Hostname: page-component-848d4c4894-2pzkn Total loading time: 0 Render date: 2024-06-02T07:19:44.864Z Has data issue: false hasContentIssue false
  • English
  • Français

Detection of abnormal ship trajectory based on the complex polygon

Published online by Cambridge University Press:  18 April 2022

Jinxian Weng Open the ORCID record for Jinxian Weng [Opens in a new window] ,
Guorong Li Open the ORCID record for Guorong Li [Opens in a new window]  and
Yahui Zhao
Jinxian Weng*
Affiliation:
College of Transport and Communications, Shanghai Maritime University, Shanghai 201306, China
Guorong Li
Affiliation:
College of Transport and Communications, Shanghai Maritime University, Shanghai 201306, China
Yahui Zhao
Affiliation:
College of Transport and Communications, Shanghai Maritime University, Shanghai 201306, China
*
*Corresponding author. E-mail: jxweng@bjtu.edu.cn; wjx206@gmail.com.
Get access Rights & Permissions [Opens in a new window]

Abstract

Ship anomaly detection is a vital aspect for monitoring navigational safety in specific water areas. Considering the effect of water channel boundaries, we propose the detection of an abnormal ship trajectory based on the complex polygon (DATCP) method to detect ship anomalies in this study. With the automatic identification systems (AIS) data from the Yangtze River estuary, a case study is created to verify the effectiveness of the proposed DATCP method. The case study results reveal that the proposed DATCP method can provide higher detection accuracy than the conventional A* algorithm. The feature analysis results indicate that ship anomalies are significantly influenced by ship type, time period, weather conditions and ship traffic characteristics.

Keywords

trajectoryship behaviourautomatic identification systems
Type
Research Article
Information
The Journal of Navigation , Volume 75 , Issue 4 , July 2022 , pp. 966 - 983
Check for updates
Copyright
Copyright © The Author(s), 2022. 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

Anneken, M., Fischer, Y. and Beyerer, J. (2015). Evaluation and Comparison of Anomaly Detection Algorithms in Annotated Datasets From the Maritime Domain. SAI Intelligent Systems Conference. IEEE.CrossRefGoogle Scholar
Hodge, V. J. and Austin, J. (2004). A survey of outlier detection methodologies. Artificial Intelligence Review, 22(2), 85–126.CrossRefGoogle Scholar
Kaluđer, H., Brezak, M. and Petrović, I. (2011). A Visibility Graph Based Method for Path Planning in Dynamic Environments. International Convention MIPRO. IEEE.Google Scholar
Lane, R. O., Nevell, D. A., Hayward, S. D. and Beaney, T. W. (2011). Maritime Anomaly Detection and Threat Assessment. International Conference on Information Fusion. IEEE.Google Scholar
Laxhammar, R. (2008). Anomaly Detection for sea Surveillance. International Conference on Information Fusion. IEEE.Google Scholar
Laxhammar, R., Falkman, G. and Sviestins, E. (2009). Anomaly Detection in sea Traffic - A Comparison of the Gaussian Mixture Model and the Kernel Density Estimator. International Conference on Information Fusion. IEEE.Google Scholar
Li, H., Liu, J., Liu, R. W., Xiong, N., Wu, K. and Kim, T. (2017). A dimensionality reduction-based multi-step clustering method for robust vessel trajectory analysis. Sensors, 17(8), 1792.CrossRefGoogle ScholarPubMed
Maria, R., Giuliana, P. and Michele, V. (2018). Maritime anomaly detection: A review. Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery, 8, e1266.Google Scholar
Pallotta, G., Vespe, M. and Bryan, K. (2013a). Traffic Knowledge Discovery From AIS Data. International Conference on Information Fusion. IEEE.Google Scholar
Pallotta, G., Vespe, M. and Bryan, K. (2013b). Vessel pattern knowledge discovery from ais data: A framework for anomaly detection and route prediction. Entropy, 15(6), 2218–2245.CrossRefGoogle Scholar
Pan, J., Jiang, Q. and Shao, Z. (2014). Trajectory clustering by sampling and density. Marine Technology Society Journal, 48(6), 74–85.CrossRefGoogle Scholar
Perera, L. P., Oliveira, P. and Soares, C. G. (2012). Maritime traffic monitoring based on vessel detection, tracking, state estimation, and trajectory prediction. IEEE Transactions on Intelligent Transportation Systems, 13(3), 1188–1200.CrossRefGoogle Scholar
Rhodes, B. J., Bomberger, N. A. and Zandipour, M. (2007). Probabilistic Associative Learning of Vessel Motion Patterns at Multiple Spatial Scales for Maritime Situation Awareness. International Conference on Information Fusion. IEEE.CrossRefGoogle Scholar
Rong, H., Teixeira, A. P. and Soares, C. G. (2019). Ship trajectory uncertainty prediction based on a Gaussian process model. Ocean Engineering, 182, 499–511.CrossRefGoogle Scholar
Shahir, H. Y., Glsser, U., Nalbandyan, N. and Wehn, H. (2014). Maritime Situation Analysis: A Multi-Vessel Interaction and Anomaly Detection Framework. Intelligence & Security Informatics Conference. IEEE.CrossRefGoogle Scholar
Soleimani, B. H., Souza, E., Hilliard, C. and Matwin, S. (2015). Anomaly Detection in Maritime Data Based on Geometrical Analysis of Trajectories. International Conference on Information Fusion. IEEE.Google Scholar
Tun, M. H., Chambers, G. S. and Tan, T. (2007). Maritime Port Intelligence Using AIS Data. RNSA Security Technology Conference, 33–43.Google Scholar
Varun, C., Arindam, B. and Vipin, K. (2009). Anomaly Detection: A Survey. Minneapolis, USA: Acm Computing Surveys.Google Scholar
Vettor, R. and Soares, C. G. (2017). Characterisation of the expected weather conditions in the main European coastal traffic routes. Ocean Engineering, 140, 244–257.CrossRefGoogle Scholar
Weng, J., Liao, S., Wu, B. and Yang, D. (2020). Exploring effects of ship traffic characteristics and environmental conditions on ship collision frequency. Maritime Policy & Management, 47(4), 523–543.CrossRefGoogle Scholar
Xu, T., Liu, X. and Xin, Y. (2012). A novel approach for ship trajectory online prediction using bp neural network algorithm. Advances in Information Sciences & Service Sciences, 4(11), 271–277.Google Scholar
Zhang, S., Shi, G., Liu, Z., Zhao, Z. and Wu, Z. (2018). Data-driven based automatic maritime routing from massive AIS trajectories in the face of disparity. Ocean Engineering, 155, 240–250.CrossRefGoogle Scholar
Zhao, L. and Shi, G. (2019). Maritime anomaly detection using density-based clustering and recurrent neural network. Journal of Navigation, 72(4), 894–916.CrossRefGoogle Scholar
Zhen, R., Jin, Y., Hu, Q., Shao, Z. and Nikitakos, N. (2017a). Maritime anomaly detection within coastal waters based on vessel trajectory clustering and naive Bayes classifier. Journal of Navigation, 70(3), 1–23.CrossRefGoogle Scholar
Zhen, R., Riveiro, M. and Jin, Y. (2017b). A novel analytic framework of real-time multi-vessel collision risk assessment for maritime traffic surveillance. Ocean Engineering, 145(15), 492–501.CrossRefGoogle Scholar
Zhou, B. and Shi, A. (2010). LSSVM and Hybrid Particle Swarm Optimization for Ship Motion Prediction. Intelligent Control and Information Processing (ICICIP), 13–15 August 2010, Dalian, China, 183–186.CrossRefGoogle Scholar