人员疏散软件SIMULEX的应用

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人群疏散仿真研究综述

人群疏散仿真研究综述发布时间：2021-07-05T08:10:42.327Z 来源：《科技新时代》2021年2期作者：马佳伟黄锦良[导读] 人群疏散仿真是研究发生突发事件时人群疏散的行为，可以为制定科学、合理的人群疏散方案提供重要的知识，有助于帮助人群快速地疏散到安全区域，减少突发事件中人员伤亡和财产损失。

目前对人群疏散仿真的研究越来越引起研究者的关注。

为了能够深入了解人群疏散仿真的发展，本文研究了目前国内外现有的常用的人群疏散模型，各个模型的优缺点，以及模型未来发展趋势。

马佳伟黄锦良（宁波工程学院电子与信息工程学院，浙江宁波 315211）摘要：人群疏散仿真是研究发生突发事件时人群疏散的行为，可以为制定科学、合理的人群疏散方案提供重要的知识，有助于帮助人群快速地疏散到安全区域，减少突发事件中人员伤亡和财产损失。

目前对人群疏散仿真的研究越来越引起研究者的关注。

为了能够深入了解人群疏散仿真的发展，本文研究了目前国内外现有的常用的人群疏散模型，各个模型的优缺点，以及模型未来发展趋势。

1) 0 前言人群疏散仿真是研究发生突发事件时人群疏散的行为，可以为制定科学合理的疏散方案提供重要的知识，有助于帮助人群快速的疏散到安全区域，以减少突发事件中人员伤亡和财产损失。

随着计算机技术的迭代，计算机技术的高速发展，以及算力的增强，用计算机进行仿真模拟各种紧急疏散的因素和过程已经成为研究人员最常用的方法[1]。

但是，研究人员的侧重点并不相同，因此人群疏散的模型也大有不同。

现在大量的仿真模型大致分为两大类：宏观仿真模型和微观仿真模型。

随着算力的增强，微观仿真模型因为更加精确、拟真等优点逐渐替代了宏观模型。

2) 1 宏观仿真模型宏观模型是早期的一种疏散仿真的计算机工具，有动力学模型、流模型、排队网络模型等。

排队网络模型是将建筑物抽象为一个平面网络，由节点和连线组成。

该模型的优点是构造简单，所需计算能力不高。

但是有一些无法克服的不足[2]，例如大量的空间信息被省略；人群的行为受所处环境的影响很大，无法模拟人与环境的动态交互[3][4]。

地铁站疏散Modeling the pedestrian’s movement and simulating evacuation

Modeling the pedestrian’s movement and simulating evacuation dynamics onstairsYunchao Qu,Ziyou Gao ⇑,Yao Xiao,Xingang LiSchool of Trafﬁc and Transportation,Beijing Jiaotong University,Beijing 100044,Chinaa r t i c l e i n f o Article history:Received 23December 2013Received in revised form 15May 2014Accepted 22May 2014Available online 26June 2014Keywords:Pedestrian ﬂowStaircase movement Dynamic characteristics Social force modela b s t r a c tThis paper presents an enhanced social force model to describe the pedestrian’s movement and evacua-tion dynamics on pared with original models that described the pedestrian’s planar motion,our model introduces some mechanisms of the staircase movement,such as the inﬂuence of staircase geometry,the restriction of the step size and the optimal velocity selection.The body shape of each pedestrian is regarded as a set of three circles to precisely quantify the movement.In addition,the rotation dynamics are included into the model to describe the congestion effect.The improved model can obtain individual velocity under different staircase geometries and the ﬂow characteristics of the evacuation dynamics.Some empirical data and a series of observations captured in two subway stations in Beijing are applied to study the characteristics and further validate the model.The results show that our model performs well consistent with the observed data.At last,simulations are implemented to ﬁnd the solutions of estimating the evacuation time and evaluating the capacity of stair.Ó2014Elsevier Ltd.All rights reserved.1.IntroductionStairs are widely used in all kinds of buildings,especially in large scale public places,i.e.,subway stations,shopping malls and ofﬁce buildings.Walking on stairs is very common and important in our daily lives,and scientiﬁc design and effective utilization of stairs are urgently needed for designers and managers (Peacock et al.,2009).In emergency,such as power failure,ﬁre,earthquake or other hazards,the elevators may be out of commission,and the stairs become the primary escape routes.If there are too many peo-ple crowded on stairs,they will pack closer together or even lead to some dangerous situations (Shields and Boyce,2009).Knowing the ﬂow characteristics and predicting the egress time are the key points to grasp the evacuation dynamics and make emergency response plans on stairs (Graat et al.,1999;Oven and Cakici,2009).The characteristics of pedestrian staircase movement are deter-mined by organizational,constructional and behavioral factors:the organizational factors,i.e.,preparation for emergencies;the constructional factors,i.e.,the staircase geometry including riser height,tread depth and step width (Graat et al.,1999;Fujiyama and Tyler,2010);the behavioral factors,i.e.,responses and move-ment characteristics of pedestrians (Yang et al.,2012;Ma et al.,2012).The study of the staircase movement is an interdisciplinaryﬁeld with different focuses,such as biomechanics,physics,physiol-ogy,phycology,computer science,safety science (i.e.,Hankin and Wright,1958;Fruin,1971;Predtechenskii and Milinskii,1978;Templer,1992;Batty,1997;Helbing et al.,2000;Hase and Yamazaki,2002;Nelson et al.,2002;Hoskin,2004;Pauls,2005;Trew,2005;Casburn et al.,2007;Hostikka et al.,2007;Galea et al.,2008;Kretz et al.,2008;Seer,2008;Xu and Song,2009;Fujiyama and Tyler,2010;Galea et al.,2010;Hoskins,2011;Halsey et al.,2012;Yang et al.,2012;Peacock et al.,2012;Burghardt et al.,2013).To make quantitative analyses and detailed descriptions of staircase movement,many researches have carried out a lot of sur-veys,experiments and evacuation drills on stairs,and have collected large amounts of experimental and observational data of staircase movement (please see Table 1for details).In these studies,the ﬂow characteristics of staircase movement are described in individual level and collective level.Pedestrian ﬂow in low densities reﬂects the characteristics in the individual level,and walking speed is inﬂu-enced by physiological feature and body function,such as gender,age,height,weight,heart rate,rate of oxygen consumption and rate of energy expenditure (Irvine et al.,1990;Teh and Aziz,2002;Halsey et al.,2012).It is also inﬂuenced by stairway geometries and movement direction.Pedestrian ﬂow in high densities reﬂects the characteristics in the collective level.The collective behaviors include pedestrians’self-organized behaviors and optimal route choice behaviors (i.e.,Helbing et al.,2000,2005;Moussaid et al.,2011).The researches of pedestrian ﬂow in the collective level focus on three aspects,(1)evaluating evacuation time,(2)reproducing/10.1016/j.ssci.2014.05.0160925-7535/Ó2014Elsevier Ltd.All rights reserved.⇑Corresponding author.Tel.:+861051688193.E-mail address:zygao@ (Z.Gao).fundamental diagram,and(3)describingﬂow characteristics,i.e., inﬂow,outﬂow,capacity.Staircase movement is a complicated three-dimensional move-ment,and modeling the movement is a quite challenging work. Nowadays,researches have integrated behavioral and construc-tional factors,and have established many models to analyze the ﬂow characteristics and simulate evacuation processes in both sin-gle-story and multi-story buildings(Table1).In our work,we mainly focus on the case of single-story staircases.These models are classiﬁed into two categories:macroscopic model and micro-scopic model(Zheng et al.,2009).The macroscopic models regard the crowd as a single entity,and focus onﬁtting the expression of fundamental diagram.Linear,piecewise linear and non-linear functions(i.e.,Fruin,1971;Warren,1984;Tanaboriboon et al., 1986;Weidmann,1993;Lam and Cheung,2000;Proulx,2002; Peacock et al.,2012;Hoskins and Milke,2012)have been applied to describe relationship between velocity and density under differ-ent stair geometries.Compared with macroscopic models,the microscopic models are able to precisely describe the individual behavior,qualitatively explain the evacuation dynamics and reproduce some self-orga-nized phenomena(Helbing et al.,2000).These microscopic models are spatial-discrete models(cellular automation model,i.e., Kirchner et al.,2004;Huang and Guo,2008;Schadschneider and Seyfried,2009)and spatial-continuous models(social force model, i.e.,Helbing et al.,2000).These models have been applied to reveal two-dimensional planar movement,but few of them have described the three-dimensional staircase movement(Song et al., 2006;Pelechano and Malkawi,2008;Xu and Song,2009;Ma et al.,2012).In addition,the spatial-discrete models are some restricted to describe the staircase movement,such as grid size,fatigue factor,route selection,and uneven use of stairs (Pelechano and Malkawi,2008).Although the spatial-continuous models are advantageous to solve most of the aforementioned problems,these models are quite rare.Social force model(Helbing and Molnar,1995)is a well-known spatial-continuous model in theﬁeld of pedestrianﬂow.The model can reproduce several self-organized phenomena,such as lane forming,arching queue,shock waves and clogging effects (Helbing et al.,2005,2007).Moussaid et al.(2011)have proposed a heuristics-based model to replace the social force with a heuris-tics intelligent optimum function.Based on the heuristic social force model,this paper introduced some special rules and estab-lished an enhanced model to describe the mechanisms of pedes-trian movement and evacuation dynamics on stairs.Firstly,the body shape of each pedestrian is regarded as a set of three circles (Thompson and Marchant,1995).Compared with traditional sin-gle-circle shape(i.e.,Helbing et al.,2000),the three-circle shape precisely represents the projection of human body and describes the rotation movement when two pedestrians collide with others. Secondly,pedestrians usually walk more carefully on stairs than on planar,so two‘safety rules’are proposed to describe staircase movement behavior.Theﬁrst rule is that a pedestrian wants to walk upstairs/downstairs with integral steps at a time,and the step-size is restricted by the staircase geometry,such as tread depth,riser height and step width.The second rule is that a pedes-trian tends to walk along the sides,i.e.,holding handrails,propping up against walls.Thirdly,the relaxation time is extended to a var-iable in our model.The relaxation time is deﬁned that a pedestrian tends to correspondingly adapt his/her actual velocity to desired velocity with a certain characteristic time s(i.e.,Helbing et al., 2000;Moussaid et al.,2011).The relaxation time is mostlyTable1The state-of-the-art of staircase movement.Author(s)Year Method Model EvacuationprocessWalking speed NoteDynamics FD Geometry Direction Hankin and Wright1958Data analysis–s d s d–Fruin1971Data analysis–s d s s Planning methodPredtechenskii and Milinskii1978Data analysis Planning model s d s s Planning methodTanaboriboon et al.1986Macro Linear function s d s s Fundamental diagram Weidmann1993Macro Non-linear function s d d d–Frantzich1996Data analysis–s d d d–Graat et al.1999Data analysis–s d s s Capacity estimation Lam and Cheung2000Macro BPR function s d d d Fundamental diagram,capacityestimation Proulx et al.2002Data analysis Non-linear function d d s s SFPENelson and Mowrer2002Data analysis Non-linear function d d s s SFPEHoskin2004Softwaresimulation Coordinate-basedmodeld d d d Simulex32Pauls2005Data analysis–d s s s–Hostikka et al.2007Data analysis–d d s s–Kretz et al.2008Data analysis–s d d d Pedestrian movement on long stairs Seer et al.2008Data analysis–d d s s Flow characteristicsPelechano and Malkawi2008SoftwaresimulationGrid based model d s s s Literature review(STEPS,EXODUS)Galea et al.2008Software Evacuation model d s s s Merging behavior at interactions Xu and Song2009Micro Multi-grid model d s s s Flow characteristics,such as in and outﬂow Fujiyama and Tyler2010Macro Linear function s s d d Individual walking speed Galea et al.2010Data analysis–d d s s Evacuation softwareHoskins2011Macro Linear function d d d d Fundamental diagramYang et al.2012Data analysis–d d d s Evacuation drillLei et al.2012Softwaresimulation–d s s s Software(FDS,EVAC) Hoskins and Milke2012Data analysis–s d s s NISTPeacock2012Data analysis–d d d s NIST,different measurement methods Ma et al.2012Data analysis CA d s s s SimulationBurghardt et al.2013Data analysis–s d s s Fundamental diagramd Represents the factor is included and s represents the factor is not included.190Y.Qu et al./Safety Science70(2014)189–201assumed to be a constant in previous models,but it is not inade-quate to describe the staircase movement.As mentioned before, the individual walking speed on stairs is inﬂuenced by many fac-tors,such as staircase size,movement direction,and physical char-acteristics.For example,pedestrians spend more energy on walking upstairs than downstairs,spend more time on walking steep stairs than gentle stairs.Pedestrians with different age, weight or gender may require different relaxation times when walking on different stairs.Considering the inﬂuence factors,the relaxation time is formulated as a linear function of individual weight,moving height,and the slope of stairs.The linear function is similar to the model(Fujiyama and Tyler,2010),and some parameters are introduced to distinguish the upstairs and down-stairs movement.In this paper,some empirical data in literatures are collected and new observations from subway stations are conducted.In Sec-tion2,the characteristics of pedestrianﬂow on the stairs are dis-cussed.Based on the data and analysis,the constructional and behavioral factors are introduced to the social force model to pre-cisely describe the individual staircase moment in Section3.To validate the model,a series of simulations are implemented,and the simulation results are compared with the observational and empirical data in Section4.Simulations are implemented to ana-lyze theﬂow characteristics and the evacuation process in subway stations in Section5.Finally,the conclusions and the further work are given in Section6.2.Data collection andﬂow characteristics on stairs2.1.Empirical data of pedestrian speedOccupant speed is a very important element of pedestrianﬂow, and pedestrian speed on stairs is mainly affected by the slope of stairway,depth of tread,height of riser,and presence and location of handrails(Gwynne et al.,2009).Graat et al.(1999)had found that speed and capacity on stairs were higher with a normal (30°)slope than a steep(38°)slope.Kretz et al.(2008)had found that some pedestrian accelerate when walking upward a short stairway,and the mean upward walking speed on the short stair-way was found to be roughly twice as large as the one on the long stairway.Fujiyama and Tyler(2010)had proposed a model to pre-dict the walking speed based on the weight,leg power and the gra-dient of the stairs.The evacuation process of a large number of people is another major concern for researchers and designers. During the evacuation,the evacuation process something likes a queuing system that contains the processes of congestion forming, propagation and dissipation(Ma et al.,2012).Besides,the stair width and capacity will affect the route/exit choice behaviors and the evacuation efﬁciency(Lei et al.,2012).Researches have obtained many observational and experimen-tal data of staircase movement.However,this study here is not intended to be an exhaustive review of all researches.The refer-ences which mentioned both staircase geometry and individual speed are taken into consideration.Ten instances of staircase are included in our paper,and more detailed data can be found in the literatures(i.e.,Weidmann,1993;Frantzich,1996;Fujiyama and Tyler,2010;Hoskins,2011;Peacock et al.,2012).From Table2, it is found that walking speeds listed in the studies are different. This may be caused by natural variation of individual capability, staircase geometry,density of crowd and other factors.Besides, different measurement methods of calculating travel distances and areas on stairs may lead to different results(Hoskins and Milke,2012).Fujiyama and Tyler(2010)have made some experi-ments and found that average upstairs and downstairs speeds were0.58m/s and0.67m/s,respectively.Peacock et al.(2012) have mentioned that average downstairs speeds in their study of 0.48±0.16m/s were observed to be quite similar to the range of literature values.Kretz et al.(2008)have pointed that the density also affected the individual speed.Ma et al.(2012)have made a series of evacuation drills to obtain the average downstairs speed of0.547m/s.Even though different researchers have come up with different values for movement on stairs,most give a maximum for density of4.5–5.5pedestrians/m2,a maximum for speed of0.7–1.2m/s,and maximum for capacity of0.8–1.5pedestrians/(m s).2.2.Observations in subway stationsSomeﬁre drill evacuations of ofﬁce buildings have been imple-mented by National Institute of Standards and Technology(NIST), and the collected staircase movement data have included a range of stair geometries and occupant densities(Peacock et al.,2012; Hoskins and Milke,2012).However,in those studies,the local speed and the density were inaccurately estimated according to the collected data.It was because the cameras did not fully record the whole of staircase movement.In the drills,cameras were set every twoﬂoors to record an overhead view of occupant move-ment.The view of each camera only covered the main landing area plus tread depth area for about4–6steps of one story.Between every two cameras,there was a mid-landing,where pedestrian movement was a planar movement,but not a staircase movement. It was impossible to dissociate the planar movement on mid-land-ing from the video,so the calculations of the travel distance and travel time of staircase movement were inaccurate.To precisely investigate the pedestrian movement characteristics on stairs,we improved the method of video recording,and conducted observa-tions of whole staircase movement in two stations of Beijing sub-way Line1.The two stations are Sihui East Station(ascendingﬂow)and Xidan Station(descendingﬂow).The Sihui East Station is a termi-nal station and all the passengers should get off the train and go to the transfer hall;therefore,the pedestrianﬂow on the observed stair is an ascendingﬂow.The Xidan Station is also a transferTable2Individual horizontal speeds in ten instances of staircases with different geometries.ID Riser height(mm)Tread depth(mm)Gradient(°)Horizontal speed(m/s)Source Note#H120021043.60.361("),0.509(;)a Frantzich(1996)Narrow stair #H215030519.00.427("),0.601(;)Lam(2000)MTR #H316327131.00.417("),0.569(;)Lam(2000)KCR #H419027035.10.423(")Kretz(2008)Long stairs #H515029027.30.538("),0.581(;)Fujiyama(2010)Elder people #H615726730.50.590("),0.721(;)Fujiyama(2010)Young people #H718623838.00.488(;)a Peacock(2012)11-Floors #H819125436.90.440(;)a Peacock(2012)18-Floors #H915028028.20.547(;)Ma(2012)SWFC #H1014028026.50.53(;)Yang(2012)Stair No.2#a The speeds were converted to horizontal speeds.Y.Qu et al./Safety Science70(2014)189–201191station and the passengers can get on or off the train by the stair, and there is an escalator on one side of the stair to relieve the coun-tering passengerﬂows.Therefore,the pedestrianﬂow is a descend-ingﬂow.The schematic diagram of the observation stations is shown in Fig.1,and the information of the stairs is shown in Table3.A HD camera was set on the transfer hall to record the trail of each passenger.The observations in the subway stations were made during the afternoon rush hours of weekend(17:30–18:30,Sunday)on May 12,2013.In our observations,383pedestrians(216males,167 females)were collected at the selected staircases in Xidan Station, and221pedestrians(129males,92females)were collected in Sihui East Station.Most of the pedestrians were young and mid-dle-aged people,and their ages mostly ranged from25to55years old.The proportion of children and elderly was very low.In our observations,most of the pedestrians carried light bags and walked in a normal speed on staircases.Because the camera was not right above on the observed region, the passengers were sometimes overlapped in the video.Each pedestrian was recorded by individual characteristics,such as gen-der,age,body size,hair,shirt and pants.Then,the pedestrians were recognized by their features,entering and leaving time.The speed of each pedestrian was calculated by the travel time(leaving time minus entering time)dividing the travel distance on the stairs.The video recordings were processed semi-manually,and the dynamic evacuation characteristics,such as average density,speed andﬂow,were analyzed.Take Xidan station for example,the evac-uation dynamic characteristics and a snapshot were illustrated in Fig.2a–c).It was found that the curve of time-varying density was divided into several segments,and each segment represented a stream of pedestrians entering and leaving the stairs.During the observation,some measures,such asﬂow restriction and guidance, had been adopted to avoid crowdedness,so the density of pedestri-ans on stairs was in a normal(low)level.There were eight local maximum points exceeding1.0pedestrians/m2,and the maximal density was about1.6(1/m2).Velocity showed an opposite trend of density.The velocityﬂuc-tuated between0.4m/s and1.0m/s,and the average velocity was about0.57m/s.The volatility of individual velocities might be caused by different individual capability and desired velocity.In a low density,the pedestrians who walked fast would overtake front pedestrians who walked slowly and blocked them.When the density became larger,the pedestrians began to slow down and follow with others,and then queues might form on stairs.In Fig.2d),the acquainted or familiar people might walk abreast, which is regarded as‘subgroup behavior’(Yang et al.,2012).If they walked slower than the surrounding people,they would form a dynamic bottleneck.Additionally,lane-forming phenomenon was also found.In Fig.3a),the distributions of the speeds during the observa-tions followed normal distributions,which were similar with the reference(Peacock et al.,2012).The average velocities of walking upstairs and downstairs were0.55m/s and0.63m/s,respectively. Affected by gravity,going upstairs was slower than going down-stairs.By gathering the observed data of unidirectionalﬂow,the relationships between the velocity and the density were shown in Fig.3(b).The velocity decreased as the density increased.It should be noted that,in a low density,the velocity of going upstairs was a little higher than downstairs.It was because some of the pedestrians were hurried out of station and ran more than one steps at one time.3.Modeling the pedestrian’s movement on stairs3.1.Body shapesIn the existing models,the projection of a pedestrian’s body shape is usually regarded as a square(i.e.,Kirchner et al.,2004), a rectangular(i.e.,Song et al.,2006;Weng et al.,2007),a circle (i.e.,Helbing et al.,2000),an ellipse(i.e.,Chraibi et al.,2010)or a set of three circles(Thompson and Marchant,1995).In these geo-metrical shapes,the three-circle shape has some geometrical and computational advantages on modeling the staircase movement. First of all,the three-circle shape is a better alternative to describe the pedestrian’s body shape.It is because the occupied space of one pedestrian is restricted by the stairs,and the shoulder width of the pedestrian is larger than lateral width(Xu and Song,2009).In addi-tion,a pedestrian walks with a relative slow speed on stairs,and his/her space requirement keeps almost constant.Secondly,in social force model,the distance of closest approach of two pedes-trians is a key parameter when calculating the self-driven force, the repulsive force and the contact force.The closet distance between two single-circle or three-circle shapes can be easily cal-culated(Thompson and Marchant,1995);however,the calculation of the closest distance of two ellipses is surprisingly difﬁcult (Zheng and Palffy-Muhoray,2007).For the convenience of calcula-tion,the three-circle shape is a better alternative than ellipse shape.Therefore,the three-circle shape is chosen to describe pedestrian’s body shape,and the schematic diagram is shown in Fig.4.3.2.Modiﬁed social force modelThe well-known social force model(Helbing and Molnar,1995; Helbing et al.,2000)is a microscopic force-based model that can reproduce several self-organized phenomena,such as lane form-ing,arching queue,shock waves and clogging effects(Helbing et al.,2005,2007;Moussaid et al.,2011).The model describes pedestrians’movement behavior by introducing the self-driven force~f Di,the contact force with pedestrians~f Cijand walls(obstacles) ~f Ciw.The self-driven force can be calculated by Eq.(1),and the total force~f exerted on pedestrian i can be formulated as Eq.(2).192Y.Qu et al./Safety Science70(2014)189–201~f D i ¼m ~m desiÀ~m isð1Þ~f i ¼~f D i þXj~f C ij þXw~f C iwð2ÞMoussaid et al.(2011)have proposed a heuristics-based modelto replace the social force with a heuristics intelligent optimum function,which can be regarded as a so-called collision prediction process.The model can overcome some difﬁculties in the original versions.Based on the model,we use the three-circle shape,intro-duce some special rules and establish an enhanced model todescribe the staircase movement.In our model,the modiﬁcations are concentrated on the calculations of optimal direction selection,self-driven force and contact force.3.2.1.Selecting optimal direction In Eq.(1),the desired velocity ~m des i can be obtained by the mag-nitude m des i multiplies by the direction ~e des iof desired velocity.The calculation of ~e desi is called ‘optimal direction selection’,which isTable 3Detailed step sizes.ID StationStep number Width (mm)Depth (mm)Height (mm)Gradient (°)Flow direction#O1Xidan Station 16240030014025.0Descending ﬂow #O2Sihui East Station15190033015726.1Ascending ﬂow(b) Change of pedestrian density with time inthe observation of staircase #O1(c) Change of pedestrian speed with time inthe observation of staircase #O1(d) A snapshot of the observations in staircase #O2(a) Change of pedestrian flow with time inthe observation of staircase #O1 Fig.2.Processed data and a snapshot.Y.Qu et al./Safety Science 70(2014)189–201193an important component of the model(Moussaid et al.,2011).In our work,the body shape is extended to three-circle shape,and the calculation becomes a little complex.To make a clear state-ment,some notation and deﬁnitions are given as follow:for pedes-trian i,the large circle’s radius is r i1,the small circle’s radius is r i2, the mass is M i,the maximum velocity is v0i,the location is~l i,the velocity is~m i and the desired destination is~D i.Assume that pedestrian i moves at the velocity v0i along the direction of direction a,and will contact with pedestrian j after D t time.The i0and j0represent the locations of i and j at time t+D t.Then,fðaÞ¼v0iÁD t is the distance to theﬁrst collision with other pedestrian or obstacle in the direction a.If no collision is expected to occur,f(a)is set to a default value d max,which repre-sents the‘maximum horizon distance’of pedestrian i.The calcula-tion of furthest distance without collision f(a)can be improved as follows:l ixm ðtþD tÞ¼l ixmðtÞþv ix D t;l iy mðtþD tÞ¼l iy mðtÞþv iy D tðm;n2f1;2;3gÞl jxn ðtþD tÞ¼l jxnðtÞþv jx D t;l jy nðtþD tÞ¼l jy nðtÞþv jy D tðl ixm ðtþD tÞÀl jxnðtþD tÞÞ2þðl iymðtþD tÞÀl jynðtþD tÞÞ2¼ðr imþr jnÞ2ð3ÞPut theﬁrst two items into the third item,we can get a qua-dratic equation with moving time D t.Given the locations and velocities of pedestrians i and j,the D t can be easily solved.Then, the value fða i m j nÞand dðaÞcan be calculated asfða i m j nÞ¼min f v i Deltat;d max g;fðaÞ¼min f fða i m j nÞg;aÃ¼argmin f dðaÞg dðaÞ¼d2maxþfðaÞÀ2d max fðaÞcosða0ÀaÞð4ÞThe optimal velocity direction~e¼ðcos aÃ;sin aÃÞ;here,aÃ¼argmin f dðaÞg is the optimal direction.Fig.5illustrates the calculation.3.2.2.Self-driven forces and contact forcesThe pedestrian’s staircase movement is a three-dimensional motion,which contains horizontal and vertical motion.Pedestrian should change his/her center of gravity to ascend or descend the stairs,which are shown in Fig.6(a)and(b).The complicated move-ment includes the pedestrian’s physiological activity and energy transformation.In our work,we mainly focus on the pedestrians’horizontal optimal choice and crowding behaviors,so the vertical motion is approximately regarded as a linear motion,which is shown in Fig.6(c).In horizontal motion,pedestrians not only over-take front people with slower speeds but also have to notice the steps and prevent themselves from falling down from stairs.To mathematically depict horizontal movement,some assumptions and rules are introduced to simplify the movement,which is illus-trated in Fig.6(d).Theﬁrst assumption is that a pedestrian wants to move forward within n steps(n is integer).And the pedestrian’s desired destina-tion of next footstep is the center of the forward step.For example, in Fig.6d),pedestrian often moves forward with integer steps,i.e., one step(point A)or two steps(point B).When he/she moves for-ward with non-integer step,i.e.,2.5steps(point C),he/she will move to the edge of the step,and may feel unstable,unsafe or even fall down from the stairs.Therefore,point C is not considered in our model.The pedestrian’s horizontal footstep length is deﬁned as d h¼nDcos b,and is restricted by the tread depths D and riser heights H of the step.Here,b is the included angle between the optimal direction aÃand the x-coordinate.If the pedestrian is obstructed by other pedestrians or obstacles,he/she will slow down and avoid collision,and the footstep length does not exceed the maximal dis-tance fðaÃÞ(Eq.(4))in the optimal direction aÃ.Finally,the footstep length is expressed as:d h¼minnDcos b;fðaÃÞ&'ð5ÞAccording to the model(Moussaid et al.,2011),a pedestrian maintains a distance from theﬁrst obstacle in the chosen walking direction that ensures a time to collision of at least a relaxation time s.In other words,desired velocity of pedestrian i is formu-lated as v des i¼d h s.Additionally,pedestrian’s speed is assumed to not exceed a maximum velocity v max.Then,the horizontal maxi-mum velocity is v max cos h,and the angle h represents the slope of the stair tan h¼HÀÁ.The horizontal desired velocity v des i can be formulated as Eq.(6):v desi¼mind hs;vmaxcos hð6Þ(a) Probability distribution(b) Fundamental diagramFig.3.Speed and fundamental diagram of theobservations.194Y.Qu et al./Safety Science70(2014)189–201。

IES(VE)软件介绍

热岛效应示意图
生态建筑设计所面临的挑战
z 能源紧缺已经成为限制经济发展，威胁国家安全的瓶颈问题之一
¾ 世界第二大石油进口国 ¾ 世界第一大煤炭生产国 ¾ GDP居世界第四位
z 建筑建设和使用过程中的一些数据：
¾ 城市用地中有30%用于住宅 ¾ 耗能达到总能耗的30%多 ¾ 耗水占城市用水47% ¾ 耗用的钢材占全国用钢量的30% ¾ 水泥用量占全国总用量的25%
¾ 降低供水管网漏损率10%，一年可节水47亿吨；推广应用节水器具，一年可节省17亿吨。
¾ 提高建筑品质，延长建筑使用寿命，推广可循环利用新型建筑材料，到2010年对不可再生资源的消耗可降低10%，到2020年在此基础上可再降低20%。
¾ 到2020年，每年可实现减少二氧化碳排放量近7亿吨
生态建筑设计所面临的挑战
小区风速分布图
小区规划图
通过小区合理的布局，提高小区的冬季防风和夏季通风，就可以达到10%左右的节能效果，同时避免热岛效应，改善住区的舒适度
建筑环境的模拟：CFD
风速云图
建筑周边的风速包面图
通过对单体建筑的CFD分析，可以有效地改善建筑周边的风环境，强化建筑的自然通风，达到降低能耗、保护环境的目的
生态建筑集成化分析模拟解决方案
武海滨博士西迪阿特信息科技（上海）有限公司
生态建筑设计应对之道：集成化分析
生态建筑的定义与发展
z 什么是生态建筑？ ¾ Arcology= architecture + ecology ¾ 以生态学方式和方法设计的建筑 ¾ 以建筑为外在表现形式的生态学系统
z 生态建筑发展背景 ¾ 1962年，美国女生物学家莱切尔﹒卡逊发表《寂静的春天》 ¾ 1972年，美国学者巴巴拉﹒沃德等为联合国环境会议起草《只有一个地球》 ¾ 1972年，罗马俱乐部丹尼斯﹒米都斯等发表《增长的极限》，提出“持续发展”和“合理的持久的均衡发展”的概念 ¾ 1987年，联合国世界与环境发展委员会发表《我们共同的未来》，正式提出可持续发展的概念 ¾ 1992年，联合国环境与发展会议通过《二十一世纪议程》，可持续发展理念正式成为全人类的共同行动纲领

人员疏散分析模型

该软件由 5 个互相关联的子模型组成，它们是人员、移动、行为、毒性和危险子模型。模型跟踪每一个人在建筑物中的移动轨迹，以及人们的模拟状态
_____________________________________________________________________ [5]
——或者疏散到安全地点，或者被火灾所伤害。模型基于行为规则和个体属性，每一个人的前进和行为由一系列启发性规则决定。行为子模型决定了人员对当前环境的响应，并将其决定传递给移动子模型。行为子模型在两个层次起作用，即全局行为和局部行为，全局行为假设人员采用最近的可用疏散出口或者最熟悉的出口来逃生；局部行为可以模拟以下现象：决定人员对疏散警报的初始响应、冲突的解决、超越以及选择可能的绕行路径等。这些都取决于人员的个体属性。毒性子模型决定环境对人员的生理影响，考虑了毒性和物理危险，包括升高的温度、热辐射、C0、C02 以及 02 含量等因素影响，并且估计了人员失去行动能力的时间。它采用“毒性比例效果剂量”模型（FED），假设火灾危险的影响由接受到的剂量而不是暴露的浓度决定，并且累计暴露期间的比例。 EXODUS 建模可以采用实验数据或者从其他模型得到数值数据,允许 CFAST 计算数据导入到 EXODUS 中。EXODUS 模拟完毕后，可以使用数据分析工具来处理数据输出文件。另外，提供了基于虚拟现实的后处理图形环境，提供疏散的三维动画演示。
3.EXIT89 软件 EXIT89 由美国消防协会的 Rita F.Fahy 开发的一个用于大量人员从高层建筑疏散而设计的疏散模型。该软件可用于模拟高密度人员的建筑的疏散。例如高层建筑，它可以跟踪个体在建筑物内的行动轨迹。从消防安全的角度来评估大型建筑设计时，该模型可以处理一些疏散场景中相关的因素，包括：（1）考虑各种不同行动能力的人员。包括限制行动能力的人员和儿童。（2）延迟时间，既包括可以用来代替移动前的准备活动的时间（由用户根据每个位置指定），也包括随机的额外时间，可以当作人员疏散开始时间。（3）提供选择路径功能—使用模型计算出来的最短路径，可以用来模拟经过良好训练的或者有工作人员协助的疏散过程；或者使用用户指定的路径，

FlexES使用说明书-报警主机

警告语言表示反应风险系数关于该环节或其他环节的相关的重要信息配置检查和调试按照国家和地方规定进行操作拆卸根据200296egweee指令的规定拆除产品后其电子设备应由制造商收回并进行妥善处理
操作说明书
FlexES 火灾报警计算机控制系统
798980.GB0 01.2010
GB
保留技术变更权!
© 2010 霍尼韦尔国际公司
5.1 重启火灾控制面板 .................................................................................................................................................15 5.2 启动或关闭消防部门警报 .....................................................................................................................................15 5.3 激活/ 禁用音响报警器 ...........................................................................................................................................15 5.4 开启/禁用火灾保护设备启动功能 ........................................................................................................................16 5.5 昼/夜模式和延迟/调查 ...........................................................................................................................................16 5.6 关闭蜂鸣器 .............................................................................................................................................................16 6 操作菜单 ........................................................................................................................................................................17

《基于SUMO平台的应急疏散交通仿真系统设计与实现》范文

《基于SUMO平台的应急疏散交通仿真系统设计与实现》篇一一、引言随着城市化进程的加速，应急疏散成为城市管理的重要一环。

为了有效应对突发事件，提高疏散效率，减少人员伤亡和财产损失，基于SUMO平台的应急疏散交通仿真系统设计与实现显得尤为重要。

本文将详细介绍该系统的设计思路、实现方法及实际应用效果。

二、系统设计背景与目标SUMO（Simulation of Urban MObility）是一款开源的交通仿真软件，能够模拟各种交通场景下的车辆、行人等交通元素的运动行为。

基于SUMO平台的应急疏散交通仿真系统旨在通过仿真技术，为城市应急管理部门提供一种高效的、可视化的疏散方案设计和评估工具。

该系统的设计背景是针对当前城市应急疏散中存在的问题，如疏散路线规划不合理、交通拥堵等，以期通过仿真技术提高疏散效率，保障人员安全。

三、系统设计原则与架构系统设计遵循以下原则：1. 实用性：系统应具备操作简便、功能齐全的特点，以满足应急管理部门的实际需求。

2. 可扩展性：系统应具备较好的可扩展性，以便未来能够适应更多场景和需求。

3. 仿真精度：系统应具备较高的仿真精度，以准确反映真实场景下的交通状况。

系统架构主要包括以下几个部分：1. 数据处理模块：负责从现实世界中收集交通数据，并转化为SUMO可识别的格式。

2. 仿真模型构建模块：根据数据处理模块提供的数据，构建仿真模型，包括道路网络、交通元素等。

3. 仿真运行模块：根据预设的仿真参数，运行仿真模型，模拟交通场景。

4. 结果分析模块：对仿真结果进行分析，为应急管理部门提供决策支持。

四、系统实现方法1. 数据处理模块实现：通过数据采集技术，从现实世界中收集交通数据，包括道路网络、交通流量等。

然后，利用数据处理技术，将数据转化为SUMO可识别的格式。

2. 仿真模型构建模块实现：根据数据处理模块提供的数据，利用SUMO的API接口，构建仿真模型。

包括道路网络的构建、交通元素的添加等。

消防工程师的火灾风险评估软件推荐

消防工程师的火灾风险评估软件推荐消防工程师在进行火灾风险评估时，需要借助专业的软件工具来准确分析和评估火灾风险。

本文将为消防工程师推荐一些在火灾风险评估方面表现出色的软件。

以下是几款推荐的火灾风险评估软件：1. FDS（Fire Dynamics Simulator）FDS 是由美国国家标准与技术研究院（NIST）开发的一款火灾仿真软件。

该软件基于计算流体动力学（CFD）原理，可以模拟火灾场景中的火势发展、烟气扩散、温度分布等情况。

消防工程师可以通过该软件对建筑物、船舶、隧道等场所的火灾风险进行全面评估。

2. EPANETEPANET 是一款由美国环境保护局（EPA）开发的水力模型软件。

虽然它主要用于供水系统的模拟和优化，但消防工程师可以借助该软件对建筑物的消防水系统进行分析。

通过EPANET，消防工程师可以评估消防水源的供水压力、水流速度以及系统的可靠性，从而提高火灾应对的效率和安全性。

3. SimulexSimulex 是一款经过验证和广泛应用的火灾模拟软件，它能够模拟建筑物内部火场的烟气扩散、人员疏散等情况。

消防工程师可以使用该软件评估建筑物在发生火灾时的疏散时间、疏散路径以及人员密集区域，从而为应急疏散方案的设计和改进提供科学依据。

4. Fire Risk Assessment Tool（FRAT）FRAT 是针对商业、工业和公共机构开发的一款火灾风险评估软件。

该软件根据用户提供的建筑物信息，自动计算各种潜在火灾风险因素的概率和严重程度，并生成相应的火灾风险报告。

消防工程师可以根据该报告评估建筑物的火灾风险，并提供相应的改进措施。

5. PyrosimPyrosim 是一款使用可视化界面的火灾模拟软件，它基于 Fire Dynamics Simulator（FDS）和 Evacuation Simulation（Simulex）等强大的火灾仿真引擎。

消防工程师可以通过该软件对建筑物内部火势发展、烟气扩散和人员疏散进行全方位模拟，从而准确评估火灾风险和制定应对策略。

疏散模拟软件STEPS与Pathfinder对比研究

疏散模拟软件STEPS与Pathfinder对比研究杜长宝1,2,3，朱国庆1,2,3，李俊毅1,2,3（1. 中国矿业大学安全工程学院，江苏徐州221116；2. 中国矿业大学煤矿瓦斯与火灾防治教育部重点实验室，江苏徐州221116；3. 中国矿业大学消防工程研究所，江苏徐州221116）摘要：为了解两款疏散模拟软件STEPS与Pathfinder的功能差异及适用范围，本文采用理论研究与实际模拟相结合的方法进行对比分析。

研究发现：STEPS软件采用元胞自动机（CA）模型，疏散规则简单，人员智能化程度低，疏散时人员行为特征与现实情况不完全相符，但路径决策系统很好地解决了上述问题，可以很真实地模拟常态下人员疏散；Pathfinder软件采用Agent-base模型，人员智能化程度高，个体可以回应环境刺激，人员可以轻松规避障碍物，人群中的个体行为特征丰富且与现实情况十分相符，但在出口选择上存在缺陷，只能选择前方的出口而忽略后方的出口，不能模拟常态下人员疏散。

分析得出Pathfinder模拟结果与真实情况相符程度更高，STEPS更适合模拟常态下人员疏散。

关键词：疏散；STEPS；元胞自动机；Pathfinder；Agent-baseComparative Study on Evacuation Simulation Software STEPS and PathfinderDU Chang-bao1,2,3, ZHU Guo-qing1,2,3,LI Jun-yi1,2,3(1 School of Safety Engineering, China University of Mining and Technology, Xuzhou Jiangsu 221116, China2 Ministry of Education Key Laboratory of Gas and Fire Control for Coal Mines, Xuzhou Jiangsu 221116, China3 Fire Research Institute, China University of Mining and Technology, Xuzhou Jiangsu 221116, China）Abstract: To know the different of two evacuation simulation software STEPS and Pathfinder in function and scope, this paper uses the analysis method of combining the theoretical research and practical simulation. Found that STEPS theory model is cellular automata (CA), its evacuation rules is simple and intelligence degree is low, the evacuation behavior is not completely consistent with reality, but path decision system solved the problems. In addition, STEPS can simulate the normal population. Pathfinder has a high intelligence degree, its individual can respond to environmental stimuli and also can easily avoid obstacles. Besides, the individual also has a rich attribute that strictly conform to the reality. But it has some flaws in door choice system, individuals can only choose the front direction ignoring the back, and can't simulate the normal evacuation. Finally, concluded that the intelligence degree of Pathfinder is high and its simulation results match the real better, STEPS is more suitable for simulating normal evacuation. Keywords: Evacuation, STEPS, Cellular Automata, Pathfinder, Agent-base1引言以往人员疏散的研究主要集中在对火灾现场的观察分析，对幸存者的访谈记录，对统计数据的拟合整理。

人员疏散的建筑EXODUS模型

人员疏散的建筑EXODUS模型及其应用性研究【摘要】随着世界现代化进程的不断深化，高层建筑的数星和高度均不断提高,建筑物构造也越来越复杂。

在其消防安全问题日益突出的同时，许多国家目前仍在使用的处方式规范设计标准越来越不适用。

世界各国都提出了以性能为基^的设计标准。

人员疏散作为性能化设计的重要方面早在二十年前人们就开始了研究，其中建立疏散模型是行之有效的主要方法。

本文首先探讨了人员疏散的有关知识及影响人员疏散的主要因素,随即介绍了运用最广泛的模型之一：建筑EXODUS模型的基本原理和相关内容。

该模型采用五个较为成熟的子模型进行模拟，不仅模拟人员与人员、建筑物、环境之间的相互作用,而且还对人员是如何被热、烟气和有毒气体等火灾危险物质吞噬进行了模拟。

此外，本文还把建筑EXODUS 模型对某剧场的疏散演习逬行了计算,从而讨论其应用性。

最后,分析了国外疏散模型在我国的应用情况以及我国的疏散模型研究。

【关键词】疏散模型、人员、疏散、逃生、子模型、运行模式、疏散演习1引言为了保证建筑结构的消防安全性能，各国有针对性地建立了一系列建筑设计规范，并且其已成为建筑设计者的设计标准以及消防工作者的监督依据。

显然，这些规范对当前建筑空间设计产生了很大的影响。

然而,随着社会的发展，建筑结构日益复杂，高层建筑的数量在我国乃至全世界处于猛增时期。

规范已经越来越不”规范",也就是说此类建筑就算消防安全系统的设计完全符合规范中的规定也不能保证其应有的安全水平，因为建筑中各部分之间的相互作用没有给予考虑。

美国、新西兰、澳大利亚、瑞典、香港以及很多其它发达国家/城市正在慢慢淘汰以规范为基础的设计方法而是更倾向于采用性能化的设计。

我国目前的消防工作还没有朝这方面发展，但是毫无疑问，性能化设计也是我国建筑设计的发展趋势。

以性能化标准为基础的消防安全设计是一种更具理性的设计，人员疏散系统的设计是其中的一个重要方面用以解决人在建筑中的安全程度这个中心问题。

人流模拟总结

现有的人群仿真研宄将其研宄重点大体可以分成三个方面,即路径规划问题、行为决策问题、动画表现问题;这三点也被称为是人群仿真研宄关注的三要素。

一．人员疏散模拟：宏观模型：（讲人群整体作为建模对象，不考虑个体间相互作用）流体力学模型。

微观模型：（对行人的详细行为进行描述）（1）连续型：(a)磁场力模型(疏散方式单一，参数由实验得出，没有计算公式)(b)社会力模型（变量所代表的物理意义是可计算的。

结果显示了现实中人群运动自组织现象，可以模拟人员的冲撞，挤压，恐慌等特征）。

（2）离散型：（时间，空间离散化）(a)元胞自动机（根据行为规则建模，行为模型根据出现的结果改变状态）(b)格子气模型（元胞自动机的特殊形式）(c)排队网络模型（基于离散事件的蒙特卡洛仿真）(d)智能体模型（agent-based对行人决策和动作方式建模, 更符合人类的行为表现;能模拟人类的思维过程，不能够真实模拟行人所处的道路网）。

模拟步骤：疏散模型相关因素：（1）楼宇空间结构：走廊过道的长度与宽度、出入口数量及宽度等（2）人流因素：人群速度，人群密度，人体所占空间，人群构成与状态等现有人群疏散研究方向：（1）单一、多个出口的房间、大厅；单向、双向，多向通道的人流交汇场景。

（2）人群流动特性分析（拥挤人群的基本特性, 如密度、速度与激波的关系）（3）房间，通道出口对人群疏散的影响（单出口，多出口，宽度，角度等因素）二．路径规划模拟以楼宇内部各节点网络的构建作为切入点，分析楼宇节点网络模型的建立、空间数据的拓扑分析和最优路径规划。

建立楼宇网络模型，主要包括点和线两种类型，即拐点、中心点、障碍点、弧段等。

进行拓扑分析和网络分析，进行基于距离最短、时间最少和有障碍物情况下等不同情况的最短路径规划。

优势是将最短路径分析的内容从距离延伸到了时间、花费、障碍物等复杂要素，从而提高该算法的适用性和可靠性。

不足是时间复杂度较高，需要人为设定要素的不同权重。

《2024年基于SUMO平台的应急疏散交通仿真系统设计与实现》范文

《基于SUMO平台的应急疏散交通仿真系统设计与实现》篇一一、引言随着城市化进程的加速，应急疏散成为城市管理的重要一环。

为了有效应对突发事件，提高疏散效率，减少人员伤亡和财产损失，本文提出了一种基于SUMO平台的应急疏散交通仿真系统设计与实现。

该系统旨在通过模拟交通流和疏散过程，为应急管理部门提供科学的决策支持。

二、系统设计（一）设计目标本系统设计的主要目标包括：实现高精度的交通仿真，提高应急疏散的效率和准确性，为决策者提供实时、准确的数据支持。

（二）系统架构本系统基于SUMO平台构建，采用分层设计的思想，分为数据层、仿真层、应用层。

数据层负责数据的输入和存储；仿真层负责模拟交通流和疏散过程；应用层则提供用户界面，实现与用户的交互。

（三）关键技术1. SUMO平台：采用SUMO平台进行交通仿真，具有高精度、高效率的特点。

2. 交通流模型：建立合理的交通流模型，包括道路网络、车辆行为、交通规则等。

3. 应急疏散模型：根据实际情况，建立应急疏散模型，包括疏散路线、疏散时间等。

三、系统实现（一）数据采集与处理系统首先需要采集道路网络、交通流量、车辆类型等数据。

通过GIS技术对数据进行处理，形成SUMO平台所需的输入文件。

（二）建立交通流模型根据实际道路网络和交通规则，建立交通流模型。

通过设置道路类型、信号灯、车辆行为等参数，使仿真更加接近实际情况。

（三）建立应急疏散模型根据应急疏散的需求，建立疏散路线、疏散时间等模型。

通过设置疏散起点、终点、疏散速度等参数，实现对应急疏散的模拟。

（四）系统集成与测试将交通流模型和应急疏散模型集成到SUMO平台中，进行系统测试。

通过模拟不同场景下的交通流和疏散过程，验证系统的准确性和效率。

四、系统应用与效果（一）应用场景本系统可应用于城市应急管理部门，为应对突发事件提供决策支持。

同时，也可用于交通规划和交通管理领域，提高交通效率和安全性。

（二）应用效果通过实际案例的仿真分析，本系统可显著提高应急疏散的效率和准确性。

消防安全人员疏散软件的比较

人员疏散软件的比较一、人员疏散软件－Simulex1Simulex软件简介Simulex的开发公司是由苏格兰集成环境解决有限公司（Integrated Environmental Solutions Ltd）的Peter Thompson博士开发，用来模拟大量人员在多层建筑物中的疏散。

年费1000英镑，永久使用2600英镑，采用C++语言编制。

只能模拟在紧急情况下人员的疏散活动，不能模拟在建筑物正常运作情况下的人流运动。

2软件应用能力2.1疏散能力可以模拟大型、复杂几何形状、带有多个楼层和楼梯的建筑物，可以容纳上千人，用户可以看到在疏散过程中，每个人在建筑物中的任意一点、任意时刻的移动。

仿真结束后，会生成一个包含疏散过程详细信息的文本文件。

2.2场景设置Simulex把一个多层建筑物定义为一系列二维楼层平面图，这些楼层平面通过楼梯相连接。

从每一个楼层进入楼梯的出口要在楼层平面窗口和楼梯窗口都指定。

楼梯和楼层片面由“link”连接，在模型中将其位置放在出口的位置。

模型中的人员可以通过连接从楼层进入楼梯，反之亦然。

Simulex无内置制图工具，楼层平面图必须来自于CAD软件包，例如AutoCAD，CADD，或者QuickCAD。

平面图必须用标准的二维DXF文件格式存储。

2.3人群设定Simulex用三个圆来代表每一个人的平面面积，精确地模拟了实际的人员。

每一个被模拟的人由一个位于中间的不完全的圆圈，和两个稍小的、与中间的圆重叠的肩膀圆圈所组成，它们排列在不完全的圆圈两侧。

如下图所示simulex人群设定2.4楼梯及连接定义Simulex假设一个楼梯可以用二维线性走廊代替，三维螺旋形楼梯也被简化成二维直线形式。

对于连接的定义，用户需要在楼层平面和楼梯之间定义特定宽度的连接，以构造一栋建筑物的三维形式。

无论何时，只要楼层平面和楼梯之间有开口就需要一个连接，每一个连接都有宽度和位置。

2.5出口定义最终出口直线代表了建筑物中人员的最终目标，当一个人到达最终出口，就认为已经逃生。

疏散模拟软件STEPS与Pathfinder对比研究

分析得出Pathfinder模拟结果与真实情况相符程度更高，STEPS更适合模拟常态下人员疏散。

高层住宅建筑火灾人员疏散仿真研究

高层住宅建筑火灾人员疏散仿真研究随着城市化进程的加速，高层住宅建筑如雨后春笋般涌现。

然而，这类建筑在提供便捷舒适生活的同时，也带来了火灾安全隐患。

一旦发生火灾，人员疏散成为至关重要的问题。

因此，对高层住宅建筑火灾人员疏散进行仿真研究具有重要的现实意义。

高层住宅建筑火灾的特点使其人员疏散面临诸多挑战。

首先，火势蔓延迅速。

高层建筑内部通常有大量的竖向通道，如楼梯井、电梯井等，这些通道容易形成“烟囱效应”，加速火势和烟雾的扩散。

其次，人员密集且疏散距离长。

高层住宅容纳的居民数量众多，而从高层疏散到安全区域需要较长的时间和路程。

再者，烟雾危害大。

火灾产生的浓烟不仅影响视线，还含有有毒气体，对人员的生命健康构成严重威胁。

为了有效地研究高层住宅建筑火灾中的人员疏散情况，仿真技术应运而生。

人员疏散仿真模型主要基于对人员行为和环境因素的模拟。

在模型中，人员的行为特征包括对火灾的感知、反应时间、行走速度以及决策能力等。

环境因素则涵盖了建筑的布局、通道的宽度和长度、楼梯的位置和数量、烟雾的分布等。

通过仿真研究，可以模拟不同火灾场景下人员的疏散过程。

例如，假设火灾发生在建筑的底层、中层或顶层，不同位置的火灾对人员疏散的影响各不相同。

在底层发生火灾时，烟雾向上蔓延，可能会阻塞高层居民的疏散通道；中层发生火灾时，上下疏散路径都可能受到威胁，人员需要做出更复杂的决策；顶层发生火灾时，人员向下疏散的压力较大，容易出现拥挤和堵塞。

同时，还可以研究不同人员构成对疏散的影响。

比如，老人、儿童、残疾人等行动不便的人员在疏散中的速度较慢，需要更多的时间和帮助。

另外，居民的消防安全意识和疏散训练水平也会对疏散效果产生显著影响。

如果居民熟悉疏散路线和逃生方法，能够在火灾发生时保持冷静并迅速行动，那么疏散的效率将会大大提高。

在仿真研究中，建筑的结构和设施对人员疏散起着关键作用。

合理的楼梯布局和足够宽的疏散通道能够减少人员拥堵。

设置防烟楼梯间和正压送风系统可以有效阻止烟雾进入疏散通道，提高疏散的安全性。

航空快件中心的消防安全疏散性能化评估

航空快件中心的消防安全疏散性能化评估摘要：随着航空快递业的迅速发展，近年来航空快件中心项目的数量越来越多，规模越来越大，功能也越来越复杂。

按照国内现行《建筑设计防火规范》，对于此类建筑的消防定性并不明确，尤其是对于多层快件中心，消防设计常常是设计难点和重点。

在消防定性难以判断时，防火安全性能化评估是目前经常采用的技术手段。

通过这种方式可以分析评估消防设计的效果和薄弱点，为消防设计和审批提供参考依据。

本文结合具体项目，对航空快件中心消防安全疏散性能化评估的过程进行了分析。

关键词：消防疏散，性能化，航空快件abstract: with the rapid development of air express industry in recent years, the number of air express center project more and more, the scale is more and more big, the function is also more and more complex. according to the current domestic the code for fire protection design of buildings, to this kind of building fire qualitative is not clear, especially for multistory express center, fire fighting design is often difficult and important design. in the fire qualitative hard to judge, performance-based fire safety assessment is now often use technology. through this kind of means can evaluate the effect of the fire fighting design and vulnerabilities, for fire protection design andprovide a reference basis for examination and approval. combining with the specific project, center for air express performance-based fire safety evacuation evaluation process is analyzed.keywords: fire evacuation, the performance-based, air express中图分类号：tu998.1 文献标识码：a文章编号：1 项目背景本项目为上海浦东国际机场西货运区航空快件枢纽工程。

软件教学在消防安全类课程中的应用

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教研
2 0 1 2. 0 1 （下旬刊）
了解客户来源，每天更新，只有不断地询盘才有机会找到客就在眼前。
户。如：阿里巴巴电子商务（ /），中国国
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教研
2 0 1 2. 0 1 （下旬刊）
浅谈软件教学在消防安全类课程中的应用
周煜琴
（西南科技大学环境与资源学院四川·绵阳６２１０１０）
中图分类号：G642
文献标识码：A
文章编号：1672- 7894（2011）01- 0054- 02
摘要如何有效地进行消防安全类课程教学一直是高校关注的热点。本文分析了消防安全教学过程中存在的问题，在此基础上结合火灾软件的特点，阐述了软件教学在消防安全课程中的应用，以便更好地提高课堂教学效果。关键词软件教学消防安全课程 A Brief Discussion on the Application of Software Teaching in Fire Safety Courses // Zhou Yuqin Abstract How to improve teaching effect in fire safety course is the focus of colleges.This paper analyzes the current drawbacks in the teaching of fire safety course.On the basis of this and fire software's features, this paper elaborates on the application of software teaching in fire safety course for improving the effectiveness. Key words software teaching;fire safety;course Author's address School of Environment and Resource,Southwest University of Science and Technology,621010,Mianyang, Sichuan,China

火灾风险评估的专业工具与软件

火灾风险评估的专业工具与软件火灾风险评估的工具与软件在建筑和消防领域中扮演着至关重要的角色。

本文将重点介绍几款专业的火灾风险评估软件，帮助读者更好地了解如何利用这些工具来提高火灾安全性。

首先，我们要介绍的是“火灾模拟软件”。

这类软件可以通过数学模型和仿真技术，模拟火灾发生时的火势蔓延情况、烟气扩散路径以及人员疏散速度，从而帮助消防人员做出合理的救援决策。

常用的火灾模拟软件包括FDS（Fire Dynamics Simulator）和Simulex等。

这些软件可以根据建筑结构、材料、人员密度等因素，精确计算火灾对建筑物和人员的影响，为火灾应急预案的制定提供重要参考。

其次，我们要介绍的是“火灾风险评估软件”。

这类软件主要用于评估建筑物或场所存在的火灾风险，帮助用户了解火灾发生的概率和可能造成的损失。

通过输入建筑物的相关信息和火灾测试数据，软件可以生成详细的火灾风险评估报告，并给出改进建议。

常见的火灾风险评估软件包括CFAST和FSE等。

这些软件的使用可以帮助建筑设计师和消防工程师在规划和设计阶段就有效地降低火灾风险。

除了上述软件外，还有一些专业的消防安全管理软件，如“火灾安全管理系统”和“火灾应急救援系统”。

这类软件通常集成了火灾报警系统、视频监控系统、灭火设备控制系统等功能，能够实时监测建筑物内的火灾风险，并在火灾发生时及时启动自动灭火装置，保障人员安全。

通过这些软件，用户可以在火灾风险较高的场所实现全面的火灾监控和管理，提高应急响应效率。

综上所述，火灾风险评估的专业工具与软件在提升火灾安全性和应急响应效率方面发挥着重要作用。

建筑设计师、消防工程师和安全管理人员应善于利用这些软件，不断提升自身的专业技能，共同为建筑和人员安全保驾护航。

希望本文能够为广大读者提供有益的参考，增强对火灾风险评估工具与软件的认识与了解。

基于理论计算和模拟仿真的轨道交通地下车站r大客流紧急疏散能力评价及比较

基于理论计算和模拟仿真的轨道交通地下车站r大客流紧急疏散能力评价及比较马培【摘要】在封闭的地下车站内,大客流紧急疏散能力是建筑设计和运营期间需重点关注的风险.本文选择某即将开通的地下二层岛式车站,分别采用理论计算和模拟仿真方法,评价其疏散能力能否满足相关规范要求.计算结果表明:无论采用哪种方法,远期高峰小时一列车进站后,列车及站台乘客从站台全部疏散至站厅区域时间均小于6min,符合国家标准规定.另外,《地铁设计规范》计算的疏散时间最短,《地铁安全疏散规范》计算所需的疏散时间最长,采用模拟软件计算居中,与《地铁安全疏散规范》的计算结果较为一致.【期刊名称】《安全》【年(卷),期】2018(039)003【总页数】4页(P35-38)【关键词】车站;大客流;疏散时间【作者】马培【作者单位】北京中安质环技术评价中心有限公司【正文语种】中文轨道交通作为一种全封闭、大运量的城市交通运载工具，具有运量大、速度快、安全、舒适、准点等特点。

但随着近年来轨道交通的快速建设和开通运营，越来越多的城市轨道交通已步入网络化运营时代。

网络化运营突出特点就是会出现客流的激增，对于封闭的地下空间，其安全风险也会越来越突出。

运营期间，仅是信号、供电、车辆等设备故障，引起的线路降级运行或停运，都会造成地下乘客大量聚集，无法及时疏散出去，形成较大的安全隐患。

若一旦发生地下火灾、爆炸或其他事故，由于地下空间人员密集、空间狭小，极易发生群死群伤事故。

突发事件发生时，如何快速的将地下乘客疏散至安全区域，自始至终都是轨道交通运营安全中迫切而又复杂的问题。

近年来国内设计和研究人员分别从建筑设计、乘客行为等方面，对轨道交通车站大客流疏散进行了大量的研究。

本文拟采用理论计算与模拟仿真相结合的方式，对地下车站进行客流疏散计算，以评价其疏散能力能否满足相关要求，并分析不同计算方式的结果差异。

1 乘客疏散影响因素轨道交通车站内客流疏散与车站建筑、站内设备设施数量和布置，以及乘客的数量、行为等存在着紧密的关系。