An adaptive trajectory compression and feature preservation method for maritime traffic analysis
Ship trajectory data extracted from Automatic Identification System (AIS) has been extensively used for maritime traffic analysis. Yet the enormous volume of AIS data has come with substantial challenges related to storing, processing, analyzing, transmitting, and transferring. Trajectory compressio...
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| Vydané v: | Ocean engineering Ročník 312; s. 119189 |
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| Hlavní autori: | , , |
| Médium: | Journal Article |
| Jazyk: | English |
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Elsevier Ltd
15.11.2024
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| ISSN: | 0029-8018 |
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| Abstract | Ship trajectory data extracted from Automatic Identification System (AIS) has been extensively used for maritime traffic analysis. Yet the enormous volume of AIS data has come with substantial challenges related to storing, processing, analyzing, transmitting, and transferring. Trajectory compression techniques have been widely investigated to remedy the challenge. However, conventional compression techniques such as Douglas-Peucker (DP) algorithm mainly depend on line simplification algorithms, falling short in accurately identifying and preserving crucial information within trajectories. Moreover, using kinematic information from AIS data has posed difficulties associated with compression threshold determination. Hence, an adaptive method capable of considering multiple information from AIS is required. In this paper, a Top-Down Kinematic Compression (TDKC) algorithm aimed at adaptive trajectory compression and feature preservation is proposed. By incorporating time, position, speed, and course attributes from AIS data, TDKC exploits a Compression Binary Tree (CBT) method to address the recursion termination problem and determine the threshold automatically. A case study was conducted to evaluate the performance of TDKC using AIS data from Gulf of Finland, where a comparison with conventional algorithms and their improved versions based on specific performance evaluation metrics was involved. The results demonstrate TDKC's superiority in facilitating maritime traffic analysis.
•Develop a novel adaptive ship trajectory compression and feature preservation method that incorporates kinematic information from AIS data.•Propose Synchronous Velocity Difference (SVD) to enhance information difference measurement.•Present the concept of Compression Binary Tree (CBT) to solve the recursion termination problem and enable adaptive threshold-setting.•Introduce a velocity-based similarity metric to fill the gap in evaluating velocity information preservation.•The proposed method is compared to 7 other well-known methods to demonstrate its superiority. |
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| AbstractList | Ship trajectory data extracted from Automatic Identification System (AIS) has been extensively used for maritime traffic analysis. Yet the enormous volume of AIS data has come with substantial challenges related to storing, processing, analyzing, transmitting, and transferring. Trajectory compression techniques have been widely investigated to remedy the challenge. However, conventional compression techniques such as Douglas-Peucker (DP) algorithm mainly depend on line simplification algorithms, falling short in accurately identifying and preserving crucial information within trajectories. Moreover, using kinematic information from AIS data has posed difficulties associated with compression threshold determination. Hence, an adaptive method capable of considering multiple information from AIS is required. In this paper, a Top-Down Kinematic Compression (TDKC) algorithm aimed at adaptive trajectory compression and feature preservation is proposed. By incorporating time, position, speed, and course attributes from AIS data, TDKC exploits a Compression Binary Tree (CBT) method to address the recursion termination problem and determine the threshold automatically. A case study was conducted to evaluate the performance of TDKC using AIS data from Gulf of Finland, where a comparison with conventional algorithms and their improved versions based on specific performance evaluation metrics was involved. The results demonstrate TDKC's superiority in facilitating maritime traffic analysis.
•Develop a novel adaptive ship trajectory compression and feature preservation method that incorporates kinematic information from AIS data.•Propose Synchronous Velocity Difference (SVD) to enhance information difference measurement.•Present the concept of Compression Binary Tree (CBT) to solve the recursion termination problem and enable adaptive threshold-setting.•Introduce a velocity-based similarity metric to fill the gap in evaluating velocity information preservation.•The proposed method is compared to 7 other well-known methods to demonstrate its superiority. |
| ArticleNumber | 119189 |
| Author | Valdez Banda, Osiris Guo, Shaoqing Bolbot, Victor |
| Author_xml | – sequence: 1 givenname: Shaoqing orcidid: 0000-0001-6839-8199 surname: Guo fullname: Guo, Shaoqing email: shaoqing.guo@aalto.fi, gsqnbsr@gmail.com organization: Department of Mechanical Engineering, Marine Technology, Research Group on Safe and Efficient Marine and Ship Systems, Aalto University, Espoo, Finland – sequence: 2 givenname: Victor orcidid: 0000-0002-1883-3604 surname: Bolbot fullname: Bolbot, Victor organization: Department of Mechanical Engineering, Marine Technology, Research Group on Safe and Efficient Marine and Ship Systems, Aalto University, Espoo, Finland – sequence: 3 givenname: Osiris surname: Valdez Banda fullname: Valdez Banda, Osiris organization: Department of Mechanical Engineering, Marine Technology, Research Group on Safe and Efficient Marine and Ship Systems, Aalto University, Espoo, Finland |
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| Cites_doi | 10.1017/S037346331900064X 10.7763/IJIET.2013.V3.322 10.3390/ijgi6110329 10.1016/j.oceaneng.2018.08.005 10.1016/j.oceaneng.2018.12.019 10.18637/jss.v099.i09 10.1155/2022/6533223 10.1016/j.oceaneng.2020.108086 10.1145/3406096 10.1155/2021/7765130 10.1145/3015457 10.3390/s19122706 10.1016/j.oceaneng.2023.114595 10.1016/j.eswa.2012.05.060 10.1016/j.ress.2021.107752 10.3390/ijgi6060184 10.1007/978-3-540-72108-6_21 10.1016/j.jocs.2022.101568 10.1109/TIT.1978.1055934 10.1016/j.oceaneng.2018.02.060 10.1016/j.trc.2022.103951 10.1109/ACCESS.2020.3001934 10.1016/j.oceaneng.2023.113617 10.1109/JIOT.2020.2989398 10.1007/s00190-012-0578-z 10.1016/j.oceaneng.2021.109256 10.1109/ACCESS.2021.3092948 10.1080/01441647.2019.1649315 10.1016/j.marpol.2019.103520 10.1016/j.trc.2019.06.004 10.1155/2016/6587309 10.1016/j.oceaneng.2022.111207 10.1080/13658816.2019.1676430 10.1016/j.oceaneng.2023.114930 10.1016/j.trc.2022.103856 10.1007/s10707-013-0184-0 10.1017/S0373463315000831 10.1109/TIP.2012.2186146 10.1155/2022/6622862 10.1017/S037346331800067X 10.1017/S0373463321000692 10.1007/s10707-014-0208-4 10.1109/ACCESS.2021.3078642 10.3390/jmse10020216 10.1049/itr2.12005 10.3390/jmse9060609 10.1016/j.jss.2017.01.003 10.1109/ACCESS.2022.3154363 10.3138/FM57-6770-U75U-7727 10.3390/jmse11102005 10.1145/2782759.2782767 10.1017/S0373463324000171 10.7717/peerj-cs.1112 10.1155/2019/7803293 10.1016/j.ress.2022.108697 10.1109/ACCESS.2019.2947111 10.1016/j.ress.2021.107772 10.12716/1001.13.04.07 10.1016/j.oceaneng.2020.106936 10.1109/ACCESS.2023.3234121 10.1145/366573.366611 10.1016/j.oceaneng.2023.113879 10.1007/s10707-021-00434-1 10.1016/j.oceaneng.2021.109041 |
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| Keywords | AIS data Feature preservation Data-driven analysis Adaptive ship trajectory compression Top-down kinematic compression Douglas-peucker algorithm |
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| Snippet | Ship trajectory data extracted from Automatic Identification System (AIS) has been extensively used for maritime traffic analysis. Yet the enormous volume of... |
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| SubjectTerms | Adaptive ship trajectory compression AIS data Data-driven analysis Douglas-peucker algorithm Feature preservation Top-down kinematic compression |
| Title | An adaptive trajectory compression and feature preservation method for maritime traffic analysis |
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