In maritime accident prevention, it is important to identify not only high-risk sea areas but also which accident-types are most likely to occur there. This study combines survey responses from 826 Korea Coast Guard practitioners with 3856 maritime accidents mapped onto an H3 grid over Korean territorial waters during 2021–2023, and proposes a practitioner-informed framework for predicting accident-type-specific risk. The survey showed limited use of quantitative, standardized accident risk criteria but high demand for AI-based prediction and area-level risk analysis. Practitioners’ perceived accident frequency differed substantially from the empirical accident distribution, whereas their prevention priorities aligned more closely with the actual pattern. Accordingly, this study treats the accident-type taxonomy not as a fixed prediction target but as a design variable of the label space for decision support. A two-stage framework first estimates accident occurrence at the H3 grid-time level and then classifies the accident-type conditional on occurrence. Comparing survey-aligned, data-aligned, union, sufficient-sample, and full administrative (7-class) framings under a common training protocol shows that accident-type organization creates trade-offs among field interpretability, coverage, class granularity, and predictive stability. The study thus reframes maritime accident prediction as an accident-type-specific decision-support problem-linking practitioner perception with empirical evidence.

Vessel traffic services (VTSs) in ports require interpretable information on vessel approaches and travel times where shared fairways branch toward multiple destinations. This study presents a gate-based Bayesian method for VTS decision support in Busan North Port. Automatic Identification System (AIS) and Port Management Information System (PORT-MIS) records from September 2024 to August 2025 were linked. Density-based clustering of turning locations and examination of traffic functions informed the placement of 22 virtual gates. A Bayesian network with 20 binary gate-passage variables was constructed from 7194 inbound trajectories. The probability of shared-fairway passage given a passenger-terminal approach was approximately 99.9%, whereas the reverse conditional probability was 20.55%. These results distinguish shared passage locations associated with a known approach from approaches associated with an observed passage. Three illustrative routes had annual mean travel times of 6.22, 6.52, and 13.38 min. Speed-conditioned travel-time probabilities were estimated from the data. On the main-fairway route, arrival in less than 6 min had a probability of 91.01% at departure speeds of 10–<12 kts. The method provides location-specific passage probabilities and route-specific travel-time information for interpreting observed traffic. Validation of predictive performance and benefits for controller decision-making remains necessary.