Abstract
Automatic identification system allows ships to automatically exchange information about themselves, mainly to avoid collisions between them. Its terrestrial segment, in which the communication is synchronized, has a range of only 74 km (40 nautical miles). To broaden this range, a satellite segment of the system was introduced. However, due to the fact that one satellite operates on multiple internally synchronized areas, the communication within satellite’s field of view suffers from message collision issue when two or more messages are received by satellite at the same time. This paper presents the first step of the approach of reconstructing lost or damaged AIS messages due to collision. The computational experiment was to cluster data (one cluster = messages from one ship) so that they can be further analysed to find abnormal ones among them and correct them. The clustering was conducted in a streaming approach, using a specific time window to mimic the time-changing character of AIS data. Numerical results that measure the clustering quality have been collected and are presented in the paper. The computational experiment proves that clustering might indeed help reconstruct AIS data and shows the best time window length from a clustering point of view.
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References
AIS transponders. https://www.imo.org/en/OurWork/Safety/Pages/AIS.aspx
He, Y.K., Zhang, D., Zhang, J.F., Zhang, M.Y.: Ship route planning using historical trajectories derived from AIS data. TransNav 13(1), 69–76 (2019). https://doi.org/10.12716/1001.13.01.06
Millefiori, L.M., Zissis, D., Cazzanti, L., Arcieri, G.: A Distributed approach to estimating sea port operational regions from lots of AIS data. In: 2016 IEEE International Conference on Big Data (Big Data), pp. 1627–1632. IEEE Press, New York (2016). https://doi.org/10.1109/BigData.2016.7840774
Lane, R.O., Nevell, D.A., Hayward, S.D., Beaney, T.W.: Maritime Anomaly Detection and Threat Assessment. In: 2010 13th Conference on IEEE Information Fusion (FUSION). IEEE Press, New York (2010). https://doi.org/10.1109/ICIF.2010.5711998
Liang, M., Liu, R.W., Zhong, Q., Liu, J., Zhang, J.: Neural network-based automatic reconstruction of missing vessel trajectory data. In: 2019 IEEE 4th International Conference on Big Data Analytics (ICBDA), pp.426–430. IEEE Press, New York (2019). https://doi.org/10.1109/ICBDA.2019.8713215
Satellite — Automatic Identification System (SAT-AIS) Overview. https://artes.esa.int/sat-ais/overview
Seta, T., Matsukura, H., Aratani, T., Tamura, K.: An estimation method of message receiving probability for a satellite automatic identification system using a binomial distribution model. Sci. J. Marit. Univ. Szczec. 46(118), 101–107 (2016). https://doi.org/10.17402/125
Prevost, R., Coulon, M., Bonacci, D., LeMaitre, J., Millerioux, J., Tourneret, J.: Extended constrained Viterbi algorithm for AIS signals received by satellite. In: 2012 IEEE First AESS European Conference on Satellite Telecommunications (ESTEL). IEEE Press, New York (2012). https://doi.org/10.1109/ESTEL.2012.6400111
Wang, T., Ye, C., Zhou, H., Ou, M., Cheng, B.: AIS ship trajectory clustering based on convolutional auto-encoder. In: Arai, K., Kapoor, S., Bhatia, R. (eds): Intelligent Systems and Applications. Proceedings of the 2020 Intelligent Systems Conference (IntelliSys), vol. 2, pp. 529–546. Springer, Cham (2020)
Patroumpas, K., Alevizos, E., Artikis, A., Vodas, M., Pelekis, N., Theodoridis, Y.: Online event recognition from moving vessel trajectories. Geoinformatica 21, 389–427 (2017). https://doi.org/10.1007/s10707-016-0266-x
Zhang, T., Zhao, S., Chen, J.: Research on ship classification based on trajectory association. In: Christos Douligeris, C., Karagiannis, D., Apostolou, D. (eds.) Knowledge Science, Engineering and Management. 12th International Conference, KSEM 2019 Athens, Greece, August 28–30, 2019 Proceedings, Part I, pp. 327–340. Springer, Cham (2019)
Wang, G., Meng, J., Li, Z., Hesenius, M., Ding, W., Han, Y., Gruhn, V.: Extraction and refinement of marine lanes from crowdsourced trajectory data. Netw. Appl. 25, 1392–1404 (2020). https://doi.org/10.1007/s11036-019-01454-w
Nikfalazar, S., Yeh, C.-H., Bedingfield, S., Hadi, A., Khorshidi, H.A.: Missing data imputation using decision trees and fuzzy clustering with iterative learning. Knowl. Inf. Syst. 62, 2419–2437 (2020). https://doi.org/10.1007/s10115-019-01427-1
U.S. CMTS. Enhancing Accessibility and Usability of Automatic Identification Systems (AIS) Data: Across theFederal Government and for the Benefit of Public Stakeholders; U.S. Committee on the Marine TransportationSystem: Washington, DC, USA (2019)
Lee, E., Mokashi, A.J., Moon, S.Y., Kim, G.: The maturity of automatic identification systems (AIS) and its implications for innovation. J. Mar. Sci. Eng. 7(9), 279 (2019). https://doi.org/10.3390/jmse7090287
Hochbaum, D.: Algorithms and complexity of range clustering. Networks 73, 170–186 (2019)
Vespe, M., Visentini, I., Bryan, K., Braca, P.: Unsupervised learning of maritime traffic patterns for anomaly detection. In: DF&TT 2012: Algorithms Applications (2012)
Recommendation ITU-R M.1371-5. https://www.itu.int/dms_pubrec/itu-r/rec/m/R-REC-M.1371-5-201402-I!!PDF-E.pdf
MacQueen, J.: Some methods for classification and analysis of multivariate observations. In: Proceedings of the Fifth Berkeley Symposium on Mathematical Statistics and Probability, vol 1, pp. 281–297. University of California Press, Berkeley, California (1967)
Ester, M., Kriegel, H.-P., Sander, J., Xu, X.: A density-based algorithm for discovering clusters in large spatial databases with noise. In: Simoudis, E., Han, J., Fayyad, U.M. (eds.). Proceedings of the Second International Conference on Knowledge Discovery and Data Mining (KDD-96), pp. 226–231. AAAI Press (1996). 10.1.1.121.9220
Rousseeuw, P.J.: Silhouettes: a graphical aid to the interpretation and validation of cluster analysis. J. Comput. Appl. Math. 20, 53–65 (1987). https://doi.org/10.1016/0377-0427(87)90125-7
Acknowledgements
Our special thanks to Mr Marcin Waraksa and Prof. Jakub Montewka from Gdynia Maritime University for sharing the raw data that we used in our experiment.
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Mieczyńska, M., Czarnowski, I. (2021). Impact of the Time Window Length on the Ship Trajectory Reconstruction Based on AIS Data Clustering. In: Czarnowski, I., Howlett, R.J., Jain, L.C. (eds) Intelligent Decision Technologies. Smart Innovation, Systems and Technologies, vol 238. Springer, Singapore. https://doi.org/10.1007/978-981-16-2765-1_3
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