Destination Port Detection for Vessels: An Analytic Tool for Optimizing Port Authorities Resources
Port authorities have many challenges in congested ports to allocate their resources to provide a safe and secure loading/unloading procedure for cargo vessels. Selecting a destination port is the decision of a vessel master based on many factors such as weather, wavelength and changes of priorities. Having access to a tool which leverages Automatic Identification System (AIS) messages to monitor vessel’s movements and accurately predict their next destination port promotes an effective resource allocation process for port authorities. In this research, we propose a method, namely, Reference Route of Trajectory (RRoT) to assist port authorities in predicting inflow and outflow traffic in their local environment by monitoring AIS messages. Our RRo method creates a reference route based on historical AIS messages. It utilizes some of the best trajectory similarity measures to identify the destination of a vessel using their recent movement. We evaluated five different similarity measures such as Discrete Frechet Distance (DFD), Dynamic Time ´ Warping (DTW), Partial Curve Mapping (PCM), Area between two curves (Area) and Curve length (CL). Our experiments show that our method identifies the destination port with an accuracy of 98.97% and an f-measure of 99.08% using Dynamic Time Warping (DTW) similarity measure.Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 361
 L. Qi and Z. ZHENG, “A measure of similarity between trajectories of vessels,” Journal of Engineering Science and Technology Review, vol. 9, pp. 17–22, 03 2016.
 E. Carlini, V. M. de Lira, A. Soares, M. Etemad, B. B. Machado, and S. Matwin, “Uncovering vessel movement patterns from ais data with graph evolution analysis.” in EDBT/ICDT Workshops, 2020.
 I. Varlamis, I. Kontopoulos, K. Tserpes, M. Etemad, A. Soares, and S. Matwin, “Building navigation networks from multi-vessel trajectory data,” GeoInformatica, pp. 1–29, 2020.
 M. Etemad, N. Zare, M. Sarvmaili, A. Soares, B. B. Machado, and S. Matwin, “Using deep reinforcement learning methods for autonomous vessels in 2d environments,” in Canadian Conference on Artificial Intelligence. Springer, 2020, pp. 220–231.
 “Ais class a ship static and voyage related data (message 5),” https://www.navcen.uscg.gov/?pageName=AISMessagesAStatic, accessed: 2021-04-02.
 S. Kos, M. Vukic, and D. Brcic, “Use of universal protocol for entering ´ the port of destination in ais device,” 2013.
 G. K. D. de Vries, W. R. van Hage, and M. van Someren, “Comparing vessel trajectories using geographical domain knowledge and alignments,” in 2010 IEEE International Conference on Data Mining Workshops, 2010, pp. 209–216.
 D. Alizadeh, A. A. Alesheikh, and M. Sharif, “Prediction of vessels locations and maritime traffic using similarity measurement of trajectory,” Annals of GIS, vol. 0, no. 0, pp. 1–12, 2020.
[Online]. Available: https://doi.org/10.1080/19475683.2020.1840434
 R. Zhen, J. Yongxing, Q. Hu, Z. Shao, and N. Nikitakos, “Maritime anomaly detection within coastal waters based on vessel trajectory clustering and na¨ıve bayes classifier,” Journal of Navigation, vol. 70, pp. 1–23, 01 2017.
 L. Alvares, V. Bogorny, and B. Kuijpers, “Towards semantic trajectory knowledge discovery,” 10 2007.
 R. d. S. Mello, V. Bogorny, L. O. Alvares, L. H. Z. Santana, C. A. Ferrero, A. A. Frozza, G. A. Schreiner, and C. Renso, “Master: A multiple aspect view on trajectories,” Transactions in GIS, vol. 23, no. 4, pp. 805–822, 2019.
[Online]. Available: https://onlinelibrary.wiley.com/doi/abs/10.1111/tgis.12526
 P. Sheng and J. Yin, “Extracting shipping route patterns by trajectory clustering model based on automatic identification system data,” Sustainability, vol. 10, p. 2327, 07 2018.
 M. Buchin, S. Dodge, and B. Speckmann, “Similarity of trajectories taking into account geographic context,” Journal of Spatial Information Science, vol. 9, 11 2014.
 M. Etemad, “Novel algorithms for trajectory segmentation based on interpolation-based change detection strategies,” 2020.
 M. Etemad, Z. Etemad, A. Soares, V. Bogorny, S. Matwin, and L. Torgo, “Wise sliding window segmentation: A classification-aided approach for trajectory segmentation,” in Canadian Conference on Artificial Intelligence. Springer, 2020, pp. 208–219.
 M. Etemad, A. Soares, E. Etemad, J. Rose, L. Torgo, and S. Matwin, “Sws: an unsupervised trajectory segmentation algorithm based on change detection with interpolation kernels,” GeoInformatica, pp. 1–21, 2020.
 Z. Xiu-Li and X. Wei-Xiang, “A clustering-based approach for discovering interesting places in a single trajectory,” in 2009 second international conference on intelligent computation technology and automation, vol. 3. IEEE, 2009, pp. 429–432.
 Y. Zheng, X. Xie, W.-Y. Ma et al., “Geolife: A collaborative social networking service among user, location and trajectory.” IEEE Data Eng. Bull., vol. 33, no. 2, pp. 32–39, 2010.
 E. Carlini, V. Monteiro, A. Soares, M. Etemad, B. Machado, and S. Matwin, “Uncovering vessel movement patterns from ais data with graph evolution analysis,” in EDBT/ICDT Workshops, 01 2020.
 M. Etemad, “Novel algorithms for trajectory segmentation based on interpolation-based change detection strategies,” Dalhousie Faculty of Graduate Studies Online Theses 2020.
[Online]. Available: http://hdl.handle.net/10222/79941
 Q. Li, Y. Zheng, X. Xie, Y. Chen, W. Liu, and W.-Y. Ma, “Mining user similarity based on location history,” in Proceedings of the 16th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems, ser. GIS ’08. New York, NY, USA: Association for Computing Machinery, 2008, p. 10.
[Online]. Available: https://doi.org/10.1145/1463434.1463477
 A. Palma, V. Bogorny, B. Kuijpers, and L. Alvares, “A clustering-based approach for discovering interesting places in trajectories,” 03 2008, pp. 863–868.
 P. Senin, “Dynamic time warping algorithm review,” 01 2009.
 M. Frechet, “Sur quelques points du calcul fonctionnel,” ´ Rendiconti del Circolo Matematico di Palermo (1884-1940), vol. 22, pp. 1–72.
 T. Eiter and H. Mannila, “Computing discrete frechet distance,” 05 1994.
 I. Cleasby, E. Wakefield, B. Morrissey, T. Bodey, S. Votier, S. Bearhop, and K. Hamer, “Using time-series similarity measures to compare animal movement trajectories in ecology,” Behavioral Ecology and Sociobiology, vol. 73, 11 2019.
 C. Jekel, G. Venter, M. Venter, N. Stander, and R. Haftka, “Similarity measures for identifying material parameters from hysteresis loops using inverse analysis,” International Journal of Material Forming, vol. 12, 05 2019.
 H. Edelhoff, J. Signer, and N. Balkenhol, “Path segmentation for beginners: An overview of current methods for detecting chang