A Data-Driven Air Traffic Sequencing Model Based on Pairwise Preference Learning

Jung, Soyeon; Hong, Sungkweon; Lee, Keumjin

doi:10.1109/tits.2018.2829863

Cited by 10 publications

(9 citation statements)

References 37 publications

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“…The aviation industry and related fields such as air traffic management systems and weather forecasting generate large-scale complex data, requiring specialized analysis and modeling to extract useful knowledge and insights. In the field of aviation big data analytics and mining, most of researchers focused on operation problems [14,15,25] and revenue management [3,55]. In general, the forecasting and analysis of flight delay, flight flow and flight trajectory is the mainstream research for advanced air traffic management.…”

Section: Aviation Data Miningmentioning

confidence: 99%

Phased Flight Trajectory Prediction with Deep Learning

Zhang¹,

Chen²

2022

Preprint

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The unprecedented increase of commercial airlines and private jets over the next ten years presents a challenge for air traffic control. Precise flight trajectory prediction is of great significance in air transportation management, which contributes to the decision-making for safe and orderly flights. Existing research and application mainly focus on the sequence generation based on historical trajectories, while the aircraft-aircraft interactions in crowded airspace especially the airspaces near busy airports have been largely ignored. On the other hand, there are distinct characteristics of aerodynamics for different flight phases, and the trajectory may be affected by various uncertainties such as weather and advisories from air traffic controllers. However, there is no literature fully considers all these issues. Therefore, we proposed a phased flight trajectory prediction framework. Multi-source and multi-modal datasets have been analyzed and mined using variants of recurrent neural network (RNN) mixture. To be specific, we first introduce spatio-temporal graphs into the low-altitude airway prediction problem, and the motion constraints of an aircraft are embedded to the inference process for reliable forecasting results. In the en-route phase, the dual-attention mechanism is employed to adaptively extract much more important features from overall datasets to learn the hidden patterns in dynamical environments. The experimental results demonstrate our proposed framework can outperform state-of-the-art methods for flight trajectory prediction for large passenger/transport airplanes.

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Section: Aviation Data Miningmentioning

confidence: 99%

Phased Flight Trajectory Prediction with Deep Learning

Zhang¹,

Chen²

2022

Preprint

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“…A framework was proposed by Jung et al (2018) in which both static and dynamic models were found to be highly accurate and scalable. An extension of this algorithm by adding various features such as weather, wind etc.…”

Section: Review On Machine Learning Algorithm For Scmmentioning

confidence: 99%

“…The study shows that a machine learning algorithm can enhance data analytics in the transportation model. A framework was proposed by Jung et al (2018) in which both static and dynamic models were found to be highly accurate and scalable. An extension of this algorithm by adding various features such as weather, wind etc.…”

Section: Data-driven Quality Management In Supply Chainmentioning

confidence: 99%

A systematic and network-based analysis of data-driven quality management in supply chains and proposed future research directions

Agrawal

Wankhede

Luthra³

2021

TQM

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PurposeThis work aims to review past and present articles about data-driven quality management (DDQM) in supply chains (SCs). The motive behind the review is to identify associated literature gaps and to provide a future research direction in the field of DDQM in SCs.Design/methodology/approachA systematic literature review was done in the field of DDQM in SCs. SCOPUS database was chosen to collect articles in the selected field and then an SLR methodology has been followed to review the selected articles. The bibliometric and network analysis has also been conducted to analyze the contributions of various authors, countries and institutions in the field of DDQM in SCs. Network analysis was done by using VOS viewer package to analyze collaboration among researchers.FindingsThe findings of the study reveal that the adoption of data-driven technologies and quality management tools can help in strategic decision making. The usage of data-driven technologies such as artificial intelligence and machine learning can significantly enhance the performance of SC operations and network.Originality/valueThe paper discusses the importance of data-driven techniques enabling quality in SC management systems. The linkage between the data-driven techniques and quality management for improving the SC performance was also elaborated in the presented study.

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“…However, this method only focused on the instantaneous situation of the arrival operation to establish the landing sequence. Jung et al proposed a new classification-based method for predicting the landing sequences to accommodate the cognitive processes of air traffic controllers [42]. Such a data-driven method first learned the pairwise preference functions between two arrivals by logistic regression.…”

Section: Introductionmentioning

confidence: 99%

A Data-Driven Method for Arrival Sequencing and Scheduling Problem

Zhang

Kang

2023

Aerospace

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Decision support tools for arrival sequencing and scheduling could assist air traffic controllers in managing the arrival aircraft in terminal areas. However, one critical issue is that the current method for dealing with the arrival sequencing and scheduling problem does not consider the dynamic traffic situation and the human working experience, which results in a deviation between the scheduled and actual landing sequences. This paper develops a data-driven method to address this issue. Firstly, the random forest model is applied to predict the estimated time of arrival (ETA). During the ETA prediction, the trajectory, operation, and airport-related factors that could increase the prediction accuracy are considered. Secondly, the landing sequence is obtained by sorting the predicted ETAs. Thirdly, two optimization methods are proposed to generate the scheduled time of arrival (STA). The former uses the predicted ETAs as inputs and then directly optimizes the landing sequence and the STA. The latter uses both the predicted ETA and the landing sequence as inputs for further optimization. Finally, these proposed methods are evaluated with three sets of historical data on arrival operations at Changsha Huanghua International Airport (ZGHA). The results show that the RF-based ETA prediction method could improve scheduling performance. Moreover, the proposed optimization methods could provide controllers with a more appropriate decision advisory. Such advisories could simultaneously reduce the operation efficiency indicators (average/maximum delay or dwell time) and the operation complexity indicators (Kendall rank correlation or position shift).

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A Data-Driven Air Traffic Sequencing Model Based on Pairwise Preference Learning

Cited by 10 publications

References 37 publications

Phased Flight Trajectory Prediction with Deep Learning

Phased Flight Trajectory Prediction with Deep Learning

A systematic and network-based analysis of data-driven quality management in supply chains and proposed future research directions

A Data-Driven Method for Arrival Sequencing and Scheduling Problem

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