Emergency evacuation simulation of commercial aircraft [original]

Vol.:(0123456789)

SN Applied Sciences (2021) 3:446 | https://doi.org/10.1007/s42452-021-04295-z

Research Article

Emergency evacuation simulation ofcommercial aircraft

Influence of body- and behaviour parameters

AndreasGobbin1 · RamanKhosravi2· AndreasBardenhagen1

Received: 24 September 2020 / Accepted: 28 January 2021 / Published online: 10 March 2021

Abstract

In order to receive certification approval for new products, aircraft manufacturers have to comply with the specifications

regarding cabin evacuation. In case of real evacuation trials, agent-based simulation can be deployed, as they are a less

cost-intensive mean of analysing passenger behaviour during the evacuation of commercial aircraft. This paper aims

at examining the suitability of agent-based simulation software to reproduce passenger behaviour during evacuation

processes. For this purpose, the algorithms and methods of the software PATHFINDER are introduced. Besides, the cabin

of a single aisle aircraft is reconstructed in a high-density configuration using software-specific tools. A representative

passenger distribution is implemented according to EASA regulations. Evacuation simulations for a single-aisle aircraft

are conducted taking EASA standards into account. The effect of vital parameters such as walking speed, body dimension,

conflict behaviour, collision response, acceleration time and exit allocation on evacuation times are examined. Results

are discussed and examined for plausibility in order to determine whether evacuation simulations of commercial aircraft

are possible using agent-based simulation software.

Keywords Evacuation simulation· Agent-based modelling· Human behaviour· Body characteristics· Conflict scenarios

List of symbols

Global cost factor (–)

Local cost factor (–)

Ctarget

Total cost factor (–)

F(x)

Functional value (–)

G(x)

Previous cost factor (–)

H(x)

Remaining cost factor (–)

Distance travelled in room (m)

Scaling factor for

kdd

(–)

kdd

Current room distance penalty (–)

kgt

Global travel time cost factor (s)

klt

Current room travel time cost factor (s)

Current room queue time cost factor (s)

kqh

Is set to

1−p

for the most recently chosen target

and 1.0 for all other targets (–)

Current door preference scaling factor of travel

(–)

Costs and global cost share (–)

tlt

Current room travel time (s)

tgt

Global travel time (s)

Current room queue time (s)

A∗

A-star search algorithm

AFT Exit in the rear of the cabin

CD Comfort distance

CRT Collision response time

CS Certification specification

EASA European Union Aviation Safety Agency

FWD Exit in the front of the cabin

GUI Graphical user interface

NTSB National transportation safety board

* Andreas Gobbin, andreas[email protected]; Raman Khosravi, ramankhosravi@yahoo.de; Andreas Bardenhagen,

andreas[email protected] | 1Chair ofAircraft Design andAerostructures, Technische Universität Berlin, 10587Berlin,

Germany. 2Technische Universität Berlin, 10623Berlin, Germany.

Vol:.(1234567890)

Research Article SN Applied Sciences (2021) 3:446 | https://doi.org/10.1007/s42452-021-04295-z

OECD Organisation for economic co-operation and

development

OWE Over wing exit

PAX Persons approximately

1 Motivation

In addition to steadily increasing passenger numbers, air

traffic is also showing success in terms of safety. Compared

to the last 60years, the frequency of fatal incidents has

steadily decreased [1, p. 9 f.]. However, an analysis of 60

NTSB reports classified as survivable aircraft accidents

shows that 78% of the fatalities occurred after impact.

Of these, 95.4% were due to smoke inhalation and burns

caused by slow and inefficient evacuation procedures. In

contrast, immediate evacuation could increase survival

rates by 98% [2, p. 8]. This shows that rapid and successful

evacuation of the passenger cabin has a significant impact

on occupant safety and survival. Therefore, according to

the certification requirement, it must be demonstrated

that in the event of an emergency evacuation, it is pos-

sible to safely exit the aircraft within 90s. The exact word-

ing of EASA CS-25.803 (c) [3] states that an aircraft with a

seating capacity of more than 44 passengers must dem-

onstrate that the maximum seating capacity, including

the number of crew members, required by the operating

rules for which certification is sought, can be evacuated

from the aircraft to the ground within 90s under simulated

emergency conditions. Not more than half of the available

emergency exits may be used in this process [3, pp. 1-APP

J-1] (Appendix J, paragraph (p)). Compliance with this must

be proven by an actual demonstration using the test crite-

ria outlined in Appendix J of the CS-25, unless the agency

determines that a combination of analysis and testing pro-

vides equivalent data that would be obtained by an actual

demonstration [3] (Paragraph 25.803 (c)).

Because certification testing is associated with high

health risks to the test subject [4, p. 5], it should be con-

sidered whether agent-based numerical evacuation

simulations are a sufficient alternative to the specified

test procedure. In accordance with the last paragraph of

the approval paragraph CS-25.803 (c) [3], this paper aims

to investigate the influence of behavioural and conflict

parameters in agent-based simulation on the total evacu-

ation time. Using agent-based simulations, it is possible

to model evacuation scenarios under consideration of

human behaviour. This enables an analysis of passenger

behaviour in stressful situations and provides information

for the safe design of passenger cabins. The results of the

following considerations are intended to provide an initial

approach to which data and analyses might be used to

represent human behaviour in evacuation simulation.

In the last 20years, more and more simulation models

have been developed that allow to take human behaviour

into account. This has shifted the focus from a pure motion

simulation to an additional consideration of psychological,

social and physiological factors. With regard to competi-

tion-oriented behaviour during an evacuation situation,

the publication of Kirchner etal. [5] and the work on the

consideration of emotions by the Autonomous Agents and

Multiagent Systems Model of Toghar and Al Barghuthi [6]

as well as Miyoshi [7] are mentioned as examples.

Various simulation environments are available to simu-

late evacuation scenarios considering human behaviour

using agent-based algorithms. The three continuously

developed tools EXODUS from the University of Green-

wich [8], STEPS from Mott MacDonald Simulation Group

[9] and PATHFINDER by Thunderhead Engineering Con-

sultants, Inc. [10] were validated in the study by Cuesta

etal. [11, p. 241 ff.]. All abovementioned tools were able

to replicate real-life evacuation tests with good results,

although each of the tools has individual strengths and

weaknesses. It should be noted that only for the tool EXO-

DUS an explicit extension for evacuation simulations of

commercial aircraft (airEXODUS) [12] exists. When using

STEPS and PATHFINDER, the modelling must be adapted to

simulate aircraft evacuations. Further tools for evacuation

simulation of commercial aircraft are listed in the publica-

tion by Togher and Al Barghuti [6, p. 277 f.].

This paper shows the suitability of numerical simula-

tion for the evacuation of commercial aircraft by using

the simulation software PATHFINDER from Thunderhead

Engineering Consultants, Inc.

The subsequent section of this paper gives an overview

of the simulation software and examines the simulation

algorithms therein. Afterwards, the simulation procedure,

reference model and assumptions for this study are pre-

sented. In the fourth section, the influence of body char-

acteristics and parameters pertaining to conflict scenar-

ios are described and the results obtained are discussed.

Finally, the sensitivity of the evacuation time to variations

of behavioural patterns is evaluated based on the results

of the parameter study and the adequacy of the simulation

software PATHFINDER is assessed.

2 Simulation software andalgorithm

PATHFINDER is an agent-based simulation software in

which structures can be modelled by a two-dimensional

navigation geometry. This navigation geometry provides

the basis for the simulation of motion flows in a room. The

creation is done by rooms, that are connected by doors,

whereby any geometry can be replicated. The limitation

of the flow rates allows an individual adaptation of the

Vol.:(0123456789)

SN Applied Sciences (2021) 3:446 | https://doi.org/10.1007/s42452-021-04295-z Research Article

geometry as well as a realistic steering of the occupants

[13]. In PATHFINDER obstacles are not shown explicitly.

However, it is possible to provide free spaces in the navi-

gation geometry where no movement simulation of occu-

pants can be performed [10]. In order to assign individual

behaviour the individual occupants, so-called profiles

are used which can be defined via the GUI. This allows to

define characteristics like body dimension, movement

speed and conflict behaviour. Behavioural patterns are

used to define various instructions for occupants. Thus,

profiles and behaviours provide the possibility to classify

occupants. Figure1 shows the Graphical User Interface of

PATHFINDER.

The movement of an occupant to a specific destination

takes place in three steps: Path Planning, Path Generation

and Path Following. A path to a specific target is created

on the navigation geometry using the A* algorithm. The

actual simulation is performed in steering-mode, which

is using the method of inverse steering. For this purpose,

several potential directions of motion (sample directions)

are evaluated based on a cost objective function and the

most cost-efficient direction is selected [10].

The results of the simulation are analysed and evaluated

based on the total evacuation time, flow rates and number

of passes per exit. In addition, the evacuation process can

be animated and visualized, which enables an accurate

analysis of conflict situations and solutions.

2.1 Path planning

Path Planning describes the process that determines the

path to a goal. Each occupant has information about local

and global targets as well as the expected waiting time.

Since the shortest route does not necessarily represent the

fastest route, a cost calculation is made for each occupant

for the respective destinations. The principle “locally quick-

est” is applied in the context of Path Planning, in order to

determine the fastest route to an occupant’s destination.

The principle is based on the assumption that an occupant

has knowledge about the presence of a room’s doors and

the queues at these doors. Besides, an occupant knows the

distance from one of the doors to the occupant’s ultimate

destination. It is conceivable that a room has multiple local

targets (e.g. multiple doors) [10, p. 18]. In this case, the

least amount of time is regarded as the target function,

whereby each occupant is assigned the door with the low-

est cost. Based on this decision, the occupant’s path to this

door is generated. The total costs

Ctarget

to a goal result

from the summation of local and global costs [10, p. 19].

Fig. 1 Graphical User Interface of simulation software PATHFINDER

Vol:.(1234567890)

Research Article SN Applied Sciences (2021) 3:446 | https://doi.org/10.1007/s42452-021-04295-z

describes the local and

the global share of the

costs. The calculation of the individual cost elements is

given in formulas 2 to 5 [10, p. 19].

The factor

in formula 5 is a function of the Current room

distance penalty

kdd

. If an occupant’s distance covered in a

room

exceeds

kdd

, the costs related to the Current room

travel time

tlt

are doubled. When setting this value low, an

occupant would prefer covering lower distances to lower

travel times. The Current room travel time

tlt

describes the

time necessary to reach a target at maximum walking

speed. The Current room travel time cost factor

klt

is a fac-

tor used to control the significance of travel time related

costs. The factor

and the distance travelled in a room

are inputs for the factor

which influences the local

and global

share of the costs. The Current room queue

time

denotes a time estimate an occupant must wait at a

door as function of the occupant’s current position and the

door flow rate. The Current room queue time cost factor

is a factor used to control the significance of the wait-time

related costs. The factor

kqh

can be calculated as a function

of the current door preference

. The current door prefer-

ence

controls the frequency of an occupant switching

its current goal. When set to 1, an occupant is prevented

from switching the initially chosen target, while for a value

below 1 it is free to switch its local target. The Global travel

time

tgt

represents the time an occupant needs to travel

from a local goal (e.g. a door) to a final exit (seek goal).

Similarly to the factors in formula 1, the factor

kgt

is used

to increase or decrease the significance of the associated

cost parameter

tgt

[10, p. 18].

2.2 Path generation

A path must exist for each local destination found by

path planning. To find this path, an A* search algorithm is

applied to a triangular navigation grid. Since A* belongs

to the class of complete and optimal algorithms, an opti-

mal solution is always found whenever a solution exists

[14, p. 104 ff.]. Furthermore, due to its optimal efficiency

(1)

Ctarget =Cl+Cg

(2)

Cl=max (pd

⋅

klt

⋅

tlt|kqh

⋅

tq)

(3)

⋅

kgt

⋅

tgt

(4)

d⋅

(5)

log(2)

kdd

property, there is no other algorithm that finds a faster

solution using the same heuristic.

The operation of the algorithm is based on the shortest

paths problem. To solve the problem, a cost calculation is

made for each waypoint on the grid based on an estima-

tion function. The heuristic applied here utilizes the linear

distance between the start and destination waypoints as

the lowest barrier. Since a connection between two points

is never shorter than the linear distance and thus does not

fall below the lowest barrier, A* always remains optimal in

this case [15, p. 13 f.]. A function value

F(x)

is allocated to

each known waypoint for optimal pathfinding. This speci-

fies how cost-intensive a path between the start and des-

tination waypoints is, especially by taking the waypoint

into consideration [14, p. 102].

In formula (6),

G(x)

denotes the costs incurred to reach

the waypoint under consideration from the origin.

H(x)

includes the remaining costs from the waypoint under

consideration to the target waypoint, which arise from

the previous heuristic.

Because of the navigation mesh in use, there is no

straight path between the start and destination waypoints.

To ensure this, the so-called "string pulling" method is used

in addition to the A* algorithm [16]. Hereby, a smoothing

of the path is realized, whereby a real movement pattern

of the occupants is created. Figure2 shows the generated

path of an occupant based on the A* algorithm.

2.3 Path following

For following the path, a combination of inverse steer-

ing algorithm [17, p. 4 ff.] and collision handling is imple-

mented. This allows occupants to react to obstacles and

deviate from the path to reach the actual target [10]. The

inverse steering method evaluates several discrete motion

directions based on the angle between the old and new

path. Each new path is combined with the cost factors

resulting from speed loss, acceleration- and waiting time.

Finally, the path with the lowest cost factor is set as an

alternative path. If the cost factor of the original path is

(6)

F(x)=G(x)+H(x)

Fig. 2 Occupant’s path and waypoints on the mesh [10, p. 21]

Vol.:(0123456789)

SN Applied Sciences (2021) 3:446 | https://doi.org/10.1007/s42452-021-04295-z Research Article

lower in spite of the waiting time at the obstacle, the fol-

lowing is set on this remaining path [10].

Due to the specific steering behaviour, each occupant

has an individual behaviour for collision handling, by

which he/she has a direct influence on the cost evaluation

during the path finding. During the movement, each occu-

pant tries to keep a predefined behaviour with respect to

distance to walls or other occupants as well as his/her

speed when walking, avoiding and turning. As steering

also offers the possibility of assigning specific characteris-

tics such as age, panic behaviour and body dimensions, a

realistic evacuation under consideration of human behav-

iour is possible. Particularly with regard to the heteroge-

neous age distribution within a passenger cabin, steering

offers profound properties for depicting this. The advan-

tages and the resulting steering behaviours are shown and

discussed in the work of B. Schneider [18, p. 121]. The pos-

sible steering behaviours in terms of representing human

behaviour and their influence on path following are also

given. Due to the additional possibility of specifying

acceleration times and the maximum achievable walking

speeds as well as the individual adjustment of the reaction

time for resolving conflicts, it is possible to simulate any

class of age.

The following Fig.3 summarizes the three steps men-

tioned above and their respective approaches and meth-

ods for implementation. Also given are the influence

parameters of the inverse steering for description of the

human behaviour. A detailed explanation of these is pro-

vided in the work of B. Schneider [18].

3 Simulation procedure andreference

model

For the simulation of the evacuation process, a typical sin-

gle-aisle aircraft with a maximum number of passengers

of 180 and 7 crew members in high-density-configuration

is modelled. The navigation geometry is implemented by

rooms and doors, whereby the realistic design of seat rows,

cockpit, passenger aisle and emergency slides is possible.

The modelled cabin layout with emergency exits and

Fig. 3 Summary of the three steps path planning, -generation and -following and their respective influencing parameters, according to [19,

p. 19]

Loading more pages...