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ICTIS.2017.8047846

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2017 4th International Conference on Transportation Information and Safety (ICTIS), August 8-10, 2017, Banff, Canada
Simulation on Occupant Evacuation during Aircraft
Emergency Based on Cellular Automata
Tianjin, China
xiaofang165@126.com
DU Hong-bing
College of Flight Technique
Civil Aviation University of China
Tianjin, China
hongbin_du@sina.com
FENG Zhen-yu
Tianjin Key Laboratory of Civil Aircraft Airworthiness and
Maintenance
Civil Aviation University of China
Tianjin, China
mhfzy@163.com
YU Xiao-fang
College of Flight Technique
Civil Aviation University of China
Abstract—The characteristics of occupant evacuation during
aircraft emergency were analyzed according to emergency
evacuation guidance systems, light intensity and evacuees.
Evacuees included occupant’s physiological characteristics and
their psychological characteristics. The occupant’s physiological
characteristics were mainly related to panic, pressure and
conformity. The psychological characteristics involved in waist
circumference, age, gender and height. The above factors were
applied to the aircraft emergency evacuation simulation on
cellular automata. Then, the cabin environment model and the
occupant model were presented. The occupant model included
individual pre-reaction time submodel, cabin channel velocity
submodel and escape time submodel of exit. Study shows that the
results could provid an auxiliary verification information for
aircraft initial certification, and could provide strategic guidance
for airline to carry out personnel evacuation and emergency
training for employees.
actual situation. In Vacate Air, all particles moving to a same
target did not match the actual situation that passengers
evacuated to more than one exit. The parameters of ETSIA
were modified according to A320-100 validation experimental
data and published literature data, and ETSIA was used to
verification experiment and the design of the cabin. AAMAS
took into account the impact of passenger panic on the escape
process
during
the
airfraft
emergency
incident.
EvacuSimulation mainly studied the population density effect
on the model, but it did not consider the individual
physiological and psychological characteristics effect on it.
GUI only created the individual physiological characteristics
on occupant evacuation process. CabinEvacu researched the
influence of gender, age and degree of panic, but it did not
considered the effect of the different cabin area to personnel
speed.
Keywords—simulation model; cellular automata; emergency
evacuation; civil aircraft
The research determined the impact factors of the civil
aircraft emergency evacuation through aircraft, the external
environment, and evacuees. These factors were applied to
cellular automata theory. Then, the cabin environment model
and the occupant model were presented. These models were
more consistent with the actual situation. The research results
could be used as an auxiliary verification method for initial
certification, and they could also provide a theoretical basis for
the design of civil aircraft emergency group simulation
software.
I.
INTRODUCTION
The 90 seconds occupant evacuation experiment is one of
the effective methods to judge the aircraft emergency
evacuation capability. At present, this experiment mainly
recruited volunteers to complete the real evacuation in the
aircraft enviorment meeting airworthiness criterion. However,
this approach requires a lot of spending and there is a risk and
hazard for the participants. With the maturity of computer
simulation method, it reflects the urgency that the 90 seconds
emergency evacuation experiment is carried out by the method.
II.
Von Neumann and others put forward the cellular automata
theory which core idea was dispersing the time and space. The
theory included cellular, time step, neighborhood, and rule,
introduced as follows:
And so far, there are some mature evacuation simulation
models in the aircraft emergency, such as air EXODUS[1~2],
Vacate
Air[4~5],
ETSIA[6~8],
AAMAS[9],
DEM[3],
[10]
EvacuSimulation ࠊGUI(Graphical User Interfaces)[11~12] ࠊ
CabinEvacu[13~14] and so on. In these models, the individual of
air EXODUS was designed to move towards the nearest
available exit in the case of no unit guidance or identification.
The individual movement in the DEM model was considered to
follow the Newtonian kinematics theorem. But many
parameters of individual behavior and motion do not match the
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CELLULAR AUTOMATA
x
Cell distributions in the discrete physical environment
and it has two states: "Occupy" and "idle". It is the
most basic element of cellular automata.
x
Time step means the discrete time size where the cell
updates the state.
2017 4th International Conference on Transportation Information and Safety (ICTIS), August 8-10, 2017, Banff, Canada
x
B. Cell Status and Update Rules
The cellular state was expanded according to the different
cabin areas. Then, a new update rule for the cellular state was
set up.
Neighborhood and rule mean the state of the cell is
updated with its own state and other cells within its
neighborhood. Update rule as follows:
Cit+1=f {Cit, Ci+1t, Ci+2t,…Ci+nt}
(1)
Cit+1=f {Cit, Ci+1t,…Ci+nt,}+φ{Pit,Pi+1t…Pi+mt}
t
Where Ci+n is the state of the n-th cell aside I cell at time t;
Cit+1 is the state of the ith cell at time t+1.
Where Ci+nt is the state of the n-th cell aside I cell at time t;
Cit+1 is the state of i-th cell at time t+1; Pi+mt is the property of
the m-th cell aside I cell at time t; f㹹㹻 is the effect of n cell
states aside i-th cell to the i-th cell state at time t; φ㹹㹻 is the
effect of m individuals aside i-th cell to the i-th cell state at
time t.
Cellular automata method could simulate complex behavior
with a simple way, and it could set different rules to reflect the
interaction between individuals and the characteristics of the
movement. Its grid is divided into uniform and large size, the
general size is 0.5m * 0.5m, or 0.5m * 0.4m. The size reflects
the accuracy of the simulation.
V. AIRCRAFT EMERGENCY EVACUATION SIMULATION
MODELS
III. AIRCRAFT EMERGENCY EVACUATION FACTORS
According to the aircraft emergency evacuation factors and
the emergency evacuation experimental data, taking the Boeing
737-800 aircraft as an example, cabin environment model and
occupant model were established.
A. Aircraft
According to the experimental condition of CCAR-25
Airworthiness Standard for Transport Aircraft, the aircraft
environmental characteristic was analyzed from the aircraft
structure and the emergency evacuation guidance systems.
A. Cabin Environment Model
1) Classification of the cabin area
B. Aircraft External Environmental
The occupant’s evacuation behaviors are affected directly
by the fire growth rate, smoke concentration, toxicity,
temperature, light intensity and so on. When an aircraft
accident happens, it is accompanied by fire, lighting system
paralysis, etc. Light intensity was the only considered factor in
the 90s certification trials of aircraft because of the limitation
of experimental condition.
According to the characteristics of personnel flow, the
cabin was divided into four areas: the obstacle, the front and
rear passenger gate, the main channel, the inter-seat channel.
The obstacle area is the places where the occupant could not
cross, including seat, partition, kitchen, etc. The front and rear
passenger gate areas refer to the places between the front
boarding gate and the front service door, and between the rear
boarding door and the rear service door. These areas are more
spacious than the cabin channel, so they could accommodate
more people. At the same time, they are also crowded easily.
The main channel area is the aisle that is used to connect the
front passenger gate and rear passenger gate. The inter-seat
channel is between the two rows of seats in the same row. That
was divided into three regions due to the different location,
which includes economy class seat channel, first class seat
channel, and the distance between the seats at the wing exit.
C. Evacuees
Combined with the emergency evacuation process of civil
aircraft and individual cognitive process, the ones of
occupant’s physical and psychical characteristics were
analyzed. The physical factors included age, sex, waist
circumference, height, etc. The psychical factors included
panic, pressure, conformity, etc.
IV. CONSTRUCTION RULES OF AIRCRAFT EMERGENCY
EVACUATION SIMULATION MODEL
2)
A. Determination of Physical Region Representation
According to the characteristics of cabin environment and
individual distance, the fine grid was chosen to divide the cabin.
In order to reduce the computational pressure of computer, and
to describe aircraft internal environment and the differences
between individual accurately, the area where the individual
distance was large and the movement between occupants was
small was set by setting the grid cell. Fig.1 (a) is the schematic
of the large size grid cell, Fig.1 (b) is the schematic of the
improved grid cell.
(a)
(b)
Fig.1. Different grid cellular schematic
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(2)
Division of the cabin grid
In order to improve the simulation accuracy, the 0.15m *
0.15m was used as the basic grid size with reference to the size
of Boeing 737-800 aircraft. Then, according to Chinese adult
human body size and the cabin actual size, the number of the
basic grid that was occupied by personnel and the different
cabin area was determined, see TABLE I.
TABLE I.
THE NUMBER OF BASIC GRID AND DISTRIBUTION OF
INDIVIDUAL
Basic Grid
Number
The Number of
Individual
Occupant
2×2
/
Economy class seat channel
6×2
3
Channel between the first row of seats
in economy class
6×2
3
The seats at the wing exit
6×3
3
First class seat channel
6×4
2
Zone Name
2017 4th International Conference on Transportation Information and Safety (ICTIS), August 8-10, 2017, Banff, Canada
The main channel
2×128
64
Male
0.93
The front and rear passenger gate area
14×6
21
Female
1.07
Economy class seat
2×2
/
1447-1625
1.09
1625-1676
First class seat
3×2
/
0.99
Bulkhead between first class and
economy class
6×2
/
1676-1727
0.95
1727-1803
0.97
Kitchen at the front of the cabin
6×2
/
1803-2006
0.99
584-787
0.85
787-864
0.90
864-965
0.95
965-1041
1.08
The cloakroom at the front of the cabin
6×2
/
The toilet at the rear right of the cabin
6×2
/
Two bathrooms at the rear left of the
cabin
6×6
/
a.
3)
Gender
Height ᧤mm᧥
Waist ᧤mm᧥
1041-1575
Continue the previous column
Definition of the grid attribute
ti1=[(α+β)×(KiA+KiG+KiH+KiW)]/5(i=1,2,3…)
2) Cabin channel velocity submodel
Cranfield University had conducted an eight-day
emergency evacuation experiment under straight channel and
dark scene, but there was an accident on the fourth day in the
experiment. The individual moving speed was determined by
analyzing these experimental data except the fourth-day's[16].
And the distance from the individual to the exit was divided
into three regions that were (2㸪5] m, (5㸪8] m, (8㸪11] m.
According to the each regional experiment data, the velocity
characteristics and distribution of each region were obtained,
see TABLE III and Fig.2.
TABLE III.
1) Pre-reaction time submodel
In initial reaction process, the pre-reaction time submodel
with unlocking the seat belt and go to channel from the seat as
the core was presented. The model considered the effects of
age, sex, height and waist on emergency evacuation. And the
impact proportions of these attributes were determined by
referring to The Aircraft Accident Statistics and Knowledge
(AASK) database V4.0 [15], see TABLE II.
Age
Impact Proportion
18-22
0.83
23-32
0.89
33-42
0.97
43-52
1.06
53-65
1.25
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THE INDIVIDUAL SPEED OF MOVEMENT AT DIFFERENT
DISTANCES FROM EXIT
Name
THE IMPACT PROPORTIONS OF OCCUPANT’S PHYSICAL
CHARACTERISTIC TO THE PRE-REACTION TIME
Attribute Ordering
(3)
Where i is the different passengers; ti1 is the pre-reaction
time of i; α is the average time for passengers to unlock the seat
belt; β is the average time for passenger leave from the seat,
KiA is the impact factor of i’s age, KiG is the impact factor of i’s
gender, KiH is the impact factor of i’s height, KiW is the impact
factor of i’s waist.
B. Occupant Model
According to the aircraft emergency evacuation process, the
occupant escape models were constructed in stages.
Base Attribute
Continue the previous column
The pre-reaction time submodel was presented as follows.
There were four main types of the cabin grid attributes,
which included grid type, grid location and scope, grid
structure, grid competition rules. The grid type indicates
whether the grid is in a state of traffic. "0" means the accessible
area grid, "2" means the non-accessible area grid, and “4"
means other obstacle grids in the non-accessible area. The grid
location and scope were the location basis for the virtual
personnel's movement. The grid structure indicates that the
individual mobility constraints in different regions. It was
divided into three attributes: the grid structure of inter-seat
channel, the main channel, the front and rear passenger gate.
When there is a competitive condition in the evacuation
process, the occupants’ movements are following rules: men
have priority over women when they are in the same position;
when they have the same gender, under 50 years old is better
than the over-50s; when they have the similar age, the small is
better than large in waist size; when there is no difference in
the nature of the personnel, the system chooses the individual
randomly to occupy the position.
TABLE II.
1.23
b.
Distance Partition
(2,5]m
(5,8]m
(8,11]m
Average speed
1.14
0.53
0.36
Maximum speed
2.54
1.09
0.53
Minimum speed
0.50
0.23
Standard deviation
0.54
0.30
0.16
0.07
2017 4th International Conference on Transportation Information and Safety (ICTIS), August 8-10, 2017, Banff, Canada
TABLE IV.
Frequency
THE IMPACT PROPORTIONS OF THE AGE AND SEX TO THE
MOVEMENT SPEED (F)
Age Group
35
30
25
20
15
10
5
0
18-22
23-32
33-42
43-49
50-52
53-62
Male
1.1
1.1
1.1
1.1
0.88
0.88
Female
0.97
0.97
0.97
0.97
0.78
0.78
Gender
The cabin channel velocity model was presented as follows.
Vi2=f×Vi0(i=1,2,3
)
(4)
WhereVi2 is I’s movement speed in the cabin channel, f is
the impact factor of i’s physical characteristics, Vi0 is the
reference speed.
Moving Speed (m/s)
3) Escape time submodel of wing exit
According to the report Study on the Influencing Factors of
Type-Ę Wing Exit Emergency Evacuation in Cabins[18], the
individual with different age, sex, waist, and height had effects
on the time that through the Type- III wing exit and the time
were determined. According to each attribute classification
corresponding to the time variance, the impact proportions of
each physiological characteristic to the escape time were
determined, see TABLE V.
(a) (2, 5] m
Frequency
60
50
40
30
20
10
0
TABLE V.
THE AVERAGE WING EXIT ESCAPE TIME OF INDIVIDUAL
WITH DIFFERENT PHYSIOLOGICAL CHARACTERISTICS
Base Attribute
Moving Speed (m/s)
Age
(b) (5, 8] m
Frequency
35
30
25
20
15
10
5
0
Gender
Height (mm)
Moving Speed (m/s)
Waist (mm)
(c) (8, 11] m
Fig.2. The regional velocity profile
Escape Time (s)
23-32
1.44
33-42
1.57
43-52
1.71
53-65
2.01
Male
1.49
Female
1.70
1447-1625
1.74
1625-1676
1.59
1676-1727
1.52
1727-1803
1.55
1803-2006
1.58
584-787
1.35
787-864
1.43
864-965
1.51
965-1041
1.72
1041-1575
1.96
Impact
Proportion
1.34
0.263
0.299
0.024
0.414
The escape time model of wing exit was presented as
follows.
The results showed that the moving space and individual
speed located in the long range were directly influenced by
forwarding individual, and needed longer waiting time to
escape. The individual barrier-free moving speed couldn’t be
reflected well by these people’s moving speed data. For these
reasons, the average velocity (1.14 m/s) from the nearest exit
area ((2㸪5] m) was selected as the reference velocity (Vi0).
This model considered these attributes, including age and sex.
And their impact proportions were determined by reference to
McLean GA’s study [17], see TABLE IV.
ti3=aMiA+bMiG+cMiH+dMiW(i=1,2,3
)
(5)
Whereti3is i spends time through the wing exit, a is the
factor’s proportion of age to individual through the wing exit, b
is the factor’s proportion of sex to individual through the wing
exit, c is the factor’s proportion of height to individual through
the wing exit, d is the factor’s proportion of waist to individual
through the wing exit, MiA is the age of individual I
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Attribute
Ordering
18-22
2017 4th International Conference on Transportation Information and Safety (ICTIS), August 8-10, 2017, Banff, Canada
corresponds to the time through the wing exit, MiG is the sex of
individual I corresponds to the time through the wing exit, MiH
is the height of individual I corresponds to the time through the
wing exit, MiW the waist of individual I corresponds to the time
through the wing exit.
4) Escape time submodel of non-wing exit
In order to obtain the average non-wing exit escape time of
individuals with different gender and degree of panic, a
jumping slide experiment was established. The impact
proportions of an individual with sex and panic to escape time
was determined by analyzing the experimental results, and the
impact proportons is shown in TABLE VI. At the same time,
the study summarized that the average decline time of the first
body in each group was 1.5s.
TABLE VI.
Base
Attribute
Gender
Level of panic
Fig.3. the simulation result
B. Analyzing the Simulation Experimental Result
Fig.4(a) shows the frequency distribution curve of the
evacuation time by analyzing the evacuation time of the 1000
simulation experiments. As can be seen from the diagram, the
frequency distribution of the evacuation time is similar to the
normal distribution. And the results were compared with
previous studies, it found that the result was similar to E.R.
Galea’s research results, see Fig.4 (b). The result proves the
validity of the experiment.
THE TIME INTERVAL OF INDIVIDUALS WITH DIFFERENT
CHARACTERISTICS TO JUMP SLIDE
Attribute
Ordering
Average Time
Interval (s)
Male
0.732
Female
Class A
0.545
0.550
Class B
0.859
Class C
1.165
Impact
Proportion
0.271
Frequen
800 cy
700
600
500
400
300
200
100
0
60
0.729
The escape time submodel of non-wing exit was presented
as follows.
ti4=jNiG+kNiP+1.5(i=1, 2, 3
)
(6)
Whereti4 is I spends time from the non-wing exit to ground,
j is the factor’s proportion of sex to individual through the
non-wing exit, k is the factor’s proportion of panic to
individual through the non-wing exit, NiG is the sex of
individual I corresponds to the average separation time through
the non-wing exit, NiP is the panic of individual I corresponds
to the average separation time through the non-wing exit.
65
70
75
80 85 90
Evacuation
time (s)
(a)
VI. SIMULATION EXAMPLES AND RESULTS
A. Simulation Example
With Visual Studio 2010 development platform, the aircraft
emergency evacuation simulation software (EES-air) was
developed using Visual C++ according to the cabin and
occupant model. In the example of Boeing 737-800, the
evacuation plan had conducted 1000 simulations using the
EES-air software, the simulation test plan is shown in TABLE
VII, and the result is shown in Fig.3.
(b)
TABLE VII.
EXPORT DISTRIBUTION PLAN
Export Part
Front (the boarding doors and the
service doors)
Middle(wing exits)
Rear(the boarding doors and the
service doors)
Fig.4. Evacuation time-frequency distribution of 1000 simulation experiments
Distribution Plan
VII. CONCLUSION
The 1st, 2nd, 11th, 12th, 13th, 14th, 15th,
16th, 17th, 18th row
The 19th, 20th, 21th, 22th, 23th, 24th,
25th, 26th row
The 27th, 28th, 29th, 30th, 31th, 32th,
33th, 34th, 35th, 36th, 37th row
Based on the above research and analysis, the following
conclusions are drawn:
1) The aircraft environmental characteristics were the
objective condition for emergency evacuation and effected the
evacuation efficiency. The characteristics of occupant’s
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2017 4th International Conference on Transportation Information and Safety (ICTIS), August 8-10, 2017, Banff, Canada
physical and psychical played an important role in the
evacuation.
[7] Hedo, J. M., ͆Modelización Computacional del Ensayo de Evacuación
2) According to the experimental data and the influence
factors of occupant evacuation in aircraft emergency, cabin
environment model and occupant model were presented. These
models were more consistent with the actual evacuation
situation.
[8]
3) The experiment was a local experiment, so the
validity of the data combination was reduced. In further studies,
a complete evacuation experiment should be considered.
[10]
[9]
[11]
ACKNOWLEDGMENT
This work was partially supported by the Civil Aviation
University of China.
[12]
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[13]
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