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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| Format: | Article |
| Language: | English |
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Wiley
2023-01-01
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| Series: | Journal of Advanced Transportation |
| Online Access: | http://dx.doi.org/10.1155/2023/8831371 |
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| author | Xinliang Zhang Shibo Zhou |
| author_facet | Xinliang Zhang Shibo Zhou |
| author_sort | Xinliang Zhang |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-19286f4ad72c40ae889f9a577ee36e67 |
| institution | OA Journals |
| issn | 2042-3195 |
| language | English |
| publishDate | 2023-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Journal of Advanced Transportation |
| spelling | doaj-art-19286f4ad72c40ae889f9a577ee36e672025-08-20T02:02:06ZengWileyJournal of Advanced Transportation2042-31952023-01-01202310.1155/2023/8831371Vessel Trajectory Data Compression Algorithm considering Critical Region IdentificationXinliang Zhang0Shibo Zhou1College of NavigationCollege of NavigationVessel 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.http://dx.doi.org/10.1155/2023/8831371 |
| spellingShingle | Xinliang Zhang Shibo Zhou Vessel Trajectory Data Compression Algorithm considering Critical Region Identification Journal of Advanced Transportation |
| title | Vessel Trajectory Data Compression Algorithm considering Critical Region Identification |
| title_full | Vessel Trajectory Data Compression Algorithm considering Critical Region Identification |
| title_fullStr | Vessel Trajectory Data Compression Algorithm considering Critical Region Identification |
| title_full_unstemmed | Vessel Trajectory Data Compression Algorithm considering Critical Region Identification |
| title_short | Vessel Trajectory Data Compression Algorithm considering Critical Region Identification |
| title_sort | vessel trajectory data compression algorithm considering critical region identification |
| url | http://dx.doi.org/10.1155/2023/8831371 |
| work_keys_str_mv | AT xinliangzhang vesseltrajectorydatacompressionalgorithmconsideringcriticalregionidentification AT shibozhou vesseltrajectorydatacompressionalgorithmconsideringcriticalregionidentification |