=Paper=
{{Paper
|id=Vol-2590/short33
|storemode=property
|title=Empirical Study on Modeling of People Behavior in Emergency
|pdfUrl=https://ceur-ws.org/Vol-2590/short33.pdf
|volume=Vol-2590
|authors=Ilya Viksnin,Julia Lyakhovenko,Nikita Tursukov,Sergey Chuprov,Ekaterina Sozinova
|dblpUrl=https://dblp.org/rec/conf/micsecs/ViksninLTCS19
}}
==Empirical Study on Modeling of People Behavior in Emergency==
https://ceur-ws.org/Vol-2590/short33.pdf
Empirical Study on Modeling of People
Behavior in Emergency
Ilya Viksnin[0000−0002−3071−6937] , Julia Lyakhovenko[0000−0001−7396−2831] ,
Nikita Tursukov[0000−0003−3848−1981] , Sergey Chuprov[0000−0001−7081−8797] , and
Ekaterina Sozinova[0000−0003−0338−783X]
ITMO University, Saint-Petersburg, Russia
wixnin@mail.ru, lyakhovenko.kam@gmail.com, stepingnik@gmail.com,
chuprov@itmo.ru, ensozinova@itmo.ru
Abstract. In this paper, we propose an advanced model and experi-
mental study results considering an unorganized group behavior in case
of an emergency. An approach to simulation of human behavior is based
on the Dirk Helbing and Peter Molnar Social Force Model. This model
depends on the informational impact on the individual behavior of group
agents. The proposed model is adapted for crowd behavior simulation in
emergencies. The model implies the behavior of an agent when it tries
to determine the optimal direction to a safe place at any point in time.
When the agent chooses the direction of movement, it takes into account
both the number of people around, the average traffic of the crowd, and
the presence of a nearby source of danger. Based on the model, a crowd
behavior simulator was implemented in the AnyLogic simulation soft-
ware, which reproduced the behavior of people during the tragedy at
the Lame horse nightclub (December 5, 2009) in Perm, Russia. To verify
our approach we conducted three groups of simulations and compared
our model with the existing method of group behavior in a multi-level
branched room, and with the method of moving agents along the shortest
path. The results, obtained by the proposed model, most closely match
the real data on the tragedy.
Keywords: Multi-agent system · simulation · crowd behavior
1 Introduction
At present, the design of premises intended for a large number of people
implies that in emergencies crowd make the proper decisions to leave it. Because
of this, the time spent on evacuation is wasted irrationally, which subsequently
increases the number of victims. There are many tragic examples, such as the fire
on 27th January 2013 at the Kiss night club, which began around 2:30 a.m. (2:00
a.m. according to other sources) in Santa Maria, Rio Grande do Sul, Brazil. The
fire was caused by the careless use of pyrotechnics in the club. That fire killed
Copyright c 2019 for this paper by its authors. Use permitted under Creative Commons License
Attribution 4.0 International (CC BY 4.0).
2 I. Viksnin et al.
242 people, and 630 were injured [1]. Due to the availability of only one narrow
emergency exit, a crush began, which increased the number of victims.
This event raised the importance of applying unorganized group (crowd)
behavior modeling methods in the events of an emergency situations (ES) to
optimize the preliminary training process and minimize victims in case of panic.
In the present work, for modeling such situations, as well as for formulating
the methods, a multi-agent approach is used [2]. Based on this approach, the
crowd is characterized as a group of agents with the ability to communicate and
interact with each other.
2 Related Work
From the existing global approaches to modeling the behavior of an unor-
ganized group, several types can be distinguished. The simplest approach is to
transfer the area along which the agents move to a discrete form, as well as to
set the rules for the agent to move to a particular area. Additionally, the behav-
ior model can be based on Newtonian mechanics or gas dynamics [3]. In these
cases, the behavior of agents in certain situations is determined by the physical
properties of the elements of the group. One of the more complex approaches is
the use of multi-agent systems that describe both the behavior of the agent and
its interaction with other participants during the simulation.
Currently, hybrid approaches [4–6] to model crowd behavior are increasingly
being used. This allows to create more flexible models with realistic behavior, as
well as reactions of groups of agents in the system.
3 Materials and Methods
The basis of the mathematical model of human behavior in ES, used in the
simulator, is the Social Force Model of Dirk Helbing and Peter Molnar [7–9].
The proposed model includes space and distance to different exit areas. It also
covers external and internal influences on humans as part of a weakly organized
mass: physiological, social and informational.
Information influence in this work is understood to be the impact of the
environmental awareness of other group members by individual visible agents
during the evacuation process. This implies the inability of the agent to choose
the optimal direction of movement to achieve the goal (salvation) based on the
choice of the average trajectory of other agents of the crowd.
To verify our mathematical model, it was implemented in the software sim-
ulator and tested. As a framework for simulation, AnyLogic 8.4 was used. The
built-in Pedestrian library allows to building models with a large amount of in-
formation about pedestrian movements. With the help of the graphical interface
and Java language functionality, it is possible to simulate the movement of agents
in the premises and to program information interaction logic between them.
Empirical Study on Modeling of People Behavior in Emergency 3
4 Our Approach to Crowd Behavior Modeling
In this section, we describe our mathematical model of the crowd behavior
in ES and approach to verify it.
The proposed model considers the catastrophe as an event with a set of time
intervals T = {t0 , t1 , . . . , tn }, i.e. it can be assumed that the system exists at
different points in time. The crowd itself is a set of agents P = {p0 , . . . , pn },
which is divided into two subsets - the number of ordinary people and the num-
ber of informed (stewards). Each agent, at any given time, solves the problem of
finding the optimal direction of movement from its location to a safer one. The
main factors determining the direction of agents movement d(pi ) are the assess-
ment of movement direction of other visible agents Si = s(pi ) and the ”weight”
w of their direction for the agent. In this case, the ”weight” of other agents
directions to a particular agent depends on the agent’s knowledge about the sit-
uation (whether a certain agent was seen before an emergency or not, whether it
is informed or not) w = {winf , wus }, where winf – “staff” agent’s weight of the
motion direction and wus – “usual” agent’s weight of motion direction, which
also can be divided into sets winf = {wsinf , wnsif n }, wus = {wsus , wnsus }, where
wsinf ,wsus – weight of the direction, which agents seen before the evacuation and
wnsif n ,wnsus – weight of the direction, which agents not seen before the ES, and
wsinf ≥ wnsif n ≥ wsus ≥ wnsus . In addition, each agent is defined by its loca-
tion in space (coordinates): coordpinf , coordpus . It follows that the direction of
agent motion can be represented as a function depending on the average motion
direction of other agents (1):
dtpi = f wdt−1 t−1 t−1
pi = f w inf dpinf + f wus dpus , (1)
where pus , pinf ∈ s. This depends on their number, mass, and direction of
movement at the present and previous time points (2):
( )
∗
× |si | + coordt−2
E dt−1
pi pi
dtpi = coordt−1 dt−1 coordpt−1
pi , E pi = , , (2)
i
|si | + 1
where E ∗ dt−1
pi is an averaged movement of all agents at time t−1, including
the coordinates of the agent pi at t time moment, calculated by (3):
coordt−1 coordt−1
P P
∗
winf pinf + wus pus
dt−1
E pi = =
|si |
coordt−1 coordt−1 (3)
P P
wsinf + wnsinf
P psinf t−1 P pnsinf t−1
+ wsus coordpsus + wnsus coordpnsus
= .
|si |
Expressing E ∗ dt−1
pi × |si | through the agents movement direction and their
locations through the weight coefficient, we obtain the final values of the agent
pi movement direction (4):
4 I. Viksnin et al.
coordt−1 coordt−1
P P
wsinf psinf + wnsinf pnsinf
P t−1 P t−1 t−2
t
t−1 + wsus coord psus + w nsus coord pnsus + coordpi
dpi = coordpi , .
|si | + 1
(4)
Figures 1 (a)-(c) demonstrate the choice of the agent’s movement direction.
Three cases are considered at time points t − 1 and t. Figure 1 (a) shows a slight
change in movement direction by the agent when it detects the change in crowd
movement. Figures 1 (b)-(c) show the behavior of an agents when they noticed
informed agents.
Fig. 1: Agent’s choice of movement direction: (a) - when crowd is detected; (b) - when
”staff” is detected; (c) - when crowd and ”staff” is detected.
Available public data on the tragedy in the night club ”Lame Horse” were
used to simulate crowd behavior in such a scenario. The number of agents in
the simulation corresponds to the actual data on the visitors in the club at the
time of the fire. The blue dots in Figure 2 represent informed agents (waiters
and staff members), while the green dots represent ordinary people (visitors). In
addition, the simulation conditions include fire, and smoke, which spreads at a
certain speed through the area. The velocity of fire propagation and the toxicity
Empirical Study on Modeling of People Behavior in Emergency 5
Fig. 2: Building plan and agents allocation in the club on the night of the tragedy.
of the smoke are based on available data in [10], on the material ignited during
the tragedy (polypropylene). Data used in the simulator:
– number of agents: 322;
– number of waiters (informed agents): 20;
– two outputs;
– evacuation start time: ≈10 seconds after the fire starts;
– time to fill the room with smoke: ≈60 seconds.
The simulation starts with the distribution of agents throughout the club
area. After the evacuation starts, the agents start moving towards the nearest
exit. In doing so, the agent continuously performs verifications such as fire de-
tection, the detection of an informed agent, or the calculation of the median
movement of the crowd. These factors influence the further movement of the
agent. The color indicates the amount of health of the agent so that once the
agent is red, it loses consciousness and stops moving. These agents, as well as
those who have lost their health, are counted with a simulator. Agents who
managed to get to the exit are also counted by the simulator.
While the simulator is running, data on the number of agents’ health is
recorded, as well as their assigned status, where: ≤ 10% health agent is dead;
> 10% and ≤ 40% health agent alive with low health points; > 40% of health
– agent is alive. In addition, information is recorded about what exactly caused
the agents to lose their health: fire or smoke. The evacuation process is shown
in Figure 3.
6 I. Viksnin et al.
Fig. 3: The process of evacuating people during a fire.
5 Empirical Study
Two models, based on existing solutions for modeling crowd behavior were
also implemented in the simulator to verify our approach. The first model, with-
out taking into account the behavior of the crowd, is implemented by the built-
in tools of AnyLogic, in which the agents move to the nearest exit. The second
model is based on panic group behavior in a multi-level branched area. In this
model, the agent’s movement is based on the existing room structure and the
presence of other agents on its path.
As a result, three groups of experiments were conducted. Each group includes
322 agents, 20 informed agents, two exits (main and emergency). Time to evac-
uate is 10 seconds from the beginning of the fire. Most people are located on the
dance floor. When the fire begins to spread, the smoke begins to fill the club.
6 Results
Table 1 compares the results obtained using the developed model with the
actual data on victims, survivors, and dead people. 1st model is a model without
a behavioral pattern, 2nd model is a model with a pattern of behavior in a
branched room.
From the comparison of the results it can be seen that the data obtained on
the simulation using the proposed model differs from real data by an average
Empirical Study on Modeling of People Behavior in Emergency 7
Table 1: Comparison of the the simulation results.
Simulation results
Groups of people Real data
1st model 2nd model Proposed model
Dead 156 202 186 151
Injured 78 14 14 45
Healthy 166 120 135 171
of 16%, which is significantly less compared to other models. This shows the
accuracy of the developed model relative to the well-known models. This fact
allows saying that the proposed model is more accurate in comparison with other
simulated models.
7 Discussion
The results obtained with the help of the simulator are very close to the real
data about the victims of the tragedy. Therefore, the obtained model of behavior
of an unorganized group allows to carry out the analysis of the emergencies that
have occurred and to test the existing and projected premises to ensure their
safety.
8 Conclusion
In the present work, we propose a model of crowd behavior in emergencies and
its validation. The model based on the information impact on the group, which
allows bringing the data closer to real conditions. Existing approaches to model
human behavior and their differences of the proposed model are described. The
model was implemented in the AnyLogic software simulator. To verify our model,
the real scenario of the emergency was simulated and the results were compared
with two existing approaches to model crowd behavior. Obtained results allow
saying that our model could produce more relevant results, that are closer to
real data compared with the simulated approaches. The presented model can be
useful in the analysis of existing and planned premises to ensure safety in the
event of panic in emergencies.
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