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80 November/December 2006 Published by the IEEE Computer Society 0272-1716/06/$20.00 © 2006 IEEE

M any applications can benefit from ani-mated virtual crowds. These applica-

tions include site planning, education, entertainment,training, and human factors analysis for building evac-uation, or other scenarios where masses of people gath-er such as sporting events, transportation centers, andconcerts.

Animating virtual crowds is often accomplished bylocal rules,1 forces,2 or flows.3 One of our objectives incrowd animation is to realistically simulate how human

communication affects the behaviorof individual agents. We have devel-oped Multi-Agent Communicationfor Evacuation Simulation (Maces)to combine local motion driven byHelbing’s model2 with high-levelwayfinding using interagent commu-nication and varied agent roles.Together, these factors automatical-ly augment an agent’s mental map ofthe environment to produce empiri-cally better building evacuation performance and realistic crowdmovements.

Crowd evacuation from large andcomplex building spaces is usuallyhindered by people not knowing itsdetailed internal connectivity. In

such circumstances, occupants might not be aware ofthe existence of suitable circulation paths or, in case ofemergencies, the most appropriate escape paths. Psy-chology studies show that building occupants usuallydecide to use familiar exits, such as where they enteredthe building. Emergency exits or exits not normallyused for circulation are often ignored. If a fire occurs,blocking some of those known paths, and smoke fur-ther obscures vision, the problem might be fatallyaggravated.

In general, building evacuation due to imminent dan-ger is accompanied by considerable physical and psy-chological stress. Since rising stress levels diminish full

sensory functioning, there is a general reduction ofawareness and increase in disorientation.

Decision skills in emergency situations are influencedby several factors such as environmental complexity,dynamically changing situations, and time pressure. Ifpeople have not been properly trained, they are likelyto feel stressed and might be incapable of making gooddecisions. On the other hand, individuals such as fire-fighters are trained to make decisions in a dynamicallychanging environment based on perception, communi-cation, and knowledge. For untrained individuals, toomuch or too little information coming at one time (sev-eral people in the same room making different decisionsand shouting different information about blockedrooms) can also promote indecision.

Many different methods exist for simulating the localmotion of individuals in a crowd such as cellular automa-ta, social forces, and rules. These models simulate peo-ple moving within a familiar environment trying to reachtheir destination while avoiding collisions with walls,obstacles, and other individuals. None of the previouswork in crowd simulation deals with unknown environ-ments where agents must explore the building and com-municate with each other to learn useful features andfind their way toward an exit as real people would do.

The main novelty of our approach to crowd simula-tion is that we are not just animating local motion, butdesigning agents that perform high-level wayfinding toobtain a building’s cognitive map. Wayfinding is theprocess of determining and following a route to somedestination; it’s the cognitive component of navigationand requires knowledge and a spatial reasoning processto get from an initial position to a goal position. Initial-ly, some individuals might have only partial informationabout the building’s connectivity, but as they explore itand communicate with other individuals they encounter,they find paths toward some of the unblocked exits.

The spatial wayfinding problem has three parts: deci-sion making, decision execution, and information pro-cessing. To carry out wayfinding, each agent needs fourcomponents:

This article considers animatingevacuation in complexbuildings by crowds who mightnot know the structure’sconnectivity, or who find routesaccidentally blocked. It takesinto account simulated crowdbehavior under two conditions:where agents communicatebuilding route knowledge, andwhere agents take differentroles such as trained personnel,leaders, and followers.

Nuria Pelechano and Norman I. BadlerUniversity of Pennsylvania

Modeling Crowd andTrained LeaderBehavior duringBuilding Evacuation

■ Cognitive map: a mental model of space.■ Orientation: its current position within the cognitive

map.■ Exploration: processes to learn the features of the

space (doors, walls, hazards, and so on).■ Navigation: processes to move it through the environ-

ment.

In our investigation of crowd wayfinding, we manip-ulate groups of 10 to 1,000 agents. We simulate the evac-uation time taken by a group of agents to find the exitswhen an emergency occurs. We assume that an acci-dent, such as a fire, occurs simultaneously at several siteswithin the building. At that moment there will be dif-ferent types of agents in the building. Some of them rep-resent individuals not familiar with the environment,and therefore will know just a few paths toward theexits. Other agents are more familiar with the buildingand will have complete knowledge about alternativeroutes. So each agent has its own cognitive (or mental)map, which will be updated as it navigates the environ-ment and communicates building path information withother agents.

Algorithm overviewMaces is a distributed multiagent system without a

centralized controller. Each agent has its own behaviorbased on simple personality variables that represent realpsychological factors. At the global level, Maces is a col-lection of reactive behaviors relying only on local per-ception and communication. Agent movement iscomputed at two levels. The high level corresponds tothe wayfinding process that generates a sequence ofrooms, while the low level corresponds to the localmotion within a room. Maces receives as an input thecharacteristics of the maze-like environment—dimen-sions, number of exits, and number of hazards—or abuilding’s floor plan, and the parameters necessary forthe simulation—number of agents, percentage oftrained agents, and the percentage of leaders.

We can either create a maze-like environment (seeFigure 1) or we can input a building floor plan (see Fig-ure 2). For the given environment, we create a cell andportal graph, and for each cell the algorithm automati-cally generates the shortest path to each exit. We caninterpret this information in two ways. On the one hand,this shortest path stored in the cell corresponds to thepath that an agent in that cell would have followed whenentering the building and therefore is the only oneknown. On the other hand, we can consider this short-est path as being the one indicated by the emergencyexit signs in a building and therefore would be followedin case of emergency.

An agent’s memory consists of a mental map: its owncell and portal graph. The mental graph removes theactual building geometry. Nodes are added as the agentnavigates and explores the building. At any time, eachagent needs to know which rooms have been fullyexplored and which still have portals that lead to roomsnot yet visited. Later, we’ll use the actual building geom-etry to compute local motion transit times and portalbottlenecks.

Another crucial information source is communica-tion with other agents. Whenever two or more agentsmeet in a room, they share two pieces of information:locations of some of the hazards that are blocking paths,and parts of the building that have been fully exploredby other agents and found to have no accessible exit(passed along by previous communications). The com-munication is local to a room, so agents exchange onlyrelevant information about neighboring rooms—thatis, do not go through that door (there is fire), do not goin that direction (there is no exit), or follow me. Thislocalized sharing of mental models is the key to Maces’wayfinding behavior.

To model different personalities that would occur ina real crowd, each agent has high-level behaviors thatdepend on leadership and training attributes:

■ Agents who are leaders, are trained, and have com-plete knowledge about the internal building connec-tivity and would help others during the evacuationprocess. Firefighters would be an example of this typeof agent.

IEEE Computer Graphics and Applications 81

1 Example of one of the mazes used for our experiments, with two exits and eight hazards.

2 Building plan used for evacuation simulations.

■ Agents who are leaders, are untrained, and corre-spond to people that can handle stress better andwould tend to help others and explore the buildingsearching for new paths.

■ Agents who are not leaders, are untrained, and rep-resent dependent people (followers) who mightpanic during an emergency situation and reach thepoint where they are incapable of making their owndecisions.

These personality types abstract the main characteristicbehaviors that would occur during real evacuationsaccording to the psychological literature.4

High level: wayfindingOnce the algorithm creates the cell and portal graph

and automatically generates the cell information, thecrowd simulation algorithm proceeds through threemain steps (see Figure 3):

1. Leaders within a room share their knowledgeabout the environment with the other agents(their mental maps contain information aboutblocked cells and local subgraphs or directionsthat have been fully explored finding no exit). Atevery time step we compute a high-level path overthe cell and portal graph, which then stores the

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82 November/December 2006

Related WorkThere have been several cognitive agent

architectures proposed to generate crowdbehaviors. They generally consist of knowledgerepresentation, algorithms that learn, andmodules that plan actions based on thatknowledge. Funge, Tu, and Terzopoulos haveworked on behavioral animation for creatingartificial life, where virtual agents are endowedwith synthetic vision and perception of theenvironment.1 Massive SW has also developed acrowd simulation system with vision-basedbehavior.

Rule-based systems can be used with dozens ofagents in real time. Reynolds describes the first useof a distributed behavioral model to produceflocking behavior.2 Brogan and Hodgins useparticle systems and dynamics for modeling themotion of groups with significant physics.3

Helbing, Farkas, and Vicsek simulates pedestriansusing a microscopic social force model that solvesNewton’s equation for the position of eachindividual by considering repulsive interactions,friction forces, dissipation, and fluctuations.4 Thesetraditional crowd simulators ignore the differencesbetween individuals and treat everyone as havingthe same behavioral set, but there are othermodels that control each agent by individual rulesor physical laws.5 In a multiagent crowd system,the agents are autonomous, typicallyheterogeneous, and concerned with coordinatingintelligent behaviors among the group.

Other models have been used in commercialtools for ship and fire evacuation. Some of themost common models include regression, routechoice, queuing, gaskinetics, and cellularautomata. Regression models use statisticallyestablished relations between flow variables topredict pedestrian flow under specificcircumstances. Route-choice models describepedestrian wayfinding based on utility: choosingdestinations to maximize the utility of their trip(such as comfort, travel time, and so on).Queuing models use Markov chains to describehow pedestrians move from one network node toanother. Gaskinetics models use fluid or gas

dynamics analogs (partial differential equations)to describe how density and velocity change overtime. Cellular automata models represent spaceby a uniform grid of cells with local statesdepending on a set of rules describing pedestrianbehaviors.

To reduce the complexity of controlling all theagents in the crowd while still guaranteeingdetailed behaviors, several systems haveattached information to the environment.6,7 TheMulti-Agent Communication for EvacuationSimulation (Maces), described in the main text,also embeds environmental information such asshortest paths. Individual agents will havedifferential access to that information and use itin different ways. Depending on their individualroles and behavior at any given moment, theywill adopt different decision-making processes.

References1. J. Funge, X. Tu, and D. Terzopoulos, “Cognitive Mod-

eling Knowledge, Reasoning and Planning for Intelli-gent Character,” Proc. ACM Siggraph, ACM Press, 1999,pp. 29-38.

2. C. Reynolds, “Flocks, Herds, and Schools: A DistributedBehavior Model,” Proc. ACM Siggraph, ACM Press,1987, pp. 25-34.

3. D. Brogan and J. Hodgins, “Group Behaviours for Sys-tems with Significant Dynamics,” Autonomous Robots,vol. 4, 1997, pp. 137-153.

4. D. Helbing, I. Farkas, and T. Vicsek, “SimulatingDynamical Features of Escape Panic,” Nature, vol. 407,2000, pp. 487-490.

5. A. Braun, B.E.J. Bodmann, and S.R. Musse, “Simulationof Virtual Crowds in Emergency Situations,” Proc. ACMSymp. Virtual Reality Software and Technology (VRST),ACM Press, 2005, pp. 244-252.

6. N. Farenc, R. Boulic, and D. Thalmann, “An InformedEnvironment Dedicated to the Simulation of VirtualHumans in Urban Context,” Proc. Eurographics, Com-puter Graphics Forum, Blackwell Publishing, 1999, pp.309-318.

7. F. Tecchia et al., “Agent Behavior Simulator (ABS): APlatform for Urban Behavior Development,” Proc.Game Technology (GTEC), CD-ROM, 2001.

order in which the cells should be visited to get toan exit.

2. Agents check their known shortest path for knownhazards. Agents use the information gatheredthrough communication or direct perception of theenvironment. If its current path is hazard free, thenthe agent will just follow it and add the next cell toits mental map.

3. Depending on their type, agents react differently ifsome hazard blocks the shortest known path. Atrained agent has a mental map containing theentire building’s connectivity graph with all the por-tals and therefore follows the next shortest pathknown from its current cell. An untrained agentexplores the building to find new routes using adepth first search (DFS). Since the untrained agentinitially lacks the entire building connectivitygraph, this DFS is implemented in an iterative way,so the agent discovers new rooms only when it seesa portal and crosses it. Untrained, follower agentswon’t know what to do and will follow the decisionstaken by the other person in the room instead ofdoing a DFS.

Low level: local motionAn agent’s local motion within a room is based on Hel-

bing’s mode,2 which describes human crowd behaviorwith a mixture of sociopsychological and physical forces.Pedestrians 1 × i × N of mass mi like to move with a cer-tain desired speed vi

0 in a certain direction ei0 and they

tend to adapt their instantaneous velocity vi(t) within acertain time interval τi. At the same time, the individu-als try to keep a distance from other individuals j andfrom the walls w using interaction forces fij and fiw. Thechange of velocity in time t is given by the accelerationequation:

This model generates realistic phenomena such as arch-ing in the portals and the faster-is-slower effect.

In Maces, the desired velocity direction within eachroom is given by an attractor point located close to thenext portal the agent must cross. We also add repulsionforces with static obstacles, such as columns. The agentwalks within a room trying to reach its next attractorpoint. Each portal has two attractor points (in front andbehind the door) to steer the agents’ movement in thedesired direction. The portal that an agent needs to crossis given by the high-level algorithm, which uses infor-mation about desired destination and distances fromcurrent position to portals to assign the next portal.

Results and analysisOur goal is to study evacuation algorithms’ perfor-

mance when large groups of agents with individual per-sonalities use communication to reduce their graphsearch space. Our motivation is to produce results thatclosely simulate real human behavior in these situations,and we do this by modeling the psychological factors—

such as following known paths, herding behavior, loss oforientation, and so on—that affect human performanceunder stress and panic.

We implemented a random search exclusively forbenchmarking purposes, since this is not a realistichuman behavior. In another comparison, we’ll see thesignificant impact that communication has on crowdbehavior when executing wayfinding. Finally, we’llexamine the impact of having trained agents in thecrowd, such as firefighters, and analyze the percentageof leaders needed to speed up the evacuation process.

For the experiments, we use three scenarios, all ofthem maze-like. We randomly generated two scenariosand created the third with a building editor to producean environment better resembling a real building. Thethree mazes each contain 100 rooms with eight of themblocked by some hazard, such as fire. For each parame-ter set, we run 25 randomly generated starting config-urations for the crowds.

The populations used for these trials range from N =20 to 200 agents. The leadership levels range from 0 to100 percent. No leaders means they are all followers,and therefore when several agents meet in a cell, onerandom agent makes a decision and the others will fol-low. Followers are dependent agents, when they findthemselves in a panic situation they will always followother agents instead of making their own decision, thus simulating the herding behavior observed in realcrowds during evacuation. On the other hand, 100 per-cent leadership means each of them will perform its owndecision-making process, with its current, completebuilding knowledge.

Random search versus depth first searchTo explore the building once an agent knows that all

the known shortest paths are blocked, we implement-

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IEEE Computer Graphics and Applications 83

3 High-level wayfinding diagram.

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ed two algorithms. The first one represents a naivesearch, where individuals explore adjacent rooms ran-domly and try not to go backward unless they find themselves trapped. In this naive search, agents lack amental map of the model nor can they create one whilenavigating the environment, therefore the emergentbehavior obtained looks quite chaotic. A DFS algorithmmakes the agents search adjacent rooms in a more struc-tured way while they create their mental maps. Theresults obtained show not only that DFS was about 15times faster than random search, but also the emergentbehavior obtained was visually closer to the behaviorexpected of a real crowd.

Communication versus noncommunication In Figure 4 we can readily observe the algorithm’s dif-

ferent performance with and without communicationfor 200 agents. The simulation with communication con-verges to 100 percent evacuated in about half of the timethat it takes the noncommunication case to converge.

Figure 5 shows the results obtained for differentcrowd sizes where all the agents represent indepen-dent (leader) individuals who make their own deci-sions during wayfinding instead of following others.In this simulation we don’t have any trained agents,therefore everyone is unfamiliar with the building con-nectivity and must discover how to evacuate basedentirely on exploration and shared communication.The graph shows the evacuation times for crowd sizesof 20, 60, 100, 150, and 200.

Evacuation time decreases as the crowd sizeincreases. This can be explained by the fact that forbigger crowds the probability of meeting anotheragent increases, and therefore the important informa-tion about hazards in the building and explored areasspreads faster among the individuals. This informa-tion helps agents to prune their graph search andtherefore find the correct path sooner. It’s importantto notice though, that this holds as long as the crowdis not so large that congestion blocks the doors, whichwill obviously decrease the evacuation time. Thisproblem can be observed for crowds of more than 500agents, where the evacuation time is constrained bythe number of exits and the flow rate through each ofthe doors (see Figure 6).

Trained versus untrained leadersWe performed 25 simulations using a crowd size of

100 with 0, 25, 50, 75, and 100 percent trained agents.Figure 7 shows the average evacuation times obtained.

As expected, the percentage of evacuees converges to100 percent faster as the percentage of trained peopleincreases. This seems an obvious result given thattrained people know how to evacuate a dangerous loca-tion because they have more information about the envi-ronment, and dependent agents will follow them.Therefore, the overall evacuation time will decrease asthe number of trained agents in the environmentincreases.

Not everyone needs to be trained, however. We canfind out what is an adequate percentage of trained lead-ers needed to have a speedy evacuation. We have pre-

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84 November/December 2006

5 Evacuation time for different crowd sizes using communicationbut 100 percent untrained leaders.

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7 Evacuation time for 0, 25, 50, 75, and 100 percent trained leaders.

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viously observed that there is not a big difference in theconvergence values between 50 percent and 100 per-cent leadership, which means that there is no need tohave a great proportion of trained leaders. Figure 8shows smaller percentages of leaders.

Here we can conclude that an optimal percentage oftrained people during an evacuation would be onlyabout 10 percent. For lower values the evacuation timefor the same percentage of evacuees takes at least twicethe time. On the other hand, having more than 10 per-cent trained people only increases evacuation time byat most 0.16 times.

Importance of leadershipIn real life, some people have a higher probability of

becoming leaders when an emergency occurs. They areusually independent individuals that by nature are ableto handle emergency situations better and also tend tohelp others. Maces models these people as untrainedleaders.

Figures 9a and 9b are two snapshots of an evacuationprocess. Figure 9a illustrates a population with a highpercentage of leaders, so that most of them tend to maketheir own decisions when attempting to exit. Figure 9bshows a population with a high percentage of depen-dent people who tend to follow any leader instead ofdeciding routes by themselves. In the first populationwe can observe an emergent behavior with lots of smallgroups of people.

In the second population, the emergent behaviorshows fewer but larger groups of individuals. When thenumber of dependent individuals is higher and thereare few leaders, the size of the groups formed tends toincrease, since dependent people will not leave a groupto try to explore new paths on their own. Instead, theytend to stay together and just follow a leader.

Conclusions and future workOur evaluation has shown a significant improvement

in evacuation rates when using interagent communi-cation. We can also observe the grouping behavior thatemerges when there is a high percentage of dependentagents in the crowd. Only a relatively small percentageof trained leaders yields the best evacuation rates. Wecan visualize these results in real time with either oursimple 2D or 3D viewer. We also created an AutodeskMaya application for higher quality renderings (see Fig-ure 10).

Areas where there is room for improvement includeadding individualism into Helbing’s model so thatagents would have different local motions dependingon their roles. The high-level wayfinding must be mod-ified because people should be less likely to enter a con-gested room when there are other possible pathsavailable. Although it’s important to closely model whatthe psychology literature reports as real behavior incrowds—studies show that people tend to have herdingbehavior even though there could be alternative doorsin a room leading to the same corridor—people underpanic still tend to all follow the same choices. In gener-al, we want to provide the agents with psychological ele-ments that will let us model more closely real human

IEEE Computer Graphics and Applications 85

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8 Evacuation times for small percentages of leaders.

9 Snapshot of crowd evacuation with (a) a high percentage of leadership and (b) a low percentage of leadership.

behavior and therefore simulate crowd behavior moreaccurately. ■

AcknowledgmentsThis research was partially supported by the Nation-

al Science Foundation grant IIS-0200983, Office ofNaval Research Virtual Technologies and Environmentsgrant N0001 4-04-1-0259, Army Research Office grantN61339-05-C-0081, and a Fulbright scholarship. Wealso thank Autodesk for its software.

References1. C. Reynolds, “Flocks, Herds, and Schools: A Distributed

Behavior Model,” Proc. ACM Siggraph, ACM Press, 1987,pp. 25-34.

2. D. Helbing, I. Farkas, and T. Vicsek, “Simulating Dynami-cal Features of Escape Panic,” Nature, vol. 407, 2000, pp.487-490.

3. S. Chenney, “Flow Tiles,” Eurographics/ACM Siggraph Proc.Symp. Computer Animation, ACM Press, 2004, pp. 233-242.

4. N. Pelechano et al., “Crowd Simulation IncorporatingAgent Psychological Models, Roles and Communication,”Proc. 1st Int’l Workshop Crowd Simulation, EPFL, 2005, pp.21-30.

For further information on this or any other computingtopic, please visit our Digital Library at http://www.computer.org/publications/dlib.

Nuria Pelechano is a postdoctoralresearcher in computer and informa-tion science at the University of Penn-sylvania. Her research interestsinclude crowd simulation for real-time 3D graphics and modeling high-level behaviors in interactive multi-agent environments. Pelechano has a

BA in computer science from the Universitat de Valencia;an MSc in vision, imaging, and virtual environments fromthe University College London; and a PhD in computer andinformation science from the University of Pennsylvania.Contact her at npelecha@seas.upenn.edu.

Norman I. Badler is a professor ofcomputer and information science atthe University of Pennsylvania, wherehe directs the Center for Human Mod-eling and Simulation. His researchinterests include animation via simu-lation; embodied agent software; andcomputational connections between

language, instructions, and action. Badler has a PhD incomputer science from the University of Toronto. He is acoeditor of Graphical Models. Contact him at badler@seas.upenn.edu.

Article submitted: 7 Sept. 2005; revised: 17 Jan. 2006;

accepted: 20 Apr. 2006.

Feature Article

86 November/December 2006