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Machine learning approach to predict aircraft boarding

Schultz, Michael and Reitmann, Stefan (2018) Machine learning approach to predict aircraft boarding. Transportation Research Part C: Emerging Technologies. Elsevier. ISSN 0968-090X (In Press)

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Abstract

Reliable and predictable ground operations are essential for punctual air traffic movements. Uncertainties in the airborne phase have significantly less impact on flight punctuality than deviations in aircraft ground operations. The ground trajectory of an aircraft primarily consists of the handling processes at the stand, defined as the aircraft turnaround, which are mainly controlled by operational experts. Only the aircraft boarding, which is on the critical path of the turnaround, is driven by the passengers' experience and willingness or ability to follow the proposed procedures. We used a recurrent neural network approach to predict the progress of a running boarding event. In particular, we implemented and trained the Long Short-Term Memory model. Since no operational data of the specific passenger behavior is available, we used a reliable, validated boarding simulation environment to provide data about the aircraft boarding events. First predictions show that uni-variate input (seat load progress) produces insufficient results, so we consider expected passenger interactions in the aircraft cabin as well. These interactions are aggregated to a prior-developed complexity metric and allow an efficient evaluation of the current boarding progress. With this multi-variate input, our Long Short-Term Memory model achieves appropriate prediction results for the boarding progress.

Item URL in elib:https://elib.dlr.de/123471/
Document Type:Article
Title:Machine learning approach to predict aircraft boarding
Authors:
AuthorsInstitution or Email of AuthorsAuthors ORCID iD
Schultz, MichaelMichael.Schultz (at) dlr.dehttps://orcid.org/0000-0003-4056-8461
Reitmann, StefanStefan.Reitmann (at) dlr.deUNSPECIFIED
Date:2018
Journal or Publication Title:Transportation Research Part C: Emerging Technologies
Refereed publication:Yes
Open Access:No
Gold Open Access:No
In SCOPUS:Yes
In ISI Web of Science:Yes
Publisher:Elsevier
ISSN:0968-090X
Status:In Press
Keywords:machine learning; prediction; turnaround; aircraft boarding
HGF - Research field:Aeronautics, Space and Transport
HGF - Program:Aeronautics
HGF - Program Themes:air traffic management and operations
DLR - Research area:Aeronautics
DLR - Program:L AO - Air Traffic Management and Operation
DLR - Research theme (Project):L - Air Traffic Concepts and Operation
Location: Braunschweig
Institutes and Institutions:Institute of Flight Control > Air traffic systems
Deposited By: Schultz, Dr. Michael
Deposited On:27 Nov 2018 12:02
Last Modified:27 Nov 2018 12:02

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