Spatial–Temporal Clustering and Optimization of Aircraft Descent and Approach Trajectories

Yang, Zhao; Tang, Rong; Chen, Yixin; Wang, Bing

doi:10.1007/s42405-021-00401-y

Cited by 3 publications

(1 citation statement)

References 37 publications

(35 reference statements)

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“…The central trajectory is the most representative one with the highest density in the traffic flow. The central trajectory can be applied to aircraft trajectory prediction [4], abnormal trajectory recognition [5], practical analysis of departure procedures [6], and airport noise control [7]. The main technical idea of central trajectory extraction is to measure the similarity between tracks and cluster large-scale track data into traffic flows with large intra-class similarity and small inter-class similarity [8][9][10].…”

Section: Introductionmentioning

confidence: 99%

Center trajectory extraction algorithm based on multidimensional hierarchical clustering

Chu¹,

Ji²,

Li³

et al. 2021

J. mechatron., artif. intell., eng.

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The existing aircraft center track extraction methods only extract the position information of the trajectory, which cannot meet the requirements of abnormal trajectory detection and trajectory prediction. This paper innovatively proposes a center locus extraction algorithm based on multidimensional hierarchical clustering. Firstly, to solve the problem that trajectory resampling is easy to lose the original trajectory features, an equal arc length interpolation resampling method is proposed to process the original trajectory data. Then, the weighted Euclidean distance matrix of the trajectory set is calculated. The calculation model of the weighted Euclidean distance matrix is novel and takes into account the influence of multidimensional features. Finally, multidimensional hierarchical clustering is used to get the traffic flow distribution and output the center trajectory. 703 departure trajectory data from the terminal area of an airport are used for example verification. The results show that compared with the traditional hierarchical clustering, this method has a significant advantage in accurately dividing traffic flow. Moreover, the extracted center locus can retain the multidimensional features of locus, which has certain practical significance.

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Section: Introductionmentioning

confidence: 99%

Center trajectory extraction algorithm based on multidimensional hierarchical clustering

Chu¹,

Ji²,

Li³

et al. 2021

J. mechatron., artif. intell., eng.

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Hierarchical Method for Mining a Prevailing Flight Pattern in Airport Terminal Airspace

Chu

Zeng

Tan

et al. 2023

Journal of Aerospace Information Systems

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Due to the variety of flight patterns in airport terminal airspace, as well as the high global similarity of different flight patterns entering and leaving from the same runway or corridor, it is difficult for current mainstream methods to achieve good clustering. To this end, this paper first constructs a truncated dynamic time warping (TDTW) trajectory similarity measurement to characterize different trajectory patterns with high global similarity and large local differences. Furthermore, a hierarchical flight pattern mining method is proposed, which is divided into four layers according to different characteristics. The first three layers of the method classify trajectories according to takeoff and landing types, runways, and corridors; whereas the fourth layer uses a [Formula: see text]-medoid clustering method based on TDTW, thereby making the mining process more controllable and in line with actual operation. Compared to dynamic time warping, the experimental results show that the intraclass compactness and interclass separation of the cluster obtained by the proposed method have decreased and increased by 44.6 and 20.1%, respectively, and the overall performance has improved by 54.1%. More refined and reasonable flight patterns have been obtained.

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A Clustering Ensemble Method of Aircraft Trajectory Based on the Similarity Matrix

2022

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Performing clustering analysis on a large amount of historical trajectory data can obtain information such as frequent flight patterns of aircraft and air traffic flow distribution, which can provide a reference for the revision of standard flight procedures and the optimization of the division of airspace sectors. At present, most trajectory clustering uses a single clustering algorithm. When other processing remains unchanged, it is difficult to improve the clustering effect by using a single clustering method. Therefore, this paper proposes a trajectory clustering ensemble method based on a similarity matrix. Firstly, a stacked autoencoder is used to learn a small number of features that are sufficiently representative of the trajectory and used as the input to the subsequent clustering algorithm. Secondly, each basis cluster is used to cluster the data set, and then a consistent similarity matrix is obtained by using the clustering results of each basis cluster. On this basis, using the deformation of the matrix as the distance matrix between trajectories, the agglomerative hierarchical clustering algorithm is used to ensemble the results of each basis cluster. Taking the Nanjing Lukou Airport terminal area as an example, the experimental results show that integrating multiple basis clusters eliminates the inherent randomness of a single clustering algorithm, and the trajectory clustering results are more robust.

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Spatial–Temporal Clustering and Optimization of Aircraft Descent and Approach Trajectories

Cited by 3 publications

References 37 publications

Center trajectory extraction algorithm based on multidimensional hierarchical clustering

Center trajectory extraction algorithm based on multidimensional hierarchical clustering

Hierarchical Method for Mining a Prevailing Flight Pattern in Airport Terminal Airspace

A Clustering Ensemble Method of Aircraft Trajectory Based on the Similarity Matrix

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