4D trajectory estimation based on nominal flight profile extraction and airway meteorological forecast revision

Tang, Xin; Chen, Ping; Zhang, Yunlong

doi:10.1016/j.ast.2015.06.001

Cited by 17 publications

(9 citation statements)

References 10 publications

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“…Dynamic space warping (DSW) algorithm was considered as an option to measure the distance between two flight altitude profiles. Meteorological forecast error from GRIdded Binary (GRIB) data, Cressman interpolation were referred to revise the original forecasts from GRIB data [11]. A trajectory planning function was later implemented and evaluated for trajectory prediction.…”

Section: Methodsmentioning

confidence: 99%

Estimating Fuel-Efficient Air Plane Trajectories Using Machine Learning

Singh¹,

Goyal²,

Ali³

et al. 2022

Computers, Materials &Amp; Continua

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Airline industry has witnessed a tremendous growth in the recent past. Percentage of people choosing air travel as first choice to commute is continuously increasing. Highly demanding and congested air routes are resulting in inadvertent delays, additional fuel consumption and high emission of greenhouse gases. Trajectory planning involves creation identification of cost-effective flight plans for optimal utilization of fuel and time. This situation warrants the need of an intelligent system for dynamic planning of optimized flight trajectories with least human intervention required. In this paper, an algorithm for dynamic planning of optimized flight trajectories has been proposed. The proposed algorithm divides the airspace into four dimensional cubes and calculate a dynamic score for each cube to cumulatively represent estimated weather, aerodynamic drag and air traffic within that virtual cube. There are several constraints like simultaneous flight separation rules, weather conditions like air temperature, pressure, humidity, wind speed and direction that pose a real challenge for calculating optimal flight trajectories. To validate the proposed methodology, a case analysis was undertaken within Indian airspace. The flight routes were simulated for four different air routes within Indian airspace. The experiment results observed a seven percent reduction in drag values on the predicted path, hence indicates reduction in carbon footprint and better fuel economy.

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Section: Methodsmentioning

confidence: 99%

Estimating Fuel-Efficient Air Plane Trajectories Using Machine Learning

Singh¹,

Goyal²,

Ali³

et al. 2022

Computers, Materials &Amp; Continua

View full text Add to dashboard Cite

show abstract

“…In addition to regression models and neural networks, other machine learning methods have also appeared [53][54][55], such as genetic algorithm (GA), ant colony algorithm, and support vector machine (SVM), etc., as a separate category here. Current trajectory prediction also uses clustering algorithms [56][57][58][59], such as K-means, Density-Based Spatial Clustering of Applications with Noise (DBSCAN), etc., and usually designs appropriate trajectory similarity metrics to improve the clustering effect. Tang et al [56] proposed an adaptive clustering method that combines the time deviation edit distance similarity measurement index with the K-means algorithm to improve the nominal flight profile accuracy.…”

Section: Machine Learning Modelsmentioning

confidence: 99%

“…Current trajectory prediction also uses clustering algorithms [56][57][58][59], such as K-means, Density-Based Spatial Clustering of Applications with Noise (DBSCAN), etc., and usually designs appropriate trajectory similarity metrics to improve the clustering effect. Tang et al [56] proposed an adaptive clustering method that combines the time deviation edit distance similarity measurement index with the K-means algorithm to improve the nominal flight profile accuracy. Combining clustering with machine learning prediction methods can significantly improve the prediction accuracy of large-scale clusterable data sets.…”

Section: Machine Learning Modelsmentioning

confidence: 99%

Multi-Aircraft Trajectory Collaborative Prediction Based on Social Long Short-Term Memory Network

Zeng

Chu

et al. 2021

Aerospace

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Aircraft trajectory prediction is the basis of approach and departure sequencing, conflict detection and resolution and other air traffic management technologies. Accurate trajectory prediction can help increase the airspace capacity and ensure the safe and orderly operation of aircraft. Current research focuses on single aircraft trajectory prediction without considering the interaction between aircraft. Therefore, this paper proposes a model based on the Social Long Short-Term Memory (S-LSTM) network to realize the multi-aircraft trajectory collaborative prediction. This model establishes an LSTM network for each aircraft and a pooling layer to integrate the hidden states of the associated aircraft, which can effectively capture the interaction between them. This paper takes the aircraft trajectories in the Northern California terminal area as the experimental data. The results show that, compared with the mainstream trajectory prediction models, the S-LSTM model in this paper has smaller prediction errors, which proves the superiority of the model’s performance. Additionally, another comparative experiment is conducted on airspace scenes with aircraft interactions, and it is found that S-LSTM has a better prediction effect than LSTM, which proves the effectiveness of the former considering aircraft interaction.

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“…There are several ways to obtain wind information: (1) The meteorological information provided by the forecast center, such as the GRIB format data published by the European Medium-Term Weather Forecast Center, can be interpolated according to the predicted time and the location of the aircraft to obtain the relevant wind speed and wind direction information to realize the track prediction, Xing [11] et al used the Cressman interpolation algorithm to spatially interpolate GRIB data to establish a route meteorological model, realized the modification of the meteorological model, and made the prediction results more accurate. (2) Aircraft Meteorological Data Relay (AMDAR), for example, Tang [12] et al used AMDAR to realize the track prediction between cities.…”

Section: Prediction Based On Particle Motionmentioning

confidence: 99%

Summary and outlook of 4D track prediction methods

Liu¹,

Zheng²,

Chang³

et al. 2020

J., mech. eng. autom., control. syst.

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Four-dimensional track prediction is the precise control of the time dimension of threedimensional space at each stage of the flight. Accurate prediction of the aircraft trajectory is a prerequisite for the automation of air traffic control. This article reviews 4D track prediction technology, that is, particle motion-based prediction method, hybrid estimation-based prediction method and machine learning-based prediction method. At the end, combined with aircraft track data, the method of data mining is used to extract the flight profile and describe the applicable scope of each method. The contents of this paper can be used for reference in the research direction of 4D track prediction technology.

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4D trajectory estimation based on nominal flight profile extraction and airway meteorological forecast revision

Cited by 17 publications

References 10 publications

Estimating Fuel-Efficient Air Plane Trajectories Using Machine Learning

Estimating Fuel-Efficient Air Plane Trajectories Using Machine Learning

Multi-Aircraft Trajectory Collaborative Prediction Based on Social Long Short-Term Memory Network

Summary and outlook of 4D track prediction methods

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