Human exit route choice in virtual crowd evacuations Nikolai W. F. Bode a, * , Edward A. Codling a, b a Department of Mathematical Sciences, University of Essex, Colchester, U.K. b School of Biological Sciences, University of Essex, Colchester, U.K. article info Article history: Received 15 March 2013 Initial acceptance 17 April 2013 Final acceptance 3 May 2013 Available online 21 June 2013 MS. number: 13-00236 Keywords: crowd behaviour decision making emergency evacuation individual-based model pedestrian interactions virtual environment The collective behaviour of human crowds emerges from the local interactions of individuals. To un- derstand human crowds we therefore need to identify the behavioural rules individual pedestrians follow. This is crucial for the control of emergency evacuations from confined spaces, for example. At a microscopic level we seek to predict the next step of pedestrians based on their local environment. However, we also have to consider ‘tactical-level’ individual behaviour that is not an immediate response to the local environment, such as the choice between different routes to exit a building. We used an interactive virtual environment to study human exit route decisions in simulated evacuations. Partici- pants had to escape from a building and had to choose between different exit routes in the presence of evacuating simulated agents. We found no inherent preference for familiar routes, but under a stress- inducing treatment, subjects were more likely to display behaviour in their route choice that was detrimental to their evacuation time. Most strikingly, subjects were less likely to avoid a congested exit by changing their original decision to move towards it under this treatment. Age and gender had clear effects on reaction times in the virtual environment. Ó 2013 The Association for the Study of Animal Behaviour. Published by Elsevier Ltd. All rights reserved. The collective behaviour of animal groups, and human crowds in particular, emerges from the local interactions of individuals (Camazine et al. 2001; Sumpter 2010). This insight has been crucial to increase our understanding of how human crowds behave and therefore our ability to control their dynamics (Helbing et al. 2000; Dyer et al. 2008; Faria et al. 2009, 2010a; Schadschneider et al. 2009). Human crowds display many examples of collective behaviour, such as the spontaneous formation of lanes in bidirec- tional pedestrian flows or pedestrian jams at bottlenecks (Moussaïd et al. 2009; Helbing & Johansson 2010). Through empirical and theoretical experiments researchers have sought to identify the set of behavioural rules individual pedestrians follow. At a microscopic level this amounts to predicting the next step or future movement direction of pedestrians given their local envi- ronment including other pedestrians and obstacles (Hoogendoorn & Bovy 2004; Antonini et al. 2006; Faria et al. 2010b; Moussaïd et al. 2011). Findings from research on this level of individual behaviour, termed ‘operational level’ (Schadschneider et al. 2009), suggest that pedestrians’ movement is dictated by the attempt to optimize quantities such as travel time or the directness of the route. While an understanding of the operational level of pedestrian behaviour is sufficient to explain many phenomena of crowd dy- namics, we also have to consider individual behaviour that is not an immediate response to the local environment. Often individuals are faced with the choice of a number of different routes to enter a building or to get from one place in a city centre to another place. Such decisions have been classified as ‘tactical level’ of pedestrian behaviour (Schadschneider et al. 2009) and a number of factors, such as the perceived distance of routes, the weather protection offered by routes and even the pleasantness of routes have been suggested to affect route choice in pedestrians (Daamen et al. 2005; Kretz et al. 2011). Understanding both the operational and tactical levels of indi- vidual behaviour is particularly important when considering evacuations of human crowds from confined spaces, such as buildings or vehicles (Helbing & Johansson 2010; Moussaïd et al. 2011). In this context, decisions on when to evacuate and which emergency exit route to use are part of the tactical level of pedes- trian behaviour and movement decisions at the operational level of behaviour can subsequently lead to potentially dangerous collec- tive phenomena at high pedestrian densities. Examples include the build-up of pressure at bottlenecks in evacuation routes or density waves increasing the risk of pedestrians falling. Theoretical and empirical work has made great strides at increasing our ability to reduce the risk of dangerous collective phenomena emerging from the operational level of behaviour (see Helbing & Johansson 2010 * Correspondence: N. Bode, Department of Mathematical Sciences, University of Essex, Colchester CO4 3SQ, U.K. E-mail address: [email protected](N. W. F. Bode). Contents lists available at SciVerse ScienceDirect Animal Behaviour journal homepage: www.elsevier.com/locate/anbehav 0003-3472/$38.00 Ó 2013 The Association for the Study of Animal Behaviour. Published by Elsevier Ltd. All rights reserved. http://dx.doi.org/10.1016/j.anbehav.2013.05.025 Animal Behaviour 86 (2013) 347e358
0003-3472/$38.00 � 2013 The Association for the Stuhttp://dx.doi.org/10.1016/j.anbehav.2013.05.025
The collective behaviour of human crowds emerges from the local interactions of individuals. To un-derstand human crowds we therefore need to identify the behavioural rules individual pedestriansfollow. This is crucial for the control of emergency evacuations from confined spaces, for example. At amicroscopic level we seek to predict the next step of pedestrians based on their local environment.However, we also have to consider ‘tactical-level’ individual behaviour that is not an immediate responseto the local environment, such as the choice between different routes to exit a building. We used aninteractive virtual environment to study human exit route decisions in simulated evacuations. Partici-pants had to escape from a building and had to choose between different exit routes in the presence ofevacuating simulated agents. We found no inherent preference for familiar routes, but under a stress-inducing treatment, subjects were more likely to display behaviour in their route choice that wasdetrimental to their evacuation time. Most strikingly, subjects were less likely to avoid a congested exitby changing their original decision to move towards it under this treatment. Age and gender had cleareffects on reaction times in the virtual environment.� 2013 The Association for the Study of Animal Behaviour. Published by Elsevier Ltd. All rights reserved.
The collective behaviour of animal groups, and human crowds inparticular, emerges from the local interactions of individuals(Camazine et al. 2001; Sumpter 2010). This insight has been crucialto increase our understanding of how human crowds behave andtherefore our ability to control their dynamics (Helbing et al. 2000;Dyer et al. 2008; Faria et al. 2009, 2010a; Schadschneider et al.2009). Human crowds display many examples of collectivebehaviour, such as the spontaneous formation of lanes in bidirec-tional pedestrian flows or pedestrian jams at bottlenecks(Moussaïd et al. 2009; Helbing & Johansson 2010). Throughempirical and theoretical experiments researchers have sought toidentify the set of behavioural rules individual pedestrians follow.At a microscopic level this amounts to predicting the next step orfuture movement direction of pedestrians given their local envi-ronment including other pedestrians and obstacles (Hoogendoorn& Bovy 2004; Antonini et al. 2006; Faria et al. 2010b; Moussaïdet al. 2011). Findings from research on this level of individualbehaviour, termed ‘operational level’ (Schadschneider et al. 2009),suggest that pedestrians’ movement is dictated by the attempt tooptimize quantities such as travel time or the directness of theroute.
atical Sciences, University of
).
dy of Animal Behaviour. Published
While an understanding of the operational level of pedestrianbehaviour is sufficient to explain many phenomena of crowd dy-namics, we also have to consider individual behaviour that is not animmediate response to the local environment. Often individuals arefaced with the choice of a number of different routes to enter abuilding or to get from one place in a city centre to another place.Such decisions have been classified as ‘tactical level’ of pedestrianbehaviour (Schadschneider et al. 2009) and a number of factors,such as the perceived distance of routes, the weather protectionoffered by routes and even the pleasantness of routes have beensuggested to affect route choice in pedestrians (Daamen et al. 2005;Kretz et al. 2011).
Understanding both the operational and tactical levels of indi-vidual behaviour is particularly important when consideringevacuations of human crowds from confined spaces, such asbuildings or vehicles (Helbing & Johansson 2010; Moussaïd et al.2011). In this context, decisions on when to evacuate and whichemergency exit route to use are part of the tactical level of pedes-trian behaviour and movement decisions at the operational level ofbehaviour can subsequently lead to potentially dangerous collec-tive phenomena at high pedestrian densities. Examples include thebuild-up of pressure at bottlenecks in evacuation routes or densitywaves increasing the risk of pedestrians falling. Theoretical andempirical work has made great strides at increasing our ability toreduce the risk of dangerous collective phenomena emerging fromthe operational level of behaviour (see Helbing & Johansson 2010
N. W. F. Bode, E. A. Codling / Animal Behaviour 86 (2013) 347e358348
for a recent review). In this study we explicitly considered thetactical level of behaviour and investigated route choice decisionsin humans in the context of evacuations.
Observations suggest that pedestrians tend to use exit routesthey are familiar with during evacuations (Proulx 2001; Johnson2005). For example, occupants of Munich Airport sought to evac-uate via the main entrance which they used to enter the building,ignoring one or even multiple fire exits along the route (Proulx2001). Similar observations were made during evacuations ofnightclubs or bars (Johnson 2005). This behaviour can lead todangerous overcrowding in these preferred exit routes which mayresult in injuries or fatalities among the evacuees (Proulx 2001;Johnson 2005). Despite these reports on specific events, the evi-dence for this route choice behaviour remains anecdotal and thereis thus a need for controlled experiments and further investigationwhich is the subject of our work.
At the operational level, empirical and theoretical research inhumans and nonhuman animals has demonstrated that stress orlevels of perceived threat strongly influence the emergent charac-teristics of group movement (Helbing et al. 2000; Bode et al. 2010).Research in psychology has investigated and identified moregenerally effects of stress or perceived threat on human decisionmaking (Keinan 1987; Svenson & Maule 1993; Starcke et al. 2008).A common theme of this research is that human decision makingcan be affected detrimentally by stress. For example, subjects’performance in simple decision-making tasks decreases under theanticipatory stress of having to give a public speech (Starcke et al.2008). We explored whether and how treatments, likely to berelated to stress levels, that increase the competitiveness or ur-gency with which people approach tasks can affect human de-cisions at the tactical level of behaviour, pertaining to route choice,for example.
While in nonevacuation route choices no consistent tendenciescould be attributed to the gender of subjects (Daamen et al. 2005),in decisions of when to move, gender appears to play an importantrole. One well-studied example for gender differences in move-ment timing decisions is pedestrian behaviour at road crossings(Rosenbloom 2009; Faria et al. 2010b). In this scenario male sub-jects often display more risky behaviour and tend to move earlierthan female subjects (Faria et al. 2010b). In addition to the questionof where to move, the question of when to move is highly relevantin evacuations and is likely to have a strong effect on the success ofindividuals’ evacuation (Zhang et al. 2008). It is therefore importantto gain a better general understanding on movement initiationtimings in the context of evacuations.
Research on human evacuations from confined spaces faces oneoverarching limiting constraint. Evacuation drills are perhaps theclosest proxy for evacuations. However, if these drills are to behighly realistic, they are potentially dangerous to participants andtherefore not ethical. When risks to participants are avoided, drillsare unlikely to convey the real stress to which evacuees areexposed. One alternative is to conduct evacuation experimentswith animals subjected to realistic stress levels (Saloma et al. 2003;Altshuler et al. 2005). The main limitation of this approach is thequestion to what extent the behaviour of evacuating animals re-flects human behaviour in evacuations. Research on decisionmaking suggests another alternative which we employed: the useof virtual environments. Simulated environments that allow hu-man subjects to interact with them are an established and well-accepted tool in research on human decisions, and in particular,in research on dynamic decision making in response to changingcircumstances (Brehmer 1992; Lipshitz et al. 2001; Gonzalez et al.2005). At a low level of sophistication for simulations, ‘table-top’pen and paper simulations are used to assess decision making ofminers, fire-fighters or military personnel in emergencies and are
subsequently used in training for emergencies (Cole et al. 1998).Highly sophisticated simulators of aircraft cockpits are used toassess decision making of pilots (Lipshitz et al. 2001, citing Orasanu& Fischer 1997). Fully immersive virtual environments have evenbeen suggested as a promising tool to calibrate future models forpedestrian movement (Kretz et al. 2011). The use of simulationexperiments with human subjects has also proven to be a fruitfulapproach to study visual perception (Bruce et al. 2003) andbehavioural ecology (Stevens et al. 2008), for example.
We used an interactive virtual environment to study human exitroute choice in simulated evacuations. Participants in our experi-ment had a top-down view of a room layout, could steer one agentand choose between two different exit routes in the presence ofevacuating simulated agents. This set-up allowed us to presentsubjects with highly controlled, yet realistic crowd evacuations andto vary stress levels in participants without exposing them to risksof injury. In particular, we investigated whether human partici-pants have a tendency to reuse routes they are familiar with andhow this behaviour and their reaction times at the onset of theevacuation are affected by a treatment that increases the compet-itiveness or urgency with which they approach the task, a treat-ment we suggest induces stress.
METHODS
Methods Summary
We recruited participants for our research at the ‘ZombieLab’event in the Science Museum in London on 3 separate days (30January, 2 and 3 February 2013). This public event was designed tocommunicate science tomuseumvisitors in the context of a zombieapocalypse. For this reason and to avoid revealing the direct pur-pose of the experiment, our instructions placed the experiment inthis context suggesting simulated pedestrians and participantswere zombies. We presented human participants with a top-downview of a virtual environment populated by 80 simulated zombiesor pedestrians, the movement of one of which could be controlledby participants via mouse clicks (locations selected with the mousewere waypoints or targets for the human-controlled pedestrian).Figure 1 shows the floor plan of this environment: the layout wassymmetrical and consisted of a central room and two corridorsconnecting this room to an entrance hall stretching over the widthof the environment. Participants could see the layout of the wholeenvironment, but the contents of the rooms they were not occu-pying were concealed from them (see Fig. 1 and Appendix Figs A2,A3 for snapshots of the virtual environment). We did not restrictmousemovement and recorded themouse click locations as well asthe resulting movement of participants in the virtual environment.At the start of the experiment, participants received instructions onhow to steer their pedestrian and on the two tasks they would beasked to complete (see Appendix for full instructions). In the firsttask, participants were asked to move their pedestrian from astarting position in the entrance hall via a designated route markedwith arrows through one of the corridors to a fixed target in thecentral room. During the first task the simulated pedestriansmoved randomly in the central room and the two corridors (seeAppendix for details). This task was designed to familiarize par-ticipants with the virtual environment and to allow them to prac-tise controlling their pedestrian. All participants successfullycompleted the first task and quickly grasped how to steer thepedestrian. We did not use data from this task in our analysis. Onceparticipants had completed the first task, a 5 s countdown startedat the end of which the second task commenced. In the second task,participants were asked to move to a new target. The location ofthis new target was revealed at the start of the second task and
Figure 1. Snapshot of the simulated environment at the start of the second task in thecontrol treatment. The circle labelled ‘FT’ indicates the location of the new and finaltarget, ‘CR’ the central room and ‘L’ the entrance hall in the virtual environment.Participants steer the pedestrian marked by a black filled circle and simulated pe-destrians are white filled circles with a line indicating their movement direction. In thefirst task, participants started at the location of FT and the target location coincidedwith the position of the black filled circle in the figure.
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coincided with the original starting position. Participants were thusfaced with a choice of two routes to the new target through eitherof the corridors: the route they had used to enter the room or theopposite route. During this second task, the simulated pedestriansperformed a simulated evacuation towards the same target. Oncethe participants reached the second target, the second task andtherefore the experiment ended. The symmetrical layout of ourexperiment allowed us to choose randomly one of the two possibleroutes into the central room for each participant in the first task tocreate a balanced experiment without directional bias. Participantswere allowed to ask questions throughout the experiment, but onlyanswers on how to steer their pedestrian were given.
Treatments
Each participant was exposed to one randomly chosen treat-ment out of four separate treatments. The different treatmentspresented subjects with different situations once they hadcompleted the first task. In the control treatment, participants sawthe 5 s countdown and, following this, the simulated pedestrianssplit evenly between the two exit routes from the central roomduring their simulated evacuation (see Fig. 1). This treatment wasdesigned to establish the baseline behaviour of participants in aperfectly symmetrical set-up. In the ‘motivation’ treatment (M),participants were presented with a motivational message invitingthem to beat the current fastest time to reach the new target duringthe countdown. In addition, counters of their own time and thecurrent fastest timewere displayed during participants’ subsequent
movement to the second target. The M treatment was designed toassess the effect of stress, motivation or competitiveness on thedecision making of participants (see Appendix Fig. A2 for an illus-tration). In the ‘asymmetric’ treatment (A), the crowd of evacuatingsimulated pedestrians split unevenly between the two possible exitroutes. An average� SD of 77.5 � 1% of the agents exited via theroute the participants had used to enter the room, while the resttook the opposite route (see Appendix Fig. A3). This treatmenttested the response of participants to the movement of simulatedagents and the exit blockages created by them. The final treatmentwas a combination of treatments A and M (AþM).
All procedures of our experiment were approved by the EthicsCommittee of the University of Essex.
Simulated Individual Behaviour
The pedestrians were simulated by using variations of previ-ously developed and well-accepted models for pedestrian move-ment (Helbing et al. 2000; Burstedde et al. 2001). Pedestriansmoved in continuous space, interacted with others via social forcesand reacted to a discrete floor field encoding the built environment(e.g. walls), as well as movement preferences (e.g. towards a target).Since we were primarily interested in human behaviour in thisresearch we did not vary model parameters between treatmentsand chose parameter values that produced reasonable pedestrianmovement allowing participants sufficient time to react to thedynamics on our computer. Before the start of the first task, thesimulated pedestrians were distributed randomly and thereforeapproximately uniformly over the central room and the two cor-ridors, avoiding pedestrianepedestrian and pedestrianewall over-laps. Pedestrians were removed from the simulation once they hadreached the final target of the simulated evacuation. Only a smallfraction of pedestrians were removed during the first task (<5%) astheir movement was not directed at the target in this task. The fulldetails of the simulation model can be found in the Appendix.
Data Collection and Statistical Analysis
The study was limited to adults aged 18 years or older. A total of188 members of the public participated. We had to exclude threesubjects from the study because they accidentally terminated thecomputer program before the complete data could be written tofiles or because they lost interest and requested to drop out of thestudy. The median age across the remaining 185 participants was25 years; the minimum and maximum ages were 18 and 51 years,respectively. The gender distribution was approximately balancedwith 90 female subjects (48.6%), 95 male subjects (51.4%) and zerosubjects stating to belong to a different gender category or notwanting to disclose their gender. We did not record data on na-tionality or ethnicity.
From the movement of participants in the virtual environmentduring the second task, we computed four summary statistics:whether or not participants used the same route to enter and to exitthe central room (‘route fidelity’), the number of mouse clicksparticipants performed (‘click number’), the number of simulationtime steps between the end of the countdown and the first mouseclick (‘reaction time’) and whether or not participants changedtheir mind when leaving the central room (‘adaptation’, see detailsbelow). We defined changes of mind as cases when participantsmoved at least one-fifth of the width of the central room in thevertical direction towards one exit before exiting through theopposite exit.
We used generalized linear models (GLMs) to test for the in-fluence of treatment, age and gender on summary statistics. For thetwo Boolean summary statistics (route fidelity, adaptation), models
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had binomial error structure with logit link functions. The othertwo summary statistics models had Gaussian error structure withthe identity link function. Reaction time data were log-transformedprior to model fitting to meet normality assumptions. All modelsincluded an intercept, the response variable was the summarystatistic and the explanatory variables were treatment (categori-cal), age and gender (categorical). Using these statistical models, weconducted pairwise treatment comparisons of ‘control versus A’,‘control versus M’, ‘M versus AþM’ and ‘A versus AþM’. We set thesignificance threshold for these statistical models to P < 0.1 toensure we captured marginally significant outcomes. However, wereport the full output of the statistical models in the Appendix.
RESULTS
Route Fidelity
We computed the probability of participants using the sameroute to enter and to exit the central room in our experiment(Fig. 2a). In the control treatment and under the motivationaltreatment M, participants did not show a consistent bias towardseither of the exits (binomial test: P ¼ 1 and P ¼ 0.78, respectively),while in treatments A and AþM in which the crowd evacuatedasymmetrically, there was a consistent bias for using the lesscrowded route opposite to the one participants used to enter thecentral room (binomial test: P < 0.05 in both cases). This suggeststhat there is no inherent bias in decisions for a known option in ourexperiments. Participants reacted to the simulated crowd andtended to avoid it. The symmetric and relatively simple layout of
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Figure 2. Human decision making in simulated evacuations. We show the summary statistifour treatments. In the control treatment, the simulated crowd split evenly between thetreatment A the crowd split asymmetrically between the exits and treatment AþM combineout of the central room. Numbers inside bars indicate the number of participants per treatmproportion of participants who changed their mind about which exit to use. The reaction timbars show SEs and the significance level is set to P < 0.1 throughout. See Methods for deta
our environment, as well as the fact that this layout was known toparticipants, could in part explain the lack of route fidelity in par-ticipants and we further discuss the implications of our experi-mental design below. Our statistical analysis comparing the controland treatment A and comparing treatments M and AþM confirmsthat treatment A had a significant effect on route fidelity (seeAppendix). Treatment M also had an effect on participants’ decisionmaking in the presence of treatment A (comparing A and AþM).This suggests that participants were more likely to follow the routethey knew when they felt under pressure after having seen amotivational message, despite the fact that this route took a lotlonger to complete, a fact readily identified by most participants.While the overall proportion of participants who chose the crow-ded route coinciding with the one they used in the first task waslow, the difference between treatments A and AþM was almost10%. There appeared to be a similar trend between the controltreatment and treatment M but this was not statistically significantin our data. Neither gender nor age had a statistically significanteffect on route fidelity.
Click Number
The number of mouse clicks people performed shows howmanytimes they adjusted their movement. This could be because theygrew impatient, because they tried to avoid the crowd or obstaclesor simply because of individual preferences for steering the agent.In Fig. 3, we illustrate the simulated environment and report thedistribution of mouse clicks within the experimental layout. Theclick distributions show that participants focused their attention
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cs extracted from participants’ movement in the simulated environment for each of thetwo exits, under treatment M participants were shown a motivational message, ind treatments A and M. (a) The proportion of participants using the same route into andent. (b) The average number of clicks, (c) the average time taken to react and (c) thee in (c) is in simulation update steps (i.e. 0.05 s of simulated time, see Appendix). Errorils on statistical analysis.
N. W. F. Bode, E. A. Codling / Animal Behaviour 86 (2013) 347e358 351
with the mouse on bottlenecks connecting different parts of theenvironment and on the final target location. Comparing treat-ments in which the crowd evacuated symmetrically (Fig. 3a, b;control and M treatment) with treatments including asymmetricevacuation (Fig. 3c, d; treatments A and AþM), we can see that theclick distributions reflected our findings on route fidelity. Themotivational message caused an increase in the average number ofmouse clicks, although this effect of treatment M was only statis-tically significant when compared to the control (Fig. 2b). Thissuggests that treatment M had an effect not only on the decisionsparticipants made, but also on their behaviour. Treatment A causeda decrease in the number of clicks which was statistically signifi-cant in our data when treatment M was present as well. Intuitivelythis makes sense, because under treatment A, most people used theless crowded exit (see Fig. 2a), were thus less impeded in theirmovement, moved faster and had less time to click or get impatient.As for route fidelity, neither gender nor age had a statistically sig-nificant effect on the number of clicks.
Reaction Time
The time it took participants to react at the start of the secondtask in our experiment could be indicative of a contemplativeperiod in which participants gather information before making adecision or it could be a measure for how fast subjects can respond
(a)
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Figure 3. Layout of simulated environment and colour-coded density maps of clicked locatshow the layout of the simulated environment with the central room (‘CR’), the two corridorone example for the movement of a subject during the second task of the experiment (dotparticipants to ensure that the route of participants into CR during the first task was alwaytreatments M, A and AþM, respectively. To construct the density maps, we binned click locatcan be identified by the high density of mouse clicks in all treatments. Under treatments A apedestrians.
at the end of the countdown. Intuitively, we might expect partici-pants to respond faster under treatment M as a result of increasedmotivation or urgency to complete the task. Indeed, treatment Mappeared to result in lower and treatment A in higher reactiontimes (Fig. 2c), but none of these effects were statistically signifi-cant in our data. However, across treatments, age and gender hadconsistent and statistically significant effects on reaction time (seeAppendix for full details). On average, female and older participantsappeared to have longer reaction times.
Adaptation
For each treatment, we computed the probability that partici-pants changed their mind about which exit to use as a proxy forparticipants’ willingness or ability to adapt their initial decisionwhen experiencing the simulated evacuation. Across treatmentsthis probability was low and significantly different from random(binomial test: P< 0.05 in all cases), suggesting participants tendedto stick with their initial decision (Fig. 2d). Age and gender did nothave statistically significant effects on this aspect of subjectbehaviour. In treatment A, when the crowd split unevenly, thehighest percentage of people changed their mind. Typically, thesesubjects walked to the exit they knew first, presumably realizedthat it was crowded, and then changed their mind. Significantlyfewer participants showed this behaviour when they had seen the
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ions accumulated over participants in different treatments. (a) Control treatment. Wes (‘C1’ and ‘C2’) and the entrance hall (‘L’), as well as the target in the first task (‘T’) andted line). Using the symmetry of the layout, we relabelled the vertical direction for alls through C2 in the plots. (bed) The click densities in the simulated environment forions into a regular 30�30 cell grid. The location for the starting position and final targetnd AþM, the exit route through C2 was jammed by a large proportion of the simulated
N. W. F. Bode, E. A. Codling / Animal Behaviour 86 (2013) 347e358352
motivational message (treatment AþM). This shows that whenpeople felt under pressure or competitive, they were less likely tochange their mind and therefore did not adapt as frequently to adeveloping situation (the simulated evacuation). Our data appear tosuggest that in the absence of treatment A, treatment M had theopposite effect on participants’ probability of changing their mind(comparing control and M). This would make sense, since in thesetreatments participants invariably got stuck in the evacuatingcrowd for some time. Motivated individuals may have felt the urgeto do something, despite the fact that this would not affect the timethey took to complete the task. However, this effect was not sta-tistically significant in our data.
DISCUSSION
We have investigated human exit route choice in simulatedevacuations. Our results show that in our experiments participantsdid not have an inherent preference for the route with which theywere familiar. However, when subjects were presented with amotivational message, they were more likely to adhere to the routeknown to them, despite the fact that this was detrimental to theirperformance in the task when the crowd split asymmetrically(treatment A). In addition, when this known route was jammed,subjects were less able or willing to adapt their decision under themotivational treatment. While the treatments did not have an ef-fect on reaction times, we found clear effects of age and gender onreaction times in the virtual environment.
The biggest limitation of our research is that it was conductedwithin a virtual environment. It is therefore not obvious whetherour findings can be extrapolated directly to human behaviour inreal environments. Nevertheless, we studied real human behaviourwhich we believe presents an important intermediate step be-tween purely theoretical studies and fully fledged evacuation drills.Our approach has revealed what can only be described as nonra-tional human decision making under the influence of the motiva-tional, potentially stress-inducing, treatment. As such, we suggestour approach could be used for initial tests of hypotheses abouthuman behaviour and then to select the most striking findings forfurther, more life-like experiments.
In our virtual environment, subjects were presented with a top-down view and could see the entire ground plan layout at all times.This is profoundly different to the way humans perceive theirenvironment and therefore raises additional questions about theapplicability of our findings to real-life situations which we discussin turn.
It is a fundamental principle of many self-organizing systems,such as human crowds, that individuals only react to the localenvironment they can perceive (Camazine et al. 2001; Sumpter2010). Despite the fact that we only displayed the positions ofother pedestrians in the same room, the top-down view in ourexperiments was likely to provide individuals with more infor-mation about the movement of others than we would expect themto have in real life. This makes it difficult to study themicroscopic oroperational movement behaviour of pedestrians using our frame-work. We studied tactical movement decisions in our data andassumed these tactical decisions were influenced by more globalfeatures of crowd movement that individuals can approximateequally well in real life as from a top-down view. For example,humans may base their movement decision on an evaluation of therelative length of queues at different exits, an estimate that can bemade without seeing the entire crowd. To what extent ourassumption holds has to our knowledge not been explored to date.
The availability of a top-down view and the symmetry andrelative simplicity of the layout may in part explain why subjectsdid not have an inherent preference for the exit route with which
they were familiar, despite the fact that observations suggestotherwise (Proulx 2001; Johnson 2005). Not making the groundplan visible or having a more complex environment could addressthis question in futurework.We suggest that the lack of an inherentpreference for a known route in our experiment could serve as ahypothesis for future research: if evacuees are familiarized with theenvironment or have access to a top-down map of their environ-ment at all times, the problem of potentially detrimental route fi-delity in evacuations could be mitigated. We do not wish to suggestthis is an entirely new concept, as it is common practice to informpublic transport passengers about the location of emergency exitsandmaps of floor plans are routinely provided in buildings (e.g. TheStationery Office 2008, Guide to Safety at Sports Grounds). How-ever, we do suggest the effect of such measures is quantifiable andtheir relative effectiveness under different stress levels could betested.
When there was a clear difference between the two exit routes(treatment A), our motivational treatment had a detrimental effecton the decisions of a significant proportion of subjects. Wedesigned our motivational treatment to increase the competitive-ness or urgency with which participants approached the task athand. This could be related to altering the stress levels of subjectsduring the experiment. The finding that stress levels can have adetrimental effect on human decision making is not new (Keinan1987; Svenson & Maule 1993; Starcke et al. 2008), but based onour experiment we can explicitly suggest effects of stress in evac-uations. We suggest that in evacuations with higher stress levelsevacuees will be more likely to use known exit routes and less ableor willing to adapt their route choices, even if this results in longerevacuation times. This constitutes a testable hypothesis and sug-gests strategies for mitigating risks for occupants of locations thatare prone to high urgency and therefore stressful evacuations. Forexample, occupants could be instructed to enter locations viadifferent routes, to avoid overcrowding of main entrance routes.
An alternative explanation for the effect of our M treatment onparticipants’ decisions could be the ‘herding effect’: a strongertendency to follow others under stress (Helbing et al. 2000;Schadschneider et al. 2009; Helbing & Johansson 2010). In ourexperiment, under treatment A, the most crowded exit route al-ways coincided with the route participants were familiar with andwe can therefore not rule out the ‘herding effect’ under this treat-ment. To determine the contribution of the herding effect on sub-jects’ behaviour under treatment M, we conducted anotherexperiment with a new treatment, A0 , in which the larger propor-tion of the crowd took the route opposite to the route participantswere familiar with. Details on data collection for this experimentcan be found in the Appendix. If treatment M induced herdingbehaviour, we would expect the number of participants using thesame route as the crowd to increase when comparing treatment A0
to treatment A0þM (or, equivalently, the number of participantsusing the route known to them should decrease). Figure A4 showsthat this is not the case. This figure also shows that the effect oftreatment M to reduce the likelihood of subjects to change theirmind is preserved in this experiment. While we can thus rule outthe herding effect in our experiment, we should point out that indifferent scenarios tendencies to follow others could be moreprominent. For example, consider the case of an environment inwhich the exit routes are less clear than in our experiment or evenentirely unknown. In such scenarios evacueesmay bemore likely toobtain information on exit routes by watching or following others.Furthermore, it is possible that participants in our experimentsrealized, despite not being told explicitly, that the other pedestrianswere simulated and therefore did not attribute asmuch attention tothe movement of others as they would if they knew they werehumans. Future work could extend our approach to populate
N. W. F. Bode, E. A. Codling / Animal Behaviour 86 (2013) 347e358 353
virtual environments with multiple human-controlled pedestriansto overcome these limitations.
We found clear effects of age and gender on the response time ofsubjects. This could be explained by general differences betweenpeople of different ages and genders in the use of and habituationto computer-based virtual environments (e.g. Lucas & Sherry 2004reported young male adults play video games more frequently).However, the demographic of our study population indicates thatmost participants were young and can therefore reasonably beexpected to be adequately computer literate for the simple tasks inour virtual environment. This poses the interesting question ofwhether there are age- or gender-related differences to respond tocrowd movements. If there were general differences in theresponse of people of different genders or ages in the time toobserve or contemplate a developing situation, this could haveprofound implications for our understanding of and our ability tocontrol crowd evacuations.
Simulations of crowd evacuations from confined spaces can beused both as a tool to inform the design of secure buildings orvehicles (Schadschneider et al. 2009) and as an educational tool tomake people aware of the potential risks in evacuations fromcrowded places (Johnson 2005).We suggest interactive scenarios inwhich participants can more directly ‘experience’ an evacuationwould provide an excellent educational tool that is, to our knowl-edge, currently underexploited.
Acknowledgments
We thank the editor and two anonymous referees for theirconstructive comments that have greatly improved the manu-script. N.W.F.B. gratefully acknowledges support from the AXAResearch Fund. We thank Mark Breckels, Nicola Lewis, DeepakPazhayamadom, Julius Piercy, Heather Rennie and the staff of theScience Museum in London for their help in collecting the data.A. Jamie Wood provided invaluable assistance on programmingissues.
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Appendix
Full Model Description
The description of our model largely follows the nomenclatureestablished by Helbing et al. (2000). In our model, 80 pedestrians i,of massmi ¼ 80 kg, had position xi(t), velocity vi(t), preferred speedv0 ¼ 0.6 m/s and radius (physical extent) ri ¼ 0.5 m. Pedestriani ¼ 0 was assigned to be steered by participants. Each agent had apreferredmovement direction ei(t) of unit length that was adjustedwith a characteristic time of s ¼ 0.5 s, while the simulation wasupdated at a rate of dt ¼ 0.05 s. For i ¼ 0, ei(t) was given by thenormalized direction vector pointing from x0(t) to the locationwhere the participant last clicked with the mouse. During the firsttask, ei(t) for simulated pedestrians (is 0) was given by thenormalized direction of vi(t � dt), rotated by an angle randomlydrawn from the interval [�0.2p, 0.2p]. During the second task, ei(t)for simulated pedestrians was obtained from computing the local
N. W. F. Bode, E. A. Codling / Animal Behaviour 86 (2013) 347e358354
gradient in the discrete floor field, as described below. In additionto attempting to move along their preferred movement direction,pedestrians were subject to velocity-dependent interaction forceswith other pedestrians j, fij, and walls, fiw. The movement of pe-destrians was then simulated according to the following equation(Helbing et al. 2000):
midvi=dt ¼ ðmiv0eiðtÞ � viðtÞÞ=sþX
jsi
f ij þX
wf iw (A1)
Interactions between pedestrians were based on distance-dependent repulsive forces motivated by the need of individualsto maintain their personal space. If pedestrians get closer thanrij ¼ ri þ rj to each other, these forces are augmented by additionalfriction forces and forces counteracting body compression (Helbinget al. 2000). Let dij ¼ jxi�xjj, be the distance between two in-dividuals and let nij ¼ ðn1ij;n2ijÞ ¼ ðxi � xjÞ=dij and tij ¼ ð�n2ij;n
1ijÞ
be the vectors pointing from j to i and the tangential direction,respectively. Then the interaction forces between pedestrians aregiven by (Helbing et al. 2000):
f ij ¼�Aexp
��rij � dij
��B�þ kg
�rij � dij
��nij þ kg
�rij � dij
�
� ��vj � vi
�,tij
�tij; ðA2Þ
where the function g(x) equals zero if dij > rij and its argumentx otherwise. A ¼ 2000 N, B ¼ 0.08 m, k ¼ 12 000 kg/s2 andk ¼ 24 000 kg/s2 are constants.
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0 0 0 0 0
0 0 0 0
0
0 0
0
0
0
0
0
0
0
0
0
0
0 0 0
0 0
0 0
6+ 7+3 2√
5+3 2√
4+3 2√
2 2√
6+2 2√
5+2 2√
5+2 2√
4+2 2√
8
7
6
5
4
3+ 2 4
3
2
2 11
√
2+ 2√
1+ 2√
3
2
1
(a) (b)
Figure A1. Illustration of floor field construction. (a) An example for a basic layout. Obstacleshown in red and is assigned value 1. In panel (b) we show the intermediate step to construcby 1 (nondiagonal) or O2 (diagonal neighbour cells). Conflicting cell values arising from thisshown in (c). This is obtained by uniquely mapping values in (b) to values between 0.1 and
Environment Construction and Interactions
While pedestrian movement was simulated in continuousspace, we implemented floor fields (Burstedde et al. 2001)encoding the environment and movement preferences discretelyat a resolution of 0.1 m ¼ 1 unit. The total environment size of15 � 15 m thus corresponded to a floor field of 150 � 150 units.Each cell in the floor field took a value between 0 and 1. Walls andother obstacles had value 0. Other cells took values between 0.1and 1 implementing a gradient of increasing values towards atarget, and adapting previously proposed methods for construct-ing floor fields (Burstedde et al. 2001; Varas et al. 2007). For anillustration on how the gradient was constructed, see AppendixFig. A1. We started by choosing a final target destination cell,assigned this the value 1 and incremented the value of neigh-bouring cells by 1 or O2, for nondiagonal and diagonal cells,respectively. Conflicts on values for cells resulting from this rulewere resolved by assigning the minimum out of the set of possiblevalues. In this way we assigned values to all cells of the floor field,apart from obstacle cells. Subsequently we created a sequencefrom 0.1 to 1 of the same length as the number of unique cellvalues and with constant increment between consecutivesequence entries. We then mapped the cell values in decreasingorder to sequence entries in increasing order, thus creating newcell values with the former maximum cell values being set to 0.1and the old minimal values, the target cells, set to 1 (obstacle cellswere not included in this). We used this floor field to computeinteractions with obstacles and to obtain preferred directions ofpedestrians in the second task of our experiment.
0
0
0
0
0
0
0
0
0
0
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0
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0
0
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0
0 0 0 0 0
0 0 0 0
0
0 0
0
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0
0
0
0
0
0
0
0
0 0
0 0
0
7+ 0.1 0.14 0.18 0.14
0.22 0.27 0.31 0.27
0.35 0.40 0.44 0.40
0.40 0.48 0.52 0.48
0.70 0.74 0.70 0.65
0.82 0.87 0.82 0.78
0.91 0.95 0.91 0.82
0.95 1 0.95 0.87
0.61 0.57
2 2√
6+2 2√
5+2 2√
4+2 2√
3+2 2√
2+2 2√
1+2 2√
+ 2√
+ 2√
+ 2√
+ 2√
+ 2√
2 3
+ 2√
+ 2√
2+ 2√+ 2√
(c)
cells (e.g. walls) are in black and are assigned value 0. The final target destination cell ist a gradient in the floor field. We start at the red cell and increment neighbouring cellsrule are resolved by choosing the minimum possible value. The final gradient used is1 to obtain a gradient of increasing values towards the target cell.
Figure A2. Snapshot of the simulated environment during the countdown betweenthe first and second task in the motivation treatment (M). In contrast to Fig. 1 andAppendix Fig. A3, the location of the new target has not yet been revealed.
Figure A3. Snapshot of the simulated environment at the start of the second task inthe asymmetric treatment (A). The crowd splits unevenly between the two possibleexit routes.
N. W. F. Bode, E. A. Codling / Animal Behaviour 86 (2013) 347e358 355
For the control treatment, when the simulated crowd splitevenly between exit routes, we assigned the target position forthe construction of the floor field to be identical with thestarting position of the pedestrian steered by the participant, i.e.centrally with respect to the vertical direction. To obtainasymmetric splitting of pedestrian crowds for the A treatment,we shifted the target position for the construction of the floorfield upwards or downwards in the vertical direction by50 units.
Let Q be the floor field described above. We computed thepreferred movement direction of individuals, ei(t), by findingthe direction of the highest gradient in the floor field Q. Wedenoted the vector from the midpoint of the grid cell (k,l) to themidpoint of the grid cell (m,o) in Q on continuous space byqk,m,l,o. Furthermore, we assumed pedestrian i was positionedon grid cell (k,l) at time t. Then, ei(t) was set to the vectorobtained by rotating the unit vector pointing in the directiongiven by the vectorial sum Sm¼k�4.kþ4$(So¼l�4.lþ4$[Qm,oeQl,k]qk,m,l,o) by an angle randomly drawn from the interval [�0.1p,0.1p]. We used a range of four cells or 0.4 m to compute thefloor field gradient. The vectorial sum equals (0,0) if all thevectors qk,m,l,o cancel or the floor field Q is homogeneous. In thiscase ei(t) is given by the normalized direction of vi(t�dt),rotated by an angle randomly drawn from the interval [�0.2p,0.2p].
In contrast to Helbing et al. (2000) we did not implementdistance-dependent repulsive forces away from obstacles orwalls. Instead, we only implemented direct interaction forcesof pedestrians with obstacles, akin to the short-range bodycompression and friction forces in the pedestrian interactions.These forces only act if a pedestrian’s circular body overlapswith an obstacle. To approximate the amount of overlap be-tween pedestrians and obstacles and the vector pointingorthogonally away from the obstacle, we determined which ofnine points on the body of pedestrians are located within anobstacle cell in the floor field. The nine points are the pedes-trian’s centre and eight equally spaced points on its circum-ference and to determine the strength of pedestrians’interactions with obstacles, we counted Niw of these pointsthat are located within obstacle cells. We approximated thedirection away from the obstacle niw ¼ ðn1iw;n2iwÞ by thenormalized sum of the vectors pointing from points withinobstacle cells to the centre of a pedestrian. Because of thesmall update steps, dt, in our simulations, pedestrians werenever positioned fully inside obstacles. The interaction of pe-destrians (including the pedestrian steered by participants)with walls is then given by:
f iw ¼ kð½2Niwri�=9Þniw þ kð½2Niwri�=9Þ½vi,tiw� tiw; (A3)
where k and k are defined as before and tiw ¼ ð�n2iw;n1iwÞ, analo-
gous to tij above.
Full Instructions given to Participants
In this section we list the text of the instructions given to par-ticipants, in addition to verbal information given to everyparticipant.
At the start of the experiments, participants were presentedwith a screen containing the following instructions: ‘Hello, Inthis game you are a zombie in a building with a lot of other
P(sa
me
exit
)
0
0.2
0.4
0.6
0.8
1
A’ A’+M
(a)
39 46
7
7.5
8
8.5
9
9.5
10
No.
of
clic
ks
A’ A’+M
(b)
200
220
240
260
280
300
320
Treatment
Tim
e to
1st
cli
ck
A’ A’+M
(c)
P(ch
ange
min
d)
0
0.05
0.1
0.15
A’ A’+M
(d)
Figure A4. Additional experiment to test for the ‘herding effect’ in human decisionmaking in simulated evacuations. All methods were the same as described in the maintext, but in treatment A0 , the crowd split asymmetrically between the exits with the
N. W. F. Bode, E. A. Codling / Animal Behaviour 86 (2013) 347e358356
zombies. You can move your zombie by clicking with the mousewhere you want it to go. To start with, follow the red arrows onthe floor into the building to your first targetea green circle.Once you have reached the target, there will be a short pause anda countdown underneath the map of the building. When this isover, a new target will appear. This is where the zombies willfind food. Move to this target as quickly as possible. When youare ready, press the START button above’. After starting thegame, all participants could see the following message in a panelunderneath the simulated environment: ‘Follow the red arrowsto the green target’.
In addition to these written instructions, participants weregiven the following verbal instructions at the start of the experi-ment: ‘You are the black dot here, and when you click somewhere,this is where you are going to move.’ and ‘Don’t worry about theother zombies, you can bump into them’.
During the second task for the control treatment andtreatment A, the message ‘Move to the new target’ was dis-played in a panel underneath the simulated environment. Fortreatments M and AþM, the message ‘Can you reach the targetfaster than any zombie before you? Currently, the fastest timeis XXXX.’ was displayed during the countdown and subse-quently during the second task the message ‘Your current time:YYYY (be faster than XXXX)’ was displayed in a panel under-neath the simulated environment. The number ‘YYYY’ countedupwards and the number ‘XXXX’ was the shortest time anyprevious participant had taken to complete the second taskwhich was set to 10 000 simulation steps at the start of theexperiment. All participants under this treatment had madetheir decision and left the central room before their timecounter exceeded the fastest time. Furthermore, all of theseparticipants finished the second task even if their time alreadyexceeded the fastest time.
majority of the crowd taking the route opposite to the route participants had followed toenter the room (i.e. in A0 the crowd moved in the opposite direction to treatment A).Treatment A0þM combined treatments A0 and M. (a) The proportion of participantsusing the same route into and out of the central room. Numbers inside bars indicate thenumber of participants per treatment. (b) The average number of clicks, (c) the averagetime taken to react and (d) the proportion of participants who changed their mindabout which exit to use. Error bars show SEs and the significance level is set to P < 0.1throughout. The full output of the statistical analysis is reported in the Appendix.
Additional experiment to test for the ‘herding effect’We conducted an additional experiment to assess the
importance of the ‘herding effect’ (increased tendency to followothers under stress) in human decision making in simulatedevacuations. All methods were the same as described in the maintext and above, but we introduced a new treatment, A0 . In thistreatment the crowd split asymmetrically between the exits withthe majority of the crowd taking the route opposite to the routeparticipants had followed to enter the room (i.e. in A0 the crowdmoved in the opposite direction to treatment A). The proportionsaccording to which the crowd split across the exits was identicalfor treatments A and A0 . Treatment A0þM combined treatmentsA0 and M. The additional experiment was performed during theNational Science Week on the 18 and 19 March 2013 on theColchester campus of the University of Essex, U.K. The medianage across the 85 participants was 20.5 years, the minimum andmaximum ages were 18 and 59 years, respectively (gender dis-tribution: 44 (51.7%) female, 40 (47.1%) male, 1 (1.2%) other/notdisclosed). Results for this experiment can be found in Fig. A4and the full output of the statistical analysis is also reported inthe Appendix tables. The ‘herding effect’ predicts a strongertendency to follow others under stress. We would thus expect adecrease in P(same exit) from A0 to A0þM as a result ofpeople following the crowd. This is not the case (Fig. A4a).While not statistically significant (P ¼ 0.11, see AppendixTable A20), the decrease in subjects’ ability to adapt from A0 toA0þM in Fig. A4d is consistent with our findings in Fig. 2 in themain text.
Full Statistical Analysis
In the Appendix tables we report the full output of the statisticalmodels fitted to the data. All statistical models include an intercept.
Binomial GLM (logit link function); response variable: Boolean indicating whetherparticipants use the same route to enter and exit the central room. Significant valueshown in bold.
Binomial GLM (logit link function); response variable: Boolean indicating whetherparticipants use the same route to enter and exit the central room. Significant valueshown in bold.
LM (identity link function); response variable: logarithm of the number of simula-tion updates before participants first clicked the mouse. Significant values shown inbold.
LM (identity link function); response variable: logarithm of the number of simula-tion updates before participants first clicked the mouse. Significant values shown inbold.
LM (identity link function); response variable: logarithm of the number of simula-tion updates before participants first clicked the mouse. Significant values shown inbold.
LM (identity link function); response variable: logarithm of the number of simula-tion updates before participants first clicked the mouse. Significant values shown inbold.
Binomial GLM (logit link function); response variable: Boolean indicating whetherparticipants changed their mind on their choice of routes when exiting the centralroom.
Binomial GLM (logit link function); response variable: Boolean indicating whetherparticipants changed their mind on their choice of routes when exiting the centralroom.
N. W. F. Bode, E. A. Codling / Animal Behaviour 86 (2013) 347e358 357
Binomial GLM (logit link function); response variable: Boolean indicating whetherparticipants changed their mind on their choice of routes when exiting the centralroom.
Binomial GLM (logit link function); response variable: Boolean indicating whetherparticipants changed their mind on their choice of routes when exiting the centralroom. Significant value shown in bold.
LM (identity link function); response variable: logarithm of the number of simula-tion updates before participants first clicked the mouse. Significant value shown inbold.
Binomial GLM (logit link function); response variable: Boolean indicating whetherparticipants changed their mind on their choice of routes when exiting the centralroom.
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