Hostname: page-component-7479d7b7d-767nl Total loading time: 0 Render date: 2024-07-11T04:55:51.791Z Has data issue: false hasContentIssue false
  • English
  • Français

An online method for ship trajectory compression using AIS data

Published online by Cambridge University Press:  31 May 2024

Zhao Liu ,
Wensen Yuan ,
Maohan Liang Open the ORCID record for Maohan Liang [Opens in a new window] ,
Mingyang Zhang Open the ORCID record for Mingyang Zhang [Opens in a new window] ,
Cong Liu ,
Ryan Wen Liu Open the ORCID record for Ryan Wen Liu [Opens in a new window]  and
Jingxian Liu
Zhao Liu
Affiliation:
School of Navigation, Wuhan University of Technology, Wuhan, PR China National Engineering Research Center for Water Transport Safety, Wuhan, PR China
Wensen Yuan
Affiliation:
School of Navigation, Wuhan University of Technology, Wuhan, PR China
Maohan Liang
Affiliation:
School of Navigation, Wuhan University of Technology, Wuhan, PR China
Mingyang Zhang*
Affiliation:
School of Engineering, Aalto University, Espoo, Finland
Cong Liu
Affiliation:
School of Engineering, Aalto University, Espoo, Finland
Ryan Wen Liu
Affiliation:
School of Navigation, Wuhan University of Technology, Wuhan, PR China
Jingxian Liu
Affiliation:
School of Navigation, Wuhan University of Technology, Wuhan, PR China
*
*Corresponding author. Mingyang Zhang; Email: mingyang.0.zhang@aalto.fi
Get access Rights & Permissions [Opens in a new window]

Abstract

Vessel trajectories from the Automatic Identification System (AIS) play an important role in maritime traffic management, but a drawback is the huge amount of memory occupation which thus results in a low speed of data acquisition in maritime applications due to a large number of scattered data. This paper proposes a novel online vessel trajectory compression method based on the Improved Open Window (IOPW) algorithm. The proposed method compresses vessel trajectory instantly according to vessel coordinates along with a timestamp driven by the AIS data. In particular, we adopt the weighted Euclidean distance (WED), fusing the perpendicular Euclidean distance (PED) and synchronous Euclidean distance (SED) in IOPW to improve the robustness. The realistic AIS-based vessel trajectories are used to illustrate the proposed model by comparing it with five traditional trajectory compression methods. The experimental results reveal that the proposed method could effectively maintain the important trajectory features and significantly reduce the rate of distance loss during the online compression of vessel trajectories.

Keywords

automatic identification systemimproved open window algorithmonline compressionvessel trajectory
Type
Research Article
Information
The Journal of Navigation , First View , pp. 1 - 22
Check for updates
Copyright
Copyright © The Author(s), 2024. Published by Cambridge University Press on behalf of The Royal Institute of Navigation

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

Cao, H., Wolfson, O. and Trajcevski, G. (2006). Spatio-temporal data reduction with deterministic error bounds. VLDB Journal, 15(3), 211–228.Google Scholar
Cao, X., Cong, G. and Jensen, C. S. (2010). Mining significant semantic locations from GPS data. Proceedings of the VLDB Endowment, 3(1-2), 1009–1020.CrossRefGoogle Scholar
Douglas, D. H. and Peucker, T. K. (1973). Algorithms for the reduction of the number of points required to represent a digitized line or its caricature. Cartographica: The International Journal for Geographic Information and Geovisualization, 10(2), 112–122.CrossRefGoogle Scholar
Ducruet, C. (Ed.) (2015). Maritime Networks: Spatial Structures and Time Dynamics. New York, NY: Routledge.CrossRefGoogle Scholar
Fu, Z., Hu, W. and Tan, T. (2005). Similarity Based Vehicle Trajectory Clustering and Anomaly Detection. In IEEE International Conference on Image Processing 2005, Vol. 2, pp. II-602. IEEE.Google Scholar
García, J., Concha, O. P., Molina, J. M. and De Miguel, G. (2006). Trajectory Classification Based on Machine-Learning Techniques Over Tracking Data. In 2006 9th International Conference on Information Fusion, pp. 1–8. IEEE.CrossRefGoogle Scholar
Huang, Y., Li, Y., Zhang, Z. and Liu, R. (2020). GPU-accelerated compression and visualization of large-scale vessel trajectories in maritime IoT industries. IEEE Internet of Things Journal, 7(11), 10794–10812.CrossRefGoogle Scholar
Kellaris, G., Pelekis, N. and Theodoridis, Y. (2013). Map-matched trajectory compression. Journal of Systems and Software, 86(6), 1566–1579.CrossRefGoogle Scholar
Keogh, E. and Ratanamahatana, C. A. (2005). Exact indexing of dynamic time warping. Knowledge and Information Systems, 7(3), 358–386.CrossRefGoogle Scholar
Keogh, E., Chu, S., Hart, D. and Pazzani, M. (2001). An Online Algorithm for Segmenting Time Series. In Proceedings 2001 IEEE International Conference on Data Mining, pp. 289–296. IEEE.CrossRefGoogle Scholar
Le Tixerant, M., Le Guyader, D., Gourmelon, F. and Queffelec, B. (2018). How can automatic identification system (AIS) data be used for maritime spatial planning? Ocean Coastal Manage, 166, 18–30.CrossRefGoogle Scholar
Li, H. and Yang, Z. (2023). Incorporation of AIS data-based machine learning into unsupervised route planning for maritime autonomous surface ships. Transportation Research Part E: Logistics and Transportation Review, 176, 103171.CrossRefGoogle Scholar
Li, H., Liu, J. and Liu, R. W. (2017). Dynamic Speed Control Model for Very Large Ship in Restricted Waters. In The 27th International Ocean and Polar Engineering Conference.Google Scholar
Li, H., Lam, J. S. L., Yang, Z., Liu, J., Liu, R. W., Liang, M. and Li, Y. (2022). Unsupervised hierarchical methodology of maritime traffic pattern extraction for knowledge discovery. Transportation Research. Part C: Emerging Technologies, 143, 103856.CrossRefGoogle Scholar
Liu, X., Cheung, Y. M., Peng, S. J., Cui, Z., Zhong, B. and Du, J. X. (2014). Automatic motion capture data denoising via filtered subspace clustering and low rank matrix approximation. Signal Process, 105, 350–362.CrossRefGoogle Scholar
Liu, J., Zhao, K., Sommer, P., Shang, S., Kusy, B. and Jurdak, R. (2015). Bounded Quadrant System: Error-Bounded Trajectory Compression on the go. In 2015 IEEE 31st International Conference on Data Engineering, pp. 987–998. IEEE.CrossRefGoogle Scholar
Liu, J., Zhou, F., Li, Z., Wang, M. and Liu, R. W. (2016). Dynamic ship domain models for capacity analysis of restricted water channels. Journal of Navigation, 69(3), 481–503.CrossRefGoogle Scholar
Liu, J., Li, H., Yang, Z., Wu, K., Liu, Y. and Liu, R. W. (2019). Adaptive Douglas-Peucker algorithm with automatic thresholding for AIS-based vessel trajectory compression. IEEE Access, 7, 150677–150692.CrossRefGoogle Scholar
Liu, J., Shi, G. and Zhu, K. (2020). Online multiple outputs least-squares support vector regression model of ship trajectory prediction based on automatic information system data and selection mechanism. IEEE Access, 8, 154727–154745.Google Scholar
Makris, A., Tserpes, K. and Anagnostopoulos, D. (2019a). Database System Comparison Based on Spatiotemporal Functionality. Proceedings of the 23rd International Database Applications & Engineering Symposium, pp. 1–7.Google Scholar
Makris, A., Tserpes, K. and Spiliopoulos, G. (2019b). Performance Evaluation of MongoDB and PostgreSQL for Spatio-temporal Data[C]//EDBT/ICDT Workshops.Google Scholar
Makris, A., Tserpes, K. and Spiliopoulos, G. (2021). MongoDB Vs PostgreSQL: A comparative study on performance aspects. GeoInformatica, 25(2), 243–268.CrossRefGoogle Scholar
Meratnia, N. and Rolf, A. (2004). Spatiotemporal Compression Techniques for Moving Point Objects. In International Conference on Extending Database Technology, pp. 765–782. Berlin, Heidelberg: Springer.Google Scholar
Muckell, J., Hwang, J. H., Lawson, C. T. and Ravi, S. S. (2010). Algorithms for Compressing GPS Trajectory Data: An Empirical Evaluation. In Proceedings of the 18th SIGSPATIAL International Conference on Advances in Geographic Information Systems, pp. 402–405.Google Scholar
Muckell, J., Hwang, J. H., Patil, V., Lawson, C. T., Ping, F. and Ravi, S. S. (2011). SQUISH: An Online Approach for GPS Trajectory Compression. In Proceedings of the 2nd International Conference on Computing for Geospatial Research & Applications, pp. 1–8.CrossRefGoogle Scholar
Muckell, J., Olsen, P. W., Hwang, J. H., Lawson, C. T. and Ravi, S. S. (2014). Compression of trajectory data: A comprehensive evaluation and new approach. GeoInformatica, 18(3), 435–460.Google Scholar
Nguyen, D., Vadaine, R., Hajduch, G., Garello, R. and Fablet, R. (2018). A Multi-Task Deep Learning Architecture for Maritime Surveillance Using ais Data Streams. In 2018 IEEE 5th International Conference on Data Science and Advanced Analytics (DSAA), pp. 331–340.CrossRefGoogle Scholar
Qi, L. and Ji, Y. (2020). Ship trajectory data compression algorithms for automatic identification system: Comparison and analysis. Journal of Water Resources and Ocean Science, 9(2), 42–47.Google Scholar
Sang, L. Z., Wall, A., Mao, Z., Yan, X. P. and Wang, J. (2015). A novel method for restoring the trajectory of the inland waterway ship by using AIS data. Ocean Eng, 110, 183–194.CrossRefGoogle Scholar
Shi, W. and Cheung, C. (2006). Performance evaluation of line simplification algorithms for vector generalization. The Cartographic Journal, 43(1), 27–44.Google Scholar
Trajcevski, G., Cao, H., Scheuermanny, P., Wolfsonz, O. and Vaccaro, D. (2006). On-line Data Reduction and the Quality of History in Moving Objects Databases. In Proceedings of the 5th ACM International Workshop on Data Engineering for Wireless and mobile Access, pp. 19–26.CrossRefGoogle Scholar
van Emde Boas, P. (1975). Preserving Order in A Forest in Less Than Logarithmic Time. In 16th Annual Symposium on Foundations of Computer Science, pp. 75–84. IEEE.CrossRefGoogle Scholar
Wang, X., Tieu, K. and Grimson, E. (2006). Learning Semantic Scene Models by Trajectory Analysis. In European Conference on Computer Vision, pp. 110–123. Berlin, Heidelberg: Springer.Google Scholar
Wang, D., Miwa, T. and Morikawa, T. (2020). Big trajectory data mining: A survey of methods, applications, and services. Sensors, 20(16), 4571.Google ScholarPubMed
Wang, X., Liu, Z., Yan, R., Wang, H. and Zhang, M. (2022). Quantitative analysis of the impact of COVID-19 on ship visiting behaviors to ports-A framework and a case study. Ocean & Coastal Management, 230, 106377.CrossRefGoogle Scholar
Wei, Z., Zhou, K., Wei, M. and Shi, G. (2016). Ship motion pattern recognition and application based on AIS data. Journal of Shanghai Maritime University, 37(2), 17–22. (in Chinese).Google Scholar
Weng, J., Shi, K., Gan, X., Li, G. and Huang, Z. (2020). Ship emission estimation with high spatial-temporal resolution in the Yangtze river estuary using AIS data. Journal of Cleaner Production, 248, 119297.Google Scholar
Wu, X., Mehta, A. L., Zaloom, V. A. and Craig, B. N. (2016). Analysis of waterway transportation in southeast Texas waterway based on AIS data. Ocean Eng, 121, 196–209.Google Scholar
Xiao, Z., Ponnambalam, L., Fu, X. and Zhang, W. (2017). Maritime traffic probabilistic forecasting based on vessels’ waterway patterns and motion behaviors. IEEE Transactions on Intelligent Transportation Systems, 18(11), 3122–3134.Google Scholar
Zhang, S. K., Shi, G. Y., Liu, Z. J., Zhao, Z. W. and Wu, Z. L. (2018). Data-driven based automatic maritime routing from massive AIS trajectories in the face of disparity. Ocean Engineering, 155, 240–250.CrossRefGoogle Scholar
Zhang, M., Montewka, J., Manderbacka, T., Kujala, P. and Hirdaris, S. (2021). A big data analytics method for the evaluation of ship-ship collision risk reflecting hydrometeorological conditions. Reliability Engineering & System Safety, 213, 107674.CrossRefGoogle Scholar
Zhang, M., Kujala, P. and Hirdaris, S. (2022). A machine learning method for the evaluation of ship grounding risk in real operational conditions. Reliability Engineering & System Safety, 226, 108697.CrossRefGoogle Scholar
Zhang, M., Kujala, P., Musharraf, M., Zhang, J. and Hirdaris, S. (2023). A machine learning method for the prediction of ship motion trajectories in real operational conditions. Ocean Engineering, 283, 114905.CrossRefGoogle Scholar
Zhang, M., Tsoulakos, N., Kujala, P. and Hirdaris, S. (2024). A deep learning method for the prediction of ship fuel consumption in real operational conditions. Engineering Applications of Artificial Intelligence, 130, 107425.CrossRefGoogle Scholar
Zhao, L. and Shi, G. (2018). A method for simplifying ship trajectory based on improved Douglas–Peucker algorithm. Ocean Eng, 166, 37–46.Google Scholar
Zhao, L. and Shi, G. (2019). A trajectory clustering method based on Douglas–Peucker compression and density for marine traffic pattern recognition. Ocean Eng, 172, 456–467.CrossRefGoogle Scholar
Zhao, L., Ochieng, W. Y., Quddus, M. A. and Noland, R. B. (2003). An extended Kalman filter algorithm for integrating GPS and low cost dead reckoning system data for vehicle performance and emissions monitoring. Journal of Navigation, 56(2), 257–275.CrossRefGoogle Scholar
Zhao, L., Shi, G. and Yang, J. (2018a). Ship trajectories pre-processing based on AIS data. Journal of Navigation, 71(5), 1210–1230.CrossRefGoogle Scholar
Zhao, Y., Shang, S., Wang, Y., Zheng, B., Nguyen, Q. V. H. and Zheng, K. (2018b). REST: A Reference-Based Framework for Spatio-Temporal Trajectory Compression. In Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2797–2806.Google Scholar
Zheng, Y. (2015). Trajectory data mining: An overview. ACM Transactions on Intelligent Systems and Technology, 6(3), 1–41.Google Scholar
Zhou, Z., Zhang, Y., Yuan, X. and Wang, H. (2023). Compressing AIS trajectory data based on the multi-objective peak Douglas–Peucker algorithm. IEEE Access, 11, 6802–6821.CrossRefGoogle Scholar