Vessel Trajectory Data Compression Algorithm considering Critical Region Identification

Vessel trajectory data are currently the most important data source for vessel trajectory data mining research. However, vessel AIS data have a short sampling time interval and a large amount of data redundancy, which hampers the efficient utilization of AIS data. In order to effectively remove redu...

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Bibliographic Details
Published in:Journal of advanced transportation Vol. 2023; pp. 1 - 18
Main Authors: Zhang, Xinliang, Zhou, Shibo
Format: Journal Article
Language:English
Published: London Hindawi 30.12.2023
John Wiley & Sons, Inc
Wiley
Subjects:
Algorithms
Analysis
Batch processing
Compression
Data compression
Data mining
Data processing
Redundancy
Sliding
Trajectory analysis
Vessels
ISSN:0197-6729, 2042-3195
Online Access:Get full text
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Summary:Vessel trajectory data are currently the most important data source for vessel trajectory data mining research. However, vessel AIS data have a short sampling time interval and a large amount of data redundancy, which hampers the efficient utilization of AIS data. In order to effectively remove redundant information from AIS data and improve its usage efficiency, a compression algorithm for vessel trajectory data compression algorithm considering critical region identification (VATDC_CCRI) is proposed. The VATDC_CCRI algorithm identifies the critical regions of a vessel’s trajectory by analyzing the distribution of node variation rates. It employs the Douglas–Peucker (DP) algorithm to compress the data in these critical regions, reducing the distortion of the trajectory after compression. Additionally, the algorithm utilizes a sliding window approach to process the initial trajectory to improve the quality of the compressed vessel trajectories and retain as many spatiotemporal characteristics of the original trajectories as possible. It combines the feature nodes from the crucial regions in the vessel’s trajectory with the results obtained from the sliding window algorithm, effectively compressing the vessel’s trajectory. Experiments conducted on individual and multiple trajectories demonstrate that the VATDC_CCRI algorithm achieves higher compression rates and exhibits faster processing speeds compared to other classical vessel trajectory compression algorithms while preserving the shape of the vessel’s trajectory significantly.
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ISSN:0197-6729
2042-3195
DOI:10.1155/2023/8831371