Extended linear regression model for vessel trajectory prediction with a-priori AIS information

Abstract

As maritime activities increase globally, there is a greater dependency on technology in monitoring, control, and surveillance of vessel activity. One of the most prominent systems for monitoring vessel activity is the Automatic Identification System (AIS). An increase in both vessels fitted with AIS transponders and satellite and terrestrial AIS receivers has resulted in a significant increase in AIS messages received globally. This resultant rich spatial and temporal data source related to vessel activity provides analysts with the ability to perform enhanced vessel movement analytics, of which a pertinent example is the improvement of vessel location predictions. In this paper, we propose a novel strategy for predicting future locations of vessels making use of historic AIS data. The proposed method uses a Linear Regression Model (LRM) and utilizes historic AIS movement data in the form of a-priori generated spatial maps of the course over ground (LRMAC). The LRMAC is an accurate low complexity first-order method that is easy to implement operationally and shows promising results in areas where there is a consistency in the directionality of historic vessel movement. In areas where the historic directionality of vessel movement is diverse, such as areas close to harbors and ports, the LRMAC defaults to the LRM. The proposed LRMAC method is compared to the Single-Point Neighbor Search (SPNS), which is also a first-order method and has a similar level of computational complexity, and for the use case of predicting tanker and cargo vessel trajectories up to 8 hours into the future, the LRMAC showed improved results both in terms of prediction accuracy and execution time.