Trajectory Compression Algorithm via Geospatial Background Knowledge
The maritime traffic status is monitored through the Automatic Identification System (AIS) installed on vessels. AIS data record the trajectory of each ship. However, due to the short sampling interval of AIS data, there is a significant amount of redundant data, which increases storage space and re...
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| Language: | English |
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MDPI AG
2025-02-01
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| Series: | Journal of Marine Science and Engineering |
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| Online Access: | https://www.mdpi.com/2077-1312/13/3/406 |
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| author | Yanqi Fang Xinxin Sun Yuanqiang Zhang Jumei Zhou Hongxiang Feng |
| author_facet | Yanqi Fang Xinxin Sun Yuanqiang Zhang Jumei Zhou Hongxiang Feng |
| author_sort | Yanqi Fang |
| collection | DOAJ |
| description | The maritime traffic status is monitored through the Automatic Identification System (AIS) installed on vessels. AIS data record the trajectory of each ship. However, due to the short sampling interval of AIS data, there is a significant amount of redundant data, which increases storage space and reduces data processing efficiency. To reduce the redundancy within AIS data, a compression algorithm is necessary to eliminate superfluous points. This paper presents an offline trajectory compression algorithm that leverages geospatial background knowledge. The algorithm employs an adaptive function to preserve points characterized by the highest positional errors and rates of water depth change. It segments trajectories according to their distance from the shoreline, applies varying water depth change rate thresholds depending on geographical location, and determines an optimal distance threshold using the average compression ratio score. To verify the effectiveness of the algorithm, this paper compares it with other algorithms. At the same compression ratio, the proposed algorithm reduces the average water depth error by approximately 99.1% compared to the Douglas–Peucker (DP) algorithm, while also addressing the common problem of compressed trajectories potentially intersecting with obstacles in traditional trajectory compression methods. |
| format | Article |
| id | doaj-art-b9f4f2d7de9c4b12837fd9a9a557100a |
| institution | OA Journals |
| issn | 2077-1312 |
| language | English |
| publishDate | 2025-02-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Journal of Marine Science and Engineering |
| spelling | doaj-art-b9f4f2d7de9c4b12837fd9a9a557100a2025-08-20T01:48:41ZengMDPI AGJournal of Marine Science and Engineering2077-13122025-02-0113340610.3390/jmse13030406Trajectory Compression Algorithm via Geospatial Background KnowledgeYanqi Fang0Xinxin Sun1Yuanqiang Zhang2Jumei Zhou3Hongxiang Feng4Faculty of Maritime and Transportation, Ningbo University, Ningbo 315211, ChinaFaculty of Maritime and Transportation, Ningbo University, Ningbo 315211, ChinaFaculty of Maritime and Transportation, Ningbo University, Ningbo 315211, ChinaFaculty of Maritime and Transportation, Ningbo University, Ningbo 315211, ChinaFaculty of Maritime and Transportation, Ningbo University, Ningbo 315211, ChinaThe maritime traffic status is monitored through the Automatic Identification System (AIS) installed on vessels. AIS data record the trajectory of each ship. However, due to the short sampling interval of AIS data, there is a significant amount of redundant data, which increases storage space and reduces data processing efficiency. To reduce the redundancy within AIS data, a compression algorithm is necessary to eliminate superfluous points. This paper presents an offline trajectory compression algorithm that leverages geospatial background knowledge. The algorithm employs an adaptive function to preserve points characterized by the highest positional errors and rates of water depth change. It segments trajectories according to their distance from the shoreline, applies varying water depth change rate thresholds depending on geographical location, and determines an optimal distance threshold using the average compression ratio score. To verify the effectiveness of the algorithm, this paper compares it with other algorithms. At the same compression ratio, the proposed algorithm reduces the average water depth error by approximately 99.1% compared to the Douglas–Peucker (DP) algorithm, while also addressing the common problem of compressed trajectories potentially intersecting with obstacles in traditional trajectory compression methods.https://www.mdpi.com/2077-1312/13/3/406AIS datatrajectory compressionDP algorithmgeospatial background |
| spellingShingle | Yanqi Fang Xinxin Sun Yuanqiang Zhang Jumei Zhou Hongxiang Feng Trajectory Compression Algorithm via Geospatial Background Knowledge Journal of Marine Science and Engineering AIS data trajectory compression DP algorithm geospatial background |
| title | Trajectory Compression Algorithm via Geospatial Background Knowledge |
| title_full | Trajectory Compression Algorithm via Geospatial Background Knowledge |
| title_fullStr | Trajectory Compression Algorithm via Geospatial Background Knowledge |
| title_full_unstemmed | Trajectory Compression Algorithm via Geospatial Background Knowledge |
| title_short | Trajectory Compression Algorithm via Geospatial Background Knowledge |
| title_sort | trajectory compression algorithm via geospatial background knowledge |
| topic | AIS data trajectory compression DP algorithm geospatial background |
| url | https://www.mdpi.com/2077-1312/13/3/406 |
| work_keys_str_mv | AT yanqifang trajectorycompressionalgorithmviageospatialbackgroundknowledge AT xinxinsun trajectorycompressionalgorithmviageospatialbackgroundknowledge AT yuanqiangzhang trajectorycompressionalgorithmviageospatialbackgroundknowledge AT jumeizhou trajectorycompressionalgorithmviageospatialbackgroundknowledge AT hongxiangfeng trajectorycompressionalgorithmviageospatialbackgroundknowledge |