Multimodal Data Fusion based Graph Neural Networks for Ship Trajectory Prediction

Global trade depends heavily on maritime transport, as ships carry more than 90% of all international goods. The Automatic Identification System (AIS) is crucial for safe and efficient maritime navigation, providing real-time ship movement data.However, precise prediction of ship behavior is difficult because of complex environmental factors, human decisions, and ship interactions. This paper introduces a novel method that uses multimodal data fusion with GNNs for improved trajectory prediction. Our model leverages AIS data in conjunction with oceanographic and meteorological data to create a ship interaction graph depicting spatial correlations. This graph-based methodology facilitates the modeling of ship dynamics, which are impacted by environmental variables and navigational regulations, by explicitly representing complex interdependencies. This proposed method yields a significant improvement in prediction accuracy, with consequential advantages for collision avoidance systems, route optimization strategies, and maritime surveillance operations. Experimental findings reveal that our model achieves a higher level of accuracy than established time-series techniques in predicting ship behavior, thus setting a new benchmark.