Navigating Complexity: Automating Maritime Decision-Making with Temporal Transformer-Based Embeddings and Scalable Clustering

The analysis of track data is fundamental to decision-making systems in domains such as maritime navigation, aviation, and logistics. Traditional reliance on handcrafted features and rule-based systems often struggles to scale with the increasing volume, variety, and complexity of data. This paper introduces an automated framework for maritime decision-making that leverages transformer-based embeddings and vector database technologies to overcome these limitations. Automatic Identification System (AIS) data, segmented into 12-hour chunks to optimize computational efficiency, is embedded into high-dimensional feature vectors using a time-series transformer. These embeddings are stored and queried in a scalable vector database, enabling efficient clustering and real-time anomaly detection. Extensive experiments validate the proposed system, demonstrating improvements in clustering accuracy, scalability, and classification performance. For instance, the Random Forest classifier achieved a weighted F1-score of 0.90, outperforming traditional handcrafted methods. Geospatial analysis confirmed the alignment of clustering results with real-world maritime patterns, underscoring the system's practical relevance. This work automates feature extraction, integrates advanced embedding and clustering techniques, and validates outputs through classification and annotation. The proposed framework not only addresses maritime challenges but also demonstrates adaptability to other domains, such as air traffic management and logistics. Future research will enhance clustering algorithms, incorporate predictive analytics, and extend the methodology to integrate multi-source data for broader applicability.