ATD-2 Field Evaluation of Pre-Departure Trajectory Option Set Reroutes in the North Texas Metroplex

Coupe, William J.; Bhadoria, Divya; Jung, Yoon; Chevalley, Eric; Juro, Greg

doi:10.1109/dasc55683.2022.9925876

Cited by 5 publications

(2 citation statements)

References 7 publications

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“…This approach resorts to the machine learning technique, specifically trajectory clustering, in order to represent the flight segments and construct flight plans [11,12,23,24]. The historical route amendments are adopted as a static foundation and trained to get the prediction model that generates the operationally acceptable flight plan options.…”

Section: Flight Segment Constructionmentioning

confidence: 99%

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Sequence‐to‐sequence transfer transformer network for automatic flight plan generation

Yang,

Qian,

Zhang

et al. 2023

IET Intelligent Trans Sys

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In this work, a machine translation framework is proposed to tackle the flight plan generation in the air transport field. Diverging from the traditional human expert‐based way, a novel sequence‐to‐sequence transfer transformer network to automatic flight plan generation with enhanced operational acceptability is presented. It allows the user to translate the departure and arrival airport pairs denoted as test sentences, into the flyable waypoint sequences denoted as the corresponding source sentences. The approach leverages deep neural networks to autonomously learn air transport specialized knowledge and human expert insights from industry legacy data. Moreover, a multi‐head attention mechanism is adopted to model the complex correlation between airport pairs. Besides, we introduce an innovative waypoint embedding layer to learn effective embeddings for waypoint sequences. Additionally, an extensive flight plan dataset is constructed utilizing real‐world data in China spanning from July to September 2019. Employing the proposed model, rigorous training and testing procedures are conducted on this dataset, yielding remarkably favourable outcomes based on automatic evaluation metrics that are BLEU and METEOR, which outperform other popular approaches. More importantly, the proposed approach achieves high performance in the operational validation and visualization, showing its application potential for real‐world air traffic operation.

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Section: Flight Segment Constructionmentioning

confidence: 99%

“…Recently, the machine learning approach is addressed to construct the operationally acceptable flight plan segments [11, 12] based on the historical flight trajectory clustering. However, the trajectories are the observations collected after the operation.…”

Section: Introductionmentioning

confidence: 99%