A novel RNN architecture to improve the precision of ship trajectory predictions

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ABSTRACT Monitoring maritime transport activities is crucial for ensuring the security and safety of people and goods. This type of monitoring often relies on the use of navigation systems such as the Automatic Identification System (AIS). AIS data has been used to support the defense teams when identifying equipment defects, locating suspicious activity, ensuring ship collision avoidance, and detecting hazardous events. In this context, Ship Trajectory Prediction (STP) has been conducted using AIS data to support the estimation of vessel routes and locations, contributing to maritime safety and situational awareness. Currently, the Ornstein-Uhlenbeck (OU) model is considered the state-of-the-art for STP. However, this model can be time-consuming and can only represent a single vessel track. To solve these challenges, Recurrent Neural Network (RNN) models have been applied to STP to allow scalability for large data sets and to capture larger regions or anomalous vessels behavior. This research proposes a new RNN architecture that decreases the prediction error up to 50% for cargo vessels when compared to the OU model. Results also confirm that the proposed Decimal Preservation layer can benefit other RNN architectures developed in the literature by reducing their prediction errors for complex data sets.