Near-miss events are incidents in which a potential accident, injury, or hazard was narrowly avoided, and are critical for identifying early warnings and preventing serious maritime incidents. The report examines methods for automatically processing and classifying free-text near-miss descriptions to improve efficiency, accuracy, and consistency. The project tested these advanced data analysis techniques within a secure, protected environment to ensure confidentiality while enhancing the value of aggregated safety data to participating companies.

Featuring a review of anonymized participant-submitted data, the report evaluates how automated text classification, taxonomy mapping, and human-in-the-loop review can reduce time-consuming manual analysis, improve data comparability, and support more rapid identification of safety risks and operational trends across diverse datasets.

The SafeMTS team’s research included a review of existing program taxonomies and data definitions, pilot testing of secure LLM workflows, and validation by subject matter experts from both maritime operations and data science fields. In addition to findings related to classification accuracy, efficiency gains, and emerging trend detection, the report identifies current barriers to full-scale implementation, such as inconsistent data formats, incomplete records, and varying reporting cadences, and also outlines recommendations for addressing these challenges before expanding the use of LLMs within the program.

The report aims to inform maritime companies, safety managers, researchers, and regulators about the potential of advanced language-based analytics to strengthen maritime safety programs. For example, the SafeMTS team has already shared detailed safety trends that have emerged from the study as part of one-on-one meetings with commercial maritime companies currently participating in the program. By enabling faster and more consistent analysis, SafeMTS seeks to support enhanced safety management practices, more actionable industry-wide insights, and stronger prevention strategies.

Download the LLM Pilot Report from the SafeMTS website’s Publications section.
 

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