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Aircraft Boarding
Data, Validation, Analysis
Dr. Michael Schultz
Institute of Flight Guidance
German Aerospace Center (DLR e.V.)
Introduction
aircraft trajectory, ground operations, boarding
• Aviation System Block Upgrades (ASBU)
• timeline to implement efficient flight paths
• Trajectory Based Operations (TBO)
• Global ATM Operational Concept (Doc. 9854)
• Flight & Flow Information for a Collaborative
Environment (FF-ICE Concept, Doc. 9965)
• need for a System Wide Information
Management (SWIM, Doc. 10039)
• Aircraft trajectory (Air-to-Air vs. Gate-to-Gate)
• A-CDM - milestone concept
• 4D ground trajectory - turnaround management
• critical path Boarding (± 3 min)
> ATM Seminar • Seattle > Michael Schultz • Aircraft Boarding - Data, Validation, Analysis > 27.06.2017 DLR.de/FL • Chart 2
Boarding Time
• Boarding time measured in the field
• 282 events, single aisle aircraft (B737, A320)
• boarding with 29 - 190 passengers
• Analysis of boarding rate (passenger per time)
• assumption of slow, medium, fast progress
• Q-Q plot to differentiate measurements and
expected distribution
• Classification of boarding rates
• linear progress (y = mx + n)
• average m = 4.5 pax/s, n = 2.3 min
• slow m = 1.0 pax/s, n = 12.3 min
• medium m = 1.2 pax/s, n = 8.2 min
• fast m = 2.2 pax/s, n = 3.5 min
> ATM Seminar • Seattle > Michael Schultz • Aircraft Boarding - Data, Validation, Analysis > 27.06.2017
0
5
10
15
20
0 50 100 150 200
bo
ard
ing
tim
e (
min
)
passengers
Boarding Time
DLR.de/FL • Chart 3
0
15
30
45
0 50 100 150 200
fre
qu
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cy o
f m
easu
rem
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t
passengers
Boarding Time Classification
fast medium slow
> ATM Seminar • Seattle > Michael Schultz • Aircraft Boarding - Data, Validation, Analysis > 27.06.2017 DLR.de/FL • Chart 4
Model Assumptions
• Problem: Boarding is owned by the passenger
• Boarding model covers operational reality of airline
boarding strategy by:
• seat load factor of current flight
• passenger arrival rate at the aircraft
• one door/ two doors configuration
• amount of hand luggage
• passenger conformance to boarding strategy
• individual passenger behavior
(speed, seat shuffle, time to store luggage)
• passenger group constellation
> ATM Seminar • Seattle > Michael Schultz • Aircraft Boarding - Data, Validation, Analysis > 27.06.2017
0
5
10
15
20
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15
pas
sen
ger
pe
r m
inu
te
boarding time (min)
Boarding Progress
Scenario A (fast) Scenario B (medium) Scenario C (slow)
DLR.de/FL • Chart 5
Boarding Strategies
> ATM Seminar • Seattle > Michael Schultz • Aircraft Boarding - Data, Validation, Analysis > 27.06.2017 DLR.de/FL • Chart 6
Validation
arrival rate
• Initial assumption of 14 passengers per minute arrival, equally distributed
• Measurement indicates exponential distribution of inter-arrival times between passengers (mtime = 3.7s)
• Deboarding is 50% faster than boarding
> ATM Seminar • Seattle > Michael Schultz • Aircraft Boarding - Data, Validation, Analysis > 27.06.2017
0%
100%
200%
300%
400%
0
5
10
15
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0 5 10 15 20
pax
pe
r m
inu
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time (min)
Boarding - Arrival Rates
Q.25 median Q.75 m flights coveredfl
igh
ts c
ove
red
0
25
50
75
100
am
ou
nt
of
pa
ss
en
ge
rs
arrival time intervalls (s)
Field measurements Exponential Distribution
0%
100%
200%
300%
400%
0
10
20
30
40
0 2 4 6 8 10
pax
pe
r m
inu
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time (min)
Deboarding - Outflow Rates
Q.25 median Q.75 m flights covered
flig
hts
co
vere
d
DLR.de/FL • Chart 7
Validation
hand luggage storage
• Initial assumption: triangular distribution
• 323 values from field trials, Weibull distribution
• With a = 1.7, b = 16.0s, xmin = 0s
> ATM Seminar • Seattle > Michael Schultz • Aircraft Boarding - Data, Validation, Analysis > 27.06.2017 DLR.de/FL • Chart 8
• Comparison of assumption and field measurements
0%
10%
20%
30%
40%
0-5 5-10 10-15 15-20 20-25 25-30 30-35 35-40 40-45
Pro
bab
ility
Time Categories (s)
Baggage Storage Time
Measurement
Weibull Distribution
Triangular Distribution(old model)
Validation
passenger seat shuffle
• Different kind of seat occupation pattern demands
for a specific amount of individual movements
• Assumption: Triangular distribution for single
movement (min = 1.8 s, mode = 2.4 s, max = 3.0 s)
> ATM Seminar • Seattle > Michael Schultz • Aircraft Boarding - Data, Validation, Analysis > 27.06.2017 DLR.de/FL • Chart 9
• Comparison of assumption and field measurements
• But: only 10 - 15 measurements per category
0
10
20
tim
e (
s)
seat shuffles
field measurement
simulated distribution
1 4 5 9
Validation
comparison to prior results
> ATM Seminar • Seattle > Michael Schultz • Aircraft Boarding - Data, Validation, Analysis > 27.06.2017
Boarding Strategies
boarding time (%)
random outside-in back-to-front block
1 door 100.0 80.9 110.5 96.2
calibrated 100.0 79.5 109.2 95.3
2 doors 74.2 63.8 75.3 76.2
calibrated 74.1 62.5 75.0 76.2
standard deviation (%)
1 door 7.1 5.5 7.9 6.6
calibrated 7.3 5.7 8.1 6.9
2 doors 4.6 2.9 4.8 5.3
calibrated 5.9 5.5 5.9 5.8
DLR.de/FL • Chart 10
Field Trials (I)
specific boarding strategies
• Field trials with two different scenarios (13 flights)
• A320/B738 aircraft
• standard gate position
• families are not separated
> ATM Seminar • Seattle > Michael Schultz • Aircraft Boarding - Data, Validation, Analysis > 27.06.2017
• Comparison of simulation and field measurements
• Accuracy of the boarding model: ± 5%
Boarding Strategies
Boarding time (%)
data sim. diff. Q.10 Q.25 Q.75 Q.90
random 102.6 100.0 2.6 -10.6 -5.7 6.2 11.8
airline - S1 94.8 98.7 -3.9 -11.5 -6.3 6.6 12.7
airline - S2 88.0 83.4 4.6 -8.9 -4.8 5.2 10.2
*airline - S2 80.8
DLR.de/FL • Chart 11
Field Trials (II)
specific boarding strategies
• Field measurements with 64 trials
• A320/B738 aircraft
• one door and two doors configuration
• seat load factor in three groups:
• A with 60%-80% (27 flights),
• B with 80%-90% (20 flights), and
• C with more than 90% (17 flights)
• remote, gate, and apron positions
• passenger classification: tourist, EU, Germany
• amount of pre-boarding passengers
• Problem: too many variations
> ATM Seminar • Seattle > Michael Schultz • Aircraft Boarding - Data, Validation, Analysis > 27.06.2017
• Comparison of simulation and field measurements
boarding time regarding to random reference
40%
60%
80%
100%
120%
140%data - outside-in data - block simulation
SLF 60% - 80%1door | 2doors
SLF 80% - 90%1door | 2doors
SLF 90% - 100%1door | 2doors
DLR.de/FL • Chart 12
Infrastructural Changes
side-slip seat
• Staggered seat approach
• Wider aisle enables passengers to pass each other
• Development of appropriate boarding strategy
• Up to 20% savings in boarding time (one door)
> ATM Seminar • Seattle > Michael Schultz • Aircraft Boarding - Data, Validation, Analysis > 27.06.2017 DLR.de/FL • Chart 13
> ATM Seminar • Seattle > Michael Schultz • Aircraft Boarding - Data, Validation, Analysis > 27.06.2017 DLR.de/FL • Chart 14
Summary / Outlook
• Stochastic model covers all operationally relevant aspects of aircraft boarding
• Field trials and measurements provide a solid database for validation
• Measurements in the field indicate both reliable set of input parameters and valid simulation approach (± 5%)
Next steps
• design of reliable, fast turnaround for short-haul flights
• first approach drafted
• online prediction of boarding progress for 4D ground trajectory
• model developed and used as input for deep learning approach
• dynamic seat allocation to regain control of the boarding sequence
• concept developed and already tested at Cologne-Bonn airport
> ATM Seminar • Seattle > Michael Schultz • Aircraft Boarding - Data, Validation, Analysis > 27.06.2017 DLR.de/FL • Chart 15
Aircraft Boarding
Data, Validation, Analysis
Deutsches Zentrum für Luft- und Raumfahrt e.V. (DLR)
German Aerospace Center
Institute of Flight Guidance
Dr.-Ing. Michael Schultz
Head of Department Air Transportation
Phone +49 531 295-2570