VTS Decision Support Through Bayesian Inference of Vessel Passage Probabilities in Busan North Port

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.