This study aims to improve vessel trajectory prediction in the inner harbor of Busan Port using Automatic Identification System (AIS) data and deep-learning techniques. The research addresses the challenge of irregular AIS data intervals through linear interpolation and focuses on enhancing the accuracy of predictions in complex port environments. Recurrent neural network (RNN), Long Short-Term Memory (LSTM), Bidirectional LSTM, Gated Recurrent Unit (GRU), and Bidirectional GRU models were developed, with LSTM delivering the highest performance. The primary scientific question of this study is how to reliably predict vessel trajectories under varying conditions in inner harbors. The results demonstrate that the proposed method not only improves the precision of predictions but also identifies critical areas where Vessel Traffic Service Operators (VTSOs) can better manage vessel movements. These findings contribute to safer and more efficient vessel traffic management in ports with high traffic density and complex navigational challenges.

This study aims to identify the key success factors for the adoption of a cloud computing system in vessel traffic service (VTS) systems and evaluate the relative importance of each factor. Through a literature review and expert Delphi surveys, 12 success factors were derived across the dimensions of technology, organization, environment, and institution. The results of the analytic hierarchy process (AHP) analysis revealed that stability in the technological dimension was the most important factor. This study provides useful implications for future decision-making in VTS cloud adoption by systematically identifying the key success factors and presenting their priorities through the application of the TOE-I framework to VTS cloud computing.

This study addresses the critical challenge of lacking quantitative measures for objective evaluation of vessel traffic service (VTS) operator workload, where current uniform staffing approaches fail to reflect center-specific operational characteristics. The VTS Operator Workload Index (VOWI) model was developed using the Delphi–AHP methodology to determine the relative importance of key factors including traffic, sea area characteristics, port facilities, and weather conditions, which formed the basis for calculating both center-wide and per-operator workload indices. Factor analysis revealed that traffic factors showed the highest importance at 0.4627, followed by sea area (0.1960), port facilities (0.1916), and weather (0.1497) factors. Application of the VOWI model to 19 VTS centers in South Korea demonstrated that per-operator workload at Busan, Incheon, and Ulsan VTS was up to three times higher than at other centers. This finding indicates that the current uniform staffing approach based on sector count inadequately reflects each center’s actual operational characteristics. The VOWI model provides objective criteria for efficient personnel management in VTS centers and is expected to contribute to improving VTS service quality.

As the maritime industry transitions toward Maritime Autonomous Surface Ships (MASS), Vessel Traffic Service Operators (VTSOs) face new challenges in managing mixed traffic of conventional and autonomous vessels. Effective VTSO selection is becoming increasingly critical for maritime safety, yet current recruitment processes rely on subjective methods that limit objective evaluation of candidate suitability. This study presents the first machine learning-based classification model for VTSO recruitment. Eight features were defined, including sea service experience, navigation career, education, certifications, and language proficiency. Due to limited access to actual recruitment data, expert-validated simulated datasets were constructed through labeling by 40 maritime professionals and density estimation-based augmentation. Four algorithms were compared, with XGBoost achieving 94.6% F1-score. Feature importance analysis revealed TOEIC score as the most critical predictor, followed by seafaring career, with 3–4 years of experience identified as optimal. These findings indicate that English proficiency for communication with shore remote control centers and practical maritime experience for assessing autonomous vessel behaviors constitute core VTSO competencies in the MASS era. The proposed model demonstrates potential to improve subjective recruitment methods by discovering quantifiable competency patterns, offering a pathway toward data-driven, standardized, and transparent decision-making for enhanced maritime safety.