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Pedestrian behavior and exit selection in evacuation of a corridor – An experimental study
Safety Science 50 (2012) 221–227
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Pedestrian behavior and exit selection in evacuation of a corridor – An experimental study Simo Heliövaara a,⇑, Juha-Matti Kuusinen a, Tuomo Rinne b, Timo Korhonen b, Harri Ehtamo a a b
Systems Analysis Laboratory, Aalto University, P.O. Box 11100, FI-00076 Aalto, Finland VTT Technical Research Centre of Finland, P.O. Box 1000, FI-02044 VTT, Finland
a r t i c l e
i n f o
Article history: Received 25 November 2010 Received in revised form 5 August 2011 Accepted 12 August 2011 Available online 9 September 2011 Keywords: Egress experiment Exit selection Pedestrian behavior Cooperation
a b s t r a c t In this paper, we present an experiment whose purpose was to study evacuees’ exit selection under different behavioral objectives. The experiment was conducted in a corridor with two exits located asymmetrically. This geometry was used to make most participants face a nontrivial decision on which exit to use. We analyze the behavior on a macroscopic level using statistical methods. Our results suggest that the members of an evacuating crowd may not be able to make optimal decisions when assessing the fastest exit to evacuate. In addition, the egress time of the whole crowd turns out to be shorter when the evacuees behave egoistically instead of behaving cooperatively. This is an interesting result because many studies on real emergencies show that evacuees tend to cooperate and act altruistically. Ó 2011 Elsevier Ltd. All rights reserved.
1. Introduction When evacuating a venue with multiple exits, each evacuee will face the decision on which exit to use. According to literature, there are many factors that influence occupants’ exit selection. For instance, people tend to prefer familiar exit routes and herding behavior, where people choose to follow the others, is a common observation in evacuations. Because people in a threatening situation have to hurry, it is obvious that one criterion when comparing different exit routes is the time it takes to exit the building. To be able to optimally select the fastest exit route, occupants should be capable to calculate estimates for the evacuation times through the different exits. It would require estimating the moving time to the exits and the queuing times in front of the exits, which depend on the actions of the other occupants. It is unlikely that people do, or even are capable of doing such calculations during egress. Instead, they are likely to use some simplified schemata to come up with an exit choice. This paper presents a study on the exit selection behavior in an evacuation scenario with two exits, where one exit is closer than the other and congestion arises at the exits. The participants were asked to evacuate through the exits as fast as possible. The specific geometry was chosen because it forces most participants to make a nontrivial decision on which exit to use, while in many other geometries the fastest exit route is obvious for a vast majority of participants. Our key research questions were: Are people able to ⇑ Corresponding author. Tel.: +358 9 470 23059. E-mail address: simo.heliovaara@tkk.fi (S. Heliövaara). 0925-7535/$ - see front matter Ó 2011 Elsevier Ltd. All rights reserved. doi:10.1016/j.ssci.2011.08.020
select the fastest exit route? How does the starting position in the crowd relate to the selected exit? How do the instructions to behave selfishly or to cooperate affect the outcome of evacuations? It is natural that many of the results may depend on the specific evacuation scenario and geometry. Hence, the results of this study cannot be directly generalized to all possible situations, which is also the case with any other single evacuation scenario. However, the results of the experiment increase understanding on human egress behavior. The empirical data on, e.g., evacuation times, exit usage, and the effect of starting position on exit selection, can also be applied to validate and test computational egress simulation models. The rest of the paper is organized as follows. In Section 1.1, we review the existing literature on the topic. Section 2 describes the experimental setting and compares our approach to the previous studies. Sections 3–6 present and analyze the results and Conclusions are given in Section 7. 1.1. Existing literature Evacuees’ exit selection behavior has been discussed in a few previous articles. These papers include analysis of real life evacuation situations (Galea et al., 2006) and experimental studies where the same evacuation scenario has been repeated several times (Muir and Cobbett, 1995; McLean et al., 1996; Drury et al., 2009; Was, 2010; Galea et al., 2011). The results of these studies do not contain information on evacuees’ ability to select the fastest exit in situations with congestion. In actual accidents, there are usually so many other factors influencing the decision that it is difficult to
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isolate this ability. In the few experimental settings, the test geometries have been such that the fastest route is a trivial choice for a large majority of the participants and no real decision making is required. Agent based evacuation simulation models have different approaches to model the exit selection. Kuligowski et al. (2010) give a comprehensive review on the topic. Some models assume that all agents use the nearest exit, while in others, agents observe the situation and make optimal decisions (Gwynne et al., 1999; Ehtamo et al., 2010). In real crowds, the behavior is not so straightforward. People’s decisions depend, e.g., on the information available and the time available for processing it. If these are limited, the decisions may not be optimal. According to literature, there are many factors that may affect the exit choice in real life evacuation situations. The estimated evacuation time through different exits is only one factor. In many cases, people tend to prefer the familiar exit routes and are unable to identify all available exits (Sime, 1985; Proulx, 1993; Pan, 2006). Herding behavior, where people choose to follow their predecessors, has also been a common observation in real crowds (Helbing et al., 2002; Pan, 2006; Low, 2000). The faster-is-slower effect describes the phenomenon, where evacuees’ attempt to move faster can cause slower flow through a bottleneck. This effect occurs because harder pushing towards the exit increases the pressure and the interpersonal friction forces and causes clogging in front of the exit (Helbing et al., 2000). Some degree of the faster-is-slower phenomenon is likely to be present also in our experiment. We will further discuss its effect when analyzing the results. Another well-known phenomenon in pedestrian flows is self-organization (Helbing et al., 2001; Hoogendoorn and Daamen, 2005). It is usually observed in multi-directional flows, where lanes and other patterns tend to form. Some form of selforganization may also occur when crowd members divide to different exits. In the literature on human behavior in evacuations, there has been a consensus for decades that panic is unlikely to occur in crowds (Sime, 1980; Johnson, 1987; Quarantelli, 2002). Many studies on actual accidents also show that evacuees do not only try to get themselves out but are rather concerned for the fate of the others and cooperate (Aguirre, 2005; Quarantelli, 2002; Cocking et al., 2009). The effect of cooperative and selfish behavior on total egress times has been previously studied in a few experiments. In the experiment of McLean et al. (1996), selfish behavior resulted in faster egress, while in the study of Muir and Cobbett (1995) the cooperative trials were faster. The results of these two experiments are not directly comparable with each other due to differences in the incentive systems as well as in the exit types of the test geometry. In their experimental study in an aircraft, Muir and Cobbett (1995) studied the effect of egoistic and cooperative behavior on egress times. According to their results, cooperative behavior results in faster egress. Muir and Cobbett (1995) produced selfish behavior, which is called competitive behavior in their work, by offering monetary rewards to the first 75% of the participants to evacuate the cabin. Cooperation was produced by giving an equal reward to all participants if the evacuation took less time than a given time limit. Hence, in the cooperative case the participants’ goal was to evacuate fast, while in the competitive case the only goal was to evacuate among the first 75% and the evacuation time did not matter. Thus, in the competitive case, the participants that were clearly in the first 75% had no incentive to hurry and, for the rest of the crowd, the rewards encouraged competition with each other instead of fast evacuation. In real evacuations, even in competitive situations, the factor that determines each occupant’s survival is whether she is able to evacuate before the conditions get lethal and the order in which they pass the exit is irrelevant. Hence,
the incentives used by Muir and Cobbett for competitive evacuation may have encouraged unrealistic behavior and the conclusions on the effect of selfish and cooperative behavior in real evacuations are questionable. Also McLean et al. (1996) studied the effect of motivation level on egress times by giving monetary rewards to the participants. In the competitive scenario, 5 egress trials were run and the seating order was rotated between the trials. The participants who averaged among the first 25%, as averaged across all 5 trials, were given a financial bonus. In the cooperative scenarios, no monetary incentives were given. In McLean’s experiment, the competitive egress turned out to be significantly faster than the cooperative. This is opposite to results of Muir and Cobbett (1995), but the difference could be largely explained by the differences in the incentive systems.
2. Experimental setup To find out how cooperative or egoistic behavior affects the egress outcome, we altered the instructions given to the participants between the trials. The objective of the participants was either to minimize their individual egress times or the egress time of the whole group. Our goal was to study, whether people are able to cooperate effectively in this sort of situations, or if the best outcome is obtained when all focus on themselves. The experiment was conducted in a corridor with a lobby at one end. The corridor had two exits to a classroom. The exits were narrowed down from 0.9 m to 0.7 m to cause heavier congestion. In addition, the exit leaves were removed. The geometry of the experiment is shown in Fig. 1. Numbers from 1 to 54 were marked to the lobby floor to indicate starting positions. They were placed so that the participants did not feel too tight when, in the beginning of each trial, they were asked to stand on the numbers facing the corridor. To avoid any bias due to individual characteristics, the starting positions were randomly assigned to the participants in every trial. As can be seen from Fig. 1, Exit 1 is closer to each starting position than Exit 2. Such an asymmetric geometry is interesting when studying exit selection because it makes the assessment of the fastest exit route more difficult than a symmetric setting. The participants have to consider the distances to the exits and the queues in front of the exits to come to the optimal decisions. In addition, in this setup, a vast majority of the participants will face a nontrivial decision on the fastest exit route. The choice is either to join the forming queue at Exit 1 or to pass the queue and head to Exit 2. Symmetric geometries would not be as interesting since the fastest exit would usually be trivial for most of the participants. Such an asymmetric and nontrivial setup is also interesting when validating and testing egress simulation models. Most models perform well in simple symmetric geometries but realistic simulations in our setting require a more sophisticated exit selection algorithm. When standing on their starting positions, the participants were instructed to evacuate the corridor by moving to the classroom with one of the following objectives: Objective 1: ‘when you hear a whistle, evacuate the corridor normally without hurry’; Objective 2: ‘when you hear a whistle, evacuate the corridor minimizing your own egress time’; Objective 3: ‘when you hear a whistle, evacuate the corridor minimizing the egress time of the whole group’. The second and third objective were intended to produce selfish and cooperative behavior, respectively. In addition, to ensure safety,
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Fig. 1. The geometry of the experiment.
the participants were instructed not to run and to move so that no one gets injured. Each trial lasted until all participants had evacuated the corridor. The trials were recorded with five digital video cameras of which two were inside the classroom and three in the corridor. From the recordings, the time from the whistle to the moment of entering the classroom was recorded for each participant. Snapshots of one of the trials are shown in Fig. 2. We used this setting in two different experiments which took place at the Aalto University School of Science and Technology at Espoo, Finland, on 2 and 24 March 2010. A mixed-sex group of 48 undergraduate students, 54% male and 46% female, participated in the first experiment, Experiment 1. The age of the participants ranged between 19 and 25 years. In total, six trials were carried out. Each of the objectives was repeated two times before moving on to the next objective. The order of the objectives was 1, 2, and 3. This order was used because randomization of the order might have confused the participants about the objective in question. The starting positions of the participants were randomized for each trial, which made each participant face a new situation every time. This randomization also prevented learning between the trials. A mixed-sex group of 54 undergraduate students, 74% male and 26% female, took part in the second experiment, Experiment 2. The age of the participants ranged between 19 and 25 years. In total, 10 trials were carried out in the same way as in the first experiment, but this time the first objective was repeated two times, and the second and the third four times. Also, the order in which the participants entered the classroom was recorded separately
for each exit. This made it possible to connect the starting positions with the exits. The starting positions were randomized for each trial. Participation to both experiments was voluntary. At the end of both experiments, the participants were asked to complete a short questionnaire. The questions regarded, e.g., the clarity of the given instructions, the grounds on which the participants selected their target exits under different objectives, and the level of effort they gave to achieve the objectives. The participants of the experiments were healthy undergraduate students, and thus, formed a homogeneous group. This population was chosen to avoid variability caused by individual differences. Our goal was to study exit selection behavior on a macroscopic level using statistical methods. In such analysis, to enable isolation of the effects of interest, it is important that the population is homogeneous. In a qualitative micro-level study, it might be more interesting to use more heterogeneous participants. To be able to really compare the outcomes of egoistic and cooperative behaviors, it is essential that the participants’ level of effort is the same in both scenarios. While definitely useful in encouraging to stronger devotion, the use of monetary rewards in egress experiments, if not carefully designed, may also create biased results. As described above, in the experiments of McLean et al. (1996) and Muir and Cobbett (1995), this equality of devotion can be questioned due to the nature of the monetary incentives. To ensure equal level of effort throughout the trials, we decided not to give any rewards to the participants. Even if the performance of the participants could be enhanced with rewards, the difference between the treatments can be studied without the
Fig. 2. Snapshots of a trial 5 and 10 s after the whistle (Experiment 2, trial 8). Exit 2 is the one closer to the camera.
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rewards as long as the devotion is on the same level in all of the treatments. Another significant difference between our study and these two previous experiments is the description of selfish behavior, or competitive behavior as it is called by McLean et al. (1996) and Muir and Cobbett (1995). In our experiment, selfish occupants tried to minimize their individual evacuation times, while in the competitive trials of McLean et al. (1996) and Muir and Cobbett (1995) monetary rewards were given to the participants according to the order in which they evacuated. It is reasonable to assume that in real evacuations, selfish occupants try to get themselves out as fast as possible and they do not care about who passes the exit first. Hence, we consider our experiment to better describe actual evacuation situations. In Sections 3–5, the data from the experiments is used to assess the evacuation outcomes from different aspects. The clearance times of the two exits are compared to find out whether the exits were used efficiently. Egress times under the egoistic and cooperative objectives are compared to see if the objective affected the outcome of the evacuation. We also study how the starting position affected the participants’ exit selection and egress times. 3. Exit-specific queue clearance times The queue clearance time for an exit is defined as the time from the whistle to the moment when the last participant who used that exit entered the classroom. Fig. 3 shows the exit-specific queue clearance times for the 16 trials. Recall that the trials of Experiment 1 had 48 participants, while Experiment 2 had 54. Based on Fig. 3, it seems that the queue clearance time is on average shorter for Exit 1 than for Exit 2. To test whether this difference is statistically significant, we applied the sign test. This test was used because it does not involve any distributional assumptions. Let K be the number of trials for which the queue clearance time was shorter for Exit 1 than for Exit 2. Under the null hypothesis that the queue clearance times are equal, K follows the Binomial distribution with n = 16 and p = 0.5. This means that if the null hypothesis is true, the queue clearance time was shorter for Exit 1 than for Exit 2 on average in half, i.e., eight, of the 16 trials. According to the data, K = 13 and the corresponding p-value, P(K P 13) = 0.0106. Hence, the null hypothesis that the queue clearance times are equal was rejected, and it was concluded that
the queue clearance time is on average shorter for Exit 1 than for Exit 2. Recall that Exit 2 was the one further from the starting positions. This result implies that many participants ended up selecting the further exit even if they would have evacuated faster through the nearer one. As the objective was to evacuate as fast as possible, the result states that the participants systematically made suboptimal decisions to head to the exit through which the egress was slower. We give two possible explanations for this outcome. Firstly, the participants may have falsely estimated that the evacuation time through Exit 2 was shorter than in through of Exit 1. This is possible because Exit 2 was further, and when estimating the queue lengths, one should consider both the people in the queues as well as the ones still moving to join the queues. This explanation is also supported by the results of the questionnaire, where the most common criterion for exit selection was the shorter queue. Hence, the participants tried to select the shorter queue but the experimental results show that they had a bias of favoring the further exit. Secondly, the setting of the experiment was such that the participants who started from the back of the group arrived to the queue in front of Exit 1 and faced the decision whether to join that queue or to keep moving to Exit 2. The fact that these participants decided to move to Exit 2, even if the queue at Exit 1 was shorter, might be explained by the idea that people in a hurry tend to prefer moving instead of standing still and queuing. This possible explanation is based on the authors’ intuition and it does not have support in the existing literature. 4. The effect of egoistic and cooperative behavior Total egress time is defined as the time from the whistle to the moment when the last participant entered the classroom using either of the two exits, i.e., the maximum of the exit-specific queue clearance times. Fig. 4 shows the total egress time as a function of objective and experiment. The total egress times for Objective 1 are not shown because we were mainly interested in the differences between Objective 2 and Objective 3. It seems that Objective 2, i.e., selfish behavior, produced faster egress. We applied analysis of variance with blocking to test whether the objective has an effect on the total egress time. Blocking is a method where the variability caused by one or more nuisance factors is removed from the data so that the experimental error will
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consist only of the random error (Montgomery, 1997). We blocked the variance caused by the fact that the experiments were run on two different occasions with a different number of participants. Blocking enabled us to draw reliable conclusions concerning the effect of the objective. The objective had a significant effect on the total egress time, F1,9 = 11.7539 and p = 0.0075. Hence, on average, egress was significantly faster under Objective 2. This result is somewhat surprising. The video recordings revealed that when the participants behaved cooperatively, they were careful not to push or even touch other participants around them. This exaggerated caution may partly explain why cooperative behavior led to slower egress times. When promoting their own interest, the participants were able to pass the doors more efficiently. Nevertheless, the effectiveness of egoistic behavior is not so surprising when considering some key findings in other fields of study. In evolutionary game theory, which studies the behavior of animal populations, egoistic behavior is found to produce strong and stable populations (Maynard Smith, 1982). In economics, the effect of individuals’ selfish behavior promoting the good of the whole society is called the invisible hand (Smith, 1776). Smith describes the invisible hand as follows: ‘‘By pursuing his own interest he frequently promotes that of the society more effectually than when he really intends to promote it’’. Hence, according to our results, the invisible hand seems to be present also in egress situations. Many studies suggest that people in egress situations tend to cooperate, behave altruistically, and do not panic (e.g., Keating, 1982; Aguirre, 2005; Cocking et al., 2009). Assuming that these suggestions are true, our experiments were realistic in the sense that they did not involve panic. Hence, it is an interesting result that cooperation may slow down egress, and in the worst case, lead to unnecessary losses of life. In this respect, it is possible that the total egress time of certain egress situations could be decreased by encouraging the evacuees to behave more egoistically. These findings are quite similar with those of McLean et al. (1996) but contradict with Muir and Cobbett (1995). The differences can be explained by the very different incentive systems used in the experiments. As we did not use any monetary incentives, the differences in the egress times were caused by the objective and not by the rewarding system. However, the results cannot necessarily be generalized to larger crowds, where the faster-isslower effect might be stronger and could slow down the evacuation under the selfish objective.
5. Connection of starting position to used exit and egress time The second experiment was conducted so that each starting position could be connected to both the used exit and the individual egress time. The average individual egress times of the four trials under Objective 2 and Objective 3 for each starting position are shown in Fig. 5. In addition, the proportions of using either of the two exits from each starting position are shown in gray scale. If the color of the bar at a starting position is black, Exit 2 was used from that position in all of the four trials. If the color is white, Exit 1 was used in all of the trials. It can be roughly concluded that the more the starting position was on the left hand side of the corridor from the participants’ point of view, the more often Exit 1 was used, and the more the starting position was on the right hand side, the more often Exit 2 was used. This result is partly explained by the geometry of the experiment. It seems, however, that this connection between starting position and target exit was stronger under Objective 3 than under Objective 2. This difference cannot be explained by the geometry of the experiment. To study the difference in exit selection between the objectives, the 54 starting positions were divided into a left and right half both
Fig. 5. Connection of starting position to used exit and egress time for Objective 2 (left) and Objective 3 (right).
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Fig. 6. Standard deviations of egress times from each starting position for Objective 2 (left) and Objective 3 (right).
including 27 positions. When averaged over the 27 positions on the left hand side, Exit 1 was used in 69.44% of the trials under Objective 2, and in 79.63% under Objective 3. When averaged over the 27 positions on the right hand side, Exit 2 was used in 65.74% of the trials under Objective 2, and in 76.85% under Objective 3. To find out whether these differences between the objectives are statistically significant, we applied the Mann–Whitney U test (Wilcoxon Rank Sum test) with continuity correction. This test was used because it does not involve any distributional assumptions and is suitable for discrete data. For the positions on the left and right hand side, the data was formed by counting the number of participants who used Exit 1 and Exit 2, respectively, for each trial and objective. The test results suggest that those who started from the positions on the left hand side used Exit 1 more often under Objective 3 than under Objective 2, z = 1.6372 and p = 0.0508, and those who started from the positions on the right hand side used Exit 2 more often under Objective 3 than under Objective 2, z = 1.9227 and p = 0.0273. The overall conclusion of these results is that the effect of starting position on exit selection was significantly stronger when the participants cooperated than when they behaved egoistically. The observation on the connection between starting position and target exit is closely related to the self-organization phenomenon of pedestrian flows, which is usually observed as lane formation in bi-directional flows (Helbing et al., 2001; Hoogendoorn and Daamen, 2005). In our experiment, the self-organization was significantly stronger when the participants tried to cooperate. It would be interesting to study whether the objective would affect the level of self-organization also in counterflow situations. Another well-known phenomenon that could affect the exit selection is herding, where people tend to follow their predecessors (Helbing et al., 2002; Pan, 2006; Low, 2000). Herding is a phenomenon that is likely present in our experiment as well as in real evacuations. It is not possible to isolate its effect from our results but it may well be one of the factors affecting the exit selection. The heights of the bars in Fig. 5 illustrate the connection between different starting positions and average egress times. A natural result is that the further the starting position is from the exits, the longer is the egress time. This holds on average for both objectives. However, for Objective 3 this effect is almost constant and there is only little variation in the average egress times of adjacent positions, while for Objective 2, the height of two adjacent bars may vary largely. The explanation for this was found from the video recordings; Under Objective 3, the participants seemed to stick to their positions within the crowd throughout the egress, while under Objective 2, overtaking was much more common. This difference also partly explains why cooperative behavior increased the total egress times; If faster people do not overtake slower predecessors, the whole crowd ends up moving in the speed of the slowest ones. This has been recognized as characteristic for people forming a group (Proulx, 1995). In this sense, Objective 3 made the participants behave as if the whole crowd was a group of people with a social connection. The heights of the bars in Fig. 6 correspond to the standard deviations of the egress times from each starting position. The standard
deviations depend on the location within the crowd for both objectives, and are in the range 0.3–4.3 s for Objective 2 and 0.3–3.7 s for Objective 3. In general, it seems that the standard deviation is smaller for the starting positions in front of the crowd. This is natural since also the egress time is shorter for the positions in front. The average proportion of the standard deviation to the egress time is about 16% for Objective 2 and about 14% for Objective 3. 6. Results of the questionnaires After both experiments, all participants completed a questionnaire. According to the results, they were able to understand the given instructions: 92% found the instructions ‘‘very clear’’ and 6% ‘‘rather clear’’. Only 2 participants thought the instructions were unclear. Also, the participants’ level of effort was high: 86% tried to achieve the given objectives ‘‘the best they could’’ and 14% ‘‘fairly’’. Nobody answered that she/he did not try to achieve the objectives. When asked to freely explain the basis on which they selected between the two exits, the most common answer was the length of the queue. This was the most common criterion mentioned for both Objective 2 and Objective 3. Many participants also mentioned that the starting positions affected their exit selection. Location on the left or right hand side of the corridor led to selecting Exit 1 or Exit 2, respectively. In general, the explanations for exit selection criteria did not differ much between the objectives. The only evident difference was that under Objective 3, if located in the front, many participants selected the further exit to give more space to the ones behind. 7. Conclusions This work presents the results of an evacuation experiment in a corridor with two exits. The goal was to study exit selection behavior in a congested asymmetric geometry, where the selection of the fastest exit was a nontrivial task for most of the participants. Another research question was the effect of selfish and cooperative behavior on the outcome of the evacuation. The results show that usually the queue at the first exit had already cleared, while there were still people queuing at the further exit. This difference was significant and it implies a constant bias of choosing the further exit even if the first one would be faster. Another result was the clear connection between the participants’ starting positions and the selected target exits. Especially when behaving cooperatively, the people starting from the right walked to the further exit and the ones starting from the left selected the first exit. This result is closely related to the self-organization phenomenon of pedestrian flow, which is usually observed as lane formation in bi-directional flows. The total evacuation times were significantly faster when the participants behaved selfishly and tried to minimize their individual evacuation times than when they tried to cooperate. This contradicts with some previous studies but the difference can be explained by the different incentive systems used in the experiments. The
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result cannot necessarily be generalized to larger crowds, where the faster-is-slower effect would be stronger and could slow down the evacuation under the selfish objective. According to many studies, people in real emergencies tend to cooperate and act altruistically. In our opinion, it is unlikely that safety designers should, or even could, influence people to behave more egoistically in evacuation situations. Regardless of this, the efficiency of egoistic behavior is a result that may have many applications for improving evacuation safety. According to our results, one of the explanations for the higher efficiency of egoistic behavior could be the fact that, when behaving egoistically, faster occupants take over their slower predecessors. When behaving cooperatively, overtaking does not occur and the whole crowd moves in the speed of its slowest members. This finding might be useful when designing the safety of buildings. For instance, egress routes could be designed in a way that overtaking becomes easier. Some buildings could also be designed so that slower individuals would use a different egress route than the ones that are able to move faster. In the future, automatic guidance systems could be giving directions to evacuating people. According to our results, to optimize the evacuation outcome, such systems should allocate people with similar physical abilities to the same egress routes. It is important to notice that the results of this or any other egress experiment may be specific to the characteristics of the experiment, e.g., the test geometry or the number of participants. Thus, the results are not necessarily generalizable to all egress situations. However, there is no reason to assume that the observed phenomena would occur only in this specific setting. The effect of selfish and cooperative behavior can be considered quite independent of the geometry. Asymmetric geometries are common in buildings and we find it likely that similar exit selection behavior would occur also in other resembling asymmetric settings. In the future, it would be interesting to study different kinds of egress scenarios and see how far these results can be generalized. Acknowledgements We would like to thank Mr. Ville Heikura, Mr. Peter Grönberg and Mr. Perttu Punakallio for their help in organizing the experiments and in processing the data. We also thank Dr. Steve Gwynne and Mr. Ilkka Mellin for their helpful comments. This research was supported by the Academy of Finland, the Finnish Fire Protection Fund, the Finnish Ministry of the Environment, the Finnish Ministry of the Interior, the Finnish Work Environment Fund, and VTT Technical Research Centre of Finland. References Aguirre, B.E., 2005. Emergency evacuations, panic and social psychology – commentary on ‘understanding mass panic and other collective responses to threat and disaster’. Psychiatry 68, 121–129.
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Cocking, C., Drury, J., Reicher, S., 2009. The psychology of crowd behaviour in emergency evacuations: results from two interview studies and implications for the fire and rescue services. The Irish Journal of Psychology 30, 59–74. Drury, J., Cocking, C., Reicher, S., Burton, A., Schofield, D., Hardwick, A., Graham, D., Langston, P., 2009. Cooperative versus competition in a mass emergency evacuation: a new laboratory simulation and a new theoretical model. Behavior Research Methods 41, 957–970. Ehtamo, H., Heliövaara, S., Korhonen, T., Hostikka, S., 2010. Game theoretic bestresponse dynamics for evacuees’ exit selection. Advances in Complex Systems 13, 113–134. Galea, E.R., Finney, K.M., Dixon, A.J.P., Siddiqui, A., Cooney, D.P., 2006. An analysis of exit availability, exit usage and passenger exit selection behaviour exhibited during actual aviation accidents. The Aeronautical Journal 110, 113–134. Galea, E.R., Filippidis, L., Wang, Z., Lawrence, P.J., Ewer, J., 2011. Evacuation analysis of 1000+ seat blended wing body aircraft configurations: computer simulations and full-scale evacuation experiment. In: Peacock, R.D., Kuligowski, E.D., Averill, J.D. (Eds.), Pedestrian and Evacuation Dynamics. Springer, US, pp. 151–161. Gwynne, S., Galea, E., Lawrence, P., Owen, M., Filippidis, L., 1999. Adaptive decision making in response to crowd formations in buildingexodus. Journal of Applied Fire Science 8, 265–289. Helbing, D., Farkas, I., Vicsek, T., 2000. Simulating dynamical features of escape panic. Nature 407, 487–490. Helbing, D., Molnar, P., Farkas, I.J., Bolay, K., 2001. Self-organizing pedestrian movement. Environment and Planning B: Planning and Design 28, 361–383. Helbing, D., Farkas, I.J., Molnar, P., Vicsek, T., 2002. Simulation of pedestrian crowds in normal and evacuation situations. In: Schreckenberg, M., Sharma, S.D. (Eds.), Pedestrian and Evacuation Dynamics. Springer, pp. 21–58. Hoogendoorn, S.P., Daamen, W., 2005. Pedestrian behavior at bottlenecks. Transportation Science 39, 147–159. Johnson, N.R., 1987. Panic and the breakdown of social order: popular myth, social theory, empirical evidence. Sociological Focus 20, 171–183. Keating, J.P., 1982. The myth of panic. Fire Journal, 56–61 and 147. Kuligowski, E.D., Peacock, R.D., Hoskins, B.L., 2010. A Review of Building Evacuation Models,, .. Technical Report Technical Note 1680, 2nd ed. National Institute of Standards and Technology, Fire Research Division, Engineering Laboratory. Low, D., 2000. Following the crowd. Nature 407, 465–466. Maynard Smith, J., 1982. Evolution and the Theory of Games. Cambridge University Press. McLean, G.A., George, M.H., Funkhouser, G.E., Chittum, C.B., 1996. Aircraft Evacuations Onto Escape Slides and Platforms I: Effects of Passenger Motivation. Technical Report DOT/FAA/AM-96/18. FAA Civil Aeromedical Institute. Montgomery, D.C., 1997. Design and Analysis of Experiments, 5th ed. John Wiley & Sons. Muir, H., Cobbett, A., 1995. Cabin Crew Behaviour in Emergency Evacuations. Civil Aviation Authority/Federal Aviation Administration Paper DOT/FAA/CT-95/16. Pan, X., 2006. Computational Modeling of Human and Social Behaviors for Emergency Egress Analysis. Dissertation. Stanford University. Palo Alto, California. Proulx, G., 1993. A stress model for people facing a fire. Journal of Environmental Psychology 13, 137–147. Proulx, G., 1995. Evacuation time and movement in apartment buildings. Fire Safety Journal 24, 229–246. Quarantelli, E.L., 2002. Sociology of panic. In: The International Encyclopedia of the Social and Behavioral Sciences. Elsevier, pp. 11020–11023. Sime, J.D., 1980. The concept of panic. In: Canter, D. (Ed.), Fires and Human Behavior. John Wiley & Sons, pp. 63–81. Sime, J.D., 1985. Movement toward the familiar person and place affiliation in a fire entrapment setting. Environment and Behavior 17, 697–724. Smith, A., 1776. An Inquiry into the Nature and Causes of the Wealth of Nations. W. Strahan and T. Cadell, London. Was, J., 2010. Experiments on evacuation dynamics for different classes of situations. In: Klingsh, W.W.F., Rogsh, C., Schadschneider, A., Schreckenberg, M. (Eds.), The Proceedings of PED2008: 4th International Conference on Pedestrian and Evacuation Dynamics. Springer, pp. 225–232.
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Pedestrian behavior and exit selection in evacuation of a corridor – An experimental study Simo Heliövaara a,⇑, Juha-Matti Kuusinen a, Tuomo Rinne b, Timo Korhonen b, Harri Ehtamo a a b
Systems Analysis Laboratory, Aalto University, P.O. Box 11100, FI-00076 Aalto, Finland VTT Technical Research Centre of Finland, P.O. Box 1000, FI-02044 VTT, Finland
a r t i c l e
i n f o
Article history: Received 25 November 2010 Received in revised form 5 August 2011 Accepted 12 August 2011 Available online 9 September 2011 Keywords: Egress experiment Exit selection Pedestrian behavior Cooperation
a b s t r a c t In this paper, we present an experiment whose purpose was to study evacuees’ exit selection under different behavioral objectives. The experiment was conducted in a corridor with two exits located asymmetrically. This geometry was used to make most participants face a nontrivial decision on which exit to use. We analyze the behavior on a macroscopic level using statistical methods. Our results suggest that the members of an evacuating crowd may not be able to make optimal decisions when assessing the fastest exit to evacuate. In addition, the egress time of the whole crowd turns out to be shorter when the evacuees behave egoistically instead of behaving cooperatively. This is an interesting result because many studies on real emergencies show that evacuees tend to cooperate and act altruistically. Ó 2011 Elsevier Ltd. All rights reserved.
1. Introduction When evacuating a venue with multiple exits, each evacuee will face the decision on which exit to use. According to literature, there are many factors that influence occupants’ exit selection. For instance, people tend to prefer familiar exit routes and herding behavior, where people choose to follow the others, is a common observation in evacuations. Because people in a threatening situation have to hurry, it is obvious that one criterion when comparing different exit routes is the time it takes to exit the building. To be able to optimally select the fastest exit route, occupants should be capable to calculate estimates for the evacuation times through the different exits. It would require estimating the moving time to the exits and the queuing times in front of the exits, which depend on the actions of the other occupants. It is unlikely that people do, or even are capable of doing such calculations during egress. Instead, they are likely to use some simplified schemata to come up with an exit choice. This paper presents a study on the exit selection behavior in an evacuation scenario with two exits, where one exit is closer than the other and congestion arises at the exits. The participants were asked to evacuate through the exits as fast as possible. The specific geometry was chosen because it forces most participants to make a nontrivial decision on which exit to use, while in many other geometries the fastest exit route is obvious for a vast majority of participants. Our key research questions were: Are people able to ⇑ Corresponding author. Tel.: +358 9 470 23059. E-mail address: simo.heliovaara@tkk.fi (S. Heliövaara). 0925-7535/$ - see front matter Ó 2011 Elsevier Ltd. All rights reserved. doi:10.1016/j.ssci.2011.08.020
select the fastest exit route? How does the starting position in the crowd relate to the selected exit? How do the instructions to behave selfishly or to cooperate affect the outcome of evacuations? It is natural that many of the results may depend on the specific evacuation scenario and geometry. Hence, the results of this study cannot be directly generalized to all possible situations, which is also the case with any other single evacuation scenario. However, the results of the experiment increase understanding on human egress behavior. The empirical data on, e.g., evacuation times, exit usage, and the effect of starting position on exit selection, can also be applied to validate and test computational egress simulation models. The rest of the paper is organized as follows. In Section 1.1, we review the existing literature on the topic. Section 2 describes the experimental setting and compares our approach to the previous studies. Sections 3–6 present and analyze the results and Conclusions are given in Section 7. 1.1. Existing literature Evacuees’ exit selection behavior has been discussed in a few previous articles. These papers include analysis of real life evacuation situations (Galea et al., 2006) and experimental studies where the same evacuation scenario has been repeated several times (Muir and Cobbett, 1995; McLean et al., 1996; Drury et al., 2009; Was, 2010; Galea et al., 2011). The results of these studies do not contain information on evacuees’ ability to select the fastest exit in situations with congestion. In actual accidents, there are usually so many other factors influencing the decision that it is difficult to
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isolate this ability. In the few experimental settings, the test geometries have been such that the fastest route is a trivial choice for a large majority of the participants and no real decision making is required. Agent based evacuation simulation models have different approaches to model the exit selection. Kuligowski et al. (2010) give a comprehensive review on the topic. Some models assume that all agents use the nearest exit, while in others, agents observe the situation and make optimal decisions (Gwynne et al., 1999; Ehtamo et al., 2010). In real crowds, the behavior is not so straightforward. People’s decisions depend, e.g., on the information available and the time available for processing it. If these are limited, the decisions may not be optimal. According to literature, there are many factors that may affect the exit choice in real life evacuation situations. The estimated evacuation time through different exits is only one factor. In many cases, people tend to prefer the familiar exit routes and are unable to identify all available exits (Sime, 1985; Proulx, 1993; Pan, 2006). Herding behavior, where people choose to follow their predecessors, has also been a common observation in real crowds (Helbing et al., 2002; Pan, 2006; Low, 2000). The faster-is-slower effect describes the phenomenon, where evacuees’ attempt to move faster can cause slower flow through a bottleneck. This effect occurs because harder pushing towards the exit increases the pressure and the interpersonal friction forces and causes clogging in front of the exit (Helbing et al., 2000). Some degree of the faster-is-slower phenomenon is likely to be present also in our experiment. We will further discuss its effect when analyzing the results. Another well-known phenomenon in pedestrian flows is self-organization (Helbing et al., 2001; Hoogendoorn and Daamen, 2005). It is usually observed in multi-directional flows, where lanes and other patterns tend to form. Some form of selforganization may also occur when crowd members divide to different exits. In the literature on human behavior in evacuations, there has been a consensus for decades that panic is unlikely to occur in crowds (Sime, 1980; Johnson, 1987; Quarantelli, 2002). Many studies on actual accidents also show that evacuees do not only try to get themselves out but are rather concerned for the fate of the others and cooperate (Aguirre, 2005; Quarantelli, 2002; Cocking et al., 2009). The effect of cooperative and selfish behavior on total egress times has been previously studied in a few experiments. In the experiment of McLean et al. (1996), selfish behavior resulted in faster egress, while in the study of Muir and Cobbett (1995) the cooperative trials were faster. The results of these two experiments are not directly comparable with each other due to differences in the incentive systems as well as in the exit types of the test geometry. In their experimental study in an aircraft, Muir and Cobbett (1995) studied the effect of egoistic and cooperative behavior on egress times. According to their results, cooperative behavior results in faster egress. Muir and Cobbett (1995) produced selfish behavior, which is called competitive behavior in their work, by offering monetary rewards to the first 75% of the participants to evacuate the cabin. Cooperation was produced by giving an equal reward to all participants if the evacuation took less time than a given time limit. Hence, in the cooperative case the participants’ goal was to evacuate fast, while in the competitive case the only goal was to evacuate among the first 75% and the evacuation time did not matter. Thus, in the competitive case, the participants that were clearly in the first 75% had no incentive to hurry and, for the rest of the crowd, the rewards encouraged competition with each other instead of fast evacuation. In real evacuations, even in competitive situations, the factor that determines each occupant’s survival is whether she is able to evacuate before the conditions get lethal and the order in which they pass the exit is irrelevant. Hence,
the incentives used by Muir and Cobbett for competitive evacuation may have encouraged unrealistic behavior and the conclusions on the effect of selfish and cooperative behavior in real evacuations are questionable. Also McLean et al. (1996) studied the effect of motivation level on egress times by giving monetary rewards to the participants. In the competitive scenario, 5 egress trials were run and the seating order was rotated between the trials. The participants who averaged among the first 25%, as averaged across all 5 trials, were given a financial bonus. In the cooperative scenarios, no monetary incentives were given. In McLean’s experiment, the competitive egress turned out to be significantly faster than the cooperative. This is opposite to results of Muir and Cobbett (1995), but the difference could be largely explained by the differences in the incentive systems.
2. Experimental setup To find out how cooperative or egoistic behavior affects the egress outcome, we altered the instructions given to the participants between the trials. The objective of the participants was either to minimize their individual egress times or the egress time of the whole group. Our goal was to study, whether people are able to cooperate effectively in this sort of situations, or if the best outcome is obtained when all focus on themselves. The experiment was conducted in a corridor with a lobby at one end. The corridor had two exits to a classroom. The exits were narrowed down from 0.9 m to 0.7 m to cause heavier congestion. In addition, the exit leaves were removed. The geometry of the experiment is shown in Fig. 1. Numbers from 1 to 54 were marked to the lobby floor to indicate starting positions. They were placed so that the participants did not feel too tight when, in the beginning of each trial, they were asked to stand on the numbers facing the corridor. To avoid any bias due to individual characteristics, the starting positions were randomly assigned to the participants in every trial. As can be seen from Fig. 1, Exit 1 is closer to each starting position than Exit 2. Such an asymmetric geometry is interesting when studying exit selection because it makes the assessment of the fastest exit route more difficult than a symmetric setting. The participants have to consider the distances to the exits and the queues in front of the exits to come to the optimal decisions. In addition, in this setup, a vast majority of the participants will face a nontrivial decision on the fastest exit route. The choice is either to join the forming queue at Exit 1 or to pass the queue and head to Exit 2. Symmetric geometries would not be as interesting since the fastest exit would usually be trivial for most of the participants. Such an asymmetric and nontrivial setup is also interesting when validating and testing egress simulation models. Most models perform well in simple symmetric geometries but realistic simulations in our setting require a more sophisticated exit selection algorithm. When standing on their starting positions, the participants were instructed to evacuate the corridor by moving to the classroom with one of the following objectives: Objective 1: ‘when you hear a whistle, evacuate the corridor normally without hurry’; Objective 2: ‘when you hear a whistle, evacuate the corridor minimizing your own egress time’; Objective 3: ‘when you hear a whistle, evacuate the corridor minimizing the egress time of the whole group’. The second and third objective were intended to produce selfish and cooperative behavior, respectively. In addition, to ensure safety,
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Fig. 1. The geometry of the experiment.
the participants were instructed not to run and to move so that no one gets injured. Each trial lasted until all participants had evacuated the corridor. The trials were recorded with five digital video cameras of which two were inside the classroom and three in the corridor. From the recordings, the time from the whistle to the moment of entering the classroom was recorded for each participant. Snapshots of one of the trials are shown in Fig. 2. We used this setting in two different experiments which took place at the Aalto University School of Science and Technology at Espoo, Finland, on 2 and 24 March 2010. A mixed-sex group of 48 undergraduate students, 54% male and 46% female, participated in the first experiment, Experiment 1. The age of the participants ranged between 19 and 25 years. In total, six trials were carried out. Each of the objectives was repeated two times before moving on to the next objective. The order of the objectives was 1, 2, and 3. This order was used because randomization of the order might have confused the participants about the objective in question. The starting positions of the participants were randomized for each trial, which made each participant face a new situation every time. This randomization also prevented learning between the trials. A mixed-sex group of 54 undergraduate students, 74% male and 26% female, took part in the second experiment, Experiment 2. The age of the participants ranged between 19 and 25 years. In total, 10 trials were carried out in the same way as in the first experiment, but this time the first objective was repeated two times, and the second and the third four times. Also, the order in which the participants entered the classroom was recorded separately
for each exit. This made it possible to connect the starting positions with the exits. The starting positions were randomized for each trial. Participation to both experiments was voluntary. At the end of both experiments, the participants were asked to complete a short questionnaire. The questions regarded, e.g., the clarity of the given instructions, the grounds on which the participants selected their target exits under different objectives, and the level of effort they gave to achieve the objectives. The participants of the experiments were healthy undergraduate students, and thus, formed a homogeneous group. This population was chosen to avoid variability caused by individual differences. Our goal was to study exit selection behavior on a macroscopic level using statistical methods. In such analysis, to enable isolation of the effects of interest, it is important that the population is homogeneous. In a qualitative micro-level study, it might be more interesting to use more heterogeneous participants. To be able to really compare the outcomes of egoistic and cooperative behaviors, it is essential that the participants’ level of effort is the same in both scenarios. While definitely useful in encouraging to stronger devotion, the use of monetary rewards in egress experiments, if not carefully designed, may also create biased results. As described above, in the experiments of McLean et al. (1996) and Muir and Cobbett (1995), this equality of devotion can be questioned due to the nature of the monetary incentives. To ensure equal level of effort throughout the trials, we decided not to give any rewards to the participants. Even if the performance of the participants could be enhanced with rewards, the difference between the treatments can be studied without the
Fig. 2. Snapshots of a trial 5 and 10 s after the whistle (Experiment 2, trial 8). Exit 2 is the one closer to the camera.
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rewards as long as the devotion is on the same level in all of the treatments. Another significant difference between our study and these two previous experiments is the description of selfish behavior, or competitive behavior as it is called by McLean et al. (1996) and Muir and Cobbett (1995). In our experiment, selfish occupants tried to minimize their individual evacuation times, while in the competitive trials of McLean et al. (1996) and Muir and Cobbett (1995) monetary rewards were given to the participants according to the order in which they evacuated. It is reasonable to assume that in real evacuations, selfish occupants try to get themselves out as fast as possible and they do not care about who passes the exit first. Hence, we consider our experiment to better describe actual evacuation situations. In Sections 3–5, the data from the experiments is used to assess the evacuation outcomes from different aspects. The clearance times of the two exits are compared to find out whether the exits were used efficiently. Egress times under the egoistic and cooperative objectives are compared to see if the objective affected the outcome of the evacuation. We also study how the starting position affected the participants’ exit selection and egress times. 3. Exit-specific queue clearance times The queue clearance time for an exit is defined as the time from the whistle to the moment when the last participant who used that exit entered the classroom. Fig. 3 shows the exit-specific queue clearance times for the 16 trials. Recall that the trials of Experiment 1 had 48 participants, while Experiment 2 had 54. Based on Fig. 3, it seems that the queue clearance time is on average shorter for Exit 1 than for Exit 2. To test whether this difference is statistically significant, we applied the sign test. This test was used because it does not involve any distributional assumptions. Let K be the number of trials for which the queue clearance time was shorter for Exit 1 than for Exit 2. Under the null hypothesis that the queue clearance times are equal, K follows the Binomial distribution with n = 16 and p = 0.5. This means that if the null hypothesis is true, the queue clearance time was shorter for Exit 1 than for Exit 2 on average in half, i.e., eight, of the 16 trials. According to the data, K = 13 and the corresponding p-value, P(K P 13) = 0.0106. Hence, the null hypothesis that the queue clearance times are equal was rejected, and it was concluded that
the queue clearance time is on average shorter for Exit 1 than for Exit 2. Recall that Exit 2 was the one further from the starting positions. This result implies that many participants ended up selecting the further exit even if they would have evacuated faster through the nearer one. As the objective was to evacuate as fast as possible, the result states that the participants systematically made suboptimal decisions to head to the exit through which the egress was slower. We give two possible explanations for this outcome. Firstly, the participants may have falsely estimated that the evacuation time through Exit 2 was shorter than in through of Exit 1. This is possible because Exit 2 was further, and when estimating the queue lengths, one should consider both the people in the queues as well as the ones still moving to join the queues. This explanation is also supported by the results of the questionnaire, where the most common criterion for exit selection was the shorter queue. Hence, the participants tried to select the shorter queue but the experimental results show that they had a bias of favoring the further exit. Secondly, the setting of the experiment was such that the participants who started from the back of the group arrived to the queue in front of Exit 1 and faced the decision whether to join that queue or to keep moving to Exit 2. The fact that these participants decided to move to Exit 2, even if the queue at Exit 1 was shorter, might be explained by the idea that people in a hurry tend to prefer moving instead of standing still and queuing. This possible explanation is based on the authors’ intuition and it does not have support in the existing literature. 4. The effect of egoistic and cooperative behavior Total egress time is defined as the time from the whistle to the moment when the last participant entered the classroom using either of the two exits, i.e., the maximum of the exit-specific queue clearance times. Fig. 4 shows the total egress time as a function of objective and experiment. The total egress times for Objective 1 are not shown because we were mainly interested in the differences between Objective 2 and Objective 3. It seems that Objective 2, i.e., selfish behavior, produced faster egress. We applied analysis of variance with blocking to test whether the objective has an effect on the total egress time. Blocking is a method where the variability caused by one or more nuisance factors is removed from the data so that the experimental error will
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consist only of the random error (Montgomery, 1997). We blocked the variance caused by the fact that the experiments were run on two different occasions with a different number of participants. Blocking enabled us to draw reliable conclusions concerning the effect of the objective. The objective had a significant effect on the total egress time, F1,9 = 11.7539 and p = 0.0075. Hence, on average, egress was significantly faster under Objective 2. This result is somewhat surprising. The video recordings revealed that when the participants behaved cooperatively, they were careful not to push or even touch other participants around them. This exaggerated caution may partly explain why cooperative behavior led to slower egress times. When promoting their own interest, the participants were able to pass the doors more efficiently. Nevertheless, the effectiveness of egoistic behavior is not so surprising when considering some key findings in other fields of study. In evolutionary game theory, which studies the behavior of animal populations, egoistic behavior is found to produce strong and stable populations (Maynard Smith, 1982). In economics, the effect of individuals’ selfish behavior promoting the good of the whole society is called the invisible hand (Smith, 1776). Smith describes the invisible hand as follows: ‘‘By pursuing his own interest he frequently promotes that of the society more effectually than when he really intends to promote it’’. Hence, according to our results, the invisible hand seems to be present also in egress situations. Many studies suggest that people in egress situations tend to cooperate, behave altruistically, and do not panic (e.g., Keating, 1982; Aguirre, 2005; Cocking et al., 2009). Assuming that these suggestions are true, our experiments were realistic in the sense that they did not involve panic. Hence, it is an interesting result that cooperation may slow down egress, and in the worst case, lead to unnecessary losses of life. In this respect, it is possible that the total egress time of certain egress situations could be decreased by encouraging the evacuees to behave more egoistically. These findings are quite similar with those of McLean et al. (1996) but contradict with Muir and Cobbett (1995). The differences can be explained by the very different incentive systems used in the experiments. As we did not use any monetary incentives, the differences in the egress times were caused by the objective and not by the rewarding system. However, the results cannot necessarily be generalized to larger crowds, where the faster-isslower effect might be stronger and could slow down the evacuation under the selfish objective.
5. Connection of starting position to used exit and egress time The second experiment was conducted so that each starting position could be connected to both the used exit and the individual egress time. The average individual egress times of the four trials under Objective 2 and Objective 3 for each starting position are shown in Fig. 5. In addition, the proportions of using either of the two exits from each starting position are shown in gray scale. If the color of the bar at a starting position is black, Exit 2 was used from that position in all of the four trials. If the color is white, Exit 1 was used in all of the trials. It can be roughly concluded that the more the starting position was on the left hand side of the corridor from the participants’ point of view, the more often Exit 1 was used, and the more the starting position was on the right hand side, the more often Exit 2 was used. This result is partly explained by the geometry of the experiment. It seems, however, that this connection between starting position and target exit was stronger under Objective 3 than under Objective 2. This difference cannot be explained by the geometry of the experiment. To study the difference in exit selection between the objectives, the 54 starting positions were divided into a left and right half both
Fig. 5. Connection of starting position to used exit and egress time for Objective 2 (left) and Objective 3 (right).
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Fig. 6. Standard deviations of egress times from each starting position for Objective 2 (left) and Objective 3 (right).
including 27 positions. When averaged over the 27 positions on the left hand side, Exit 1 was used in 69.44% of the trials under Objective 2, and in 79.63% under Objective 3. When averaged over the 27 positions on the right hand side, Exit 2 was used in 65.74% of the trials under Objective 2, and in 76.85% under Objective 3. To find out whether these differences between the objectives are statistically significant, we applied the Mann–Whitney U test (Wilcoxon Rank Sum test) with continuity correction. This test was used because it does not involve any distributional assumptions and is suitable for discrete data. For the positions on the left and right hand side, the data was formed by counting the number of participants who used Exit 1 and Exit 2, respectively, for each trial and objective. The test results suggest that those who started from the positions on the left hand side used Exit 1 more often under Objective 3 than under Objective 2, z = 1.6372 and p = 0.0508, and those who started from the positions on the right hand side used Exit 2 more often under Objective 3 than under Objective 2, z = 1.9227 and p = 0.0273. The overall conclusion of these results is that the effect of starting position on exit selection was significantly stronger when the participants cooperated than when they behaved egoistically. The observation on the connection between starting position and target exit is closely related to the self-organization phenomenon of pedestrian flows, which is usually observed as lane formation in bi-directional flows (Helbing et al., 2001; Hoogendoorn and Daamen, 2005). In our experiment, the self-organization was significantly stronger when the participants tried to cooperate. It would be interesting to study whether the objective would affect the level of self-organization also in counterflow situations. Another well-known phenomenon that could affect the exit selection is herding, where people tend to follow their predecessors (Helbing et al., 2002; Pan, 2006; Low, 2000). Herding is a phenomenon that is likely present in our experiment as well as in real evacuations. It is not possible to isolate its effect from our results but it may well be one of the factors affecting the exit selection. The heights of the bars in Fig. 5 illustrate the connection between different starting positions and average egress times. A natural result is that the further the starting position is from the exits, the longer is the egress time. This holds on average for both objectives. However, for Objective 3 this effect is almost constant and there is only little variation in the average egress times of adjacent positions, while for Objective 2, the height of two adjacent bars may vary largely. The explanation for this was found from the video recordings; Under Objective 3, the participants seemed to stick to their positions within the crowd throughout the egress, while under Objective 2, overtaking was much more common. This difference also partly explains why cooperative behavior increased the total egress times; If faster people do not overtake slower predecessors, the whole crowd ends up moving in the speed of the slowest ones. This has been recognized as characteristic for people forming a group (Proulx, 1995). In this sense, Objective 3 made the participants behave as if the whole crowd was a group of people with a social connection. The heights of the bars in Fig. 6 correspond to the standard deviations of the egress times from each starting position. The standard
deviations depend on the location within the crowd for both objectives, and are in the range 0.3–4.3 s for Objective 2 and 0.3–3.7 s for Objective 3. In general, it seems that the standard deviation is smaller for the starting positions in front of the crowd. This is natural since also the egress time is shorter for the positions in front. The average proportion of the standard deviation to the egress time is about 16% for Objective 2 and about 14% for Objective 3. 6. Results of the questionnaires After both experiments, all participants completed a questionnaire. According to the results, they were able to understand the given instructions: 92% found the instructions ‘‘very clear’’ and 6% ‘‘rather clear’’. Only 2 participants thought the instructions were unclear. Also, the participants’ level of effort was high: 86% tried to achieve the given objectives ‘‘the best they could’’ and 14% ‘‘fairly’’. Nobody answered that she/he did not try to achieve the objectives. When asked to freely explain the basis on which they selected between the two exits, the most common answer was the length of the queue. This was the most common criterion mentioned for both Objective 2 and Objective 3. Many participants also mentioned that the starting positions affected their exit selection. Location on the left or right hand side of the corridor led to selecting Exit 1 or Exit 2, respectively. In general, the explanations for exit selection criteria did not differ much between the objectives. The only evident difference was that under Objective 3, if located in the front, many participants selected the further exit to give more space to the ones behind. 7. Conclusions This work presents the results of an evacuation experiment in a corridor with two exits. The goal was to study exit selection behavior in a congested asymmetric geometry, where the selection of the fastest exit was a nontrivial task for most of the participants. Another research question was the effect of selfish and cooperative behavior on the outcome of the evacuation. The results show that usually the queue at the first exit had already cleared, while there were still people queuing at the further exit. This difference was significant and it implies a constant bias of choosing the further exit even if the first one would be faster. Another result was the clear connection between the participants’ starting positions and the selected target exits. Especially when behaving cooperatively, the people starting from the right walked to the further exit and the ones starting from the left selected the first exit. This result is closely related to the self-organization phenomenon of pedestrian flow, which is usually observed as lane formation in bi-directional flows. The total evacuation times were significantly faster when the participants behaved selfishly and tried to minimize their individual evacuation times than when they tried to cooperate. This contradicts with some previous studies but the difference can be explained by the different incentive systems used in the experiments. The
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result cannot necessarily be generalized to larger crowds, where the faster-is-slower effect would be stronger and could slow down the evacuation under the selfish objective. According to many studies, people in real emergencies tend to cooperate and act altruistically. In our opinion, it is unlikely that safety designers should, or even could, influence people to behave more egoistically in evacuation situations. Regardless of this, the efficiency of egoistic behavior is a result that may have many applications for improving evacuation safety. According to our results, one of the explanations for the higher efficiency of egoistic behavior could be the fact that, when behaving egoistically, faster occupants take over their slower predecessors. When behaving cooperatively, overtaking does not occur and the whole crowd moves in the speed of its slowest members. This finding might be useful when designing the safety of buildings. For instance, egress routes could be designed in a way that overtaking becomes easier. Some buildings could also be designed so that slower individuals would use a different egress route than the ones that are able to move faster. In the future, automatic guidance systems could be giving directions to evacuating people. According to our results, to optimize the evacuation outcome, such systems should allocate people with similar physical abilities to the same egress routes. It is important to notice that the results of this or any other egress experiment may be specific to the characteristics of the experiment, e.g., the test geometry or the number of participants. Thus, the results are not necessarily generalizable to all egress situations. However, there is no reason to assume that the observed phenomena would occur only in this specific setting. The effect of selfish and cooperative behavior can be considered quite independent of the geometry. Asymmetric geometries are common in buildings and we find it likely that similar exit selection behavior would occur also in other resembling asymmetric settings. In the future, it would be interesting to study different kinds of egress scenarios and see how far these results can be generalized. Acknowledgements We would like to thank Mr. Ville Heikura, Mr. Peter Grönberg and Mr. Perttu Punakallio for their help in organizing the experiments and in processing the data. We also thank Dr. Steve Gwynne and Mr. Ilkka Mellin for their helpful comments. This research was supported by the Academy of Finland, the Finnish Fire Protection Fund, the Finnish Ministry of the Environment, the Finnish Ministry of the Interior, the Finnish Work Environment Fund, and VTT Technical Research Centre of Finland. References Aguirre, B.E., 2005. Emergency evacuations, panic and social psychology – commentary on ‘understanding mass panic and other collective responses to threat and disaster’. Psychiatry 68, 121–129.
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Cocking, C., Drury, J., Reicher, S., 2009. The psychology of crowd behaviour in emergency evacuations: results from two interview studies and implications for the fire and rescue services. The Irish Journal of Psychology 30, 59–74. Drury, J., Cocking, C., Reicher, S., Burton, A., Schofield, D., Hardwick, A., Graham, D., Langston, P., 2009. Cooperative versus competition in a mass emergency evacuation: a new laboratory simulation and a new theoretical model. Behavior Research Methods 41, 957–970. Ehtamo, H., Heliövaara, S., Korhonen, T., Hostikka, S., 2010. Game theoretic bestresponse dynamics for evacuees’ exit selection. Advances in Complex Systems 13, 113–134. Galea, E.R., Finney, K.M., Dixon, A.J.P., Siddiqui, A., Cooney, D.P., 2006. An analysis of exit availability, exit usage and passenger exit selection behaviour exhibited during actual aviation accidents. The Aeronautical Journal 110, 113–134. Galea, E.R., Filippidis, L., Wang, Z., Lawrence, P.J., Ewer, J., 2011. Evacuation analysis of 1000+ seat blended wing body aircraft configurations: computer simulations and full-scale evacuation experiment. In: Peacock, R.D., Kuligowski, E.D., Averill, J.D. (Eds.), Pedestrian and Evacuation Dynamics. Springer, US, pp. 151–161. Gwynne, S., Galea, E., Lawrence, P., Owen, M., Filippidis, L., 1999. Adaptive decision making in response to crowd formations in buildingexodus. Journal of Applied Fire Science 8, 265–289. Helbing, D., Farkas, I., Vicsek, T., 2000. Simulating dynamical features of escape panic. Nature 407, 487–490. Helbing, D., Molnar, P., Farkas, I.J., Bolay, K., 2001. Self-organizing pedestrian movement. Environment and Planning B: Planning and Design 28, 361–383. Helbing, D., Farkas, I.J., Molnar, P., Vicsek, T., 2002. Simulation of pedestrian crowds in normal and evacuation situations. In: Schreckenberg, M., Sharma, S.D. (Eds.), Pedestrian and Evacuation Dynamics. Springer, pp. 21–58. Hoogendoorn, S.P., Daamen, W., 2005. Pedestrian behavior at bottlenecks. Transportation Science 39, 147–159. Johnson, N.R., 1987. Panic and the breakdown of social order: popular myth, social theory, empirical evidence. Sociological Focus 20, 171–183. Keating, J.P., 1982. The myth of panic. Fire Journal, 56–61 and 147. Kuligowski, E.D., Peacock, R.D., Hoskins, B.L., 2010. A Review of Building Evacuation Models,, .. Technical Report Technical Note 1680, 2nd ed. National Institute of Standards and Technology, Fire Research Division, Engineering Laboratory. Low, D., 2000. Following the crowd. Nature 407, 465–466. Maynard Smith, J., 1982. Evolution and the Theory of Games. Cambridge University Press. McLean, G.A., George, M.H., Funkhouser, G.E., Chittum, C.B., 1996. Aircraft Evacuations Onto Escape Slides and Platforms I: Effects of Passenger Motivation. Technical Report DOT/FAA/AM-96/18. FAA Civil Aeromedical Institute. Montgomery, D.C., 1997. Design and Analysis of Experiments, 5th ed. John Wiley & Sons. Muir, H., Cobbett, A., 1995. Cabin Crew Behaviour in Emergency Evacuations. Civil Aviation Authority/Federal Aviation Administration Paper DOT/FAA/CT-95/16. Pan, X., 2006. Computational Modeling of Human and Social Behaviors for Emergency Egress Analysis. Dissertation. Stanford University. Palo Alto, California. Proulx, G., 1993. A stress model for people facing a fire. Journal of Environmental Psychology 13, 137–147. Proulx, G., 1995. Evacuation time and movement in apartment buildings. Fire Safety Journal 24, 229–246. Quarantelli, E.L., 2002. Sociology of panic. In: The International Encyclopedia of the Social and Behavioral Sciences. Elsevier, pp. 11020–11023. Sime, J.D., 1980. The concept of panic. In: Canter, D. (Ed.), Fires and Human Behavior. John Wiley & Sons, pp. 63–81. Sime, J.D., 1985. Movement toward the familiar person and place affiliation in a fire entrapment setting. Environment and Behavior 17, 697–724. Smith, A., 1776. An Inquiry into the Nature and Causes of the Wealth of Nations. W. Strahan and T. Cadell, London. Was, J., 2010. Experiments on evacuation dynamics for different classes of situations. In: Klingsh, W.W.F., Rogsh, C., Schadschneider, A., Schreckenberg, M. (Eds.), The Proceedings of PED2008: 4th International Conference on Pedestrian and Evacuation Dynamics. Springer, pp. 225–232.