Accurate Prediction of Maritime Trajectories From Historical AIS Data Using Grid-Based Methods

Abstract

In order to aid prediction of future maritime vessel trajectories, it is useful to examine historical vessel information. It is mandatory for large maritime vessels to broadcast, among other fields, spatial, speed, and course information using Automatic Identi- fication System (AIS) transponders. By processing a large historical dataset, it is possible to predict future vessel trajectories. The region of interest is discretized into a grid. Then, using offline computations, the historical data are used to determine second-order transition probabilities and speed information. Predictions will be car- ried out as an online process. If the destination is known, Dijkstra’s Algorithm is used to predict the vessel’s path. If the destination is not known, a path can still be de- termined using transition probabilities, but the prediction will be less accurate. The path is then smoothed using a line of sight algorithm to produce more realistic paths. Finally, the speed information is used to predict travel times. Real data were used to build the graph structure, and predictions were judged against real trajectories.