# Grid-Based Vessel Deviation from Route Identification with Unsupervised Learning
## 5 Discussion and Conclusions  
This research work focused on providing a study for the problem of route prediction, exploring machine learning techniques to automatically extract route patterns from historical AIS data, from which main routes are obtained to provide guidelines for future movement. The research involvement of academia/research, industry, authorities and society organizations allowed leverage to develop technologies, frameworks and innovative solutions for improving the sustainability of the sea. The present work presents a solution proposition for the scalability limitation of grid-based methods, described by [PERSON] et al. [22]. The authors state that grid-based methods, such as [12; 13; 14; 15; 16; 17], are only effective on small-scale surveillance, requiring heavy computation as the area of surveillance increases, due to the complex discretization operation and the difficulty of selecting an appropriate grid cell value. The proposed dynamic grid size, along with the binary search tree algorithm for vessel trip discretization allows for grid-based algorithms to be utilized on a larger scale for maritime abnormal trajectory detection. The ability of the system to learn over time, as new data is added to the system also poses an advantage over other proposed systems in the literature. The ability of the system to automatically generate alerts on the fly that can warn the system operators and maritime authorities about potentially suspicious or abnormal events occurring in the maritime space contributes toward a better knowledge of the maritime domain and for more informed decisions to be taken by operators.  
This method comes with some limitations, as new and never-before-seen vessels on the coast will not have an associated leap rate model. In order to solve this problem, the model of a vessel with similar characteristics or routes, or an ensemble of models from similar vessels may be used in this case. The efficiency of such methods is yet to be tested. The inherently convoluted definition as to which poses to be a deviation from a normal route makes the proposed methods difficult to compare and evaluate their performance as the data is unlabelled, some deviations from route may be justifiable, and several vessels, mainly tourism-related and leisure boats, may have unpredictable routes.  
Conceptualization, N.A.; methodology, N.A. and J.R.; software, N.A. and J.P.; validation, N.A. and J.P.; formal analysis, N.A.; investigation, N.A.; resources, N.A.; data curation, N.A.; writing--original draft preparation, N.A.; writing--review and editing, N.A. and J.C.F.; visualization, N.A.; supervision, N.A. and J.R.; project administration, N.A. and J.R.; funding acquisition, J.R. All authors have read and agreed to the published version of the manuscript.  
This work has received funding from the European Union's Horizon 2020 Research and Innovation Programme under Grant Agreement No 883374 (project EFFECTOR). This article reflects only the authors' views and the Research Executive Agency (REA) is not responsible for any use that may be made of the information it contains.  
Not applicable.  
The AIS data used was kindly provided to us by the Portuguese Navy. The dataset has not been made publicly available by the project consortium.  
The authors declare no conflict of interest.  
The following abbreviations are used in this manuscript:  
\begin{tabular}{l l} AD & Anomaly Detector \\ AIS & Automatic Identification System \\ BST & Binary Search Tree \\ C2 & Command and Control \\ COG & Course Over Ground \\ DBSCAN & Density-based spatial clustering of applications with noise \\ DP & Douglas Peucker \\ LRIT & Long-range identification and tracking \\ LSTM & Long short-term memory \\ MMSI & Maritime Mobile Service Identity \\ OPTICS & Ordering points to identify the clustering structure \\ SOG & Speed Over Ground \\ VRNN & Variational Recurrent Neural Network \\ \end{tabular}