<![CDATA[This research develops an AIS-based probabilistic collision risk assessment model and evaluates its use as both an industry decision-support tool and an educational resource in maritime safety courses. Large-scale AIS data from a selected Traffic Separation Scheme (TSS) are processed to derive traffic density, encounter patterns, and near-miss events based on ship domain and relative motion criteria. A probabilistic framework combining encounter frequency and consequence severity is used to estimate collision risk levels across different route segments and times of day. These risk maps are then incorporated into classroom and simulator exercises in which cadets and port traffic controllers-in-training analyze high-risk zones, propose speed or routing adjustments, and evaluate the effect of different traffic management strategies. Learning outcomes are measured by pre- and post-tests on risk concepts, as well as qualitative assessment of students’ risk-based decision-making. Feedback from VTS operators, pilots, and safety managers supports the practical relevance of the model. The study shows how AIS-based risk analytics can simultaneously enhance operational safety planning and enrich maritime education by exposing learners to authentic, data-driven scenarios.]]>
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