aircraft boarding - data, validation, analysis€¢a320/b738 aircraft •standard gate position...

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

en

cy o

f m

easu

rem

en

t

passengers

Boarding Time Classification

fast medium slow

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


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)


Boarding Strategies


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


0%

100%

200%

300%

400%

0

5

10

15

20

0 5 10 15 20

pax

pe

r m

inu

te

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

te

time (min)

Deboarding - Outflow Rates

Q.25 median Q.75 m flights covered

flig

hts

co

vere

d


Validation

hand luggage storage

• Initial assumption: triangular distribution

• 323 values from field trials, Weibull distribution

• With a = 1.7, b = 16.0s, xmin = 0s


• 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)


• 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


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


Field Trials (I)

specific boarding strategies

• Field trials with two different scenarios (13 flights)

• A320/B738 aircraft

• standard gate position

• families are not separated


• 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


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


• 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




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)


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


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

[email protected]

mailto:[email protected]

aircraft boarding - data, validation, analysis€¢a320/b738 aircraft •standard gate position...

Documents