# Method for Select Best AIS Data in Prediction Vessel Movements and Route Estimation
## 7 Conclusions  
The present proposal, called Method for Select Best AIS Data in Prediction Vessel Movements and Route Estimation (PreMovEst), is related to obtaining reports on vessels' status that allows for accurate and real-time monitoring.  
It applies to all vessels navigating within a designated maritime area and predicting future positions of a vessel based on positions already traveled within the designated maritime area. The information is shown to the user through general and specific vessel status reports.  
Figure 11: The route prediction results with MICE.  
Figure 10: The route prediction results with MICE.  
The PreMovEst method was tested using a small dataset. However, the correct selection of data for analysis and prediction in maritime routes is essential before applying the method.  
The PreMovEst method uses a filter for the routes whose behavior are similar to obtain the best routes collection through a Chi-squared selection process. The execution time was reduced from 16 to 11 min on average. The experiments were run on a Core i7 processor with 16 GB of RAM.  
The results show a prediction accuracy of 80.5-84%. The amount of information used in the second example was more than the first example, which allowed the accuracy to be better, at 84%. The historical AIS data samples are similar to the block to be estimated, out of 10 predictions. The absolute mean difference for latitude is 0.0015655 degrees, while the absolute mean difference for longitude is 0.00211949 degrees.  
The results concern the assets of the vessels that move in the seas, especially in economic terms. The paper shows the PreMovEst method's application and testing of its accuracy using ARNN and MICE techniques. Everyday, vessels move billions of items across the oceans from one country to another, a drawback of which is handling all of this information. The process of selecting data to train the neural network is a function of the number of trips made by the vessel from the port of departure to the arrival port.  
One of the challenges to be undertaken is to obtain better accuracy while reducing the execution time.  
Future work will be to accelerate the prediction model with Graphical Process Units (GPU). This requires image processing, which implies supercomputers to process such data, limiting its effectiveness for real-time monitoring of vessels navigating within a designated maritime area and increasing the amount of processed information.  
Conceptualization, R.B.-S., and J.J.S.-E.; software, R.B.-S., L.I.B.-S. and J.J.S.-E. investigation, R.B.-S., L.I.B.-S. and J.J.S.-E. writing--original draft preparation, R.B.-S., L.I.B.-S. and J.J.S.-E.; writing R.B.-S., L.I.B.-S. and [PERSON]-E. All authors have read and agreed to the published version of the manuscript.  
This research was founding by Research Council (CONACyT), Mexico.  
The data that support the findings of this study are available on request from the corresponding author.  
This research was supported by the Sciences Research Council (CONACyT) through the research project number 262756 called \"The use of GNSS data for tracking maritime flow for sea security\" [[http://navigationsnssproject.net/index.html](http://navigationsnssproject.net/index.html)]([http://navigationsnssproject.net/index.html](http://navigationsnssproject.net/index.html)) accessed on 28 February 2021.  
The authors declare no conflict of interest.