Notice of Retraction: An Improved High-Density Sub Trajectory Clustering Algorithm

Liu, Xiaoming; Dong, Luxi; Shang, Chunlin; Wei, Xiangda

doi:10.1109/access.2020.2974059

Cited by 7 publications

(3 citation statements)

References 31 publications

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“…Zhao and Shi proposed a novel similarity measure based on the dynamic time warping distance, which considers the course change of track points and the meaning at the route level [41]. Some other research tried to improve clustering methods or clustering efficiency [22,34]. For example, Li et al proposed a dimensionality reduction-based multi-step clustering method for robust vessel trajectory analysis to improve the clustering performance in terms of quantitative and qualitative evaluations.…”

Section: Trajectory Clusteringmentioning

confidence: 99%

See 1 more Smart Citation

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

Yang

Tang

Chen

et al. 2021

Int. J. Aeronaut. Space Sci.

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This study presents a procedure for the spatial-temporal clustering and optimization of aircraft descent and approach trajectories. First, the spatial-temporal similarity between two trajectories is defined. Clustering analysis are conducted to identify the prevailing trajectories. The clustering centers obtained based on spatial-temporal distance are compared with those obtained based on the traditional Euclidean distance. Second, a multi-objective optimization model is established to minimize fuel consumption, aircraft emission and noise impact considering flight constraints. The Pareto solution that has the highest similarity with the prevailing trajectories is selected as the final optimized trajectory. The performance indicators of the optimized trajectory are compared with the average values of historic trajectories. It is found that travel time, fuel consumption and noise impact for the optimized trajectory are reduced by 5.34%, 0.96% and 11.86%, respectively. The percentages are 0.96%, 1.32%, 9.18%, 3.54% and 4.00% for CO 2 , SO x , NO x , CO and HC, respectively. Also, the performance indicators for the two clustering centers based on spatial-temporal distance are generally closer to average performance of original trajectories, as well as those of the optimized trajectories, as compared with the two clustering centers based on Euclidean distance. The spatial-temporal clustering methods may help to discover the valuable information that lies in those indicators associated with features reflected in time dimension.

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Section: Trajectory Clusteringmentioning

confidence: 99%

“…According to formula (25), the noise upper limit outdoor is 89 dB for SEL. Thus, an approximate impact area (S) can be calculated as where D is calculated with assign L to 89 dB in formula (22), H represents the height between aircraft and ground; H p is the barometric altitude.…”

Section: Aircraft Noisementioning

confidence: 99%

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

Yang

Tang

Chen

et al. 2021

Int. J. Aeronaut. Space Sci.

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“…Yuan [45] divides the trajectory into a set of stationary sub-trajectory (SST) based on the detection and evaluation of trajectory position disturbance. The HTRACLUS_DL algorithm proposed by Liu [46] improved the accuracy of TRACLUS algorithm, in which a sub-trajectory parallel boundary method is constructed. Zhang [47] is also based on the MDL principle, which transforms the trajectory segmentation problem into the shortest path problem of undirected graph and then uses Dijkstra algorithm to get the best segmentation, which has higher complexity.…”

Section: Introductionmentioning

confidence: 99%

PSOTSC: A Global-Oriented Trajectory Segmentation and Compression Algorithm Based on Swarm Intelligence

Ouyang

Ding

et al. 2021

IJGI

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Linear approximate segmentation and data compression of moving target spatio-temporal trajectory can reduce data storage pressure and improve the efficiency of target motion pattern mining. High quality segmentation and compression need to accurately select and store as few points as possible that can reflect the characteristics of the original trajectory, while the existing methods still have room for improvement in segmentation accuracy, reduction of compression rate and simplification of algorithm parameter setting. A trajectory segmentation and compression algorithm based on particle swarm optimization is proposed. First, the trajectory segmentation problem is transformed into a global intelligent optimization problem of segmented feature points, which makes the selection of segmented points more accurate; then, a particle update strategy combining neighborhood adjustment and random jump is established to improve the efficiency of segmentation and compression. Through experiments on a real data set and a maneuvering target simulation trajectory set, the results show that compared with the existing typical methods, this method has advantages in segmentation accuracy and compression rate.

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Parallel grid-based density peak clustering of big trajectory data

et al. 2021

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Notice of Retraction: An Improved High-Density Sub Trajectory Clustering Algorithm

Cited by 7 publications

References 31 publications

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

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

PSOTSC: A Global-Oriented Trajectory Segmentation and Compression Algorithm Based on Swarm Intelligence

Parallel grid-based density peak clustering of big trajectory data

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