Trajectory Clustering, Modeling and Selection with the focus on Airspace Protection

Eerland, Willem; Box, Simon

doi:10.2514/6.2016-1411

Cited by 14 publications

(5 citation statements)

References 5 publications

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“…Pan et al [13] constructed a multi-factor Hausdorff distance as a similarity measure and proposed a density-based multi-dimensional trajectory clustering algorithm. Eerland et al [14] clustered the trajectory data and generated a probability model for each cluster, and weighted the trajectory based on the probability model to generate a representative trajectory. Mahboubi et al [15] adopted a method based on trajectory turning point recognition and clustering.…”

Section: Introductionmentioning

confidence: 99%

Aircraft Trajectory Clustering in Terminal Airspace Based on Deep Autoencoder and Gaussian Mixture Model

Zeng

Cai

et al. 2021

Aerospace

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The aircraft trajectory clustering analysis in the terminal airspace is conducive to determining the representative route structure of the arrival and departure trajectory and extracting their typical patterns, which is important for air traffic management such as airspace structure optimization, trajectory planning, and trajectory prediction. However, the current clustering methods perform poorly due to the large flight traffic, high density, and complex airspace structure in the terminal airspace. In recent years, the continuous development of Deep Learning has demonstrated its powerful ability to extract internal potential features of large dataset. Therefore, this paper mainly tries a deep trajectory clustering method based on deep autoencoder (DAE). To this end, this paper proposes a trajectory clustering method based on deep autoencoder (DAE) and Gaussian mixture model (GMM) to mine the prevailing traffic flow patterns in the terminal airspace. The DAE is trained to extract feature representations from historical high-dimensional trajectory data. Subsequently, the output of DAE is input into GMM for clustering. This paper takes the terminal airspace of Guangzhou Baiyun International Airport in China as a case to verify the proposed method. Through the direct visualization and dimensionality reduction visualization of the clustering results, it is found that the traffic flow patterns identified by the clustering method in this paper are intuitive and separable.

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Section: Introductionmentioning

confidence: 99%

Aircraft Trajectory Clustering in Terminal Airspace Based on Deep Autoencoder and Gaussian Mixture Model

Zeng

Cai

et al. 2021

Aerospace

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“…In 2015, Eerland W. J. [22] selected the Gaussian equation to model uncertainty based on historical data statistics. The method he proposed could choose to filter the track.…”

Section: Model-based Clusteringmentioning

confidence: 99%

Research on track clustering algorithm based on historical radar data

Mao¹,

Liu²,

Li³

2020

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

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With the advent of the big data era of air traffic control systems, the application of trajectory clustering in the field of air traffic has received widespread attention. This article reviews the trajectory clustering methods proposed in domestic and foreign literature. According to the different similarity measures and clustering evaluation criteria, the application of different clustering methods in trajectory clustering is introduced from several aspects, and the advantages and applicable scenarios of each algorithm are summarized. Then, based on the real flight history radar data information, the performance of the two different clustering algorithms in track clustering is analyzed. It mainly includes two aspects: First, in the data preprocessing part, data cleaning, filtering, and interpolation are performed. After processing, the data is resampled to obtain research data. Then, according to the characteristics of the track data, the two hyperparameters of the DBSCAN algorithm and the value of the K-means algorithm are determined, and the clustering results are visually displayed in combination with the real flight data from the Capital International Airport to Shanghai Hongqiao International Airport.

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“…As a result, all the methods that have previously been developed have one or more of the above disadvantages. Some of the essential reasons for restricting the practical applications of the relevant results presented in many previous works [2,8,10,16,[19][20][21], especially in cases of analyzing flight data, are as follows:…”

Section: Introductionmentioning

confidence: 99%

Optimizing the mutual arrangement of pilot indicators on an aircraft dashboard and analysis of this procedure from the viewpoint of quantum representations

Kuravsky¹,

Greshnikov²

2021

J Appl Eng Science

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The purpose of this work is to present the first attempt to provide quantitative analysis and objective justification for designers’ decisions that relate to the arrangement of pilot indicators on an aircraft dashboard with the use of video oculography measurements. To date, such decisions have been made only based on the practical experience accumulated by designers and subjective expert assessments. A new method for optimizing the mutual arrangement of the dashboard indicators is under consideration. This is based on iterative correction of the gaze transition probability matrix between the selected zones of attention, to minimize the difference between the stationary distribution of relative frequencies of gaze that are staying in these zones and the corresponding desirable target eye movements that are given for distribution for qualified pilots. When solving the subsequent multidimensional scaling problem, the gaze transition probability matrix that is obtained is considered to be the similarity matrix, the elements of which quantitatively characterize the proximity between the zones of attention. The main findings of this novel work are as follows: the use of oculography data to justify dashboard design decisions, the optimizing method itself, and its mathematical components, as well as analysis of the optimization in question from the viewpoint of quantum representations, all revealed design mistakes. The results that were obtained can be applied for prototyping variants of aircraft dashboards by rearranging the display areas associated with the corresponding zones of attention.

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Trajectory Clustering, Modeling and Selection with the focus on Airspace Protection

Cited by 14 publications

References 5 publications

Aircraft Trajectory Clustering in Terminal Airspace Based on Deep Autoencoder and Gaussian Mixture Model

Aircraft Trajectory Clustering in Terminal Airspace Based on Deep Autoencoder and Gaussian Mixture Model

Research on track clustering algorithm based on historical radar data

Optimizing the mutual arrangement of pilot indicators on an aircraft dashboard and analysis of this procedure from the viewpoint of quantum representations

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