Taking trajectory based operations to the next level: Management by trajectory

Fernandes, Alicia; Atkins, Stephen; Leiden, Kenneth; Bagnall, Timothy; Kaler, Curt; Evans, Mark I.; Bell, A. J.; Kilbourne, Todd; Jackson, Michael R.

doi:10.1109/icnsurv.2018.8384865

Cited by 3 publications

(2 citation statements)

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“…Thus, the automatic flight plan generation (FPG) has become an urgent topic for air transport field. Some novel initiatives are widely discussed in both academia and industry, such as coded departure routes (CDRs) [2], dynamic weather routes (DWR) [3,4], collaborative trajectory options program (CTOP) [5], and other transportation areas [6][7][8][9]. Note that, the flight plan is generated via solving the optimization program to meet a set of operational metrics.…”

Section: Introductionmentioning

confidence: 99%

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: Introductionmentioning

confidence: 99%

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

Yang,

Qian,

Zhang

et al. 2023

IET Intelligent Trans Sys

View full text Add to dashboard Cite

show abstract

“…To cope with the continuous and rapid growth of traffic flow, improve the safety level of flight operation, and reduce flight delay, in 2004, Europe proposed the Single European Sky ATM Research (SESAR) [2]; in 2005, the United States proposed the Next Generation Air Transportation System (NextGen) plan [3]; and in 2012, the International Civil Aviation Organization (ICAO), with the vision of the Global Air Traffic Management Operational Concept (GATMOC), proposed the long-awaited Aviation System Block Upgrade (ASBU) plan [4]. However, whether it is the United States' NextGen, Europe's SESAR development plan, or the ICAO's ASBU plan, trajectory-based operations will be the core technology of air traffic management in the future.…”

Section: Introductionmentioning

confidence: 99%

An Efficient Trajectory Negotiation and Verification Method Based on Spatiotemporal Pattern Mining

Liu,

Wang,

Zhong

et al. 2023

International Journal of Aerospace Engineering

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In trajectory-based operations, trajectory negotiation and verification are conducive to using airspace resources fairly, reducing flight delay, and ensuring flight safety. However, most of the current methods are based on route negotiation, making it difficult to accommodate airspace user-initiated trajectory requests and dynamic flight environments. Therefore, this paper develops a framework for trajectory negotiation and verification and describes the trajectory prediction, negotiation, and verification processes based on a four-dimensional trajectory. Secondly, users predict flight trajectories based on aircraft performance and flight plans and submit them as requested flight trajectories to the air traffic management (ATM) system for negotiation in the airspace. Then, a spatiotemporal weighted pattern mining algorithm is proposed, which accurately identifies flight combinations that violate the minimum flight separation constraint from four-dimensional flight trajectories proposed by users, as well as flight combinations with close flight intervals and long flight delays in the airspace. Finally, the experimental results demonstrate that the algorithm efficiently verifies the user-proposed flight trajectory and promptly identifies flight conflicts during the trajectory negotiation and verification processes. The algorithm then analyzes the flight trajectories of aircrafts by applying various constraints based on the specific traffic environment; the flight combinations which satisfy constraints can be identified. Then, based on the results identified by the algorithm, the air traffic management system can negotiate with users to adjust the flight trajectory, so as to reduce flight delay and ensure flight safety.

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A 4D Trajectory Prediction Model Based on the BP Neural Network

Tian

2019

Journal of Intelligent Systems

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Abstract To solve the problem that traditional trajectory prediction methods cannot meet the requirements of high-precision, multi-dimensional and real-time prediction, a 4D trajectory prediction model based on the backpropagation (BP) neural network was studied. First, the hierarchical clustering algorithm and the k-means clustering algorithm were adopted to analyze the total flight time. Then, cubic spline interpolation was used to interpolate the flight position to extract the main trajectory feature. The 4D trajectory prediction model was based on the BP neural network. It was trained by Automatic Dependent Surveillance – Broadcast trajectory from Qingdao to Beijing and used to predict the flight trajectory at future moments. In this paper, the model is evaluated by the common measurement index such as maximum absolute error, mean absolute error and root mean square error. It also gives an analysis and comparison of the predicted over-point time, the predicted over-point altitude, the actual over-point time and the actual over-point altitude. The results indicate that the predicted 4D trajectory is close to the real flight data, and the time error at the crossing point is no more than 1 min and the altitude error at the crossing point is no more than 50 m, which is of high accuracy.

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Taking trajectory based operations to the next level: Management by trajectory

Cited by 3 publications

References 0 publications

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

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

An Efficient Trajectory Negotiation and Verification Method Based on Spatiotemporal Pattern Mining

A 4D Trajectory Prediction Model Based on the BP Neural Network

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