Machine learning approach to predict aircraft boarding

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. The authors used a recurrent neural network approach to predict the progress of a running boarding event. In particular, the authors implemented and trained the Long Short-Term Memory model. Since no operational data of the specific passenger behavior is available, the authors 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 the authors 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, the authors' Long Short-Term Memory model achieves appropriate prediction results for the boarding progress.

• Record URL:
  • https://doi.org/10.1016/j.trc.2018.09.007
• Record URL:
  • http://www.sciencedirect.com/science/article/pii/S0968090X18312580
• Availability:
  • Find a library where document is available. Order URL: http://worldcat.org/issn/0968090X
• Supplemental Notes:
  • © 2018 Elsevier Ltd. All rights reserved. Abstract reprinted with permission of Elsevier.
• Authors:
  • Schultz, Michael
  • 0000-0003-4056-8461
  • Reitmann, Stefan
• Publication Date: 2019-1

Language

• English

Media Info

• Media Type: Web
• Features: Figures; References; Tables;
• Pagination: pp 391-408
• Serial:
  • Transportation Research Part C: Emerging Technologies
  • Volume: 98
  • Issue Number: 0
  • Publisher: Elsevier
  • ISSN: 0968-090X
  • Serial URL: http://www.sciencedirect.com/science/journal/0968090X

Subject/Index Terms

• TRT Terms: Air transportation; Aircraft; Boarding; Machine learning; Mathematical prediction; Neural networks; Simulation
• Subject Areas: Aviation; Operations and Traffic Management; Passenger Transportation; Planning and Forecasting;

Filing Info

• Accession Number: 01691394
• Record Type: Publication
• Files: TRIS
• Created Date: Jan 25 2019 10:34AM