人员疏散软件SIMULEX的应用
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;aüargmin f dðaÞg dðaÞ¼d2maxþfðaÞÀ2d max fðaÞcosða0ÀaÞð4ÞThe optimal velocity direction~e¼ðcos aÃ;sin aÃÞ;here,aüargmin 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
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
_____________________________________________________________________ [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
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FlexES 火灾报警计算机控制系统
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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。
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人员疏散软件S I M U L E X的应用
2009年10月
1.构建建筑模型
该建筑总共四层，每层建筑模型如图1。
1st floor
2nd floor
3rd floor
4th floor
图1 建筑各层模型图
2.设置安全出口
安全出口在楼层平面图中用于一个类似与“工”的符号表示，布置在建筑边界之外，离建筑嘴边界上对外门洞距离不小于一米的位置。
图2即为安全出口示意图，exit1，exit2 分别是2处安全出口。
图2 安全出口示意图
3．设置楼梯1）Staircase1-1
2）Staircase1-2 3）Staircase2-1 4）Staircase2-2 5）Staircase3-1
6）Staircase3-2
4．定义连接
每个楼梯必须通过两条“连接”与两个楼层相连，这两个“连接”分别位于楼梯的两端。
每个“连接”在窗口中以类似的“T”型符号标识。
在楼层平面与楼梯平面中的“连接”标识“T”前后，分别应留有的净空间。
这样可以使人员自由通过设定的“连接”，确保人员横穿设定的“连接”时不会出现故障。
1）floor1
2）floor2
3）floor3
4）floor4
5．等距图的计算
当所有的楼梯和出口设置完成后，点击Distance map菜单中的Calculate 子项，将弹出对话框显示将计算的等距图是系统默认等距图，点击ok，完成等距图的计算。
图3即为等距示意图。
1st floor
2nd floor
3rd floor
4th floor
图3 各层等距图
6．定义人员
等距图计算完成以后，向建筑物中设置人员，本次实验采取成组定义方法。
定义人员，首先设置人员属性，包括设置人员类型（人员类型、人员的尺寸、分布及疏散速度等）。
具体如图4所示。
图4 人员定义
7．模拟
当人员定义完成后，工程文件保存后，就可以开始进行人员疏散模拟了。
8．模拟结果
1）各层平面参数和楼梯参数
Number of Floors = 4
Number of Staircases = 6
Number of Exits = 2
Number of Links = 12
Number of People = 83
-----------------------------------------------
Floor 1 (DXF file: 1st ) (Size: , metres)
Number of People Initially in This Floor = 13
Link 1 : , m), degrees, m wide, connected to Staircase1-1 Link 2 : , m), degrees, m wide, connected to Staircase 1-2 Exit 1 : , m), degrees, m wide
Exit 2 : , m), degrees, m wide
-----------------------------------------------
Floor 2 (DXF file: 2nd ) (Size: , metres)
Number of People Initially in This Floor = 20
Link 3 : , m), degrees, m wide, connected to Staircase1-1 Link 4 : , m), degrees, m wide, connected to Staircase 2-1 Link 5 : , m), degrees, m wide, connected to Staircase 1-2 Link 6 : , m), degrees, m wide, connected to Staircase 2-2 -----------------------------------------------
Floor 3 (DXF file: 3rd ) (Size: , metres)
Number of People Initially in This Floor = 24
Link 7 : , m), degrees, m wide, connected to Staircase 2-1 Link 8 : , m), degrees, m wide, connected to Staircase 3-1 Link 9 : , m), degrees, m wide, connected to Staircase 2-2 Link 10 : , m), degrees, m wide, connected to Staircase 3-2 -----------------------------------------------
Floor 4 (DXF file: 4th ) (Size: , metres)
Number of People Initially in This Floor = 26
Link 11 : , m), degrees, m wide, connected to Staircase 3-1 Link 12 : , m), degrees, m wide, connected to Staircase 3-2 -----------------------------------------------
Staircase1-1 (Size: , metres)
Number of People Initially in This Stair = 0
Link 1 : , m), degrees, m wide, connected to Floor 1
Link 3 : , m), degrees, m wide, connected to Floor 2
-----------------------------------------------
Staircase 1-2 (Size: , metres)
Number of People Initially in This Stair = 0
Link 2 : , m), degrees, m wide, connected to Floor 1
Link 5 : , m), degrees, m wide, connected to Floor 2
----------------------------------------------- Staircase 2-1 (Size: , metres)
Number of People Initially in This Stair = 0
Link 4 : , m), degrees, m wide, connected to Floor 2 Link 7 : , m), degrees, m wide, connected to Floor 3 ----------------------------------------------- Staircase 2-2 (Size: , metres)
Number of People Initially in This Stair = 0
Link 6 : , m), degrees, m wide, connected to Floor 2 Link 9 : , m), degrees, m wide, connected to Floor 3 ----------------------------------------------- Staircase 3-1 (Size: , metres)
Number of People Initially in This Stair = 0
Link 8 : , m), degrees, m wide, connected to Floor 3 Link 11 : , m), degrees, m wide, connected to Floor 4 ----------------------------------------------- Staircase 3-2 (Size: , metres)
Number of People Initially in This Stair = 0
Link 10 : , m), degrees, m wide, connected to Floor 3 Link 12 : , m), degrees, m wide, connected to Floor 4 2）计算结果：
All people reached the exit in 2:.
3）各时间段内从出口、连接处逃出的人员
各时间段内从出口、连接处逃出的人员如表一所示。