Interpolation-Based Inference of Vessel Trajectory Waypoints from Sparse AIS Data in Maritime

Abstract

The availability of automatic identification system (AIS) data for tracking vessels has paved the way for improvements in maritime safety and efficiency. However, one of the main challenges in using AIS data is often the low quality of the data. Practically, AIS-based trajectory data of vessels are available at irregular time intervals; consequently, large temporal gaps often exist in the historical AIS data. Meanwhile, certain tasks such as waypoint detection using historical data, which involves finding locations along the trajectory where the vessel changes its course (and possibly speed, acceleration, etc.), require AIS messages with a high temporal resolution. High-resolution AIS data are especially required for waypoint detection in critical areas where vessels maneuver carefully because of, e.g., narrow pathways or the presence of islands. One possible solution to address the problem of insufficient AIS data in vessel trajectories is interpolation. In this paper, we address the problem of detecting waypoints in a single representative trajectory with insufficient data using various interpolation-based methods. To this end, a two-step approach is proposed, in which the trajectories are first interpolated, and then the waypoint detection method is applied to the merged trajectory containing both interpolated and observed AIS messages. The numerical results demonstrate the effectiveness of exploiting various interpolation methods for waypoint detection. Moreover, the results of the numerical experiments show that the proposed methodology is effective for waypoint detection in envisaged settings with insufficient data, and outperforms the competing algorithm.