Big Data Driven Vessel Trajectory and Navigating State Prediction with Adaptive Learning, Motion Modeling and Particle Filtering Techniques

The predictive vessel surveillance is one of the indispensable functional components in intelligent maritime traffic system. Vessel trajectory prediction serves as a prerequisite for collision detection and risk assessment. Perceiving the forthcoming traffic situation in advance helps decide the succeeding actions to mitigate the potential risk. The availability of maritime big data brings great potential to extract vessel movement patterns to support trajectory forecasting. In this paper, a novel vessel trajectory and navigating state prediction methodology is proposed based on AIS data, which synergizes properly designed learning, motion modelling and knowledge base assisted particle filtering processes. The primary contributions of this work also comprise several critical research findings to handle the key challenges in vessel trajectory and navigating state prediction problem, such as the adaptive training window determination for the learning process and effective knowledge storage and searching algorithm intended to reduce the query time of waterway pattern retrieval. The studies for these challenges are still missing in the reported literatures but they are essentially important for improving the prediction accuracy, efficiency and practicality. With the maritime traffic data collected for Singapore water, a thorough evaluation of the prediction performance has been conducted for different navigating scenarios. It is also observed that better prediction outperforms on account of allowing earlier alert in risk detection.