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• W2912998124 endingPage "2341" @default.
• W2912998124 startingPage "2330" @default.
• W2912998124 abstract "Reliable and predictable ground operations are essential for 4D aircraft trajectories. 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 propose a machine learning approach predict the boarding time. A validated boarding simulation provides data input for a recurrent neural network approach (discrete time series of boarding progress). In particular we use a Long Short-Term Memory model to learn the characteristic passenger behaviors over time." @default.
• W2912998124 created "2019-02-21" @default.
• W2912998124 creator A5001283900 @default.
• W2912998124 creator A5001839757 @default.
• W2912998124 date "2018-12-09" @default.
• W2912998124 modified "2023-09-26" @default.
• W2912998124 title "Prediction of aircraft boarding time using LSTM network" @default.
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• W2912998124 doi "https://doi.org/10.5555/3320516.3320795" @default.
• W2912998124 hasPublicationYear "2018" @default.
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