Flight taxi-out time prediction based on deep learning

He, Yujian; Hu, Minghua; Ligang, Yuan; Jiang, Hao

doi:10.1117/12.2623856

Cited by 3 publications

(1 citation statement)

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“…Comparatively, this model outperformed other machine learning algorithms in terms of predictive performance. In 2022, He [33] extended the consideration of feature variables and classified 12 of them into numerical and categorical variables. Analyzing the magnitude of correlation between these variables and taxi time, they constructed a taxi-out time prediction model based on a deep multilayer feed-forward neural network.…”

Section: Neural Networkmentioning

confidence: 99%

Prediction and Analysis of Airport Surface Taxi Time: Classification, Features, and Methodology

Yin,

Zhang,

et al. 2024

Applied Sciences

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Airport arrival and departure movements are characterized by high dynamism, stochasticity, and uncertainty. Therefore, it is of paramount importance to predict and analyze surface taxi time accurately and scientifically. This paper conducts a comprehensive review of existing studies on surface taxi time prediction and analysis. Firstly, the overall research framework of surface taxi time prediction and analysis is categorized from three perspectives: taxi time type, movement type, and modeling method. Then, focusing on the two means of taxi time analytical modeling and simulation modeling, the existing mainstream models and methods are categorized, and the main ideas and scope of application of the various methods are analyzed. Finally, the paper presents the future development direction of surface taxi time prediction prospects. The research results are aimed at providing basic support and methodological guidance for reducing the uncertainty in airport surface operation and enhancing the level of control and decision-making ability of airport surface operation.

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Section: Neural Networkmentioning

confidence: 99%