A method for compressing AIS trajectory based on the adaptive core threshold difference Douglas–Peucker algorithm
Traditional trajectory compression algorithms, such as the siliding window (SW) algorithm and the Douglas–Peucker (DP) algorithm, typically use static thresholds based on fixed parameters like ship dimensions or predetermined distances, which limits their adaptive capabilities. In this paper, the ad...
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| Published in | Scientific reports Vol. 14; no. 1; pp. 21408 - 17 |
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| Main Authors | , , |
| Format | Journal Article |
| Language | English |
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London
Nature Publishing Group UK
13.09.2024
Nature Publishing Group Nature Portfolio |
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| Online Access | Get full text |
| ISSN | 2045-2322 2045-2322 |
| DOI | 10.1038/s41598-024-71779-4 |
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| Abstract | Traditional trajectory compression algorithms, such as the siliding window (SW) algorithm and the Douglas–Peucker (DP) algorithm, typically use static thresholds based on fixed parameters like ship dimensions or predetermined distances, which limits their adaptive capabilities. In this paper, the adaptive core threshold difference-DP (ACTD-DP) algorithm is proposed based on traditional DP algorithm. Firstly, according to the course value of automatic identification system (AIS) data, the original trajectory data is preprocessed and some redundant points are discarded. Then the number of compressed trajectory points corresponding to different thresholds is quantified. The function relationship between them is established by curve fitting method. The characteristics of the function curve are analyzed, and the core threshold and core threshold difference are solved. Finally, the compression factor is introduced to determine the optimal core threshold difference, which is the key parameter to control the accuracy and efficiency of the algorithm. Five different algorithms are used to compress the all ship trajectories in the experimental water area. The average compression ratio (ACR) of the ACTD-DP algorithm is 87.53%, the average length loss ratio (ALLR) is 23.20%, the AMSED (mean synchronous Euclidean distance of all trajectories) is 68.9747 mx, and the TIME is 25.6869 s. Compared with the other four algorithms, the ACTD-DP algorithm shows that the algorithm can not only achieve high compression ratio, but also maintain the integrity of trajectory shape. At the same time, the compression results of four different trajectories show that ACTD-DP algorithm has good robustness and applicability. Therefore, ACTD-DP algorithm has the best compression effect. |
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| AbstractList | Traditional trajectory compression algorithms, such as the siliding window (SW) algorithm and the Douglas–Peucker (DP) algorithm, typically use static thresholds based on fixed parameters like ship dimensions or predetermined distances, which limits their adaptive capabilities. In this paper, the adaptive core threshold difference-DP (ACTD-DP) algorithm is proposed based on traditional DP algorithm. Firstly, according to the course value of automatic identification system (AIS) data, the original trajectory data is preprocessed and some redundant points are discarded. Then the number of compressed trajectory points corresponding to different thresholds is quantified. The function relationship between them is established by curve fitting method. The characteristics of the function curve are analyzed, and the core threshold and core threshold difference are solved. Finally, the compression factor is introduced to determine the optimal core threshold difference, which is the key parameter to control the accuracy and efficiency of the algorithm. Five different algorithms are used to compress the all ship trajectories in the experimental water area. The average compression ratio (ACR) of the ACTD-DP algorithm is 87.53%, the average length loss ratio (ALLR) is 23.20%, the AMSED (mean synchronous Euclidean distance of all trajectories) is 68.9747 mx, and the TIME is 25.6869 s. Compared with the other four algorithms, the ACTD-DP algorithm shows that the algorithm can not only achieve high compression ratio, but also maintain the integrity of trajectory shape. At the same time, the compression results of four different trajectories show that ACTD-DP algorithm has good robustness and applicability. Therefore, ACTD-DP algorithm has the best compression effect. Abstract Traditional trajectory compression algorithms, such as the siliding window (SW) algorithm and the Douglas–Peucker (DP) algorithm, typically use static thresholds based on fixed parameters like ship dimensions or predetermined distances, which limits their adaptive capabilities. In this paper, the adaptive core threshold difference-DP (ACTD-DP) algorithm is proposed based on traditional DP algorithm. Firstly, according to the course value of automatic identification system (AIS) data, the original trajectory data is preprocessed and some redundant points are discarded. Then the number of compressed trajectory points corresponding to different thresholds is quantified. The function relationship between them is established by curve fitting method. The characteristics of the function curve are analyzed, and the core threshold and core threshold difference are solved. Finally, the compression factor is introduced to determine the optimal core threshold difference, which is the key parameter to control the accuracy and efficiency of the algorithm. Five different algorithms are used to compress the all ship trajectories in the experimental water area. The average compression ratio (ACR) of the ACTD-DP algorithm is 87.53%, the average length loss ratio (ALLR) is 23.20%, the AMSED (mean synchronous Euclidean distance of all trajectories) is 68.9747 mx, and the TIME is 25.6869 s. Compared with the other four algorithms, the ACTD-DP algorithm shows that the algorithm can not only achieve high compression ratio, but also maintain the integrity of trajectory shape. At the same time, the compression results of four different trajectories show that ACTD-DP algorithm has good robustness and applicability. Therefore, ACTD-DP algorithm has the best compression effect. Traditional trajectory compression algorithms, such as the siliding window (SW) algorithm and the Douglas-Peucker (DP) algorithm, typically use static thresholds based on fixed parameters like ship dimensions or predetermined distances, which limits their adaptive capabilities. In this paper, the adaptive core threshold difference-DP (ACTD-DP) algorithm is proposed based on traditional DP algorithm. Firstly, according to the course value of automatic identification system (AIS) data, the original trajectory data is preprocessed and some redundant points are discarded. Then the number of compressed trajectory points corresponding to different thresholds is quantified. The function relationship between them is established by curve fitting method. The characteristics of the function curve are analyzed, and the core threshold and core threshold difference are solved. Finally, the compression factor is introduced to determine the optimal core threshold difference, which is the key parameter to control the accuracy and efficiency of the algorithm. Five different algorithms are used to compress the all ship trajectories in the experimental water area. The average compression ratio (ACR) of the ACTD-DP algorithm is 87.53%, the average length loss ratio (ALLR) is 23.20%, the AMSED (mean synchronous Euclidean distance of all trajectories) is 68.9747 mx, and the TIME is 25.6869 s. Compared with the other four algorithms, the ACTD-DP algorithm shows that the algorithm can not only achieve high compression ratio, but also maintain the integrity of trajectory shape. At the same time, the compression results of four different trajectories show that ACTD-DP algorithm has good robustness and applicability. Therefore, ACTD-DP algorithm has the best compression effect.Traditional trajectory compression algorithms, such as the siliding window (SW) algorithm and the Douglas-Peucker (DP) algorithm, typically use static thresholds based on fixed parameters like ship dimensions or predetermined distances, which limits their adaptive capabilities. In this paper, the adaptive core threshold difference-DP (ACTD-DP) algorithm is proposed based on traditional DP algorithm. Firstly, according to the course value of automatic identification system (AIS) data, the original trajectory data is preprocessed and some redundant points are discarded. Then the number of compressed trajectory points corresponding to different thresholds is quantified. The function relationship between them is established by curve fitting method. The characteristics of the function curve are analyzed, and the core threshold and core threshold difference are solved. Finally, the compression factor is introduced to determine the optimal core threshold difference, which is the key parameter to control the accuracy and efficiency of the algorithm. Five different algorithms are used to compress the all ship trajectories in the experimental water area. The average compression ratio (ACR) of the ACTD-DP algorithm is 87.53%, the average length loss ratio (ALLR) is 23.20%, the AMSED (mean synchronous Euclidean distance of all trajectories) is 68.9747 mx, and the TIME is 25.6869 s. Compared with the other four algorithms, the ACTD-DP algorithm shows that the algorithm can not only achieve high compression ratio, but also maintain the integrity of trajectory shape. At the same time, the compression results of four different trajectories show that ACTD-DP algorithm has good robustness and applicability. Therefore, ACTD-DP algorithm has the best compression effect. |
| ArticleNumber | 21408 |
| Author | Wang, Zhiming Wang, Peiliang Zhang, Ting |
| Author_xml | – sequence: 1 givenname: Ting surname: Zhang fullname: Zhang, Ting organization: Merchant Marine College, Shanghai Maritime University, Shandong Transport Vocational College – sequence: 2 givenname: Zhiming surname: Wang fullname: Wang, Zhiming organization: Merchant Marine College, Shanghai Maritime University – sequence: 3 givenname: Peiliang surname: Wang fullname: Wang, Peiliang email: gfy5216@126.com organization: Merchant Marine College, Shanghai Maritime University, Shandong Vocation College of Science and Technology |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/39271771$$D View this record in MEDLINE/PubMed |
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| Cites_doi | 10.1016/j.trc.2022.103856 10.1109/ICBDA55095.2022.9760355 10.1007/978-3-540-24741-8_44 10.1016/j.joes.2021.03.001 10.1007/s00773-023-00945-6 10.13340/j.jsmu.202208070214 10.1109/ICOSEC51865.2021.9591958 10.1109/ACCESS.2021.3078642 10.1007/s13131-020-1638-5 10.1016/j.enavi.2016.06.002 10.1007/s11804-020-00138-2 10.1016/j.oceaneng.2023.114930 10.1109/OCEANS.2012.6404872 10.1002/9780470669488.ch2 10.1016/j.oceaneng.2020.108086 10.1016/j.oceaneng.2023.114503 10.1007/s12517-021-06930-w 10.1016/j.isci.2023.106383 10.1109/ACCESS.2023.3234121 10.1016/j.ress.2023.109489 10.1016/j.oceaneng.2018.08.005 10.1109/ICMA54519.2022.9856198 10.1007/s10707-021-00434-1 10.1109/ICTIS54573.2021.9798623 10.1016/j.oceaneng.2018.02.060 10.23919/APNOMS.2019.8892899 10.1016/j.oceaneng.2021.109041 10.3390/s19122706 10.1016/j.oceaneng.2022.113036 |
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| References | MakrisAEvaluating the effect of compressing algorithms for trajectory similarity and classification problemsGeoInformatica20212567971110.1007/s10707-021-00434-1 Lei, P.-R., Yu, P.-R. & Peng, W.-C. A Framework for maritime anti-collision pattern discovery from AIS network. In 2019 20th Asia-Pacific Network Operations and Management Symposium (APNOMS). 1–4 https://doi.org/10.23919/APNOMS.2019.8892899 (IEEE, 2019). ShukaiZZhengjiangLXiankuZGuoyouSYaoCA method for AIS track data compression based on Douglas–Peucker algorithmJ. Harbin Eng. Univ.201536595599 HanPYangXBig data-driven automatic generation of ship route planning in complex maritime environmentsActa Oceanol. Sin.20203911312010.1007/s13131-020-1638-5 Barco, S. G., Lockhart, G. G. & Swingle, W. M. Using RADAR & AIS to investigate ship behavior in the Chesapeake Bay ocean approach off of Virginia, USA. In 2012 Oceans. 1–8 https://doi.org/10.1109/OCEANS.2012.6404872 (IEEE, 2012). MathWorks, I. Matlab. https://www.mathworks.com/products/matlab/ (2024). JeongSKimT-WGenerating a path-search graph based on ship-trajectory data: Route search via dynamic programming for autonomous shipsOcean Eng.202328311450310.1016/j.oceaneng.2023.114503 GaoMShiG-YShip spatiotemporal key feature point online extraction based on AIS multi-sensor data using an improved sliding window algorithmSensors20191927062019Senso..19.2706G10.3390/s19122706312081316630457 AnKE-navigation services for non-SOLAS shipsInt. J. e-Navigat. Maritime Econ.20164132210.1016/j.enavi.2016.06.002 LiHUnsupervised hierarchical methodology of maritime traffic pattern extraction for knowledge discoveryTransport. Res. Part C Emerg. Technol.202214310385610.1016/j.trc.2022.103856 ZhuFMaZShip trajectory online compression algorithm considering handling patternsIEEE Access20219701827019110.1109/ACCESS.2021.3078642 Guo, Y. & Ding, Z. Application of big data in analyzing the impact of explosive cyclone on ship navigation safety. In 2021 2nd International Conference on Smart Electronics and Communication (ICOSEC). 910–913 https://doi.org/10.1109/ICOSEC51865.2021.9591958 (IEEE, 2021). Meratnia, N. & De By, R. A. Spatiotemporal compression techniques for moving point objects. In (Goos, G. et al. eds.) Advances in Database Technology-EDBT 2004. Lecture Notes in Computer Science. Vol. 2992. 765–782 https://doi.org/10.1007/978-3-540-24741-8_44 (Springer, 2004). Douglas, D. H. & Peucker, T. K. Algorithms for the reduction of the number of points required to represent a digitized line or its caricature. In Classics in Cartography (Dodge, M. ed.). 1 Ed. 15–28 https://doi.org/10.1002/9780470669488.ch2 (Wiley, 2011). Liu, H., Liu, Y. & Zong, Z. Research on ship abnormal behavior detection method based on graph neural network. In 2022 IEEE International Conference on Mechatronics and Automation (ICMA). 834–838 https://doi.org/10.1109/ICMA54519.2022.9856198 (IEEE, 2022). Jie, G., Xin, S., Xiaowei, S. & Daofang, C. Online compression algorithm of fishing ship trajectories based on improved sliding window. J. Shanghai Maritime Univ.44, 17–24 https://doi.org/10.13340/j.jsmu.202208070214 (2023). ZhouZZhangYYuanXWangHCompressing AIS trajectory data based on the multi-objective peak Douglas–Peucker algorithmIEEE Access2023116802682110.1109/ACCESS.2023.3234121 BaiXXieZXuXXiaoYAn adaptive threshold fast DBSCAN algorithm with preserved trajectory feature points for vessel trajectory clusteringOcean Eng.202328011493010.1016/j.oceaneng.2023.114930 LiuZLiYZhangZYuWDuYSpatial modeling and analysis based on spatial information of the ship encounters for intelligent navigation safetyReliabil. Eng. Syst. Saf.202323810948910.1016/j.ress.2023.109489 TangCA method for compressing AIS trajectory data based on the adaptive-threshold Douglas–Peucker algorithmOcean Eng.202123210904110.1016/j.oceaneng.2021.109041 Hao, Y., Zheng, P. & Han, Z. Automatic generation of water route based on AIS big data and ECDIS. Arab. J. Geosci.14, 1–8 https://doi.org/10.1007/s12517-021-06930-w (Springer, 2021). ZhangS-KShiG-YLiuZ-JZhaoZ-WWuZ-LData-driven based automatic maritime routing from massive AIS trajectories in the face of disparityOcean Eng.20181552402502018OcEng.155..240Z10.1016/j.oceaneng.2018.02.060 ZhaoLShiGA method for simplifying ship trajectory based on improved Douglas–Peucker algorithmOcean Eng.201816637462018OcEng.166...37Z10.1016/j.oceaneng.2018.08.005 BlindheimSJohansenTAUtneIBRisk-based supervisory control for autonomous ship navigationJ. Mar. Sci. Technol.20232862464810.1007/s00773-023-00945-6 Cui, C. & Dong, Z. Ship space-time AIS trajectory data compression method. In 2022 7th International Conference on Big Data Analytics (ICBDA). 40–44 https://doi.org/10.1109/ICBDA55095.2022.9760355 (IEEE, 2022). WeiZXieXZhangXAIS trajectory simplification algorithm considering ship behavioursOcean Eng.202021610808610.1016/j.oceaneng.2020.108086 Qi, Z., Yi, C., Li, X. & Wen, G. Improved sliding window trajectory compression algorithm considering motion characteristics. J. Geomat. Sci. Technol.37, 622–627 (2020) (5 citations (CNKI)[2024-3-13]). HuangCQiXZhengJZhuRShenJA maritime traffic route extraction method based on density-based spatial clustering of applications with noise for multi-dimensional dataOcean Eng.202326811303610.1016/j.oceaneng.2022.113036 Chen, J., Zhang, J., Chen, H., Zhao, Y. & Wang, H. A TDV attention-based BiGRU network for AIS-based vessel trajectory prediction. iScience26, 106383 https://doi.org/10.1016/j.isci.2023.106383 (2023). Huang, C. et al. A simulation model for marine traffic environment risk assessment in the traffic separation scheme. In 2021 6th International Conference on Transportation Information and Safety (ICTIS). 213–221 https://doi.org/10.1109/ICTIS54573.2021.9798623 (IEEE, 2021). KimESensitive resource and traffic density risk analysis of marine spill accidents using automated identification system big dataJ. Mar. Sci. Appl.202019173181408666710.1007/s11804-020-00138-2 MurrayBPereraLPShip behavior prediction via trajectory extraction-based clustering for maritime situation awarenessJ. Ocean Eng. Sci.2022711310.1016/j.joes.2021.03.001 S Jeong (71779_CR15) 2023; 283 71779_CR16 71779_CR17 71779_CR9 71779_CR7 71779_CR12 71779_CR13 71779_CR5 71779_CR4 71779_CR1 S-K Zhang (71779_CR22) 2018; 155 P Han (71779_CR6) 2020; 39 S Blindheim (71779_CR8) 2023; 28 K An (71779_CR3) 2016; 4 Z Zhou (71779_CR28) 2023; 11 71779_CR32 C Huang (71779_CR24) 2023; 268 71779_CR31 X Bai (71779_CR21) 2023; 280 Z Liu (71779_CR10) 2023; 238 L Zhao (71779_CR29) 2018; 166 M Gao (71779_CR19) 2019; 19 B Murray (71779_CR14) 2022; 7 71779_CR27 F Zhu (71779_CR18) 2021; 9 Z Shukai (71779_CR25) 2015; 36 E Kim (71779_CR2) 2020; 19 H Li (71779_CR26) 2022; 143 Z Wei (71779_CR23) 2020; 216 A Makris (71779_CR11) 2021; 25 71779_CR20 C Tang (71779_CR30) 2021; 232 |
| References_xml | – reference: HanPYangXBig data-driven automatic generation of ship route planning in complex maritime environmentsActa Oceanol. Sin.20203911312010.1007/s13131-020-1638-5 – reference: Liu, H., Liu, Y. & Zong, Z. Research on ship abnormal behavior detection method based on graph neural network. In 2022 IEEE International Conference on Mechatronics and Automation (ICMA). 834–838 https://doi.org/10.1109/ICMA54519.2022.9856198 (IEEE, 2022). – reference: BlindheimSJohansenTAUtneIBRisk-based supervisory control for autonomous ship navigationJ. Mar. Sci. Technol.20232862464810.1007/s00773-023-00945-6 – reference: Huang, C. et al. A simulation model for marine traffic environment risk assessment in the traffic separation scheme. In 2021 6th International Conference on Transportation Information and Safety (ICTIS). 213–221 https://doi.org/10.1109/ICTIS54573.2021.9798623 (IEEE, 2021). – reference: JeongSKimT-WGenerating a path-search graph based on ship-trajectory data: Route search via dynamic programming for autonomous shipsOcean Eng.202328311450310.1016/j.oceaneng.2023.114503 – reference: Cui, C. & Dong, Z. Ship space-time AIS trajectory data compression method. In 2022 7th International Conference on Big Data Analytics (ICBDA). 40–44 https://doi.org/10.1109/ICBDA55095.2022.9760355 (IEEE, 2022). – reference: MurrayBPereraLPShip behavior prediction via trajectory extraction-based clustering for maritime situation awarenessJ. Ocean Eng. Sci.2022711310.1016/j.joes.2021.03.001 – reference: ZhuFMaZShip trajectory online compression algorithm considering handling patternsIEEE Access20219701827019110.1109/ACCESS.2021.3078642 – reference: GaoMShiG-YShip spatiotemporal key feature point online extraction based on AIS multi-sensor data using an improved sliding window algorithmSensors20191927062019Senso..19.2706G10.3390/s19122706312081316630457 – reference: Lei, P.-R., Yu, P.-R. & Peng, W.-C. A Framework for maritime anti-collision pattern discovery from AIS network. In 2019 20th Asia-Pacific Network Operations and Management Symposium (APNOMS). 1–4 https://doi.org/10.23919/APNOMS.2019.8892899 (IEEE, 2019). – reference: KimESensitive resource and traffic density risk analysis of marine spill accidents using automated identification system big dataJ. Mar. Sci. Appl.202019173181408666710.1007/s11804-020-00138-2 – reference: WeiZXieXZhangXAIS trajectory simplification algorithm considering ship behavioursOcean Eng.202021610808610.1016/j.oceaneng.2020.108086 – reference: Guo, Y. & Ding, Z. Application of big data in analyzing the impact of explosive cyclone on ship navigation safety. In 2021 2nd International Conference on Smart Electronics and Communication (ICOSEC). 910–913 https://doi.org/10.1109/ICOSEC51865.2021.9591958 (IEEE, 2021). – reference: Chen, J., Zhang, J., Chen, H., Zhao, Y. & Wang, H. A TDV attention-based BiGRU network for AIS-based vessel trajectory prediction. iScience26, 106383 https://doi.org/10.1016/j.isci.2023.106383 (2023). – reference: MathWorks, I. Matlab. https://www.mathworks.com/products/matlab/ (2024). – reference: ShukaiZZhengjiangLXiankuZGuoyouSYaoCA method for AIS track data compression based on Douglas–Peucker algorithmJ. Harbin Eng. Univ.201536595599 – reference: MakrisAEvaluating the effect of compressing algorithms for trajectory similarity and classification problemsGeoInformatica20212567971110.1007/s10707-021-00434-1 – reference: ZhaoLShiGA method for simplifying ship trajectory based on improved Douglas–Peucker algorithmOcean Eng.201816637462018OcEng.166...37Z10.1016/j.oceaneng.2018.08.005 – reference: Qi, Z., Yi, C., Li, X. & Wen, G. Improved sliding window trajectory compression algorithm considering motion characteristics. J. Geomat. Sci. Technol.37, 622–627 (2020) (5 citations (CNKI)[2024-3-13]). – reference: TangCA method for compressing AIS trajectory data based on the adaptive-threshold Douglas–Peucker algorithmOcean Eng.202123210904110.1016/j.oceaneng.2021.109041 – reference: Barco, S. G., Lockhart, G. G. & Swingle, W. M. Using RADAR & AIS to investigate ship behavior in the Chesapeake Bay ocean approach off of Virginia, USA. In 2012 Oceans. 1–8 https://doi.org/10.1109/OCEANS.2012.6404872 (IEEE, 2012). – reference: Hao, Y., Zheng, P. & Han, Z. Automatic generation of water route based on AIS big data and ECDIS. Arab. J. Geosci.14, 1–8 https://doi.org/10.1007/s12517-021-06930-w (Springer, 2021). – reference: Douglas, D. H. & Peucker, T. K. Algorithms for the reduction of the number of points required to represent a digitized line or its caricature. In Classics in Cartography (Dodge, M. ed.). 1 Ed. 15–28 https://doi.org/10.1002/9780470669488.ch2 (Wiley, 2011). – reference: AnKE-navigation services for non-SOLAS shipsInt. J. e-Navigat. Maritime Econ.20164132210.1016/j.enavi.2016.06.002 – reference: LiuZLiYZhangZYuWDuYSpatial modeling and analysis based on spatial information of the ship encounters for intelligent navigation safetyReliabil. Eng. Syst. Saf.202323810948910.1016/j.ress.2023.109489 – reference: HuangCQiXZhengJZhuRShenJA maritime traffic route extraction method based on density-based spatial clustering of applications with noise for multi-dimensional dataOcean Eng.202326811303610.1016/j.oceaneng.2022.113036 – reference: LiHUnsupervised hierarchical methodology of maritime traffic pattern extraction for knowledge discoveryTransport. Res. Part C Emerg. Technol.202214310385610.1016/j.trc.2022.103856 – reference: BaiXXieZXuXXiaoYAn adaptive threshold fast DBSCAN algorithm with preserved trajectory feature points for vessel trajectory clusteringOcean Eng.202328011493010.1016/j.oceaneng.2023.114930 – reference: Jie, G., Xin, S., Xiaowei, S. & Daofang, C. Online compression algorithm of fishing ship trajectories based on improved sliding window. J. Shanghai Maritime Univ.44, 17–24 https://doi.org/10.13340/j.jsmu.202208070214 (2023). – reference: ZhouZZhangYYuanXWangHCompressing AIS trajectory data based on the multi-objective peak Douglas–Peucker algorithmIEEE Access2023116802682110.1109/ACCESS.2023.3234121 – reference: ZhangS-KShiG-YLiuZ-JZhaoZ-WWuZ-LData-driven based automatic maritime routing from massive AIS trajectories in the face of disparityOcean Eng.20181552402502018OcEng.155..240Z10.1016/j.oceaneng.2018.02.060 – reference: Meratnia, N. & De By, R. A. Spatiotemporal compression techniques for moving point objects. In (Goos, G. et al. eds.) Advances in Database Technology-EDBT 2004. Lecture Notes in Computer Science. Vol. 2992. 765–782 https://doi.org/10.1007/978-3-540-24741-8_44 (Springer, 2004). – volume: 143 start-page: 103856 year: 2022 ident: 71779_CR26 publication-title: Transport. Res. Part C Emerg. Technol. doi: 10.1016/j.trc.2022.103856 – ident: 71779_CR27 doi: 10.1109/ICBDA55095.2022.9760355 – volume: 36 start-page: 595 year: 2015 ident: 71779_CR25 publication-title: J. Harbin Eng. Univ. – ident: 71779_CR16 doi: 10.1007/978-3-540-24741-8_44 – volume: 7 start-page: 1 year: 2022 ident: 71779_CR14 publication-title: J. Ocean Eng. Sci. doi: 10.1016/j.joes.2021.03.001 – volume: 28 start-page: 624 year: 2023 ident: 71779_CR8 publication-title: J. Mar. Sci. Technol. doi: 10.1007/s00773-023-00945-6 – ident: 71779_CR17 doi: 10.13340/j.jsmu.202208070214 – ident: 71779_CR9 doi: 10.1109/ICOSEC51865.2021.9591958 – volume: 9 start-page: 70182 year: 2021 ident: 71779_CR18 publication-title: IEEE Access doi: 10.1109/ACCESS.2021.3078642 – ident: 71779_CR32 – volume: 39 start-page: 113 year: 2020 ident: 71779_CR6 publication-title: Acta Oceanol. Sin. doi: 10.1007/s13131-020-1638-5 – volume: 4 start-page: 13 year: 2016 ident: 71779_CR3 publication-title: Int. J. e-Navigat. Maritime Econ. doi: 10.1016/j.enavi.2016.06.002 – volume: 19 start-page: 173 year: 2020 ident: 71779_CR2 publication-title: J. Mar. Sci. Appl. doi: 10.1007/s11804-020-00138-2 – volume: 280 start-page: 114930 year: 2023 ident: 71779_CR21 publication-title: Ocean Eng. doi: 10.1016/j.oceaneng.2023.114930 – ident: 71779_CR4 doi: 10.1109/OCEANS.2012.6404872 – ident: 71779_CR31 doi: 10.1002/9780470669488.ch2 – volume: 216 start-page: 108086 year: 2020 ident: 71779_CR23 publication-title: Ocean Eng. doi: 10.1016/j.oceaneng.2020.108086 – volume: 283 start-page: 114503 year: 2023 ident: 71779_CR15 publication-title: Ocean Eng. doi: 10.1016/j.oceaneng.2023.114503 – ident: 71779_CR7 doi: 10.1007/s12517-021-06930-w – ident: 71779_CR12 doi: 10.1016/j.isci.2023.106383 – ident: 71779_CR20 – volume: 11 start-page: 6802 year: 2023 ident: 71779_CR28 publication-title: IEEE Access doi: 10.1109/ACCESS.2023.3234121 – volume: 238 start-page: 109489 year: 2023 ident: 71779_CR10 publication-title: Reliabil. Eng. Syst. Saf. doi: 10.1016/j.ress.2023.109489 – volume: 166 start-page: 37 year: 2018 ident: 71779_CR29 publication-title: Ocean Eng. doi: 10.1016/j.oceaneng.2018.08.005 – ident: 71779_CR13 doi: 10.1109/ICMA54519.2022.9856198 – volume: 25 start-page: 679 year: 2021 ident: 71779_CR11 publication-title: GeoInformatica doi: 10.1007/s10707-021-00434-1 – ident: 71779_CR1 doi: 10.1109/ICTIS54573.2021.9798623 – volume: 155 start-page: 240 year: 2018 ident: 71779_CR22 publication-title: Ocean Eng. doi: 10.1016/j.oceaneng.2018.02.060 – ident: 71779_CR5 doi: 10.23919/APNOMS.2019.8892899 – volume: 232 start-page: 109041 year: 2021 ident: 71779_CR30 publication-title: Ocean Eng. doi: 10.1016/j.oceaneng.2021.109041 – volume: 19 start-page: 2706 year: 2019 ident: 71779_CR19 publication-title: Sensors doi: 10.3390/s19122706 – volume: 268 start-page: 113036 year: 2023 ident: 71779_CR24 publication-title: Ocean Eng. doi: 10.1016/j.oceaneng.2022.113036 |
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| Snippet | Traditional trajectory compression algorithms, such as the siliding window (SW) algorithm and the Douglas–Peucker (DP) algorithm, typically use static... Traditional trajectory compression algorithms, such as the siliding window (SW) algorithm and the Douglas-Peucker (DP) algorithm, typically use static... Abstract Traditional trajectory compression algorithms, such as the siliding window (SW) algorithm and the Douglas–Peucker (DP) algorithm, typically use static... |
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| SubjectTerms | 639/705 639/705/1046 639/705/117 704/829 ACTD-DP algorithm AIS Algorithms Compression Humanities and Social Sciences multidisciplinary Science Science (multidisciplinary) Ship trajectory Threshold limits Trajectory compression |
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| Title | A method for compressing AIS trajectory based on the adaptive core threshold difference Douglas–Peucker algorithm |
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