Modeling Crowd and Trained Leader Behavior during Building Evacuation

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1 Modelng Crowd and Traned Leader Behavor durng Buldng Evacuaton Nura Pelechano and rman I. Badler Unversty of Pennsylvana Ths artcle consders anmatng evacuaton n complex buldngs by crowds who mght not know the structure s connectvty, or who fnd routes accdentally blocked. It takes nto account smulated crowd behavor under two condtons: where agents communcate buldng route knowledge, and where agents take dfferent roles such as traned personnel, leaders, and followers. Many applcatons can beneft from anmated vrtual crowds. These applcatons nclude ste plannng, educaton, entertanment, tranng, and human factors analyss for buldng evacuaton, or other scenaros where masses of people gather such as sportng events, transportaton centers, and concerts. Anmatng vrtual crowds s often accomplshed by local rules, 1 forces, 2 or flows. 3 One of our objectves n crowd anmaton s to realstcally smulate how human communcaton affects the behavor of ndvdual agents. We have developed Mult-Agent Communcaton for Evacuaton Smulaton (Maces) to combne local moton drven by Helbng s model 2 wth hgh-level wayfndng usng nteragent communcaton and vared agent roles. Together, these factors automatcally augment an agent s mental map of the envronment to produce emprcally better buldng evacuaton performance and realstc crowd movements. Crowd evacuaton from large and complex buldng spaces s usually hndered by people not knowng ts detaled nternal connectvty. In such crcumstances, occupants mght not be aware of the exstence of sutable crculaton paths or, n case of emergences, the most approprate escape paths. Psychology studes show that buldng occupants usually decde to use famlar exts, such as where they entered the buldng. Emergency exts or exts not normally used for crculaton are often gnored. If a fre occurs, blockng some of those known paths, and smoke further obscures vson, the problem mght be fatally aggravated. In general, buldng evacuaton due to mmnent danger s accompaned by consderable physcal and psychologcal stress. Snce rsng stress levels dmnsh full sensory functonng, there s a general reducton of awareness and ncrease n dsorentaton. Decson sklls n emergency stuatons are nfluenced by several factors such as envronmental complexty, dynamcally changng stuatons, and tme pressure. If people have not been properly traned, they are lkely to feel stressed and mght be ncapable of makng good decsons. On the other hand, ndvduals such as frefghters are traned to make decsons n a dynamcally changng envronment based on percepton, communcaton, and knowledge. For untraned ndvduals, too much or too lttle nformaton comng at one tme (several people n the same room makng dfferent decsons and shoutng dfferent nformaton about blocked rooms) can also promote ndecson. Many dfferent methods exst for smulatng the local moton of ndvduals n a crowd such as cellular automata, socal forces, and rules. These models smulate people movng wthn a famlar envronment tryng to reach ther destnaton whle avodng collsons wth walls, obstacles, and other ndvduals. ne of the prevous work n crowd smulaton deals wth unknown envronments where agents must explore the buldng and communcate wth each other to learn useful features and fnd ther way toward an ext as real people would do. The man novelty of our approach to crowd smulaton s that we are not just anmatng local moton, but desgnng agents that perform hgh-level wayfndng to obtan a buldng s cogntve map. Wayfndng s the process of determnng and followng a route to some destnaton; t s the cogntve component of navgaton and requres knowledge and a spatal reasonng process to get from an ntal poston to a goal poston. Intally, some ndvduals mght have only partal nformaton about the buldng s connectvty, but as they explore t and communcate wth other ndvduals they encounter, they fnd paths toward some of the unblocked exts. The spatal wayfndng problem has three parts: decson makng, decson executon, and nformaton processng. To carry out wayfndng, each agent needs four components: vember/december 6 Publshed by the IEEE Computer Socety /6/$. 6 IEEE

2 Cogntve map: a mental model of space. Orentaton: ts current poston wthn the cogntve map. Exploraton: processes to learn the features of the space (doors, walls, hazards, and so on). Navgaton: processes to move t through the envronment. In our nvestgaton of crowd wayfndng, we manpulate groups of to 1, agents. We smulate the evacuaton tme taken by a group of agents to fnd the exts when an emergency occurs. We assume that an accdent, such as a fre, occurs smultaneously at several stes wthn the buldng. At that moment there wll be dfferent types of agents n the buldng. Some of them represent ndvduals not famlar wth the envronment, and therefore wll know just a few paths toward the exts. Other agents are more famlar wth the buldng and wll have complete knowledge about alternatve routes. So each agent has ts own cogntve (or mental) map, whch wll be updated as t navgates the envronment and communcates buldng path nformaton wth other agents. Algorthm overvew Maces s a dstrbuted multagent system wthout a centralzed controller. Each agent has ts own behavor based on smple personalty varables that represent real psychologcal factors. At the global level, Maces s a collecton of reactve behavors relyng only on local percepton and communcaton. Agent movement s computed at two levels. The hgh level corresponds to the wayfndng process that generates a sequence of rooms, whle the low level corresponds to the local moton wthn a room. Maces receves as an nput the characterstcs of the maze-lke envronment dmensons, number of exts, and number of hazards or a buldng s floor plan, and the parameters necessary for the smulaton number of agents, percentage of traned agents, and the percentage of leaders. We can ether create a maze-lke envronment (see Fgure 1) or we can nput a buldng floor plan (see Fgure 2). For the gven envronment, we create a cell and portal graph, and for each cell the algorthm automatcally generates the shortest path to each ext. We can nterpret ths nformaton n two ways. On the one hand, ths shortest path stored n the cell corresponds to the path that an agent n that cell would have followed when enterng the buldng and therefore s the only one known. On the other hand, we can consder ths shortest path as beng the one ndcated by the emergency ext sgns n a buldng and therefore would be followed n case of emergency. An agent s memory conssts of a mental map: ts own cell and portal graph. The mental graph removes the actual buldng geometry. des are added as the agent navgates and explores the buldng. At any tme, each agent needs to know whch rooms have been fully explored and whch stll have portals that lead to rooms not yet vsted. Later, we ll use the actual buldng geometry to compute local moton transt tmes and portal bottlenecks. Another crucal nformaton source s communcaton wth other agents. Whenever two or more agents meet n a room, they share two peces of nformaton: locatons of some of the hazards that are blockng paths, and parts of the buldng that have been fully explored by other agents and found to have no accessble ext (passed along by prevous communcatons). The communcaton s local to a room, so agents exchange only relevant nformaton about neghborng rooms that s, do not go through that door (there s fre), do not go n that drecton (there s no ext), or follow me. Ths localzed sharng of mental models s the key to Maces wayfndng behavor. To model dfferent personaltes that would occur n a real crowd, each agent has hgh-level behavors that depend on leadershp and tranng attrbutes: Agents who are leaders, are traned, and have complete knowledge about the nternal buldng connectvty and would help others durng the evacuaton process. Frefghters would be an example of ths type of agent. 1 Example of one of the mazes used for our experments, wth two exts and eght hazards. 2 Buldng plan used for evacuaton smulatons. IEEE Computer Graphcs and Applcatons 81

3 Related Work There have been several cogntve agent archtectures proposed to generate crowd behavors. They generally consst of knowledge representaton, algorthms that learn, and modules that plan actons based on that knowledge. Funge, Tu, and Terzopoulos have worked on behavoral anmaton for creatng artfcal lfe, where vrtual agents are endowed wth synthetc vson and percepton of the envronment. 1 Massve SW has also developed a crowd smulaton system wth vson-based behavor. Rule-based systems can be used wth dozens of agents n real tme. Reynolds descrbes the frst use of a dstrbuted behavoral model to produce flockng behavor. 2 Brogan and Hodgns use partcle systems and dynamcs for modelng the moton of groups wth sgnfcant physcs. 3 Helbng, Farkas, and Vcsek smulates pedestrans usng a mcroscopc socal force model that solves Newton s equaton for the poston of each ndvdual by consderng repulsve nteractons, frcton forces, dsspaton, and fluctuatons. 4 These tradtonal crowd smulators gnore the dfferences between ndvduals and treat everyone as havng the same behavoral set, but there are other models that control each agent by ndvdual rules or physcal laws. 5 In a multagent crowd system, the agents are autonomous, typcally heterogeneous, and concerned wth coordnatng ntellgent behavors among the group. Other models have been used n commercal tools for shp and fre evacuaton. Some of the most common models nclude regresson, route choce, queung, gasknetcs, and cellular automata. Regresson models use statstcally establshed relatons between flow varables to predct pedestran flow under specfc crcumstances. Route-choce models descrbe pedestran wayfndng based on utlty: choosng destnatons to maxmze the utlty of ther trp (such as comfort, travel tme, and so on). Queung models use Markov chans to descrbe how pedestrans move from one network node to another. Gasknetcs models use flud or gas dynamcs analogs (partal dfferental equatons) to descrbe how densty and velocty change over tme. Cellular automata models represent space by a unform grd of cells wth local states dependng on a set of rules descrbng pedestran behavors. To reduce the complexty of controllng all the agents n the crowd whle stll guaranteeng detaled behavors, several systems have attached nformaton to the envronment. 6,7 The Mult-Agent Communcaton for Evacuaton Smulaton (Maces), descrbed n the man text, also embeds envronmental nformaton such as shortest paths. Indvdual agents wll have dfferental access to that nformaton and use t n dfferent ways. Dependng on ther ndvdual roles and behavor at any gven moment, they wll adopt dfferent decson-makng processes. References 1. J. Funge, X. Tu, and D. Terzopoulos, Cogntve Modelng Knowledge, Reasonng and Plannng for Intellgent Character, Proc. ACM Sggraph, ACM Press, 1999, pp C. Reynolds, Flocks, Herds, and Schools: A Dstrbuted Behavor Model, Proc. ACM Sggraph, ACM Press, 1987, pp D. Brogan and J. Hodgns, Group Behavours for Systems wth Sgnfcant Dynamcs, Autonomous Robots, vol. 4, 1997, pp D. Helbng, I. Farkas, and T. Vcsek, Smulatng Dynamcal Features of Escape Panc, Nature, vol. 7,, pp A. Braun, B.E.J. Bodmann, and S.R. Musse, Smulaton of Vrtual Crowds n Emergency Stuatons, Proc. ACM Symp. Vrtual Realty Software and Technology (VRST), ACM Press, 5, pp N. Farenc, R. Boulc, and D. Thalmann, An Informed Envronment Dedcated to the Smulaton of Vrtual Humans n Urban Context, Proc. Eurographcs, Computer Graphcs Forum, Blackwell Publshng, 1999, pp F. Teccha et al., Agent Behavor Smulator (ABS): A Platform for Urban Behavor Development, Proc. Game Technology (GTEC), CD-ROM, 1. Agents who are leaders, are untraned, and correspond to people that can handle stress better and would tend to help others and explore the buldng searchng for new paths. Agents who are not leaders, are untraned, and represent dependent people (followers) who mght panc durng an emergency stuaton and reach the pont where they are ncapable of makng ther own decsons. These personalty types abstract the man characterstc behavors that would occur durng real evacuatons accordng to the psychologcal lterature. 4 Hgh level: wayfndng Once the algorthm creates the cell and portal graph and automatcally generates the cell nformaton, the crowd smulaton algorthm proceeds through three man steps (see Fgure 3): 1. Leaders wthn a room share ther knowledge about the envronment wth the other agents (ther mental maps contan nformaton about blocked cells and local subgraphs or drectons that have been fully explored fndng no ext). At every tme step we compute a hgh-level path over the cell and portal graph, whch then stores the 82 vember/december 6

4 order n whch the cells should be vsted to get to an ext. 2. Agents check ther known shortest path for known hazards. Agents use the nformaton gathered through communcaton or drect percepton of the envronment. If ts current path s hazard free, then the agent wll just follow t and add the next cell to ts mental map. 3. Dependng on ther type, agents react dfferently f some hazard blocks the shortest known path. A traned agent has a mental map contanng the entre buldng s connectvty graph wth all the portals and therefore follows the next shortest path known from ts current cell. An untraned agent explores the buldng to fnd new routes usng a depth frst search (DFS). Snce the untraned agent ntally lacks the entre buldng connectvty graph, ths DFS s mplemented n an teratve way, so the agent dscovers new rooms only when t sees a portal and crosses t. Untraned, follower agents won t know what to do and wll follow the decsons taken by the other person n the room nstead of dong a DFS. Step 1 Step 2 Step 3 Leader? Explore buldng Communcate and share mental maps Get shortest path Blocked? Traned? Traned? Get alternatve path Follow the leader s behavor Low level: local moton An agent s local moton wthn a room s based on Helbng s mode, 2 whch descrbes human crowd behavor wth a mxture of socopsychologcal and physcal forces. Pedestrans 1 N of mass m lke to move wth a certan desred speed v n a certan drecton e and they tend to adapt ther nstantaneous velocty v (t) wthn a certan tme nterval τ. At the same tme, the ndvduals try to keep a dstance from other ndvduals j and from the walls w usng nteracton forces f j and f w. The change of velocty n tme t s gven by the acceleraton equaton: Random search versus depth frst search To explore the buldng once an agent knows that all the known shortest paths are blocked, we mplementm d v v ( t) e ( t) v ( t) = m + f + f j dt τ j( ) Ths model generates realstc phenomena such as archng n the portals and the faster-s-slower effect. In Maces, the desred velocty drecton wthn each room s gven by an attractor pont located close to the next portal the agent must cross. We also add repulson forces wth statc obstacles, such as columns. The agent walks wthn a room tryng to reach ts next attractor pont. Each portal has two attractor ponts (n front and behnd the door) to steer the agents movement n the desred drecton. The portal that an agent needs to cross s gven by the hgh-level algorthm, whch uses nformaton about desred destnaton and dstances from current poston to portals to assgn the next portal. Results and analyss Our goal s to study evacuaton algorthms performance when large groups of agents wth ndvdual personaltes use communcaton to reduce ther graph search space. Our motvaton s to produce results that closely smulate real human behavor n these stuatons, and we do ths by modelng the psychologcal factors w w 3 Hgh-level wayfndng dagram. such as followng known paths, herdng behavor, loss of orentaton, and so on that affect human performance under stress and panc. We mplemented a random search exclusvely for benchmarkng purposes, snce ths s not a realstc human behavor. In another comparson, we ll see the sgnfcant mpact that communcaton has on crowd behavor when executng wayfndng. Fnally, we ll examne the mpact of havng traned agents n the crowd, such as frefghters, and analyze the percentage of leaders needed to speed up the evacuaton process. For the experments, we use three scenaros, all of them maze-lke. We randomly generated two scenaros and created the thrd wth a buldng edtor to produce an envronment better resemblng a real buldng. The three mazes each contan rooms wth eght of them blocked by some hazard, such as fre. For each parameter set, we run 25 randomly generated startng confguratons for the crowds. The populatons used for these trals range from N = to agents. The leadershp levels range from to percent. leaders means they are all followers, and therefore when several agents meet n a cell, one random agent makes a decson and the others wll follow. Followers are dependent agents, when they fnd themselves n a panc stuaton they wll always follow other agents nstead of makng ther own decson, thus smulatng the herdng behavor observed n real crowds durng evacuaton. On the other hand, percent leadershp means each of them wll perform ts own decson-makng process, wth ts current, complete buldng knowledge. IEEE Computer Graphcs and Applcatons 83

5 Communcaton communcaton Smulaton tme steps 4 Communcaton versus no communcaton. Untraned leaders (%) 1 Smulaton tme steps 5 Evacuaton tme for dfferent crowd szes usng communcaton but percent untraned leaders. 6 Congeston at doors. Traned leaders (%) Smulaton tme steps 7 Evacuaton tme for, 25,, 75, and percent traned leaders. ed two algorthms. The frst one represents a nave search, where ndvduals explore adjacent rooms randomly and try not to go backward unless they fnd themselves trapped. In ths nave search, agents lack a mental map of the model nor can they create one whle navgatng the envronment, therefore the emergent behavor obtaned looks qute chaotc. A DFS algorthm makes the agents search adjacent rooms n a more structured way whle they create ther mental maps. The results obtaned show not only that DFS was about 15 tmes faster than random search, but also the emergent behavor obtaned was vsually closer to the behavor expected of a real crowd. Communcaton versus noncommuncaton In Fgure 4 we can readly observe the algorthm s dfferent performance wth and wthout communcaton for agents. The smulaton wth communcaton converges to percent evacuated n about half of the tme that t takes the noncommuncaton case to converge. Fgure 5 shows the results obtaned for dfferent crowd szes where all the agents represent ndependent (leader) ndvduals who make ther own decsons durng wayfndng nstead of followng others. In ths smulaton we don t have any traned agents, therefore everyone s unfamlar wth the buldng connectvty and must dscover how to evacuate based entrely on exploraton and shared communcaton. The graph shows the evacuaton tmes for crowd szes of,,, 1, and. Evacuaton tme decreases as the crowd sze ncreases. Ths can be explaned by the fact that for bgger crowds the probablty of meetng another agent ncreases, and therefore the mportant nformaton about hazards n the buldng and explored areas spreads faster among the ndvduals. Ths nformaton helps agents to prune ther graph search and therefore fnd the correct path sooner. It s mportant to notce though, that ths holds as long as the crowd s not so large that congeston blocks the doors, whch wll obvously decrease the evacuaton tme. Ths problem can be observed for crowds of more than agents, where the evacuaton tme s constraned by the number of exts and the flow rate through each of the doors (see Fgure 6). Traned versus untraned leaders We performed 25 smulatons usng a crowd sze of wth, 25,, 75, and percent traned agents. Fgure 7 shows the average evacuaton tmes obtaned. As expected, the percentage of evacuees converges to percent faster as the percentage of traned people ncreases. Ths seems an obvous result gven that traned people know how to evacuate a dangerous locaton because they have more nformaton about the envronment, and dependent agents wll follow them. Therefore, the overall evacuaton tme wll decrease as the number of traned agents n the envronment ncreases. t everyone needs to be traned, however. We can fnd out what s an adequate percentage of traned leaders needed to have a speedy evacuaton. We have pre- 84 vember/december 6

6 vously observed that there s not a bg dfference n the convergence values between percent and percent leadershp, whch means that there s no need to have a great proporton of traned leaders. Fgure 8 shows smaller percentages of leaders. Here we can conclude that an optmal percentage of traned people durng an evacuaton would be only about percent. For lower values the evacuaton tme for the same percentage of evacuees takes at least twce the tme. On the other hand, havng more than percent traned people only ncreases evacuaton tme by at most.16 tmes. Importance of leadershp In real lfe, some people have a hgher probablty of becomng leaders when an emergency occurs. They are usually ndependent ndvduals that by nature are able to handle emergency stuatons better and also tend to help others. Maces models these people as untraned leaders. Fgures 9a and 9b are two snapshots of an evacuaton process. Fgure 9a llustrates a populaton wth a hgh percentage of leaders, so that most of them tend to make ther own decsons when attemptng to ext. Fgure 9b shows a populaton wth a hgh percentage of dependent people who tend to follow any leader nstead of decdng routes by themselves. In the frst populaton we can observe an emergent behavor wth lots of small groups of people. In the second populaton, the emergent behavor shows fewer but larger groups of ndvduals. When the number of dependent ndvduals s hgher and there are few leaders, the sze of the groups formed tends to ncrease, snce dependent people wll not leave a group to try to explore new paths on ther own. Instead, they tend to stay together and just follow a leader. Conclusons and future work Our evaluaton has shown a sgnfcant mprovement n evacuaton rates when usng nteragent communcaton. We can also observe the groupng behavor that emerges when there s a hgh percentage of dependent agents n the crowd. Only a relatvely small percentage of traned leaders yelds the best evacuaton rates. We can vsualze these results n real tme wth ether our smple 2D or 3D vewer. We also created an Autodesk Maya applcaton for hgher qualty renderngs (see Fgure ). Areas where there s room for mprovement nclude addng ndvdualsm nto Helbng s model so that agents would have dfferent local motons dependng on ther roles. The hgh-level wayfndng must be modfed because people should be less lkely to enter a congested room when there are other possble paths avalable. Although t s mportant to closely model what the psychology lterature reports as real behavor n crowds studes show that people tend to have herdng behavor even though there could be alternatve doors n a room leadng to the same corrdor people under panc stll tend to all follow the same choces. In general, we want to provde the agents wth psychologcal elements that wll let us model more closely real human Traned leaders (%) Smulaton tme steps 8 Evacuaton tmes for small percentages of leaders. (a) (b) 9 Snapshot of crowd evacuaton wth (a) a hgh percentage of leadershp and (b) a low percentage of leadershp. Close-up vew of an Autodesk Maya anmaton. IEEE Computer Graphcs and Applcatons 85

7 behavor and therefore smulate crowd behavor more accurately. Acknowledgments Ths research was partally supported by the Natonal Scence Foundaton grant IIS-983, Offce of Naval Research Vrtual Technologes and Envronments grant N , Army Research Offce grant N C-81, and a Fulbrght scholarshp. We also thank Autodesk for ts software. References 1. C. Reynolds, Flocks, Herds, and Schools: A Dstrbuted Behavor Model, Proc. ACM Sggraph, ACM Press, 1987, pp D. Helbng, I. Farkas, and T. Vcsek, Smulatng Dynamcal Features of Escape Panc, Nature, vol. 7,, pp S. Chenney, Flow Tles, Eurographcs/ACM Sggraph Proc. Symp. Computer Anmaton, ACM Press, 4, pp N. Pelechano et al., Crowd Smulaton Incorporatng Agent Psychologcal Models, Roles and Communcaton, Proc. 1st Int l Workshop Crowd Smulaton, EPFL, 5, pp For further nformaton on ths or any other computng topc, please vst our Dgtal Lbrary at computer.org/publcatons/dlb. Nura Pelechano s a postdoctoral researcher n computer and nformaton scence at the Unversty of Pennsylvana. Her research nterests nclude crowd smulaton for realtme 3D graphcs and modelng hghlevel behavors n nteractve multagent envronments. Pelechano has a BA n computer scence from the Unverstat de Valenca; an MSc n vson, magng, and vrtual envronments from the Unversty College London; and a PhD n computer and nformaton scence from the Unversty of Pennsylvana. Contact her at npelecha@seas.upenn.edu. rman I. Badler s a professor of computer and nformaton scence at the Unversty of Pennsylvana, where he drects the Center for Human Modelng and Smulaton. Hs research nterests nclude anmaton va smulaton; emboded agent software; and computatonal connectons between language, nstructons, and acton. Badler has a PhD n computer scence from the Unversty of Toronto. He s a coedtor of Graphcal Models. Contact hm at badler@ seas.upenn.edu. Artcle submtted: 7 Sept. 5; revsed: 17 Jan. 6; accepted: Apr vember/december 6