Consideration of Passenger Interactions for the Prediction of Aircraft Boarding Time

Aerospace

Michael Schultz¹  Stefan Reitmann¹ 

[1] Institute of Flight Guidance, German Aerospace Center, 38108 Braunschweig, Germany;

关键词: aircraft turnaround;    boarding prediction;    machine learning;    neural network;   

DOI  :  10.3390/aerospace5040101

来源: DOAJ

【 摘 要 】

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.

【 授权许可】

Unknown