Predicting Destinations by Nearest Neighbor Search on Training Vessel Routes

September 28, 2018 ยท Declared Dead ยท ๐Ÿ› DEBS 2018, Proceedings of the 12th ACM International Conference on Distributed and Event-based Systems, Pages 224-225

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Authors Valentin RoลŸca, Emanuel Onica, Paul Diac, Ciprian Amariei arXiv ID 1810.00096 Category cs.LG: Machine Learning Cross-listed stat.ML Citations 0 Venue DEBS 2018, Proceedings of the 12th ACM International Conference on Distributed and Event-based Systems, Pages 224-225 Last Checked 4 months ago
Abstract
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.
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