Vessel Trajectory Prediction Using Historical Automatic Identification System Data

For maritime safety and security, vessels should be able to predict the trajectories of nearby vessels to avoid collision. This research proposes three novel models based on similarity search of trajectories that predict vessels' trajectories in the short and long term. The first and second prediction models are, respectively, point-based and trajectory-based models that consider constant distances between target and sample trajectories. The third prediction model is a trajectory-based model that exploits a long short-term memory approach to measure the dynamic distance between target and sample trajectories. To evaluate the performance of the proposed models, they are applied to a real automatic identification system (AIS) vessel dataset in the Strait of Georgia, USA. The models' accuracies in terms of Haversine distance between the predicted and actual positions show relative prediction error reductions of 40 center dot 85% for the second model compared with the first model and 23% for the third model compared with the second model.

Automated summary

Three novel trajectory prediction models based on similarity search are proposed in this research for predicting vessels' trajectories in the short and long term. Applied to a real AIS vessel dataset, the second model shows a relative prediction error reduction of 40.85% compared to the first model, and the third model shows a reduction of 23% compared to the second model in terms of Haversine distance accuracy.