movement direction as follows: – if the current direction is perpendiculars to the surface of an obstacle/wall, the new direction could be either right or left (Fig 6, case of ‘‘90 touch’’), – if the current direction is not perpendicular to the surface of an obstacle/wall, the new direction will be the nearest direction to the current one which enables the evacuee to follow the obstacle/wall (Fig 6, case of ‘‘normal touch’’)  Blind movement rule 3: if an evacuee is tracking an obstacle/wall, he continues to track until the end of the obstacle/wall (Fig 6, case of ‘‘during tracking’’)  Blind movement rule 4: at the end of an obstacle, the evacuee continues to move along the current movement direction (Fig 6, case of ‘‘getting out of obstacle’’)  Blind movement rule 5: at the ‘‘end’’ of a wall, the evacuee continues to follow the next face of the wall until he reaches an exit (Fig 6, case of ‘‘getting out of wall’’) Note that in the blind movement rule 5, there could be an exception when the wall is a closed block in the building, the evacuee thus could repeat his movement around the wall forever Therefore, he could not get out of the building This Fig The movement principle to avoid obstacles 50 M.H Nguyen et al / Simulation Modelling Practice and Theory 36 (2013) 44–59 Fig The blind movement principle Table Simulation parameters (based on [41,43,44]) Parameter Values Number of simulations for each scenario Number of evacuee agents Length of recent passed positions list Influence factor of smoke (b) Safety smoke intensity threshold (h) 100 1000 20 0.01 12.5% problem could be solved by using the proposed recent passed positions list technique: if the evacuee is aware that he is repeating the evacuation path around a wall, he will change to the blind movement rule 4: at the end of the closed block wall, he continues to move as the current direction by considering the closed block wall as an obstacle 3.2.3 Toxic level evolution If an evacuee agent is in the smoke, he is poisoned by toxic fumes The higher the smoke’s intensity at the agent’s position is, the more it is poisoned Assume that pti and ptỵ1 are the toxicity poisoned of evacuee i at the time t and t + 1, we have: i ptỵ1 i if pti ỵ b ðIt ðpÞ À hÞ < 0% > < 0% t t ẳ pi ỵ b I pị hị if 0% pti ỵ b It pị hÞ 100% > : 100% if 100% < pti þ b Á ðIt ðpÞ À hÞ ð1Þ where b is the influence factor of smoke; It(p) is the intensity of smoke at the evacuee’s position p (of the agent i) at the time t; h is a safety smoke intensity threshold Following Europe guideline (CFPA-E No 19:2009 [41]), every inspiration has 16% of oxygen concentration An evacuee shows serious symptoms if the oxygen concentration is lower than 14% When the oxygen concentration decreases to 14%, the smoke in the air must be 100 À 14 Ã 100/16 = 12.5%.1 Therefore the safety smoke intensity threshold is chosen in this model is h = 12.5% (Table 2) It means that if the smoke intensity is over 12.5%, the evacuee starts to be poisoned Note that, following formula (1), the toxic level of an evacuee will be decreased if the evacuee enters in a zone having the smoke intensity lower than h And inversely, the toxic level will be increased if the evacuee enters in a zone having the smoke intensity higher than h The more the smoke intensity is high, the faster the toxic level is increased The evacuee will be supported to be died if his or her toxic level is equal to 100% In a normal condition: there are 16 l of oxygen in 100 l of normal air Assume that now there are x litres of smoke in 100 l or polluted air, it means that there are (100 À x) litre(s) of normal air So the oxygen in these (100 À x) litre(s) of normal air are 16⁄(100 À x)/100 It takes less than 14% means that 16⁄(100 À x)/ 100 < = 14 It equals to x > = 100⁄(1 À 14/16) = 12.5 M.H Nguyen et al / Simulation Modelling Practice and Theory 36 (2013) 44–59 51 Fig Relation between smoke intensity and evacuee’s observable range and speed 3.2.4 Evacuee observable range reduce principle Like the evacuee’s power, the evacuee’s observable range is decreased if the evacuee is in the smoke The higher the intensity of the smoke at the position of the evacuee is, the less the evacuee is able to observe around it (this is well modelled in the model of Kang [42]) The relationship between the smoke’s intensity and the observable range of evacuee is depicted in Fig Assume that r 0i and r ti are the observable range of evacuee i at the beginning (without any smoke) and at the time t In order to keep the model as simple as possible, we use a function which states that the observable range is inversely proportional to the smoke intensity: rti ẳ It pịị r 0i ð2Þ where It(p) is the intensity of smoke at the evacuee’s position p (of the agent i) at the time t (normalised in the interval of [0, 1]) 3.3 Fire agents This agent represents fires The smoke agent is created from fire agents Fire agent could propagate  Duration: its time to live Normally, the duration of a smoke is longer than that of a fire  Propagation speed: the speed of propagation of the fire or the smoke This speed changes by time and by the quantity of fire agents in the building  Affected zone: the space around a fire which can affect evacuees inside it  Smoke creation speed: The smoke could not create other smoke, but fire could it This attribute of fire defines the speed to create smoke of a fire The fire agent’s behaviours are presented in Fig 3b: From its start, a fire burns until its ‘‘time to live’’ is equal to zero During its burning, a fire continues to create smoke with its smoke creation speed And the fire could also propagate by creating other fire near by its position with its propagation speed 3.4 Smoke agents This agent represents smoke The smoke agent is created from fire agents: (i) smoke, once being born from fire, is relatively independent from fire and (ii) smoke could move in some unpredictable directions: During moving around inside the building, smoke changes the smoke intensity at a given position by time We therefore model smoke as an agent  Direction: The direction to propagate The direction is determined based on the following principle: the smoke moves from the position with higher smoke intensity to the position with lower smoke intensity  Propagation speed: the speed of propagation of the smoke The speed of smoke is determined based on the following principle: the more the difference of smoke intensity at the two positions, the higher the speed of smoke The smoke agent’s behaviours are presented in Fig 3c: From its creation, a smoke updates its speed and direction at every simulation step After updating these two attributes, the smoke moves to the next position If the position is already outside of the building, it dies Otherwise, it continues to update its attributes 3.5 Alarm agents This agent represents a fire alarm with following attributes: Main properties:  Ringing duration: the duration of ringing when fire/smoke was detected  Detection range: this agent rings if there is fire or smoke appearing in this zone 52 M.H Nguyen et al / Simulation Modelling Practice and Theory 36 (2013) 44–59 Fig The evacuation plan of the Metro supermarket of Hanoi The alarm agent’s behaviours are presented in Fig 3d: In a normal condition, the alarm agent does not ring It starts to ring if and only if either it detects the fire/smoke inside its detection range This ringing leads evacuee agents to evacuate It stops ringing when the ringing duration is over passed 3.6 Sign and plan agents They represent the evacuation signs and plan They are non-movable agents They provide the information about the direction to emergency exits Case study: simulation with the Metro supermarket of Hanoi In this section, we apply the proposed model to simulate the fire evacuation in the supermarket Metro of Hanoi Our objective is to use simulation experiments to study what is the best evacuation strategy in the supermarket environment This section is organised as following: Section 4.1 presents the setting up of environment for simulations; Section 4.2 validates the model of smoke and blind evacuation; Section 4.3 validates the fire expert’s recommendation; Section 4.4 optimises the evacuation strategies in normal condition 4.1 Simulation setup This section presents the setting up of environment for simulations: Section 4.1.1 presents the setting up of the evacuation plans; Section 4.1.2 sets up simulation parameters; Section 4.1.3 presents analysis and evaluation criteria 4.1.1 The evacuation plan of Metro supermarket of Hanoi The environment of simulation is a representation of GIS data as shown in Fig The Metro supermarket of Hanoi is situated on one floor, with eight emergency exits: three on the front, two on the left, and three on the right In the simulation, the emergency exits are represented by red rectangles People can directly get to the left and right emergency exits from inside While in order to go to the front emergency exits, people have to pass two more gate layers: first, the cashiers layer with 12 main outputs, each is divided by two to have 24 cashiers in total; second, the security layer with four doors, and then the two main exits to the parking Another exit is the entrance which can use as an emergency exit The inside configuration of Metro2 supermarket can be decomposed into three main zones First, the left zone is the one for clothes and electronic materials There are two rows of shelves One is tall so people cannot look over to see the signs, therefore the evacuation signs are putted on the shelves One other is short so people can look over shelves to see the evacuation signs: there is only one common evacuation sign above them The evacuation signs in this zone indicate the direction to the two left emergency exits This plan is taken based on the inside configuration of the Metro supermarket of Hanoi on the September 12th 2011 M.H Nguyen et al / Simulation Modelling Practice and Theory 36 (2013) 44–59 53 Second, the central zone presents in majority dry and cosmetic goods There are three identical rows of shelves The nine shelves on the left are tall so the evacuation signs are also pasted on them The three remain on the right are short for frozen goods and therefore there is only one evacuation sign above them The evacuation signs in this zone represent the direction to three front emergency exits Third, the right zone presents the vegetables and foods This is divided into three small blocks, each has an right emergency exit The evacuation signs in this zone represent the direction to the three right emergency exits 4.1.2 Simulation parameters In order to make the results comparable, we use the same values for input parameters of all simulations: steps of simulations; number of people; initial distribution These values are estimated based on the Europe guideline on Fire safety engineering concerning evacuation from buildings (CFPA-E No 19:2009 [41]), the Human factors: Life safety strategies Occupant evacuation, behaviour and conditions (PD7974-6:2004 [43]), and the Fire and Smoke: Understanding the Hazards of The Committee on Fire Toxicology, Board on Environmental Studies and Toxicology, National Research Council [44] These parameters are shown in Table 4.1.3 Analysis and evaluation criteria For each evacuation strategy, we run the simulations many times (100 times at least) with the same value of initial parameters: the number of people (N = 1000), and the speed of people At the output, we need to calculate the following parameters:  Percentage of survivals The simulated environment is one floor building, so a person is considered as escaped if s/he passed one of emergency exits  Percentage of death A person is considered as dead when his or her toxic level reaches 100%  Average time for a person to be escaped It is the average time duration from the moment when s/he starts to evacuate until s/he escapes  Average rate of toxic level of survivals In comparing these parameters among strategies for each experiment, we will see which strategy of occupants is better in this realistic environment of the Metro supermarket of Hanoi A strategy is considered as better if three following observations are true: (1) the % of survivals is higher (% of death is lower); (2) the average time to escape is shorter; and (3) the average rate of toxic poisoned of survivals is lower 4.2 Validation of smoke and blind evacuation model The development of smoke during fire is presented in some snapshots of simulation interfaces (Fig 9) Following the time of fire, the smoke increases the propagation space and intensity These are correspond to the results of the modelling of smoke in Section 3.4 In order to indicate the effect of smoke on the movement of evacuees, at the visual level, we tracked the evacuation paths of some evacuees in the condition of limited visibility due to smoke (Fig 10a) These evacuees are modelled with the evacuation strategy recommended by fire evacuation experts: tracking the walls or obstacles The results in this case show that in majority time of movement, evacuee really tracks the walls and/or obstacles Fig The visualisation of fire and smoke during simulation 54 M.H Nguyen et al / Simulation Modelling Practice and Theory 36 (2013) 44–59 Fig 10 Comparison of the movements of three evacuees between two cases: with and without smoke model Let consider further, we compare the evacuation paths of the same evacuee (at the same start position) in two conditions: with smoke (limited visibility – Fig 10a) and without smoke (the evacuee could observe the evacuation signs to follow them – Fig 10b) The results show that these two paths are different That is reasonable because in the limited visibility, evacuee does not see the evacuation signs to follow them, it has to follow the wall or obstacle, so, its path could be significantly longer than in the case that evacuee could observe the signs to go to the approximate nearest exit 4.3 Validation of fire expert’s recommendation This section validates the fire expert’s recommendation: Section 4.3.1 presents the evacuation contexts and the considered evacuation strategies; Section 4.3.2 presents and analyses the obtained results 4.3.1 Evacuation context and strategies In order to verify if our model simulate correctly the advice of fire evacuation specialists and/or experts in a blind evacuation condition, we compare the given strategy of tracking the wall to the wander moving strategies, including: going ahead and random moving  Blind evacuation strategy 1: Tracking the wall The evacuee goes straight until touching an obstacle or a wall It then tracks following boundaries of the obstacle At the end of these boundaries, it continues to go straight These actions are repeated until the evacuee gets to an exit  Blind evacuation strategy 2: Going ahead The evacuee goes ahead until touching an obstacle or a wall Once it touches an obstacle, it changes its direction and continues to move These actions are repeated until the evacuee gets to an exit  Blind evacuation strategy 3: Random moving The evacuee randomly moves until finding an exit 4.3.2 Results We analysis the results at two levels: vision and statistics At the level of vision, our objective is to verify whether the agent of each strategy behaves as expected (described) We did track the evacuation paths of three agents which are randomly chosen as representatives of agents corresponding to three blind evacuation strategies as depicted in Fig 11: the cycle line corresponds to the path of tracking the wall, the square line corresponds to the path of going ahead, and the rectangle line corresponds to the path of random moving Intuitively, each agent evacuates corresponding to its defined strategy: the path given by the strategy of tracking the wall seem to be based on the walls; that of going ahead seem to be straight until the evacuate direction is changed when the agent touches an obstacle; and that of random moving seem to be unpredictable because its evacuate direction is randomly changed We could consider that the simulated behaviours of three kinds of agent are real enough to be acceptable At the statistic level, our objective is to quantitatively compare the output parameters to see whether the recommended strategy of tracking the wall is better than the two others in case of blind evacuation The results of the % of survivals by the simulation steps are depicted in Fig 12 For more detail, the % of survivals in the case of tracking the wall is higher than those in the case of going ahead or random moving The % of survivals (or death) at the end of simulation is shown in Fig 13a: the % of survivals in the case of tracking the wall is higher than that in the case of going ahead (M(tracking) = 49.12%, M(ahead) = 35.37%, significant difference with p-value < 0.001) or random moving (M(tracking) = 49.12%, M(random) = 21.75%, significant difference with p-value < 0.001) Without any contradiction, the average rate of toxicity level (in %) of survivals is also significant (as depicted in Fig 13b): in the case of tracking the wall, people is poisoned lower toxic fumes than going ahead (M(tracking) = 69.85%, M.H Nguyen et al / Simulation Modelling Practice and Theory 36 (2013) 44–59 55 Fig 11 The different paths among three blind evacuation strategies Fig 12 The % of survivals by simulation steps among three blind evacuation strategies Fig 13 Comparison of output parameters among three blind evacuation strategies M(ahead) = 77.44%, significant difference with p-value < 0.001) or random moving (M(tracking) = 69.85%, M(random) = 83.68%, significant difference with p-value < 0.001) In the same line, the average time to escape (Fig 13c) in the case of tracking the wall is significantly shorter than going ahead (M(tracking) = 138.91, M(ahead) = 166.45, significant difference with p-value < 0.001) or random moving (M(tracking) = 138.91, M(random) = 192.55, significant difference with p-value < 0.001) The first simulation’s results show that in the case of blind evacuation due to smoke, tracking the wall brings the higher % of survivals, the lower toxic level, and the shorter time to escape than going ahead or random moving (Table 3) These 56 M.H Nguyen et al / Simulation Modelling Practice and Theory 36 (2013) 44–59 Table Summary on 95% confidence interval of output parameter values among three blind evacuation strategies Parameter Tracking of wall Going ahead Random moving % Of survivals Toxicity level (%) Time to escape 47.7–55.5 68.9–70.7 136.0–141.7 33.8–36.9 76.2–78.8 162.9–169.9 20.0–23.7 82.4–85.0 188.8–196.2 simulation results confirm the advice of fire evacuation experts that in the condition of limited visibility, evacuees should track the wall instead of wander moving 4.4 Optimisation of evacuation strategies in normal condition This section optimises the evacuation strategies in normal condition: Section 4.4.1 presents the evacuation contexts and considered evacuation strategies; Section 4.4.2 presents and analyses the obtained results; Section 4.4.3 discusses about the obtained results regarding our setting up of simulations 4.4.1 Evacuation context and strategies In the first experiment, we considered in a tide situation with assumption that the smoke has completely filled inside of the building The evacuee thus is no more able to observe anything in around to choose the direction However, in a realistic situation, especially at the beginning of the fire, the evacuee is still able to find the direction to evacuate because there is not too much of smoke So the evacuee could dynamically change its evacuate strategy depended on the real situation: observable or not In the blind condition, the experiment pointed out that it is better if the evacuee uses the strategy of tracking the wall We aims to see what is the best strategy in visible condition considering three main strategies to evacuate in a fire evacuation: follow the self (the own’s path), or follow the crowd, or follow the evacuation signs  Normal evacuation strategy 1: Evacuees follow their own path (Fig 14a) The own path is the direction to the emergency exit which the agent has seen the last time Therefore the goal exit of agent does not change during evacuation If agent does not see anything because of smoke, it tracks following the wall to find an exit  Normal evacuation strategy 2: Evacuees follow the crowd (Fig 14b) If agent can still see around, it observes the crowd and follows it If agent does not see anything because of smoke, it tracks following the wall to find an exit Fig 14 Three normal evacuation strategies M.H Nguyen et al / Simulation Modelling Practice and Theory 36 (2013) 44–59 57  Normal evacuation strategy 3: Evacuees follow the evacuation signs (Fig 14c) If agent can still see around, it observes the signs and follows its indication direction If agent does not see anything because of smoke, it tracks following the wall to find an exit In order to compare the three strategies in a more general and realistic situation, we run the simulation with the random initiation of these parameters: the initial position of fire, the people’s fire exposure level, speed, observable range 4.4.2 Results Following the time of simulation, the % of survivals in the case of following the evacuation signs is higher than those in the case of following the crowd or following the own’s path (Fig 15) The % of survivals (or death) at the end of simulation is shown in Fig 16a: the % of survivals in the case of following the evacuation signs is higher than that in the case of following the crowd (M(signs) = 82.48%, M(crowd) = 70.97%, significant difference with p-value < 0.005) or following the own’s path (M(signs) = 82.48%, M(own path) = 58.55%, significant difference with p-value < 0.001) Likely, the average of toxicity level (in %) of survivals is also significant (as depicted in Fig 16b): in the case of following the evacuation signs, people is poisoned lower toxic fumes than following the crowd (M(signs) = 18.46%, M(crowd) = 29.93%, significant difference with p-value < 0.005) or following the one’s own path (M(signs) = 18.46%, M(own path) = 42.22%, significant difference with p-value < 0.001) In the same tendency, the average time to escape (Fig 16c) in the case of following the evacuation signs is significantly shorter than following the crowd (M(signs) = 104.61, M(crowd) = 129.72, significant difference with p-value < 0.001) or following the one’s own path (M(signs) = 104.61, M(own path) = 149.20, significant difference with p-value < 0.001) To summary, these results show that in the case of following the evacuation signs, the % of survivals is higher, the average of toxic level is lower, and the time to escape is shorter than following the crowd or following the own’s path (Table 4) 4.4.3 Discussions These results are just obtained in only one case of initial population The population for these simulations is initiated as following aspects First, the proportion of people using one of three evacuation strategies is approximately equal It means that among 1000 people for each simulation, there are approximately one third people following each strategy: following the signs, following the crowd, and following the own’s path The evacuation strategy of each evacuee is randomised, in each simulation, as long as the rate of people using each strategy is about one third We aim to run simulations with different proportions of people following each strategy This enables us to test the effects of initial proportion of people on the goodness of these three evacuation strategies In other word, this leads us to verify the question whether following the signs is always better to fire evacuate in this supermarket with any proportion of people using these three strategies This is one of our works in the next period Second, the initial positions of people are also randomised This may make us, in some circumstances, out of the realistic spatial distribution of people inside the supermarket We thus aim to initiate the people spatial distribution as realistic as possible by apply some intuitive rules such as: there are more people at the shelves of promotion or entertainment zones, there are more children at the shelves of toys, and more women at the shelves of food and cooking products, etc By applying these facts, we could evaluate how the spatial distribution of people effect on the goodness of these three evacuation strategies This is also one of our work in the near future An other field that this paper does not yet address is the optimisation at the configuration level We also aim to run simulations with different configurations of signs distribution, configurations of shelves positioning, configurations of emergency exits, etc These potential simulations could help us to find out which configuration (or inside design of building) is better than the current one This is also one of our next works in the line Fig 15 The % of survivals by simulation steps among three standard evacuation strategies 58 M.H Nguyen et al / Simulation Modelling Practice and Theory 36 (2013) 44–59 Fig 16 Comparison of output parameters among three standard evacuation strategies Table Summary on 95% confidence interval of output parameter values among three evacuation strategies in standard condition Parameter Signs Crowd Own’s path % Of survivals Toxicity level (%) Time to escape 78.8–86.2 14.5–22.4 99.7–109.5 64.2–77.7 23.3–36.6 121.2–138.3 53.3–63.8 36.8–47.6 145.8–152.5 Conclusion and future works This paper presented an agent-based model with Smoke Effect and Blind Evacuation Strategy (SEBES) for simulation of fire evacuation inside buildings This model respects a recommendation of fire evacuation experts on the fact that evacuees should follow the boundaries of obstacles or walls to find the exits once their visibility is limited by smoke, by integrating the model of smoke and its effect on the evacuee’s visibility, speed, and evacuation strategy The proposed model is then applied to simulate of human behaviours in a fire evacuation in a realistic plan of the Metro supermarket of Hanoi with GIS data The simulations are carried out with the optimisation in two conditions First, normal evacuation strategies include: following the crowd, following the one’s own path, or following the evacuation signs Second, blind evacuation strategies include: tracking the wall, go ahead, and random moving The simulations results indicated that following the evacuation signs is the best visible evacuation strategy in three given ones, and tracking the wall is the best invisible evacuation strategy in three given ones This results confirm one more time the effectiveness of a recommendation given by fire evacuation experts The simulations also provided an animation tool for fire evacuation experts to train people about fire evacuation in more visible ways Developing a framework for simulation of 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Toxicology, N.R.C Toxicology, Fire and Smoke: Understanding the Hazards, The National Academies Press, 1986  ... including a Smoke Effect and Blind Evacuation Strategy (SEBES) module for fire evacuation simulation Section presents the application of our model to a real case study, including two types of scenario:... SEBES: an agent-based simulation model This section presents our agent-based model including a Smoke Effect and Blind Evacuation Strategy (SEBES) module for fire evacuation simulation: Section 3.1... of environment for simulations; Section 4.2 validates the model of smoke and blind evacuation; Section 4.3 validates the fire expert’s recommendation; Section 4.4 optimises the evacuation strategies