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Research Article

A novel machine learning approach to analyzing geospatial vessel patterns using AIS data

ORCID Icon, &
Pages 1473-1490 | Received 29 Apr 2022, Accepted 24 Aug 2022, Published online: 20 Sep 2022

References

  • Bagnall, A. J., and G. J. Janacek. 2004. “Clustering Time Series from ARMA Models with Clipped Data.” In Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining (KDD '04). Association for Computing Machinery, New York, NY, USA, 49–58. doi:10.1145/1014052.1014061.
  • Coscia, P., P. Braca, L. M. Millefiori, F. A. N. Palmieri, and P. Willett. 2018. “Multiple Ornstein–Uhlenbeck Processes for Maritime Traffic Graph Representation.” IEEE Transactions on Aerospace and Electronic Systems (IEEE) 54: 2158–2170. doi:10.1109/TAES.2018.2808098.
  • d’Afflisio, E., P. Braca, L. M. Millefiori, and P. Willett. 2018a. “Detecting Anomalous Deviations from Standard Maritime Routes Using the Ornstein–Uhlenbeck Process.” IEEE Transactions on Signal Processing (IEEE) 66: 6474–6487. doi:10.1109/TSP.2018.2875887.
  • d’Afflisio, E., P. Braca, L. M. Millefiori, and P. Willett. 2018b. “Maritime Anomaly Detection Based on mean-reverting Stochastic Processes Applied to a real-world Scenario.” In 2018 21st International Conference on Information Fusion (FUSION), Cambridge, UK, 1171–1177. IEEE.
  • Ester, M., H.-P. Kriegel, J. Sander, X. Xiaowei. 1996. “A density-based Algorithm for Discovering Clusters in Large Spatial Databases with Noise.” kdd 96 (34): 226–231.
  • Forti, N., L. M. Millefiori, and P. Braca. 2019. “Unsupervised Extraction of Maritime Patterns of Life from Automatic Identification System Data.” In OCEANS, Marseille, France, 1–5. IEEE.
  • Forti, N., L. M. Millefiori, P. Braca, and P. Willett. 2019. “Anomaly Detection and Tracking Based on mean–reverting Processes with Unknown Parameters.” ICASSP 2019-2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Brighton, UK, 8449–8453.
  • Forti, N., L. M. Millefiori, P. Braca, and P. Willett. 2021. “Bayesian Filtering for Dynamic Anomaly Detection and Tracking.“ In IEEE Transactions on Aerospace and Electronic Systems 58 (3): 1528–1544. doi:10.1109/TAES.2021.3122888.
  • Gloaguen, P., S. Mahévas, E. Rivot, M. Woillez, J. Guitton, Y. Vermard, and M.-P. Etienne. 2015. “An Autoregressive Model to Describe Fishing Vessel Movement and Activity”. Environmetrics Wiley Online Library 26: 17–28. doi:10.1002/env.2319.
  • Guo, S., J. Mou, L. Chen, and P. Chen. 2021. “An Anomaly Detection Method for AIS Trajectory Based on Kinematic Interpolation.” Journal of Marine Science and Engineering, no. 6: 609. doi:10.3390/jmse9060609.
  • Hendrawati, T., A. H. Wigena, I. M. Sumertajaya, and B. Sartono. 2021. “Clustering of Commodity Inflation Pattern Based on Estimated ARIMA Model.” In Journal of Physics: Conference Series, Bogor, Indonesia, 012058. IOP Publishing.
  • Huang, J., F. Zhu, Z. Huang, J. Wan, and Y. Ren. 2021. “Research on Real-Time Anomaly Detection of Fishing Vessels in a Marine Edge Computing Environment.” Mobile Information Systems 2021: 1–15. doi:10.1155/2021/5598988.
  • Hyndman, R. J., and G. Athanasopoulos. 2021. “Forecasting: Principles and Practice, 3rd edition.” Melbourne, Australia: OTexts. Accessed on August 31, 2022. OTexts.com/fpp3
  • Ismail, A., and A. Vigneron. 2015. “A New Trajectory Similarity Measure for GPS Data.” Proceedings of the 6th ACM SIGSPATIAL International Workshop on GeoStreaming, Bellevue, WA, USA, 19–22.
  • Jiang, H., W. U. Yao, L. Y. U. Kuilin, and W. A. N. G. Huijiao. 2019. “Ocean Data Anomaly Detection Algorithm Based on Improved k-medoids.” 2019 Eleventh International Conference on Advanced Computational Intelligence (ICACI), Guilin, China, 196–201.
  • Kalpakis, K., D. Gada, and V. Puttagunta. 2001. “Distance Measures for Effective Clustering of ARIMA time-series.” In Proceedings 2001 IEEE international conference on data mining, San Jose, CA, USA, 273–280. IEEE.
  • Karatas, G. B., P. Karagoz, and O. Ayran. 2021. “Trajectory Pattern Extraction and Anomaly Detection for Maritime Vessels.” Internet of Things 16: 100436. Elsevier.
  • Kontopoulos, I., I. Varlamis, and K. Tserpes. 2021. “A Distributed Framework for Extracting Maritime Traffic Patterns.” International Journal of Geographical Information Science 35: 767–792. doi:10.1080/13658816.2020.1792914.
  • Laxhammar, R. 2008. “Anomaly Detection for Sea Surveillance.“ 11th International Conference on Information Fusion, Cologne, Germany, 1-8.
  • Lee, J.-G., J. Han, and K.-Y. Whang. 2007. “Trajectory Clustering: A partition-and-group Framework.” Proceedings of the 2007 ACM SIGMOD international conference on Management of data, Beijing, China, 593–604.
  • Li, H., J. Liu, W. Kefeng, Z. Yang, R. Wen Liu, and N. Xiong. 2018. “Spatio-temporal Vessel Trajectory Clustering Based on Data Mapping and Density.” IEEE Access (IEEE) 6: 58939–58954. doi:10.1109/ACCESS.2018.2866364.
  • Li, H., J. Liu, R. Wen Liu, N. Xiong, W. Kefeng, and T.-H. Kim. 2017. “A Dimensionality reduction-based multi-step Clustering Method for Robust Vessel Trajectory Analysis.” Sensors (Multidisciplinary Digital Publishing Institute) 17: 1792.
  • Liang, M., R. Wen Liu, L. Shichen, Z. Xiao, X. Liu, and L. Feng. 2021. “An Unsupervised Learning Method with Convolutional auto-encoder for Vessel Trajectory Similarity Computation.” Ocean Engineering 225: 108803.
  • Liu, B., E. N. de Souza, S. Matwin, and M. Sydow. 2014. “Knowledge-based Clustering of Ship Trajectories Using density-based Approach.” 2014 IEEE International Conference on Big Data (Big Data), Washington, DC, USA, 603–608.
  • Liu, R. W., J. Nie, S. Garg, Z. Xiong, Y. Zhang, and M. Shamim Hossain. 2020. “Data-driven Trajectory Quality Improvement for Promoting Intelligent Vessel Traffic Services in 6G-enabled Maritime IoT Systems.“ IEEE Internet of Things Journal 8 (7): 5374–5385. doi:10.1109/JIOT.2020.3028743.
  • Logan, J. D., and W. Wolesensky. 2009. Mathematical Methods in Biology. Vol. 96. Toronto, ON, CA: John Wiley & Sons.
  • Luo, T., X. Zheng, X. Guangluan, F. Kun, and W. Ren. 2017. “An Improved DBSCAN Algorithm to Detect Stops in Individual Trajectories.” ISPRS International Journal of Geo-Information (Multidisciplinary Digital Publishing Institute) 6: 63. doi:10.3390/ijgi6030063.
  • Magdalene, J. J. C., and B. S. E. Zoraida. 2022. “Predicting the Usage of Energy in a Smart Home Using Improved Weighted K-Means Clustering ARIMA Model.” JOURNAL OF ALGEBRAIC STATISTICS 13: 1770–1777.
  • Mascaro, S., A. E. Nicholso, and K. B. Korb. 2014. “Anomaly Detection in Vessel Tracks Using Bayesian Networks.” International Journal of Approximate Reasoning 55: 84–98. doi:10.1016/j.ijar.2013.03.012.
  • Mazzarella, F., M. Vespe, A. Alessandrini, D. Tarchi, G. Aulicino, and A. Vollero. 2017. “A Novel Anomaly Detection Approach to Identify Intentional AIS on-off Switching.” Expert Systems with Applications (Elsevier) 78: 110–123. doi:10.1016/j.eswa.2017.02.011.
  • Murray, B., and L. Prasad Perera. 2022. “Ship Behavior Prediction via Trajectory extraction-based Clustering for Maritime Situation Awareness.” Journal of Ocean Engineering and Science 7: 1–13. doi:10.1016/j.joes.2021.03.001.
  • Newaliya, N., and Y. Singh. 2021. “A Review of Maritime Spatio-temporal Data Analytics.” In 2021 International Conference on Computational Performance Evaluation (ComPE), Shillong, India, 219–226. IEEE.
  • Ng, A., M. Jordan, and Weiss, Y. 2001. “On spectral clustering: Analysis and an algorithm.” In Advances in neural information processing systems 14.
  • Nguyen, D., R. Vadaine, G. Hajduch, R. Garello, and R. Fablet. 2021. “GeoTrackNet–A Maritime Anomaly Detector Using Probabilistic Neural Network Representation of AIS Tracks and A Contrario Detection.“ IEEE Transactions on Intelligent Transportation Systems 23 (6): 5655–5667. doi:10.1109/TITS.2021.3055614.
  • Nielsen, F. 2016. “Hierarchical Clustering.” In Introduction to HPC with MPI for Data Science Undergraduate Topics in Computer Science, 195–211. Cham, Germany: Springer.
  • Pallotta, G., M. Vespe, and K. Bryan. 2013. “Vessel Pattern Knowledge Discovery from AIS Data: A Framework for Anomaly Detection and Route Prediction.” Entropy (Multidisciplinary Digital Publishing Institute) 15: 2218–2245.
  • Patmanidis, S., I. Voulgaris, E. Sarri, G. Papavassilopoulos, and G. Papavasileiou. 2016. “Maritime Surveillance, Vessel Route Estimation and Alerts Using AIS Data.” 2016 24th Mediterranean Conference on Control and Automation (MED), Athens, Greece, 809–813.
  • Peiguo, F., H. Wang, K. Liu, H. Xiaohui, and H. Zhang. 2017. “Finding Abnormal Vessel Trajectories Using Feature Learning.” IEEE Access (IEEE) 5: 7898–7909. doi:10.1109/ACCESS.2017.2698208.
  • Riveiro, M., G. Falkman, and T. Ziemke. 2008. “Visual Analytics for the Detection of Anomalous Maritime Behavior.” 2008 12th International Conference Information Visualisation, London, UK, 273–279.
  • Riveiro, M., G. Pallotta, and M. Vespe. 2018. “Maritime Anomaly Detection: A Review.” Wiley Interdisciplinary Reviews. Data Mining and Knowledge Discovery 8: e1266.
  • Rong, H., A. P. Teixeira, and C. Guedes Soares. 2020. “Data Mining Approach to Shipping Route Characterization and Anomaly Detection Based on AIS Data.” Ocean Engineering 198: 106936.
  • Ross, S. M. 2014. Introduction to Probability Models 11 ed. Washington, DC: Academic press.
  • Rousseeuw, P. J. 1987. “Silhouettes: A Graphical Aid to the Interpretation and Validation of Cluster Analysis.” Journal of Computational and Applied Mathematics (Elsevier) 20: 53–65. doi:10.1016/0377-0427(87)90125-7.
  • Shahir, H. Y., U. Glasser, A. Yaghoubi Shahir, and H. Wehn. 2015. “Maritime Situation Analysis Framework: Vessel Interaction Classification and Anomaly Detection.” In 2015 IEEE International Conference on Big Data (Big Data), Santa Clara, CA, USA, 1279–1289. Ieee.
  • Stella, X. Y., and J. Shi. 2003. “Multiclass Spectral Clustering.” Proceedings Ninth IEEE International Conference on Computer Vision, Nice, France, 1: 313–319. doi:10.1109/ICCV.2003.1238361.
  • Tadayon, M., and Y. Iwashita. 2020. “A Clustering Approach to Time Series Forecasting Using Neural Networks: A Comparative Study on distance-based Vs. feature-based Clustering Methods.“ arXiv preprint arXiv:2001.09547.
  • Üstünel, A. S. 2006. An Introduction to Analysis on Wiener Space. cham, switzerland: Springer.
  • Vinh, N. X., J. Epps, and J. Bailey. 2010. “Information Theoretic Measures for Clusterings Comparison: Variants, Properties, Normalization and Correction for Chance.” The Journal of Machine Learning Research 11: 2837–2854.
  • Watson, J. R., and J. Woodil. 2019. “Anticipating Illegal Maritime Activities from Anomalous Multiscale Fleet Behaviors Measured from Space.” arxiv preprint arXiv:1910.05424.
  • Xiao, Z., L. Zhang, F. Xiuju, W. Zhang, J. Tianyi Zhou, and R. Siow Mong Goh. 2019. “Concurrent Processing Cluster Design to Empower Simultaneous Prediction for Hundreds of Vessels’ Trajectories in near real-time.“ IEEE Transactions on Systems, Man, and Cybernetics: Systems 51 (3): 1830–1843. doi:10.1109/TSMC.2019.2906381
  • Yao, D., C. Zhang, Z. Zhu, J. Huang, and B. Jingping 2017. “Trajectory Clustering via Deep Representation Learning.” 2017 international joint conference on neural networks (IJCNN), Anchorage, AK, USA.
  • Zhao, L., and G. Shi. 2019. “Maritime Anomaly Detection Using density-based Clustering and Recurrent Neural Network.” The Journal of Navigation 72: 894–916. doi:10.1017/S0373463319000031.