Predicting Destinations by Nearest Neighbor Search on Training Vessel Routes

TL;DR

This paper presents a vessel route prediction method using nearest neighbor search on AIS data, incorporating route partitioning, parameter tuning with genetic algorithms, and improvements to reduce prediction fluctuations, achieving better accuracy.

Contribution

It introduces a novel approach combining route partitioning, nearest neighbor search, and automated parameter tuning for maritime destination prediction.

Findings

Significant improvement in prediction accuracy.

Effective route partitioning enhances nearest neighbor relevance.

Automated parameter tuning optimizes model performance.

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.