LifeBelt: Crowd Evacuation Based on Vibro-Tactile Guidance

Published by the IEEE CS n 1536-1268/10/$26.00 © 2010 IEEE PERVASIVE computing 33

H O S T I L E E N V I R O N M E N T S

O n 11 November 2000, at the Kaprun resort in Austria, 162 passengers boarded a funicu-lar Gletscherbahn train for an early-morning trip to the ski

slopes. As the train traveled through a 3,300-meter tunnel at 10 m/sec to an altitude of 2,450 meters above sea level, a heater at the car-riage’s lower end caught fire. The fire destroyed the braking system pipes carrying flammable hydraulic fluid, resulting in the loss of fluid pres-sure and causing the train to stop suddenly.

The train conductor was able to raise a fire alarm at the control center, but the fire had burned the power cables, yielding a total blackout. Meanwhile, many passengers

had lost consciousness due to toxic smoke by the time the train conductor eventually man-aged to unlock the hydraulic doors. Most of the remaining passengers tried to escape up the tunnel—away from the fire—while 11 of them headed downward. The tunnel, acting as a chimney, drew oxygen from the bottom open-ing and blew fire and toxic smoke upward. The disaster claimed a total of 155 lives. Twelve pas-sengers survived—basically those who decided to escape via the downward exit. Although the control center’s system contained the proper exit strategy, this information didn’t reach the individuals on the train.

In emergency situations such as this, guiding individuals toward exits is often complicated by dynamic exit choices and a lack of appropriate infrastructure, such as exit signs and loudspeak-ers. What appears to be an effective exit recom-mendation one moment might be disastrous the next. In addition, modality options have limita-tions. Visual guidance via exit signs or direction maps might prove inefficient in limited visibility or total darkness, for instance, due to smoke. Auditory guidance via cues such as speaker an-nouncements might be ineffective because of high noise levels of alarm sirens and a panick-ing crowd. In addition, panicked individuals’ limited attentiveness often prevents successful delivery of guidance recommendations. Fear of-ten replaces cogent thinking, leading to seem-ingly irrational behavior. Mindless following—a phenomenon observed in emergencies such as the Kaprun disaster—emerges when trust in the crowd’s behavior replaces individual planning.

Technology that delivers timely and reliable exit recommendations could have helped save lives. Emergencies such as the Kaprun train fire, the Thailand tsunami in December 2004, the 11 September New York City attacks in 2001, the 7 July London bombing in 2005, the 2010 Haiti earthquake, or the 2010 Duisburg Love Parade disaster could have benefitted from early alert-ing and controlled evacuation with pervasive computing and communication technologies.

To improve individuals’ escape strategies in

In emergency situations, a wearable computer can collect an individual’s position and orientation information, share this information with an emergency coordination system, and guide the wearer to his or her optimal exit.

Alois Ferscha and Kashif ZiaUniversity of Linz

LifeBelt: Crowd Evacuation Based on Vibro-Tactile Guidance

34 PERVASIVE computing www.computer.org/pervasive

HOSTILE ENVIRONMENTS

crowd evacuations, we created Life-Belt, a wearable computer that guides individuals to exits in emergency situa-tions. The LifeBelt collects individuals’ position and orientation information from embedded sensors, shares this information with background emer-gency coordination systems, receives an individualized optimized escape plan, and guides each individual to the recommended exit via vibro-tactile stimulation.

Crowd Evacuation StudiesA crowd evacuation process’s success depends on both group and individual behavioral studies. But understanding crowd dynamics in emergency situ-ations is difficult because of internal factors—such as the psychological, physical, and social attributes of indi-viduals constituting a crowd and the ef-fects due to nearby activities—as well as external factors—such as threat level and location, damaged regions, and the area’s geometry, to name a few. Collec-tive effects include jamming, density waves, lane formation, oscillations at bottlenecks, patterns at intersections, and panic. In addition, interaction among individuals results in formation of group primitives—namely, collision avoidance, following, dispersion, ag-gregation, homing, flocking, and to-getherness. All these factors must be considered when designing an evacua-tion system.

Recent research has focused on mod-eling and simulation of crowd evacua-

tion processes.1,2 The need for realistic simulation is rooted in the lack of real evacuation data and staged evacuation trials. Researchers often borrow social science behavior models for simulation studies, which provide the psychologi-cal and anthropological basis for crowd behavioral rules, usually at a micro-scopic level.

Crowd behavioral patterns used in crowd simulation systems are influ-enced by studies in which environmen-tal input is available through sight and hearing. Although these established behavior patterns might be realistic for human activities under normal cir-cumstances, they might not apply in emergency situations. In cases of fire, natural disaster, or other catastrophe, one can’t assume the power infrastruc-ture will be intact. Even during the day, many spaces aren’t exposed to natural light (for example, underground sub-stations, tunnels, and certain build-ings), so evacuations possibly happen in partial or total darkness. Furthermore, because hearing is tied to vision, func-tional performance based on hearing would also deteriorate.

Many research efforts have con-sidered evacuation of spaces without visibility, including statistical charac-teristics for the random walk model.3 For instance, Motonari Isobe, Dirk Helbing, and Takashi Nagatani vali-date experimental results with a sim-ulation of an evacuation process from a smoky room.4 They report that even when they added more exits, jamming

still occurred because most people are attracted by nonlocal acoustic interac-tion and move toward the first discov-ered exit. Hence, acoustic guidance de-creases evacuation efficiency if there are multiple exits.

Recent publications also focus on exit selection on the basis of occupant density. Researchers have proposed theoretical models for exit selection in multiple-exit environments5 and have improved this model, particularly in cases in which obstacles hinder mobil-ity.6 But neither model considers lim-ited visibility; they assume that indi-viduals are aware of exit-area activities, irrespective of the exit’s distance.

Vibro-Tactile Guidance in Emergency SituationsMotivated by situations in which hu-man senses are overloaded with visual and auditory stimulation, we created a vibro-tactile wearable notification system.7 During disasters, effectively raising human attention is difficult. Tactile stimulation is a subtle yet ef-fective communication channel that can raise attention in an unobtrusive yet demanding manner.8 The LifeBelt system varies vibro-tactile stimulation intensity, duration, and frequency through tactor elements embedded into a hip-worn belt (see Figure 1) to indicate both orientation and direc-tional guidance. Eight vibrator ele-ments are lined up in the fabric of a hip belt and connected to the belt control-ler shown in Figure 2. The controller

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Figure 1. The LifeBelt microcontroller and vibro-element array. (a) The waist-worn LifeBelt with eight vibro elements, (b) tactor (evacuation) control unit with the vibro elements, (c) a closer view of tactor control unit, and (d) assembly inside the vibro-element casing.

OCTOBER–DECEMBER 2010 PERVASIVE computing 35

uses an ATmega32-based microcon-troller board, and activates the vibra-tor switches according to commands received wirelessly from a global evac-uation control unit.9

The LifeBelt serves two purposes. It indicates the direction toward the optimal exit (as recommended by the evacuation control unit) for each in-dividual. It also assists individuals in making next-step decisions by sens-ing the neighborhood for a variety of ranges (distances) and relative orienta-tions (angles) toward the recommended exit, indicating potential hazards us-ing predefined vibration patterns. The LifeBelt can sense the neighborhood and extract other individuals’ relative spatial relations while interfacing with a back-end system (evacuation control unit) and interacting with the wearer in a natural and unobtrusive way.

Evacuation ModelingTo model evacuation process dynam-ics, we use a cellular automata (CA) technique.9,10 A CA model is based on a grid in which a single individual oc-cupies each cell. The space and local rules describe the next cells an individ-ual can occupy in the next time step. Space rules are based on surrounding cells’ states, whereas local rules are based on an individual’s characteris-tics with regard to his or her neighbor-hood. Usually a model’s appropriate-ness depends on the adequacy of the

next-step behavior expressed with rules. Thus, individuals are bound by strict rules and can only move to a nearby cell on the basis of transi-tion probabilities related to three fac-tors: direction of motion (for instance, shortest path to an exit), interactions with other individuals, and interac-tions with the structure (for example, obstacles and walls).

Here we study an optimal-exit model based on three parameters—the exit population (EP), the exit capacity (EC), and the time to reach to an exit area (TEA). EC is the number of evac-uees that can possibly escape through the exit per unit time. TEA represents the steps needed to reach to an exit and is essentially a best-case calcula-tion. Congestion along the exit path and local movement patterns can in-crease the TEA value. EP is the number of individuals present at an exit area (EA). As an individual approaches an EA, the LifeBelt marks the exit as its final destination.

We also consider a variant of EP for comparison. As an individual in an EA tries to move toward an exit, the effort might succeed or fail. In case of failure, the cell’s panic measure increases by 1, expressing a growing stress level that comes from the fear of getting stuck in the crowd without hope of escape. In case of success, the panic measure resets to 0, irrespective of previous value. The EP’s panic indicator is the

sum of EA cells’ panic values. The exits considered are one-way, situated in the evacuation space’s outer walls (elimi-nating the complexities of cross-flows through exits). The predicted exit time (PET) is calculated as PETi = TEAi + (EPi/ECi) where i represents an exit.

Naturally, the exit with the lowest PET value is the LifeBelt’s optimal exit. To describe individuals’ next-step behavior, researchers introduced a CA model based on a square lat-tice.11 The lattice is comprised of four cells (at 0-, 90-, 180-, and 270-degree angles) around the source cell. Out of these four possibilities, the model chooses the source cell occupant’s next step on the basis of his or her orien-tation. Possible orientation values are up (0 degrees), down (180 degrees), left (270 degrees), and right (90 degrees). However, we build on Moore’s neigh-borhood model,12 extending the cell’s neighborhood to eight cells.

We also generalized the concept of orientation (which can be any angle between 0 and 359) and devised the following simple mechanism to choose between the candidate cells, using Tobias Kretz and Michael Schrecken-berg’s “Moore and More and Symme-try.”13 If empty, the cell at 0 degrees relative to the current cell is preferred, followed by cells at ±45 degrees, then ±90 degrees. Given the choice be-tween right and left, most people tend to move toward the right.14 Thus, the

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Figure 2. LifeBelt wearable notification system. (a) The LifeBelt assembly as used in the experiment, (b) an experiment participant wearing LifeBelt, and (c) the InterSense transceiver, used for information exchange. Information is exchanged through InterSense transceiver.



LifeBelt gives right preference over left. Figure 3 shows this mechanism. The cell with an arrow represents the individual. Green cells represent the cells occupied by an object, such as an obstacle or another individual, and gray cells show the adopted choice.

If none of the first five cells are avail-able, the individual’s next move would be counterproductive—away from the planned destination (cells 5, 7, and 8 in Figure 3f). In this model, we don’t allow this move. Rather, an individual in such a situation doesn’t perform any action (remains in wait state).

We verified the strategy for next-cell calculation at the local level. We con-ducted a simulated crowd evacuation scenario with 30 participants and in-structed them to rush toward one of two exits from the center of a class-room. To avoid preplanning, the tar-get exit was chosen ad hoc. To mimic obstacles while preventing injury from bumping into physical objects such as tables and chairs, each student was assigned one of two roles—blocker

or mover. The blocker’s role was to permanently occupy a position of their liking after a few random initial steps. We used this strategy to ensure an initial move toward the exit, hence avoiding immediate jamming. The mover’s role was to evacuate as early as possible. We conducted three ex-periments with 10, 25, and 50 percent of individuals acting as blockers. The experiment validated the cell-choice mechanism and yielded the following conclusions:

• People initially tend to wait in place if they’re unable to move forward. As soon as they realize that waiting won’t improve the situation, they try moving sideways, then backward.

• People tend to prefer right over left. • A preemptive decision depends on

the detail levels in a relatively for-ward direction. Knowing the sur-roundings at a larger scale helps prevent jamming.

See “On the Efficiency of LifeBelt Based Crowd Evacuation”15 for more information.

Simulating Exit RecommendationsTo study the LifeBelt system’s effect in a real-world evacuation scenario, we first conducted a NetLogo simulation.9 In NetLogo, a “world” is a collection of cells that might contain an individual. We used a 101 × 41-cell world, with the origin at its center. The cells’ mini-mum and maximum x-coordinates are -50 and 50, respectively, and the mini-

mum and maximum y- coordinates are -20 and 20, respectively. For the simulation, each cell could accommo-date one individual, located at the cell’s center. We performed simulations for 1,000 individuals for different evacua-tion strategies, keeping the exit setting (number, locations, and width) and vis-ibility unchanged.

Figure 4 shows this simulation’s ini-tial setup. There are four exits. Exit 1 is the strip of black cells on the bottom right ([24, -20], [25, -20], [26, -20]). Exit 2 is the strip of black cells on the bottom left ([-24, -20], [-25, -20], [-26, -20]). Exit 3 is the top-left black cell ([-25, 20]), and Exit 4 is the top-right black cell ([25, 20]). Exit 1 and exit 2’s EC is three times greater than exits 3 and 4. The gray region repre-sents each exit’s EA, defined by vis-ibility level. The visibility level is cur-rently 5, which means that individuals can view up to a distance of five cells. The cell’s direction of motion variable stores the angle (direction) from the cell to the nearest exit cell. The exit-id variable stores the identity of the exit to which the nearest exit cell belongs. For the initial setup, we placed 1,000 individuals at random locations.

We studied three evacuation strategies:

• Strategy 1—nearest exit. We initially assigned a similar number of individ-uals to each exit, measuring a linear decrement in the number of individu-als residing in exit areas (see Figure 5a). Because of increased width, the decrement factor in exits 1 and 2 was

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Figure 3. Possible configurations of an individual moving at a 45-degree angle. The cell with an arrow represents the individual. Green cells are occupied by an object, such as an obstacle or other individual. Gray cells show the adopted choice.

Figure 4. Simulation setup with 101 × 41 cells, 1,000 randomly placed individuals, and four diametrically placed exits. The black cells represent exits, and the gray region represents the exit area, defined by visibility level.


much higher than that of exits 3 and 4. Exits 1 and 2 exhausted all the in-dividuals around iteration 130. Ex-its 3 and 4 required approximately 290 iterations. We can improve exit performance if we move individuals from exits 3 and 4 to exits 1 and 2.

• Strategy 2—recommended exit, with optimization based on the number of individuals at the EA. We diverted individuals related to exits 3 and 4 to exits 1 and 2 in ini-tial iterations (Figure 5b). This ini-tial migration completed after ap-proximately 15 iterations, and then we observed a linear decrement in individuals from all exits. When the EP reached maximum for all exits, the factor EPi/ECi becomes ineffec-tive and the optimal exit depends entirely on distance. Later in the simulation, when exits 3 and 4’s EP decreased briskly, a few individuals diverted from exits 1 and 2 toward exits 3 and 4, near iteration 70 to 80. Overall, the evacuation performance is better than the first strategy—190 iterations, instead of 290.

• Strategy 3—recommended exit, with optimization based on EA panic levels. In this strategy, the exits’ usage was more variant (non-linear) (see Figure 5c). Throughout this simulation, there were high fluctuations of individuals related (distance-wise) to different exits. This means that more individuals were at the world’s center, which af-fected the evacuation performance at the later stages of the evacuation. We can see that evacuation perfor-mance (approximately 230 itera-

tions) is better than strategy 1 but, as expected, worse than strategy 2.

Comparing strategies 2 and 3 yields interesting results. The migration pat-terns in strategy 2 are more definite, meaning the population might mi-grate from one exit to another, but mi-

gration is sustained for a considerable period of time until the EP changes significantly. Strategy 3 is the oppo-site of that, meaning migration and remigration occur frequently because of changing aggregate panic values at an EA. Both strategies have positive and negative aspects. A high degree

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Figure 5. Simulation results for 1,000 randomly placed individuals and four diametrically placed exits. We tested three exit strategies: (a) nearest exit, (b) recommended exit with optimization based on the number of individuals at the exit area, and (c) recommended exit with optimization based on exit area panic levels.



of variation in exit choice (strategy 3) can result in panic reduction; however, the evacuation process might be inef-ficient. Moreover, individuals might lose trust in the evacuation device ow-ing to the frequent, contradicting no-tifications. Alternatively, fewer migra-tions (strategy 2) can result in a more efficient evacuation; however, this can increase the panic factor substantially. Hence the choice between strategy 2 and strategy 3 depends on both the en-vironment and the population.

Simulating LifeBelt-Based Evacuations for Linz Main StationOn the basis of these findings, we con-ducted evacuation experiments on the Linz Main Station—the second- largest railway station in Austria—using the Exodus simulation environment. The

Linz Main Station has three levels. At the ground level (see Figure 6), the transit hall connects to the road net-work. Most travelers are familiar with several central exits, which are directly accessible from the outside road (E1 to E6). We assume that the majority of travelers are unaware of the exits at the back and side of the building (E7 through E12), which are not directly accessible anyway.

The ground-level transit hall (G) connects to an underground main hall (UG1) via two staircases. Figure 7a shows the left staircase, SC1, and Fig-ure 7b shows the right staircase, SC2. Figures 7c and 7d are photos of the two sides of the main hall, both taken from ground floor.

Figure 8 shows the main hall at UG1. We divided the hall into four regions, three of them defined by exits—exit

13 (region 2), exit 14 (region 3), and exit 15 (region 1)—and a central re-gion. These exits are less familiar than the central region that connects to the transit hall via the staircases (SC1 and SC2). The central region also connects with an underground tram station (UG2) via staircases.

The main hall has offshoots to the train platforms and connects to the underground tram station (UG2) through left and right staircases. Figures 9a and 9b show the station’s two platforms, and Figure 9c shows the geometrical view.

In our scenario, a fire at exit 16 in the tram station causes an evacuation. Exit 16 is blocked, and all passengers at the tram station must evacuate using the staircases connected to the main hall. From the main hall, there are two ways to evacuate—through the exits at re-

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Figure 6. The Linz Main Station’s ground-level transit hall. Only six outer exits (E1 through E6) that connect to the road network are accessible. Two staircases (SC1-G-UG1 and SC2-G-UG1) connect to the main hall.

Figure 7. Linz Main Station’s central staircases. (a) Left and (b) right staircases connect the transit hall on the ground level to the underground (c, d) main hall.


gions 1, 2, and 3, and up the staircases to the transit hall. People have different levels of familiarity with the main hall and the transit hall. Exodus’ external exit parameter potential calculates exit familiarity by a node’s distance from the nearest exit. Without directional guidance, all evacuees target the exit on the potential map (nearest exit). Figure 10 shows a potential map of the three exits at the main hall (the central region is served by transit hall exits). Figure 11 shows the essentially ineffi-cient exit routes, based on the poten-tial maps, for the entire station. In the tram station (see Figure 11a), evacuees proceed upward toward the main hall; in the main hall (see Figure 11b), the central staircases must accommodate the evacuees from the central region of the main hall and from the tram sta-tion; and in the transit hall (see Figure 11c), after exhausting native evacuees (the indigenous transit hall popula-tion), due to incoming evacuees from the main hall, exits 2 and 5 are busy until the end of the simulation, whereas other exits are unused.

By changing potentials from their de-fault value of 100 percent, we can re-adjust the evacuees’ paths according to our requirements. In our scenario, we consider only the tram station evacu-ees, analyzing the effects of providing them with the LifeBelt to counter the visibility and mindless-following con-straints. We based our directional guid-

ance strategy on the expected time to evacuate measure, which considers an evacuee’s distance from an exit as well as the exit area’s population density.15

Through repeated simulations, we concluded that only exit 15 could play an effective role for tram station evacuees. The other two exits in the main hall are so distant that expected time to evacuate would be lower. The default route (based on the potential map) for the evacuees leaving the tram station using the two platform stair-cases is through the transit hall using the left staircase (SC1). We provided directional guidance to all tram sta-tion evacuees (with different adher-ence levels) to judge efficiency im-provements. Again, we didn’t provide directional guidance to the main hall evacuees because we’re only targeting a portion of the population. In addi-tion, in initial experiments, we found that attracting a portion of the main hall evacuees toward exit 15 degraded the performance due to congestion, in-creasing the expected time to evacuate.

We simulated an overall population of 2,000 evacuees—500 in the transit

hall, 1,000 in the main hall, and 500 in the tram station. We began with the default case in which all the in-dividuals follow the potential maps, then provided directional guidance to individuals in the tram station. We experimented with three adherence levels—25, 50, and 90 percent. Figure 12 shows the simulations of the four cases, concentrating on the central region of the main hall. Overall, the congestion around the left staircase decreases with increased adherence levels. The time series graph also vali-dates this claim (see Figure 13a on p. 42). We improved performance by 90 percent when using an adherence level of 90 percent.

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Figure 8. Linz Main Station’s main hall. We divided the main hall into four regions, three of them defined by exits—exit 13 (region 2), exit 14 (region 3), and exit 15 (region 1)—and a central region.

Figure 9. Linz Main Station’s tram station. The tram station has (a, b) two platforms. (c) Two staircases—SC3-UG2-UG1 and SC4-UG2-UG1—connect UG2 with UG1. In our scenario, a fire at exit 16 causes an evacuation.



In this scenario, only situations at exits 2 and 15 change. The LifeBelt exit rec-ommendation system was able to relieve congestion at exit 2, diverting a portion of the population heading toward exit 2 to the less crowded exit 15. Figure 13b

shows a comparison between the num-ber of individuals who opted for exit 15 in all four cases. All the individuals originally starting from the tram station and heading toward exit 2 in the default potential map case diverted to exit 15.

These results deliver evidence for a potential improvement in evacuation efficiency by using strategies based on EA dynamics (population size and aggregate panic levels) rather than nearest-exit maps, especially with high adherence levels.

I n addition to guiding individuals toward unknown exits, the Life-Belt can also divert the wearer from inaccessible or congested

exits as well as reduce panic. As a personal life-saver technology, Life-Belt can lead wearers whose senses of vision and hearing are overwhelmed (due to perceptual overload and stress) or not operational (due to smoke or power failure) to safety. As crowd-saver technology, it provably reduces time to successful evacuation.

ACKNOWLEDGMENTSThe FP7 ICT Future Enabling Technologies pro-gram of the European Commission supported this work under grant agreement 231288 (SOCIONI-CAL) and grant agreement 225938 (OPPORTU-NITY). The LifeBelt system was developed under grant FACT, Siemens AG, CT-SE 2, Munich. The ÖBB (ÖsterreichischeBundesbahn) supported the experiments in Linz Main Station. R. Neunteufel (ÖBB), Ch. Neumann (ILF), and W. Ammerstor-fer (ÖBB) provided valuable train-station-specific information and insight.

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Figure 10. Linz Main Station—main hall potential map. (a) Exit 15, (b) exit 13, and (c) exit 14. The central region is served by transit hall exits. Without directional guidance, all the evacuees target the exit on the potential map.

Figure 11. Exit routes based on potential maps. The exit strategy’s inefficiency is evident. In (a) the tram station, all evacuees proceed upward toward main hall due to inaccessible outer exits. In (b) the main hall, the outer exits are almost exhausted; the central staircases must accommodate the evacuees from central region of the main hall and from tram station. In (c) the transit hall, two central exits (exits 2 and 5) remain busy until the end of the simulation, while other exits are unused.


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Simulation time units = 45

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Directional guidance with 90 percent adhering to the exit recommendation

Figure 12. Congestion at main hall staircases at (a) 45, (b) 90, and (c) 225 simulation time units. We tested evacuation efficiency by comparing individuals without directional guidance (potential map) to those with LifeBelt directional guidance, using various compliance levels.



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8. M. Grunwald, Human Haptic Percep-tion: Basics and Applications, Birkhäuser Verlag, 2009.

9. A. Ferscha and K. Zia, “LifeBelt: Silent Directional Guidance for Crowd Evacu-ation,” Proc. 13th Int’l Symp. Wearable Computers (ISWC 09), IEEE CS Press, 2009, pp. 19−26.

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the AUTHORSAlois Ferscha is a full professor at the University of Linz. His research interests include pervasive and ubiquitous computing, networked embedded systems, embedded software systems, wireless communication, multiuser cooperation, distributed interaction, and distributed interactive simulation. Ferscha has a PhD in informatics from the University of Vienna. Contact him at [email protected].

Kashif Zia is a PhD candidate in the Institute for Pervasive Computing at the University of Linz. His research interests include socio-technical systems, focus-ing on crowd dynamics and simulation. Zia has an MS in computer science from the University of the Punjab and a BE from the University of Engineering and Technology in Lahore, Pakistan. Contact him at [email protected].

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Potential map

Directional guidance,adherence = 25%




Potential map



Figure 13. Evacuation efficiency. The graphs show time elapsed versus number of evacuees for (a) all exits and (b) exit 15. The black line represents the potential map, blue represents directional guidance with 25 percent adherence, green represents 50 percent adherence, and red represents 90 percent adherence.