Dem based group behavior model

Journal of Natural Sciences Research www.iiste.org
ISSN 2224-3186 (Paper) ISSN 2225-0921 (Online)
Vol.4, No.13, 2014 – Special Issue
1
Selection and peer review by the scientific conference committee under responsibility of the Australian Society
for Commerce, Industry and Engineering
DEM-based Group Behaviour Model and Its Impact in Tsunami
Evacuation Simulation
Noorhazlinda Abd Rahman*
Eiji Harada Hitoshi Gotoh
Department of Civil and Earth Resources Engineering, Kyoto University, Katsura Campus, Nishikyo-ku,
Kyoto, 615-8584, Japan
* E-mail of the corresponding author: abd.noorhazlinda.76v@st.kyoto-u.ac.jp
Abstract
Most of the existing crowd behavior models simulate crowd motions based on interactions among isolated
walking individuals. In reality, a crowd not only contains individuals who walk alone, but walk in groups as well.
Hence, the interactions should be extended among individuals who belong to the same group as well as among
individuals and different groups’ persons. Limited number of works has incorporate group behavior in crowd
behavior models. This topic is still new and its impact in crowd dynamics is more to be discovered. Driven by
the belief of group behavior modeling can lead to a more realistic crowd motions, this paper is objectively to
model group behavior in crowd behavior simulation and its impact in tsunami evacuation process. For this,
observations and video footages of public area had been conducted as a basis of group behavior model
development. Relying on observations made, the existing Distinct Element Method (DEM)-based Crowd
Behavior Simulator for Disaster Evacuation (CBS-DE) is extended to incorporate group behavior by introducing
in-group and out-group interactions. Validation on the extended CBS-DE was executed in two stages work; (1)
looking for a suitable grouping scenario from the observations conducted; and (2) the simulation of a suitable
scenario in (1). It shows that the CBS-DE with group behavior model can realistically simulate group behavior.
Later, the new model was demonstrated in the simulation of tsunami evacuation process. The effects of the group
behavior model were shown in the context of evacuation completion time by comparison with the CBS-DE
without group behavior model.
Keywords: DEM, group behaviour, in-group interaction, out-group interaction.
1. Introduction
The study of crowd behavior has initiate great interest in pedestrian facilities and traffic management studies
(Helbing et al., 2001, Teknomo, 2006, Usher et al., 2010, Gotoh et al., 2012), and in the study of crowd safety
during mass events or evacuation processes (Helbing et al., 2000, Gawronski et al., 2011, Kwak et al., 2013).
Different crowd behavior models have been developed to date (e.g., cellular automata-, social force-, agent-
based- and DEM-based model). In this paper, the DEM-based model is a preferred model. It models each
individual distinctly by tracking the trajectory and rotation of each individual in a domain in order to evaluate
individual’s position and orientation through the calculation of the interactions between individuals and between
individual and boundaries. Hence, the Crowd Behavior Simulator for Disaster Evacuation (CBS-DE) has been
developed by implementing the DEM. The capability of this simulator has been demonstrated in the previous
study (Gotoh et al., 2004, 2008 2009).
Most of the existing crowd behavior models simulate crowd motions based on interactions among isolated
walking individuals. In reality, majority of individuals do not walk alone, but in groups who have social ties and
purposely walk together. For example family members walk side-by-side at a park or in a shopping mall friends
stay together and maintain the group during their motion. Grouping is a common phenomenon in a crowd and as
discussed by Aveni (1997), crowds contained both isolated individuals and persons in groups. Moussaid et al.
(2010) in their article claimed that about 70% of observed pedestrians in a commercial street are walking in
group with the most frequent size of group is two to four members. Group behaviour is still new and has not
been incorporated in the existing crowd behaviour models. Its impact on crowd dynamics is still more to be
discovered.
Some of the works incorporates the influence of grouping in crowd dynamic model are Sarmady et al. (2009),
Singh et al. (2009), Moussaid et al. (2010) and Qiu et al. (2010). The groups of persons may have an important
effect to the flow of a crowd. Sarmady et al. (2009) reported that groups of persons act as a virtual barrier since a
group normally move slower than individual person and therefore slow down the crowd. Similarly as discussed
by Qiu et al. (2010), different group structures can result in slow movement and affect evacuation efficiency in

2
emergency situations. Meanwhile, Moussaid et al. (2010) discussed the group walking behavior from the
sociological sense of view and its impact on the crowd dynamics by extending the social force model to include
social interactions among people walking in groups. Singh et al. (2009) modified the existing DEM-based model,
the CrowdDMX, in studying the subgroup behaviour in crowd dynamics simulation. Previously the CrowdDMX
(Langston et al., 2006) was limited to modelling individual move independently which led to discrepancies in
the simulation with unrealistic splitting of subgroups.
The works mentioned are limited to the pedestrians’ behavioral studies. Driven by the belief of group modelling
can lead to a more realistic crowd behavior, this paper is objectively to model group behavior in crowd behavior
simulation and its impact in tsunami evacuation process. In this contribution, the existing CBS-DE of DEM-
based simulator is extended to incorporate group behavior on the basis of observations made. Part of activities
involved were observations at different spotted locations of a crowd. Hence, Section 2 of this paper describes the
outcomes of observations of crowd behavior at public area. Later, in Section 3, the outline of group behavior
model is presented. This model is an individual-based model of person behavior which describes how a person
interacts with other persons in the same group and with other persons in different groups. The validation between
simulated scenario and the real situation was compared. This part of explanation is presented in Section 4. A two
stages work were conducted in validation work; (1) looking for a suitable grouping scenario from the
observations conducted; and (2) the simulation of a suitable scenario in (1). In Section 5, a further application of
the extended CBS-DE with group behaviour model is demonstrated in simulation of tsunami evacuation process.
The effect of the new model is shown in the context of evacuation completion time by comparison with the CBS-
DE without group behaviour model. Finally in Section 6 the conclusion is presented.
2. Observations of the Crowd
2.1 Empirical Observations
The first part of this study involved observations and video recordings at public areas. The observations were
made under normal condition at two locations; a park area (location A) and an area of travel activities near a
train station (location B). Two locations are observed to ensure the data collected is reliable. The selected public
areas also suitable to view persons walk for a sufficient length of time to evident their walking behaviour.
There were 1,325 people observed in this study with 162 people at location A and 1,163 people at location B.
The proportions of people belong to a group at location A and B is 67.1% and 42.1%, respectively. Higher
frequency of groups was found at location A than location B. This is possibly related to the different activities of
locations. Location A is a sightseeing place where more groupings observed and location B involved travel
activities where mostly single persons travel. Table 1 and Fig. 1 show the detail of frequency distribution of the
group size observed. From the findings, groups composed of two persons are the most frequent, following by
groups of three and four persons, whereas groups of size five and larger are infrequent. Further, the average
walking velocity of observed people in groups were determined. With the growing size of a group, the average
walking velocity of a group is decreasing. This is evident from the data summarized in Table 1.
3. DEM-based CBS-DE with group behaviour model
In this study, the model developed is an individual-based model of person behavior which describes how a
person interacts with other persons in the same group and with other persons in different groups. A crowd is
considered consists of single persons and groups of persons. An isolated walking person is considered to be a
group of itself. The motion of a crowd is defined based on local interactions in a crowd. Local interactions
Table 1. Frequency distribution of group size
Group
Size
No. of
Group
Population
Group Percentage
(%)
Group Proportion
Average Walking
Velocity (m/s)
A B A B A B A B A B
Single 28 435 28 435 32.9 57.9 0.33 0.58 1.19 1.21
2 45 242 90 484 52.9 32.2 0.53 0.32 1.15 1.17
3 6 55 18 165 7.1 7.3 0.07 0.07 0.95 1.02
4 4 16 16 64 4.7 2.1 0.05 0.02 0.86 0.86
> 5 2 3 10 15 2.4 0.4 0.02 0.00 0.80 0.70
Total 85 751 162 1163 100.00 100.00 1.00 1.00

3
Figure 1. Group size proportion
were model from two aspects, in-group and out-group interactions. The in-group interaction refers to the
interaction among persons who belong to the same group, while the out-group interaction refers to the interaction
of a person with other persons of different groups and with environment. The existing simulator, the CBS-DE, is
extended to include these in-group and out-group interactions.
Each person is governed by the translational and rotational equations of motion in which the motion of each
person is described by the vector addition of the separate force terms: an autonomous driving force, Faw and
interaction forces, Fint and a torque, Tint due to interaction force. The complete governing equations are describes
as follows;
; (1)
(2)
where m and I are the mass and moment of inertia of a person, respectively. The person is represented as a
cylindrical element. xi is the positional vector of the person i and ωi is the angular velocity of the person i. a is
the acceleration vector. Each person i is assumed to accelerate up to the specific equilibrium velocity, vlimit by the
autonomous walking force Faw.
The interaction forces are defined as in Eq. 3;
(3)
where Fin is the in-group interaction force acting between a person and his/her relative neighbor in the same
group and Fout is the out-group interaction force acting between groups and group-wall element.
In the existing CBS-DE, the interaction forces only consider the repulsion effects which defined based on the
overlapping between two isolated persons elements or between person element and wall element. Here
Figure 2. The domain of the interaction
the estimation of the in-group and out-group interactions forces are made by the Voigt model in reference to the
awint
i
t
m FF
x
+=2
2
d
d
int
i
t
I T
ω
=
d
d
outininhi FFF +=
aF maw =

4
physical contact area introduced for each person to hold the group formation and the perception domain for each
group to avoid collision with other groups. Figure 2 illustrates the domain of the interaction.
3.1 In-group interaction
The in-group interaction, Fin is defined by the attraction effects which postulated based on the separation
between two person elements who belong to the same group. In addition, to avoid overlap between persons
elements in the same group, the repulsion effect also included. To hold the group in formation, the reference is
made based on the relative position of each person in the group to his/her relative neighbor who belongs to the
same group. In reference to Fig. 2, the 2nd
person is a relative neighbor to the 1st
and the 3rd
person.
Total the in-group interaction force on the person i is described as follows:
(4)
(5)
where repF is the net physical repulsive force and attF is the net psychological attractive force, igrepf is the local
in-group physical repulsive force and igattf is the local in-group psychological attractive force. These local
forces are given as follows;
(6)
where en
and et
denote the components of the repulsive or attractive force due to the spring and dn
and dt
denote
the components of the repulsive or attractive force due to the dashpot. kn
and kt
are the spring constants in the
normal and tangential direction, respectively, while cn
and ct
are the dashpot constants in the normal and
tangential direction, respectively. Dx is the relative displacement between contact elements, “con.” indicates the
in-group interaction, “pre” indicates the previous numerical time step and “*” indicates the contact elements.
Each person is having the physical or psychological contact area. The psychological attractive force will be
triggered if the relative position between the person i and his/her relative neighbor j is satisfying the condition in
Eq. 7. Meanwhile, if the person and his/her relative neighbor are in physical contact, the physical repulsive force
is triggered, as in Eq. 8.
(7)
(8)
where xi is the positional vector of person i, xj is the positional vector of relative neighbor j and dh is the diameter
of person (0.379 m).
3.2 Out-group interaction
To avoid collision with other groups, the out-group interaction considers the repulsion effects which defined
based on the overlapping between isolated group elements or between group elements and wall elements. For
this, the formulation is developed by considering a group as a single entity (see Fig. 3). The gravity centre of
each group i, xgi is determined by calculating the arithmetic mean of positions of persons in a group i, as in Eq. 9.
Besides, the mean velocity of each group i, vgi is determined by finding the arithmetic mean velocity of persons
in a group i, as in Eq. 10.
(9)
(10)
where xk is the positional vector of a person in a group i, n is total number of persons in a group i. Each group is
having the perception domain with the radius 4dh and angle of vision 120o
. The repulsive forces will only be
2
hjhi
ji
dd +
>- xx
2
hjhi
ji
dd +
£- xx
å=
=
n
k
k
n
gi
1
1
xx
attrepin FFF +=
( ) ( )
åå ¹¹
+=
ij
igatt
ij
igrep
ff
def +=.con
ttnn
dede +++=
ttttt
*
prennn
*
nn
*
pre
ckck ***
xxexxe D+D++D+D+= . .
å=
=
n
k
k
n
gi
1
1
vv

5
triggered if Eq. 11 is satisfied;
; (11)
where dW is the diameter of the wall (0.379 m).
The interaction between isolated group elements or between group element and wall element is defined as the
following;
(12)
repF is the net repulsive force between the group i and j. The net repulsive forces between groups and
group-wall elements are quantified by the summation of the local repulsive forces between groups and group-
wall elements.
(13)
where ogrepf is the local out-group repulsive force. Similarly, the local out-group repulsive force is
determined in the similar way in Eq. 6 above. And this time, “con.” indicates the out-group interaction.
3.3 Model parameter
In the CBS-DE, the model parameters for the acceleration as the driving force of a person need to be calibrated.
In this study, the model parameters in the CBS-DE with self-evasive action model (Gotoh et. al., 2012) are used.
For the normal case, the acceleration is taken as 0.837 m/s2
. Meanwhile, the magnitude of specific equilibrium
walking velocity, vlimit is taken as 1.47 m/s.
As for the interaction force, the Voigt model is a mechanical joint composed of a spring, dashpot and frictional
slider is introduced. The spring and dashpot are arranged in both normal and tangential directions on the
tangential plane. In the present study, the spring and dashpot constants using in the DEM are given according to
the setup procedure proposed by Kiyono et al. (1996, 1998) as shown in Table 2.
Table 2 Model parameter
Physical Psychological
Parameter Value Unit Parameter Value Unit
n
phyk 4
101.26 ´ N/m
n
psyk 106.01´ N/m
t
phyk 2
106.30 ´ N/m
t
psyk 3.01 N/m
n
phyc 3
101.69 ´ N s/m
n
psyc 2
101.17´ N s/m
t
phyc 2
103.77 ´ N s/m
t
psyc 102.61´ N s/m
(a) Perspective view (b) Schematic diagram
Figure 3. The observation trap and the initial position
repout FF =
å= ogreprep fF
hdgjgi 4£- xx WdxWgi 4£-x
( )0F =attQ

6
4. Validation of Group Behaviour Model
Two procedures were performed in validation work and only location B was considered. The first procedure was
looking for preferred scenario from the footages. The footages were manually examined. The criteria for the
preferred scenario were contra-flow and involved multiple groups including persons who walk alone. The chosen
scenario was then saved in sequence images of png format. Afterwards, the number of groups, number of people
in a specific group and the average walking velocity of each group were determined.
In the second procedure, simulation of the chosen scenario was performed by employing the CBS-DE with group
behaviour model. Concurrently, simulation also performed using the existing CBS-DE. Further the simulated
results were compared to the footage from the observation.
4.1 Preferred Scenario from the Footage
For validation work, the observation trap was as shown in Figure 3. Figure 3(a) shows the perspective view
taken from the video footage and Fig. 3(b) shows the schematic diagram of the observation trap. The dimension
of the observation trap is 17.5 m length and 5.5 m width.
Beforehand, the footage at location B was converted into sequence images of png format. After reviewing the
entire video, one preferred scenario was considered. The scenario is in interval of 10 s length. Then, the
snapshots (png format) of the preferred scenario were sorted for every 2 s. The initial position of persons in
specific group and persons who walk alone were determined using the in-house code written in MAYA
Embedded Language (MEL), named Human Behavior Simulator (HBS), which is a plug-in for Autodesk®
MAYA® software to project the position of persons on a 2D plane. The initial position for preferred scenario is
as shown in Figure 3.
4.2 Validation Outcomes
The outcome of the validation works for the preferred scenario can be evident in the snapshots shown in Figure 4.
Model 1 is a model without group behavior and Model 2 is a model with group behavior. The simulations were
conducted and comparisons were made with the observation. From Model 1, obviously
Photos Observation
Without group behavior
Model 1
With group behavior
Model 2
t = 2.0 s
t = 6.0 s
t = 10.0 s

7
Figure 4. The snapshots of validations
the unrealistic situations can be seen clearly particularly at time t = 6.0 s where groups G1 and G3 began to split
when met head-on with other person and group in contra-flow. Hence, Model 1 which being the conventional
CBS-DE shows significant limitations in the group behavior.
In Model 2, the simulator models grouping realistically which show reasonably good agreement with the
observation. These can be evident in the snapshots, where the groups formation were maintained during the
motion.
5. Tsunami Evacuation Process
5.1 Simulation Setup
In this contribution, the CBS-DE with group behaviour model is demonstrated further in tsunami evacuation
process. A local airport located at the Langkawi Island, Malaysia is chosen as the study area. The airport is
facing the Andaman Sea. Historically, the giant earthquake off Sumatra Island caused serious damage by tsunami
along the Andaman Sea coast. Hence, it is possible to study the tsunami evacuation process at the Langkawi
Airport, Malaysia for the disaster prevention and mitigation.
Figure 5. Floor plan and the initial position of person at
the airport
Figure 7. Time series of the evacuation completion
time

8
Table 3. The composition of the person distribution at
the airport
Group
size
No. of
Group
No. of
people
Group
percentage
Group
proportion
Velocity
[m/s]
Single 232 232 51.9 0.52 1.21
2 136 272 30.4 0.30 1.17
3 51 153 11.4 0.11 1.02
4 20 80 4.5 0.05 0.86
5 8 40 1.8 0.02 0.70
Total 447 777 100 1.00
Figure 6. Evacuation route and the trap area
Figure 5 shows the floor plan together with the initial position of persons in the airport. The composition of
persons distribution was observed during survey conducted. The composition and the walking velocity applied to
persons either walk alone or in groups are as shown in Table 3.
Two simulations (Sim-1 and Sim-2) of tsunami evacuation process were performed. Sim-1 refers to simulation
using the CBE-DE without group behaviour model, while Sim-2 refers to the simulation of evacuation process
by employing the CBS-DE with group behaviour model. For both simulations, the same persons composition
and walking velocity (as in Table 3) are employed. The distinction between these two simulations was discussed
from the viewpoint of the evacuation completion time and the grouping formation maintain. In this simulation,
the evacuation place was set at the level 2 of the nearest hotel (as shown in Fig. 6).
5.2 Effect of Group Behavior Model
To demonstrate the effect of the group behaviour, the trap area is chosen as shown in Fig. 6. The length of the
trap area is 100 m. The trap area is used to record the motions of the evacuees and the formation of groupings.
t = 280 s t = 330 s t = 380 s
Sim-1Sim-2
Figure 8. The top view snapshots of the trap area for Sim-1 and Sim-2
Figure 7 shows the time series of the evacuation completion time for the Sim-1 and Sim-2. From the figure it is
clearly shows that the groups of persons have an important effect to the flow of a crowd. The time to complete
the evacuation for the Sim-2 is 1,536 s which is 26.6% longer than Sim-1, 1,213 s. Groups move slower than
individual person and therefore slow down the crowd, hence have a tendency to affect evacuation efficiency in
emergency situations.
Figure 8 shows the snapshots at the trap area for the Sim-1 and Sim-2.To show the grouping phenomenon, the
colours of persons for different group size were used. Persons who walk independently were denoted by red,
while the groups of size 2 were coloured with green, the groups of size 3 were coloured with blue, the groups of
size 4 in orange and the groups of size 5 were coloured with purple.

9
From the snapshots, it can be seen the splitting of the group occurred in the Sim-1 in which the person are
walking independently. Meanwhile, in the Sim-2, the improvement of the CBS-DE can be evident. For instance,
at the times t=280s, 330s and 380s, the group formation was maintained during the motion. The group of size
four and five were still walk in the group. These demonstrate the validity of the algorithmic changes made to the
existing CBS-DE.
6. Conclusion
This paper is objectively to model group behavior in a crowd and to show its impact in tsunami evacuation
process. The existing simulator, the CBS-DE, is extended to incorporate the group behavior. Previously, the
CBS-DE was developed based on the interaction among the isolated individuals which bring significant
limitations in simulate grouping phenomenon as can be evident in the validation work. The in-group and out-
group interactions were introduced in the CBS-DE to overcome the limitations. The in-group interaction
maintained a group so that persons who belong to the same group keep together while walking. And the out-
group interaction was to ensure persons in the different groups are not colliding each other.
The improvements were validated on a visual basis through simulation and direct comparison to the filmed
footage. The development is reasonably successful in simulating the group behavior. Further, the CBS-DE with
group behavior was demonstrated in the tsunami evacuation process. The impact of the group behavior during
evacuation can be seen in comparison to the CBS-DE without group behavior model. The groups of persons
have an important effect to the flow of a crowd. Groups move slower than individual person and therefore slow
down the crowd, hence affect evacuation efficiency in emergency situations.
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