Unimpeded Taxi-Time Prediction Based on the Node–Link Model

Jeong, Myeong-Sook; Eun, Yeonju; Jeon, Daekeun; Bang, Hyochoong

doi:10.2514/1.i010823

Cited by 8 publications

(7 citation statements)

References 6 publications

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“…Impressively, its validation demonstrated a prediction accuracy of 96.62% within a margin of ±5 min. Jeong et al [4] innovated a prediction method tailored based on the airport node-link model for unimpeded taxiing time, which specifically targets both departure and arrival taxiing time at Incheon International Airport. Jiao et al [5] introduced an advanced method based on attention mechanism, which synergizes long-and short-term memory with a deep neural network (D-LSTM) to predict taxiing time.…”

Section: Related Workmentioning

confidence: 99%

“…(1) Most of the existing studies on taxiing time prediction focus on single-flight departure taxiing time [2][3][4][5][6][7][8][9][10][11][12][13][14] and less on the average departure taxiing time [1]. (2) All existing studies use macroscopic scene flow features [2,3,[5][6][7][8][9][10][11][12][13] but do not consider the influence of scene structures, such as connecting corridors.…”

Section: Research Gapmentioning

confidence: 99%

“…Meanwhile, refs. [3][4][5][6][7][8] and other studies utilized machine learning methods, including random forest, neural networks, and support vector machines, highlighting the superiority of machine learning approaches. In feature studies, there has been a predominant focus on macro features like taxiing distance and scene flow [9][10][11].…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

A CNN-GRU Hybrid Model for Predicting Airport Departure Taxiing Time

Yuan,

Liu,

Chen

et al. 2024

Aerospace

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Scene taxiing time is an important indicator for assessing the operational efficiency of airports as well as green airports, and it is also a fundamental parameter in flight regularity statistics. The accurate prediction of taxiing time can help decision makers to further optimize flight pushback sequences and improve airport operational efficiency while increasing flight punctuality. In this paper, we propose a hybrid deep learning model for departure taxiing time prediction based on the new influence factors of taxiing time. Taking Pudong International Airport as the research object, after analyzing the scene operation mode, we construct the origin–destination pairs (ODPs) with stand groups and runways and then propose two structure-related factors, corridor departure flow and departure flow proportion of ODP, as the new features. Based on the new feature set, we construct a departure taxiing dataset for training the prediction model. Then, a departure taxiing time prediction model based on convolutional neural networks (CNNs) and gated recurrent units (GRUs) is proposed, which uses a CNN model to extract the high-dimensional features from the taxiing data and then inputs them to a GRU model for taxiing time prediction. Finally, we conduct a series of comparison experiments on the historical taxiing dataset of Pudong Airport. The prediction results show that the proposed hybrid prediction model has the best performances compared with other deep learning models, and the proposed structure-related features have high correlations with departure taxiing time. The prediction results of taxiing time for different ODPs also verify the generalizability of the proposed model.

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Section: Related Workmentioning

confidence: 99%

Section: Research Gapmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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A CNN-GRU Hybrid Model for Predicting Airport Departure Taxiing Time

Yuan,

Liu,

Chen

et al. 2024

Aerospace

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“…(5) Other methods from the literature Jeong et al [27] devised a novel model for estimating reference time, utilizing the node-link structure of airports and Airport Surface Detection Equipment (ASDE) ground surveillance data. The data were organized based on taxiing sections, aircraft wake, and aircraft type.…”

Section: Reference Timementioning

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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“…At present, neither the universal display test standards [4] nor the automotive industry standards has been issued for the testing and evaluation of the temperature characteristics of vehicle displays. Many researchers have begun to pay attention to this problem and have also carried out related research and improvements [1][2][3], but now the temperature dependence test and evaluation methods of the vehicle display are not standardized.…”

mentioning

confidence: 99%