Predicting Destinations by Nearest Neighbor Search on Training Vessel Routes
This work addresses the specific problem of early destination and arrival time prediction for vessel trips using AIS data, which is incremental with parameter tuning enhancements.
The paper tackles the maritime route prediction problem from the DEBS Grand Challenge 2018 by using nearest neighbor search on training vessel routes partitioned by destination port, with improvements like reducing port prediction changes and automated parameter tuning, achieving significant improvements in the final score.
The DEBS Grand Challenge 2018 is set in the context of maritime route prediction. Vessel routes are modeled as streams of Automatic Identification System (AIS) data points selected from real-world tracking data. The challenge requires to correctly estimate the destination ports and arrival times of vessel trips, as early as possible. Our proposed solution partitions the training vessel routes by reported destination port and uses a nearest neighbor search to find the training routes that are closer to the query AIS point. Particular improvements have been included as well, such as a way to avoid changing the predicted ports frequently within one query route and automating the parameters tuning by the use of a genetic algorithm. This leads to significant improvements on the final score.