Preparing VTS for the MASS Era: A Machine Learning-Based VTSO Recruitment Model

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.