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Aerospace 2018, 5(4), 101;

Consideration of Passenger Interactions for the Prediction of Aircraft Boarding Time

Institute of Flight Guidance, German Aerospace Center, 38108 Braunschweig, Germany
Author to whom correspondence should be addressed.
Received: 4 September 2018 / Revised: 27 September 2018 / Accepted: 29 September 2018 / Published: 30 September 2018
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In this paper we address the prediction of aircraft boarding using a machine learning approach. Reliable process predictions of aircraft turnaround are an important element to further increase the punctuality of airline operations. In this context, aircraft turnaround is mainly controlled by operational experts, but the critical aircraft boarding is driven by the passengers’ experience and willingness or ability to follow the proposed procedures. Thus, we used a developed complexity metric to evaluate the actual boarding progress and a machine learning approach to predict the final boarding time during running operations. A validated passenger boarding model is used to provide reliable aircraft status data, since no operational data are available today. These data are aggregated to a time-based complexity value and used as input for our recurrent neural network approach for predicting the boarding progress. In particular we use a Long Short-Term Memory model to learn the dynamical passenger behavior over time with regards to the given complexity metric. View Full-Text
Keywords: aircraft turnaround; boarding prediction; machine learning; neural network aircraft turnaround; boarding prediction; machine learning; neural network

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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).

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Schultz, M.; Reitmann, S. Consideration of Passenger Interactions for the Prediction of Aircraft Boarding Time. Aerospace 2018, 5, 101.

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