Prediction of passenger boarding progress using

neural network approach

Michael Schultz and Stefan Reitmann German Aerospace Center (DLR e.V.)

Institute of Flight Guidance

• Background

• Boarding model

• Long short-term memory model

• Results

• Outlook

Outline

carmodel2011.blogspot.com

ICRAT • Castelldefels > Prediction of passenger boarding progress using neural network approach > June 2018 DLR.de • Chart 2

• International Civil Aviation Organization (ICAO), 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 (Doc. 9965)

• Need for a System Wide Information Management (SWIM, Doc. 10039)

• Aircraft trajectory – not only flight path

• Milestone concept available - A-CDM

• 4D ground trajectory – including ground operations (turnaround)

• Critical path

• 70€ per delay minute

• Boarding is always on critical path (particularly important for flights

with high number of rotations, short haul)

Background

aircraft trajectory, ground operations, boarding

ICRAT • Castelldefels > Prediction of passenger boarding progress using neural network approach > June 2018 DLR.de • Chart 3

Eurocontrol, CODA

Background

aircraft trajectory, ground operations, boarding

ICRAT • Castelldefels > Prediction of passenger boarding progress using neural network approach > June 2018 DLR.de • Chart 4

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departure time taxi-out phase flight phase taxi-in phase arrival time

tim

e (

min

)

range (80th-20th percentile) standard deviation

Variability on intra-European flights (2008-2015)

H. Fricke, M. Schultz, Delay impacts onto turnaround performance, ATM Seminar, 2009

Background

aircraft trajectory, ground operations, boarding

ICRAT • Castelldefels > Prediction of passenger boarding progress using neural network approach > June 2018 DLR.de • Chart 5

Ready for boarding

ICRAT • Castelldefels > Prediction of passenger boarding progress using neural network approach > June 2018 DLR.de • Chart 6

Data-based vs. model-based approaches

input data, models, new concepts

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lute

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passengers

fast medium slow

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ard

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passengers

M. Schultz, Field Trial Measurements to Validate a Stochastic Aircraft Boarding Model, Aerospace 2018, 5(1), 27 M. Schultz, Dynamic change of aircraft seat condition for fast boarding, J. of Transp. Res. Part C 2017 85:131-147

ICRAT • Castelldefels > Prediction of passenger boarding progress using neural network approach > June 2018 DLR.de • Chart 7

Model - A320, 29 rows, 174 seats

• grid-based, stochastic processes (ASEP), fast implementation

• Operational aspects, e.g. seat load, arrival rates, boarding strategy

• Individual passenger behaviour, e.g. speed

Aircraft boarding

stochastic, individual-based model

M. Schultz, Implementation and application of a stochastic aircraft boarding model, J. of Transp. Res. Part C 2018 90:334-349

ICRAT • Castelldefels > Prediction of passenger boarding progress using neural network approach > June 2018 DLR.de • Chart 8

Reliable simulation environment

Aircraft boarding

stochastic, individual-based model

M. Schultz, Implementation and application of a stochastic aircraft boarding model, J. of Transp. Res. Part C 2018 90:334-349

ICRAT • Castelldefels > Prediction of passenger boarding progress using neural network approach > June 2018 DLR.de • Chart 9

Implementation

boarding strategies and operational constraints

ICRAT • Castelldefels > Prediction of passenger boarding progress using neural network approach > June 2018 DLR.de • Chart 10

Pax could disturb the

whole boarding progress

(connection flights, pax

handling in terminal -

security)

Problem to solve

boarding is owned by the passenger

Different kind of seat

occupation pattern

demands for a specific

amount of individual

movements

1 3 5 7 9 11 13 15

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arrival (pax per minute)

boarding time (min)

Scenario A (fast)

Scenario B (medium)

Scenario C (slow)

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probability (%)

time (s)

triangular distribution (old model)

Weibull distribution

field measurement

Distribution and amount of

hand luggage pieces

results in blocked aisle

when storing into the

overhead compartment

M. Schultz, Field Trial Measurements to Validate a Stochastic Aircraft Boarding Model, Aerospace 2018, 5(1), 27

ICRAT • Castelldefels > Prediction of passenger boarding progress using neural network approach > June 2018 DLR.de • Chart 11

How to predict passenger-controlled process?

boarding progress

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1boarding time (s)

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random boarding

outside-in boarding

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boarding time (s)

random boarding

outside-in boarding

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M. Schultz, A metric for the real-time evaluation of the aircraft boarding progress, J. of Transp. Res. Part C 2018 86:467-487

ICRAT • Castelldefels > Prediction of passenger boarding progress using neural network approach > June 2018 DLR.de • Chart 12

• System state of seat rows and cabin

(using future cabin management system and sensors)

Evaluation of seat status

complexity metric

step 0

aisle window

n

n

realized movements

expected movements free seat

occupied seat (from step 2) current seat

occupied seat (from step 1)

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10.5

8.0

3.5

8.3

M. Schultz, A metric for the real-time evaluation of the aircraft boarding progress, J. of Transp. Res. Part C 2018 86:467-487

ICRAT • Castelldefels > Prediction of passenger boarding progress using neural network approach > June 2018 DLR.de • Chart 13

Complexity metric

progress prediction 0 200 400 600 800 1000 1200

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1boarding time (s)

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se

at

loa

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random boarding

outside-in boarding

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Pair

cra

ft

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boarding time (s)

random boarding

outside-in boarding

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1boarding time (s)

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outside-in boarding

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cra

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boarding time (s)

random boarding

outside-in boarding

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slow boarding

fast boarding

ICRAT • Castelldefels > Prediction of passenger boarding progress using neural network approach > June 2018 DLR.de • Chart 14

• Artificial Neural Networks (ANN) are characterized by interconnected neurons

• Neurons structured in different layers, connection depends on computed

weights, result in blocking or passing of information

Machine learning approach

artificial neural networks

ICRAT • Castelldefels > Prediction of passenger boarding progress using neural network approach > June 2018 DLR.de • Chart 15

• Training neural networks demands for error estimation (backpropagation)

Machine learning approach

recurrent neural network

input layer hidden layers output layer information loop

ICRAT • Castelldefels > Prediction of passenger boarding progress using neural network approach > June 2018 DLR.de • Chart 16

• Information to be passed from one step of the network to the next

• Long-term dependency problem (need for more context)

Machine learning approach

short-term memory

http://colah.github.io/posts/2015-08-Understanding-LSTMs/

ICRAT • Castelldefels > Prediction of passenger boarding progress using neural network approach > June 2018 DLR.de • Chart 17

• Repeating module in a RNN and LSTM

Machine learning approach

long short-term memory (LSTM)

forget gate

input gate

(store information)

update cell state output gate

http://colah.github.io/posts/2015-08-Understanding-LSTMs/

ICRAT • Castelldefels > Prediction of passenger boarding progress using neural network approach > June 2018 DLR.de • Chart 18

• Implementation in Python 3.6, TensorFlow (backend), Keras (frontend)

• Basic model: core layers (Dense, Dropout), recurrent layers (LSTM)

• Boarding model: concatenation of 1 - 3 separate LSTM branches

Machine learning approach

LSTM implementation

ICRAT • Castelldefels > Prediction of passenger boarding progress using neural network approach > June 2018 DLR.de • Chart 19

Machine learning approach

LSTM implementation

ICRAT • Castelldefels > Prediction of passenger boarding progress using neural network approach > June 2018 DLR.de • Chart 20

Input data – complexity measures

boarding simulation to train and evaluate

M. Schultz and S. Reitmann, Machine learning approach to predict aircraft boarding, J. of Transp. Res. Part C 2018 (review)

ICRAT • Castelldefels > Prediction of passenger boarding progress using neural network approach > June 2018 DLR.de • Chart 21

Prediction of boarding progress

M. Schultz and S. Reitmann, Machine learning approach to predict aircraft boarding, J. of Transp. Res. Part C 2018 (review)

ICRAT • Castelldefels > Prediction of passenger boarding progress using neural network approach > June 2018 DLR.de • Chart 22

Outlook

handle input data - smooth

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S. Reitmann and M. Schultz, Advanced Recurrent Neural Network based Aircraft Boarding Prediction. ATRS conference 2018

ICRAT • Castelldefels > Prediction of passenger boarding progress using neural network approach > June 2018 DLR.de • Chart 23

Prediction of passenger boarding progress using

neural network approach

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

michael.schultz@dlr.de

Thank you.

ICRAT • Castelldefels > Prediction of passenger boarding progress using neural network approach > June 2018 DLR.de • Chart 24

Schultz, M. 2018. Implementation and Application of a Stochastic Aircraft Boarding Model. Journal of

Transportation Research Part C: Emerging Technologies 90, 334-349.

Schultz, M. 2017. Field Trial Measurements to Validate a Stochastic Aircraft Boarding Model. Aerospace

5(1), 27.

Schultz, M. 2017. Dynamic Change of Aircraft Seat Condition for Fast Boarding. Journal of

Transportation Research

Part C: Emerging Technologies 85, 131-147.

Schultz, M. 2018. A metric for the real-time evaluation of the aircraft boarding progress. Journal of

Transportation Research Part C: Emerging Technologies 86, 467-487.

S. Reitmann and M. Schultz, Advanced Recurrent Neural Network based Aircraft Boarding Prediction.

ATRS conference 2018

References

ICRAT • Castelldefels > Prediction of passenger boarding progress using neural network approach > June 2018 DLR.de • Chart 25