[type=editor, role=Graduate Researcher, orcid=0000-0001-8785-8007]

[type=editor, role=PI, orcid=0000-0001-5035-9925]

[type=editor, role=Collaborator, orcid=0000-0001-9280-7837] [1]

[cor1]Corresponding author

# Use of immersive virtual reality-based experiments to study tactical
decision-making during emergency evacuation

Laura M. Harris<EMAIL_ADDRESS>Subhadeep Chakraborty<EMAIL_ADDRESS>Aravinda Ramakrishnan Srinivasan<EMAIL_ADDRESS>Mechanical
Aerospace and Biomedical Engineering, The University of Tennessee, Knoxville
Institute for Transport Studies, University of Leeds

###### Abstract

Humans make their evacuation decisions first at strategic/tactical levels,
deciding their exit and route choice and then at operational level, navigating
to a way-point, avoiding collisions. What influences an individuals at
tactical level is of importance, for modelers to design a high fidelity
simulation or for safety engineers to create efficient designs/codes. Does an
unlit exit sign dissuades individual(s) to avoid a particular exit/route and
vice versa? What effect does the crowd’s choices have on individual’s decision
making? To answer these questions, we studied the effect of exit signage
(unlit/lit), different proportions of crowd movement towards the exits, and
the combined (reinforcing/conflicting) effect of the sign and the crowd
treatment on reaction times and exit choices of participants in an immersive
virtual reality(VR) evacuation experiment. We found that there is tolerance
for queuing when different sources of information, exit signage and crowd
movement reinforced one another. The effect of unlit exit signage on
dissuading individuals from using a particular exit/route was significant. The
virtual crowd was ineffective at encouraging utilization of a particular
exit/route but had a slight repulsive effect. Additionally, we found some
similarities between previous studies based on screen-based evacuation
experiments and our VR-based experiment.

###### keywords:

Emergency evacuation Virtual reality User study Tactical decision making

##  1 Introduction

The behavior of individuals and crowds is an important topic of research which
has helped in preventing crowd disasters and in improving pedestrian flow and
safety [1, 2]. Crowd behavior has been studied from different viewpoints such
as animal swarms, judgment formation, and consumer behaviors [3, 4, 5, 6, 7].
Individual’s decision-making related to emergency evacuation situations has
been studied with screen-based experiments in order to understand different
factors influencing individuals during evacuation like scenarios [8, 9, 10].
Researchers have broadly categorized the decision-making mechanism into
strategic, tactical and operational level [11, 12, 2]. The strategic level is
related to when the individual(s) make the decision to start their evacuation,
the tactical decisions refers to individual’s exit and route choice, and
operational level takes care of local interactions like collision avoidance as
defined by Warren et al. [13]. In reality, the tactical decisions of
individuals have a profound effect on the overall evacuation process for the
entire crowd. For example, if every evacuee from a building with multiple exit
choices/routes chose to exit via the shortest path in order to optimize chance
of successful egress, it can lead to dangerous overcrowding at that particular
exit/route and thus eventually become a non-optimal choice for both the
individual and the crowd [14].

###  1.1 Social Influence

A crowd’s behavior evolves over time and the behavioral propagation can occur
through interactions between the individuals within the crowd [15] which can
lead to interesting crowd dynamics such as lane formation and propensity to
choose a certain exit [3]. The social influence of the crowd on individuals
can be attractive, neutral, or repulsive as discussed by Warren et al. [13].
The attractive social influence is more generally known as social imitation
(or herding) [16, 17]. It can sometimes lead to sub-optimal performances due
to overcrowding of routes and exits [18]. The overcrowding can lead to
increased congestion and exit times [16, 19]. Conversely, individuals have
been found to display a repulsive social influence to crowd thus avoiding
them. Some studies have found repulsive (avoiding) or neutral influence of the
crowd on individuals [8, 20, 21, 22] as well. Additionally, queue lengths
along with quickest and shortest path to safety have been found to be an
influencing factor on route and exit choice [10].

Lin et al. [23] studied effect of social influence in an emergency evacuation
situation. They utilized a virtual train station with simulated fire
emergency. The study concluded that in an unevenly split $(80-20)$ crowd,
individuals tend to follow the bigger crowd. Haghani et al. [24] compared the
stated and revealed choices collected through survey in a virtual evacuation.
They found that participants exhibited similar decision pattern in both type
of data collection. They also found that people chose the most crowded exit
more often. Nilsson et al. [25] found that social influence is based on
distance and plays an important role in the initial evacuation response. They
concluded that physically closer people had more influence than people at
distance. Additionally, they found that social influence is more important
when the evacuation cue is unclear or uninformative. Kinateder et al. [26]
verified that a virtual crowd exerted social influence on participants in a
cave immersive virtual reality (VR) system. The virtual crowd also affected
route choice of participants. Moussaid et al. [27] found that social imitation
was more due to a density effect rather than social imitation. Additionally,
they concluded that VR experiment elicited similar decision pattern to real-
life [27]. Kinateder et al. found that participants in their VR experiment
chose to exit through familiar doors and found that this effect was increased
when their virtual neighbors also left through the familiar door [28].
Surrounding persons were found to influence evacuee exit decision [29].
Additionally, Zhu et al. found that strangers affected people similarly to
non-strangers [29]. A stated choice survey was conducted to elicit the effect
of social influence and distance to exits by Lovreglio et al. [30].
Subsequently, the fitted a discrete choice models to better understand exit
choices of individuals. Thus, it is clear that crowd has an effect on
individual’s tactical decision during an emergency evacuation.

###  1.2 Sign Influence

Static sources such as signs, perceptual access, architectural
differentiation, and plan configurations are also important factors in
understanding the tactical decision making of individuals during emergency.
Building signage are common static directional information source. Exit signs
also provide information about emergency exits from a building. Cognition of
signs as well as the effectiveness of the content of signs were studied and
applied to exit designs [31, 32, 33, 34]. The size of the content was studied
and larger content was found to be more effective [35]. The color of the
content for the signs were studied and green or red lettering have been found
to increase recognition distance [36]. Sign design and illumination have been
found to be important for sign visibility [37]. Signs which update have been
found to be more trusted than fully static signs [38]. Comprehensive knowledge
of the environment was found to be not necessary for reasonable decisions to
be made [39].

Fu et al. [40] studied how signs affect people with and without the presence
of a crowd. Signs were found to be more effective in the absence of crowd
influence. Additionally, the average decision time of those who did not follow
signage was not atypically out of normal. Olander et al. [41] found that
dissuasive signs, like a red X added on the sign to indicate no entry, to be
effective in providing directional information. Tang et al. [42] summarized
that exit signage were important for way-finding decisions, but individuals do
not always follow them. Importantly, evacuations were slower when no signage
were present [42]. Bode et al. [9] utilized an interactive $2D$ VR experiment
and found that signs have a significant influence on exit choice of
participants. Kinateder et al. [43] found that green colored signs were most
attractive for exit utilization. The influence of visual information by means
of exit signs and corridor illumination were studied by Dachner et al. with a
small pool of participants and both were found to influence emergency
decision-making [44]. Galea et al. [45] found that dynamic exit signage
increased the visibility of the exit signs from $38\%$ to $77\%$ compared to
static exit signage. Galea et al. [46] found that dissuasive signage with
reinforcement from a voice alarm system successfully redirected people towards
the optimal exit by $66\%$.

###  1.3 Use of VR in Egress Literature

Researchers have used methods such as surveys[18, 22] and evacuation drills
[47, 48] to study interactive evacuation scenarios [20, 9, 49, 40, 50]. More
recently, immersive VR has been a promising avenue of information gathering
which minimizes risks and cost associated with evacuation experiments. VR has
been used in many fields to study human factors and has been found to be an
effective tool to study real world behaviors [51]. Several studies have found
that VR elicits similar non-emotional responses as real-world experiments [52,
53, 54, 55, 56, 57] and adequate emotional responses as situation demands [26,
58, 59, 60].

There are also known limitations in ergonomics and technology which can
prevent complete reliability of the VR experiment e.g. unrealistic AI,
unrealistic movement, unrealistic environments, unrealistic hazards, motion
sickness [61], and lack of inherent danger. But, some validation has been
performed on VR experiment by comparing results from VR to real-world data.
Kobes et al. [51] performed a validation study for use of serious games for
behavioral evacuation studies where VR-generated data was compared to real-
world data and found to elicit similar behavior. Kinateder et al. [62] found
some interesting correlation between real world and VR experiments. Reactions
to an alarm were reduced, a comparable response to positive influence from
bystanders, and a weaker response to negative influences where observed in VR.
Kinateder et al.[63] provided an in-depth comparison of real world and virtual
world behaviors and the capabilities of VR.

In summary, it is important for modelers and safety engineers to account for
all the different factors that can influence an individual’s tactical
decision. What factors can encourage individuals to take a particular route,
what factors discourage individuals to take a particular route, does the crowd
movement influence the decision making, if yes, whether it is a positive
influence or a negative influence, does the exit signage being lit or unlit
have opposite effect on the individual’s choice? Also, does virtual reality
(VR) based experiment elicit different response compared to screen-based
experiments? Bode et al. [8, 9, 10] have performed comprehensive analysis of
factors affecting the tactical decision making with a screen-based virtual
evacuation experiment. We are interested in comparing the results from them
with data collected in an immersive, first-person view, VR-based experiment.
More particularly, the current study expands on the work reported by Bode et
al. [9] on directional information source on exit choice of humans with 2D
virtual experiment. Specifically, this study attempts to address how lit and
unlit exit signs and crowd configuration affect evacuee exit choice behaviors
in an immersive VR emergency evacuation simulation. The primary differences
between this work and that of Bode et al. were the use of immersive VR
environment, the addition of an uneven crowd configuration and the lit and
unlit exit signage.

Figure 1: A screen capture of the immersive environment: Participant
perspective of scenario 2: Sign

##  2 Methods

Figure 2: 2D Representation of Experimental Rooms. All participants had their
VR avatar start at the equidistant position from the exit as marked by the
orange X facing towards the exits as indicated by the arrow mark in all
scenarios

###  2.1 Experimental Design

Unreal Engine 4 was used to develop the immersive VR environments. A head-
mounted display, a HTC Vive Pro (with 1440x1600 pixels resolution per eye),
was used to provide the participants with a fully immersive experience.
Participants navigated their virtual avatar in the simulated environment with
a joystick. A simple room layout was preferred to explore the effect of
directional information provided by crowd and exit signage on exit choice.
This is similar to the simple room layout utilized in literature [8, 9, 10].
The room allowed the participants full visual access to both exits and exit
signage. An example room (Figure 1) and a 2D representation of two
representative room layouts is provided in Figure 2. The room was $40$ meters
wide and $44$ meters long. The exit signs were $1$ meter wide and $0.4$ meters
in height. The signage were designed to be bigger than the standard dimensions
to counter the loss of clarity due to pixelation in the HTC Vive screen. We
prioritized being able to read the signs over strictly adhering to sizing
standards. The exit doorways were 2 meters wide which conforms with the US
Department of Labor’s Occupational Safety and Health Administration (OSHA)
guidelines for Emergency Exit Routes (osha.gov) [64]. The exits were located
either against the far wall from the participant starting point or at the end
of the side walls. The participants’ start location was in the central bottom
portion of the room (marked with an “X”) as can be seen in 2D representation
of the room in Figure 2. Furthermore, a crowd of co-evacuees consisting of
programmed non-player characters (NPCs) was positioned around the participant
within the participants’ virtual field-of-view rather than throughout the
room. A total of six scenarios were designed. The scenarios are summarized in
Table 1. All scenarios had two exits (the left and the right exit) and each
participant started from a position equidistant from the exits as indicated in
Figure 2.

Table 1: Experimental parameters for each of the 6 scenarios tested. # | Scenario | Lit Signs | Crowd: Left | Crowd: Right
---|---|---|---|---
1 | Control | Neither | 25 | 25
2 | Sign | Left Only | 25 | 25
3 | Crowd | Neither | 0 | 50
4 | Sign+Crowd | Left Only | 50 | 0
5 | Sign-Crowd | Right Only | 50 | 0
6 | Uneven Crowd | Neither | 15 | 35

The appearances of the NPCs in VR experiments have been found to not have any
significant effect on decision-making by Llobera et al. [65] and Bruneau et
al.[66]. Thus, all NPCs in our experiments appeared as males and were
identical to each other. The NPCs used simple way-finding to find the closest
path to their designated exit. The NPCs and participant moved at the same
speed of 1.5 meters per second (3.4 miles per hour) for a faster than normal
walking speed for the evacuation [67]. Table 1 details the experimental
parameters for each of the 6 Scenarios tested. Scenario 1 was designed to be
the control, scenario 2 was the base sign control test, scenario 3 was
designed to study the effect of the crowd on the decision-making, scenario 4
was a reinforcing-treatments scenario with crowd and sign in agreement,
scenario 5 was the conflicting-treatments scenario and scenario 6 was an
uneven crowd treatment scenario with an unevenly split crowd.

The NPCs acted as a potential influence on the participants’ exit decision. To
further elicit the crowding behaviors during an evacuation, the NPCs were
programmed to not have collision avoidance with other evacuees to allow
crowding at exits. It is to noted that the NPCs can never overlap but can be
blocked by each other when we mean they can collide. The NPCs were randomly
assigned to an exit according to the scenario description. Additionally, the
exits were visible in all scenarios. The participants could not see into the
room beyond the exits. One or both the exit signs were unlit to provide a
dissuasive sign effect to the participants according to the scenarios. The
dissuasive effect is hypothesized as lit exit signs are expected whereas unlit
exit sign(s) were present thus biasing the participants not to use that
particular exit. We refer to this as an updated sign in this work. A klaxon
alarm activated in the virtual environment when the evacuation scenario
started and ended when the participants’ avatars exited the room.

The order that the participants performed the scenarios was randomly chosen
and tracked. A within subjects design was used. The number of participants who
saw a given scenario as their first scenario, as opposed to later in the
experiment, are provided in Table 2. The participants had the opportunity to
complete all six scenarios. Participants who performed multiple scenarios may
have had a source of self-feedback [68] from prior scenarios, but this was
found to not be a significant influence in [23]. The path and the exit choice
of the participants were recorded. In order to avoid participants expecting
the same scenarios, the room for each scenario was given unique wall, floor
and ceiling textures. Additionally, two sets of room layouts were used in an
attempt to further reduce habituation as seen in Figure 2.

Table 2: Summary of scenarios which were seen as the first scenario and those which were not seen as the first. # | Scenario | First | Not First | Total
---|---|---|---|---
1 | Control | 11 | 46 | 57
2 | Sign | 10 | 45 | 55
3 | Crowd | 10 | 44 | 54
4 | Sign+Crowd | 10 | 44 | 54
5 | Sign-Crowd | 10 | 43 | 53
6 | Uneven Crowd | 10 | 47 | 57

###  2.2 Data Analysis Approach

The three analyzed summary statistics were 1) the participants’ probability to
follow directional information provided by the environment in the form of
signage and crowds’ exit choice, 2) the participant’s desire to change their
exit choice after initial decision, and 3) the time they took to make their
initial decision to choose an exit.

To determine the probability that the participants changed their exit
decision, P(change decision), the event "change decision" was defined as where
the participant started to walk towards an exit and then began to walk to the
other exit after moving at least one-fifth the lateral distance in the
direction of the former exit. This is consistent with the definition by Bode
et al. [9] in literature. The probability that the participant went to the
exit with a particular treatment, P(follow treatment), was found from the
proportion of the population which chose the exit indicated by the treatment.
When two treatments reinforced each other, e.g. (Sign+Crowd), the summary
statistics for each treatment was equal to the probability to follow the
treatment, P(Follow Sign)=P(Follow Crowd)=P(Follow Treatment). When two
treatments conflicted with each other, e.g. (Sign-Crowd), the follow treatment
summary statistic for each treatment was found by the probability of the
participants to choose the exit of the respective treatment. The decision time
was chosen as the time from the start of the simulation until the instant when
the participant started to move towards an exit. Since the start position of
the participant’s avatar was set by the simulation, the start of the movement
could be identified by tracking the participant’s avatar position in post-
processing. The initial exploratory movements such as turning to search for
exits or looking at the NPCs were thus not considered to be an actual
determined movement towards exit, but rather counted as part of the decision
time.

The significance level of $95\%$ was chosen for all models and tests
performed. Cochran’s Q-tests 111Ben Jann, 2004. ”COCHRAN: Stata module to test
for equality of proportions in matched samples (Cochran’s Q),” Statistical
Software Components S4444105, Boston College Department of Economics, revised
27 Oct 2004. https://ideas.repec.org/c/boc/bocode/s444105.html were used to
determine if the follow treatment and change decision statistics had
significantly different proportions for each relevant scenario. A pairwise
comparison between the scenarios and Control, Sign, and Crowd were performed
to test desired comparisons ex ante. A Sidak correction was used. A one-way
repeated measures ANOVA was performed to determine if there was significance
across participants and across scenarios for decision time. After performing
the ANOVA, a pairwise comparison between scenarios was performed with a Sidak
correction.

The data was also split into the participants who performed a given scenario
first and those who had completed the same scenario after completing another
scenario. The data was analyzed for each scenario separately to discover
learning effects from observation of the means and standard deviations.

###  2.3 Participants

Table 3: Demographics Summary Age Demographics |  |
---|---|---
Age (years) | Frequency | Percent (%)
18-24 | 27 | 44.26
25-34 | 17 | 27.87
35 and over | 16 | 26.23
NA | 1 | 1.64
Total | 61 | 100
Gender Demographics |  |
Gender | Frequency | Percent (%)
Man | 42 | 68.85
Woman | 18 | 29.51
Other | 1 | 1.64
Total | 61 | 100

Prior to any data collection, approval was sought from the local ethics
committee, the Institutional Review Board (IRB), University of Tennessee,
Knoxville. This study was approved by the IRB (approval number UTK
IRB-17-04159-XM). The data collection was conducted prior to the Covid-19
pandemic. The data collection itself was anonymized. No personally
identifiable information were recorded. The data have been stored according to
local IRB recommendations.

Participants were recruited from the staff and students at the University of
Tennessee, Knoxville campus. No incentives were provided for participation. A
total of 64 participants were recruited through an open call for volunteers.
However, only the data of 61 participants was used. Participants were eligible
if they were 18 years or older and had no known sickness to VR-based
experiments. The demographics of the participants are listed in Table 3. For
analysis purpose, gender was converted to 0 for participants who listed
themselves as man and 1 for participants who listed themselves as woman.

At least $10$ participants were desired to perform each scenario as the first
one and at least $30$ participants total for each scenario given the
precedence set by previous works [44, 54].

###  2.4 Procedure

Each participant was provided with an information sheet about the study and a
consent form, both approved for this specific experiment by the local ethics
committee. The participants had opportunity to discuss their participation and
potential benefit (to the scientific community) with the researchers before
they signed the consent form. After signing the informed consent form, each
participant was given a demographic survey. Next, the participants were asked
to wear an HTC Vive Pro virtual reality headset and placed in a "VR
preparatory setup" which consisted of 2 rooms surrounded by corridors and
several exits. The participant started from one of the rooms with an exit and
an exit sign above it. Upon leaving this room, the participant made a series
of left or right decisions to exit out of the building. The participants then
repeated this same scenario except with the addition of NPCs who also were
egressing the building. No data from the preparatory scenarios was used in our
analysis in this work, but it provided an opportunity for participants to get
comfortable with the immersive VR environment and the navigation control with
the joystick.

After the VR preparatory environment, the participants then completed as many
scenarios in the actual experimental setup (described in subsection 2.1) as
they were comfortable with. The number of scenarios varied between
participants due to their comfort with the VR and time involved. The 1-room
simplified environment, reported in this paper, was designed to test
fundamental exit choices rather than more complex time-dependent tests for
memory, familiarity, etc. This was done in an attempt to limit the VR exposure
time, which can cause motion sickness, but still provide the time needed to
develop the feeling of involvement and engagement in the evacuation process.
Moreover, a warning siren sound played through the VR headphones throughout
the experiment, to provide a sense of urgency during the experiment.

Finally, when the participants completed as many scenarios as possible, they
were asked to complete an exit survey. The participants were asked to rate how
different factor affected their decision-making during a scenario. The rated
factors were 1) the exit sign lit, 2) perceived time-to-exit, 3) follow crowd,
4) avoid crowd, and 5) previous exit choice(s). Lit sign was provided for
participants in the rating to inform us about how effectiveness of exit sign
lighting versus non-lighting in influencing their exit choice. Time-to-exit
was provided for participants to describe if the time to leave the room was an
important factor in determining their exit decisions. Both follow crowd and
avoid crowd were provided to ascertain the effect of the crowd on individuals.
The previous exit choice(s) was provided to determine what effect the previous
exits decision (when participating in multiple scenarios) had on the
participant’s exit choice. The rating scale was from 1 (lowest) to 5
(highest). Additionally, they were requested to elaborate their thought
process behind the ratings. A screenshot of the exit survey is provided in
Figure 3. The experimenter’s script for the entire data collection process is
provided in Appendix A.

Figure 3: A screenshot of participant’s exit survey

##  3 Results

###  3.1 Survey

Figure 4: A plot visualizing the mean score for each of the influencing
factors along with their standard deviation.

Fifty of the sixty-four participants completed the survey. The studied factors
were lit sign, time-to-exit, follow crowd, avoid crowd, and previous exit
choice(s). The participants were asked to specify how much they thought each
factor affected their exit choices. The values ranged from 1 as the lowest and
5 as the highest possible scores. The averages for the survey responses are
provided in Figure 4. The lit sign was listed as the most influential followed
by avoid crowd, time-to-exit, follow crowd, and previous exit choice(s) as the
least influential.

Table 4: Chose Left Summary Statistics: First vs. Not First # | Scenario | First (%) | Not-First (%) | Combined (%)
---|---|---|---|---
1 | Control | $27.3$ | $54.3$ | $49.1$
2 | Sign | $80.0$ | $82.2$ | $81.8$
3 | Crowd | $20.0$ | $54.5$ | $47.3$
4 | Sign+Crowd | $100.$ | $72.7$ | $77.8$
5 | Sign-Crowd (Sign) | $40.0$ | $16.3$ | $21.8$
6 | Uneven Crowd | $40.0$ | $46.8$ | $43.9$

###  3.2 First vs. Not-First Participant Results

The first versus not-first data for the chose left statistics were provided in
Table 4. The chose left statistics refers to the choice to take the left exit
from the participant’s perspective. The primary difference in the first versus
not-first results was observed in Control and Crowd scenarios. Small
differences occurred in the other scenarios but the behaviors of the
participants is similar for the first and not-first scenarios. The probability
of the participants to choose an exit was not significantly different from
chance when the not-first and first scenario participant data was combined for
the Control scenario; however, the participants who saw Control as their first
scenario chose the left exit $27.2\%$ of the time and those that did not see
the control as their first scenario chose the left exit $54.3\%$ of the time.
Eleven participants saw Control as their first scenario compared to forty-six
who saw it after their first scenario. The participants who saw Crowd as their
first scenario chose to avoid the crowd (left) $20.0\%$ of the time, but those
that saw Crowd after their first scenario chose to avoid the crowd $54.5\%$ of
the time. Ten participants saw Crowd as their first scenario and forty-four
participants saw it after their first scenario. These differences indicate a
potential right bias in the case of the Control and a repulsive effect of the
crowd when the participants had been exposed to previous scenarios.

Figure 5: Proportion of participants who followed the respective treatment(s) (combined percentage) Figure 6: Average decision time for each scenario Table 5:  Decision Time Summary Statistics: First vs. Not First # | Scenario | First (s) | Not-First (s) | Combined (s)
---|---|---|---|---
1 | Control | $5.12\pm 2.72$ | $10.4\pm 7.24$ | $9.39\pm 6.92$
2 | Sign | $5.82\pm 2.86$ | $6.23\pm 4.13$ | $6.16\pm 3.91$
3 | Crowd | $7.48\pm 5.19$ | $7.65\pm 5.01$ | $7.55\pm 5.01$
4 | Sign+Crowd | $6.20\pm 2.66$ | $5.66\pm 4.67$ | $5.86\pm 4.40$
5 | Sign-Crowd | $10.2\pm 5.40$ | $6.41\pm 6.07$ | $7.13\pm 6.09$
6 | Uneven Crowd | $10.1\pm 7.82$ | $10.4\pm 7.32$ | $10.4\pm 7.34$

The first versus not-first data for the decision time were provided in Table
5. Upon visual inspection, the data appeared to be within standard deviation
for each scenario as seen in Table 5. This would indicate that a learning
effect likely did not apply to the decision time. The proportion of
participants who followed the treatment for each scenario is presented in
Figure 5. The proportion of those who followed the treatment for Sign-Crowd
was given to those who followed the sign rather than the crowd.

###  3.3 Decision Time Results

Table 6: ANOVA Summary Variable | SS | df | MS | F
---|---|---|---|---
Scenario # | $4778$ | $5$ | $185.1$ | $8.09$
Residual | $6013$ | $263$ | $22.86$ |
Table 7: Pairwise Decision Time Comparison Summary Scenario | Contrast | Std. Err. | t
---|---|---|---
Sign vs Control | $-3.29$ | $0.914$ | $-3.60$
Sign+Crowd vs Control | $-3.63$ | $0.924$ | $-3.93$
Uneven Crowd vs Sign | $4.28$ | $0.915$ | $4.68$
Uneven Crowd vs Crowd | $3.06$ | $0.921$ | $3.33$
Uneven Crowd vs Sign+Crowd | $4.63$ | $0.926$ | $5.00$
Uneven Crowd vs Sign-Crowd | $3.49$ | $0.918$ | $3.81$

A summary of the decision time can be found in Figure 6. A one-way repeated
measure ANOVA was used to determine if there were significant differences
between the means of the treatment scenarios and across participants for the
decision time. The results can be found in Table 6. There was a significant
effect of scenario number on decision time, $F(5,263)=8.09,p=0.0000$, so we
reject the null hypothesis that the means of decision time are equal between
scenarios. Therefore, a pairwise comparison with a Sidak correction was used.
No ex ante relationships were assumed between scenarios and decision time. The
significant results were between Sign vs Crowd, Sign-Crowd vs Control, Uneven
Crowd vs Sign, Uneven Crowd vs Crowd, Uneven Crowd vs Sign+Crowd, and Uneven
Crowd vs Sign-Crowd as summarized in Table 7. The contrasts found agree with
the statistics provided in Table 5. This seems to indicate that the
contrasting treatments produced hesitancy compared to the single or
reinforcing treatments. Additionally, decision time is the lowest for
scenarios with the sign treatment, (Sign, Sign+Crowd, and Sign-Crowd).

Table 8: Pairwise Follow Information Comparison Summary Scenario | $\chi^{2}$ | p-value | Sidak
---|---|---|---
Sign vs Control | $10.8$ | $0.0014$ | $0.0167$
Sign+Crowd vs Control | $8.33$ | $0.0059$ | $0.0462$
Sign-Crowd (Sign) vs Control | $9.14$ | $0.0037$ | $0.0400$
Crowd vs Sign | $9$ | $0.0041$ | $0.0403$
Crowd vs Sign-Crowd (Sign) | $8.91$ | $0.0043$ | $0.0403$

###  3.4 Following Information Results

A Cochran’s Q-Test was used to determine if the null hypothesis that the
proportions of following information between scenarios were equal. The test
reported that significantly different proportions existed for follow
information between the different scenarios, $\chi^{2}=30.3,p=0.0000$. It was
desired ex ante to compare all relevant scenarios with different baseline
scenarios. The baselines scenarios were Control, Sign, and Crowd. Therefore, a
pairwise comparison with a series of Cochran’s Q-Tests with a Sidak correction
was used. As summarized in Table 8, [Sign vs Control, Sign+Crowd vs Control,
Sign-Crowd (Sign) vs Control, Crowd vs Sign, and Crowd vs Sign-Crowd (Sign)]
were significantly different. All of the significant pairwise comparisons
included the sign treatment.

##  4 Discussion

It is interesting to analyze how each scenario affected the participants’
probability to follow the treatment information, to change their decision, and
to quickly make their decision. While the Control scenario did not have
treatment information, it was expected to have an equal proportion of exits to
be chosen. This was not observed as the participants chose the right exit more
than the left exit, but only slightly so. The deviation from the expected
value was not significant but it may indicate a bias in the participants to
choose the right exit. It may be conjectured that this bias is a result of
right-handedness of the participants or the layout of the joystick rather than
any other factors. This handed bias was found in a work by Veeraswamy et al.
[69] that studied way-finding in buildings.

When the participants saw the Sign-Crowd scenario as their first one, their
preference to choose the exit with sign(right exit) or the crowd(left exit)
was approximately evenly distributed. However, a drop in following crowd(left
exit) was observed when participants saw the Sign-Crowd as a non-first
scenario. This is indicative of a learned repulsive effect of the crowd. This
may be related to the effect of the physicality of the crowd on the
participant while attempting to exit. Furthermore, the survey responses found
that the sign was the most important factor on average and avoiding the crowd
was the second most important factor. These responses agree with the observed
trends in the collected trajectory/exit choice data. Another potential
relationship was that the time-to-exit was the third most important factor
reported on the surveys, this may indicate that the participant wanted to
avoid the crowd to egress faster through the less crowded exit.

Participants generally chose to follow the lit exit sign over any other
treatment, and did so consistently for all scenarios with the lit sign
treatment. This agrees with the findings by Bode et al. [9]. The lit exit sign
treatment was always with the left exit except when both the sign and crowd
treatments were conflicting (Crowd left and Sign right). Additionally, the
participants made their decisions sooner, on average, when they chose to
follow the lit exit sign treatment. Furthermore it was observed that as exit
signs are expected to be lit, the unlit or dissuasive signs discouraged exit
utilization and the lit signs encouraged exit utilization in general. The
findings from these results agree with existing literature [41, 45, 46].

The effectiveness of the lit signs to attract participants towards that
particular exit and a similar effectiveness of the sign treatment when
reinforced by the crowd treatment may suggest an increased tolerance for
queuing and thus a cause for congestion when individuals trusted the
information provided by the environment.

##  5 Limitations

While the data is correct and useful for furthering the understanding of exit
choice during an evacuation, it is important to recognize that there are
limitations present from the recruitment pool and from the experiment set up
itself. The participants were primarily recruited from the graduate student
population of the University’s College of Engineering which will likely not
encompass the full range of the population demographics. Lastly, additional
scenarios for the uneven crowd would have been beneficial for better
understanding of crowd phenomena, such as the effects of number of people in
the crowd. These can be addressed in future studies. Also, there will always
be a question about the transferability of VR-based or screen-based study to
real world evacuation situation. It is to be noted that VR provides a good
compromise in terms of immersive experience and also a safe environment for
data collection.

##  6 Conclusions

The effect of lit exit signs and crowd movement on the exit choice behaviors
of participants in an immersive VR experiment were studied. Specifically, the
effect of an updated exit sign, different proportions of crowd movement toward
each exit, and the effect of reinforcing and conflicting effects between the
sign and crowd treatments were studied. Crowd and lit (updated) sign
treatments were found to produce significant effects for following the
treatment and time to initiate egress towards an exit. The sign treatment was
found to be effective at reducing the decision time and increasing utilization
of the exit with the lit sign. The utilization of the exit with the lit sign
was not significantly reduced when the crowd treatment was reinforcing the
sign treatment. Based on the results, the sign was an effective treatment and
the crowd had an insignificant repulsive effect but was overall ineffective.
These results agree with the literature. Specifically that the crowd can be
repulsive [8, 20, 21, 22] and a dissuasive sign can produces the intended
effect [41, 45, 46]. Furthermore, a potential learning effect was found,
possibly due to the effect of the physicality of crowding more realistically
perceived in immersive VR experiments. This may have also shown a difference
between immersive VR and non-immersive screen-based experiments [8, 9, 10]
because the participants are able to perceive the crowd more realistic in an
immersive VR experiment they reacted to the crowd more strongly one way or
another.

Summarizing the implications of this work for human factors/evacuation
planning/building design there is an indication that a queuing tolerance
exists when crowds all move towards a trusted source (lit sign). Crowds are
ineffective at encouraging utilization of a particular exit and may have a
slight repulsive effect if the evacuee becomes uncomfortable with the crowding
or feels delayed by the queuing. A sign being updated through
lighting/unlighting is an effective solution to influence exit choices.
Particularly, when exit sign and crowd reinforced each other, the increased
tolerance for queuing may causes increased congestion, thus a dynamic lighting
of signage could be used to alleviate the crowding problem at
bottlenecks/exits. This idea has potential to help handle a mass evacuation
better in terms of average egress time for the crowd. In other words, dynamic
signage has potential to help prevent over-crowding of exits which could in
turn help with better flow of the crowd during emergency evacuation. This is
an interesting direction for future research.

## Acknowledgement

The authors would like to thank Mr. Hema Sumanth for his help with the data
collection. This research was funded by NSF CPS Program (Award Number
1932505).

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## Appendix

##  Appendix A Building Evacuation Experimenter Script

  * •

Have the participant sign the informed consent form.

"Please read and sign the informed consent form. If you have any questions or
concerns, then please let me know."

  * •

Have the participant seat in seating area for the simulation.

"Please take a seat here."

  * •

Instruct the participant of the controls and demonstrate with the controller.

"The left joystick controls the movement and the right joystick controls
turning."

  * •

Explain the importance of the experiment.

"We are testing evacuation behaviors during emergency situations to better
provide safe evacuation outcomes by understanding what factors influence their
evacuation decisions."

  * •

Help the participant place the VR headset on their head.

  * •

Start simulation and perform VR preparatory Scenarios.

  * •

Explain VR preparatory experimental setup.

"You will need to walk through the initial doorway and make a left or right
decision, then walk through another doorway and make a left or right decision,
and finally proceed until a final exit is reached."

  * •

For the second set of the preparatory scenarios, explain to the participant
that there will now be others who are also trying to exit.

"As far as you are aware, this scenario will be the same as the last except
now others will also be trying to evacuate."

  * •

After completing TR 2, have the participant take off the headset and complete
survey 1 and the demographic form.

"Please remove the headset and complete this survey and this demographic
form."

  * •

Ensure that the participant has time to be outside of the headset and ask if
they are okay to continue.

"Are you okay to continue?"

  * •

If they are okay to continue, then inform them that there will be two exits
from a room with provided exit information that they can choose to use. Also,
upon their exit, they will receive a message stating “Success” for a
successful exit or “Hazard” for an unsuccessful exit. Tell them to inform you
if they have seen the message.

"There will be two exits from a room with provided exit information that they
can choose to use."

  * •

After completing the scenarios, please have the participant take off the
headset and perform survey 2.

"Please take off the headset and complete this survey."

  * •

Thank the participant for contributing towards evacuation safety.

"Thank you for contributing towards evacuation safety."