Open Collections

Abstract

Maritime shipping underpins over 80% of global trade, making accurate vessel destination prediction essential for efficient port operations, optimized resource allocation, and strategic decision-making. However, existing methods face persistent challenges, including high computational costs for trajectory comparisons and limited effectiveness and prediction stability during the early stages of a voyage, when only trajectory signals are available. This thesis addresses these gaps by developing three novel methodologies: TrajBERT Deep Semantic Similarity Model (DSSM), CrossRanker, and TrajReducer to improve prediction accuracy, robustness, and efficiency across all travel stages. Firstly, TrajBERT-DSSM is proposed to capture spatiotemporal correlations, geometric attributes, and vessel movement in trajectory comparisons. By applying geohash encoding to filter redundant trajectories and employing a contextual embedding model (TrajBERT) combined with DSSM, TrajBERT-DSSM effectively identifies destinations based on historical vessel paths. Experimental results demonstrate enhanced accuracy and stability, highlighting the benefits of integrating comprehensive trajectory features. Secondly, CrossRanker addresses the limitations of single-dimensional approaches by fusing temporal, spatial, and static signals for destination prediction. Through a two-stage ranking process, CrossRanker combines trajectory-based similarity with feature-level measurements encompassing vessel type, design characteristics, real-time draft, travel distance, and time. This cross-dimensional signal fusion enhances early-stage prediction accuracy and consistently outperforms state-of-the-art methods in terms of Top-1 accuracy and robustness metrics, thereby reducing the uncertainty inherent in partial trajectory data. Finally, TrajReducer addresses computational inefficiency in large-scale applications by clustering past trajectories based on spatial characteristics and selectively comparing them using static and dynamic vessel metadata. This approach reduces the search space for trajectory comparisons while preserving high accuracy throughout all travel stages. In evaluations, TrajReducer achieves a high reduction ratio and maintains superior accuracy even with limited trajectory signals. Overall, the proposed methods form a comprehensive framework that advances the state of the art in vessel destination prediction in terms of accuracy, efficiency, and robustness. These contributions have significant implications for the maritime industry, ranging from enhanced port scheduling and traffic management to more sustainable operational strategies, thereby supporting the growing global demand for reliable maritime logistics solutions.