As interest in eco-friendly ships increases, methods for status monitoring and forecasting using in-service data from ships are being developed. Models for predicting the energy efficiency of a ship in real time need to effectively process the operational data and be optimized for such an application. This paper presents models that can predict fuel consumption using in-service data collected from a 13,000 TEU class container ship, along with statistical and domain-knowledge methods to select the proper input variables for the models. These methods prevent overfitting and multicollinearity while providing practical applicability. To implement the prediction model, either an artificial neural network (ANN) or multiple linear regression (MLR) were applied, where the ANN-based models showed the best prediction accuracy for both variable selection methods. The goodness of fit of the models based on ANN ranged from $0.9709$ to $0.9936$. Furthermore, sensitivity analysis of the draught under normal operating conditions indicated an optimal draught of 14.79 m, which was very close to the design draught of the target ship, and provides the optimal fuel consumption efficiency. These models could provide valuable information for ship operators to support decision making to maintain efficient operating conditions.

To ensure ship safety, safety culture is a critical factor in the organization of shipping companies. The safety climate has been evaluated to determine the level of a particular group’s safety culture. This study investigated South Korean seafarers’ safety culture awareness to compare whether there are differences between those who work on ships engaged in domestic and international voyages. In the latter, reinforced international regulations are applied. A questionnaire survey was conducted with 261 Korean seafarers using seven indicators representing the safety climate used in the aviation and maritime fields. Results showed that seafarers engaged in ocean-going navigation had a higher awareness of management involvement, organizational commitment, learning, and reporting systems, which yielded more positive effects than those engaged in domestic navigation. However, this did not significantly affect communication or employee empowerment. The survey methodology in this study can be used as an effective measure to assess the maritime safety climate; thus, it is possible to prepare policies and educational programs aimed at improving maritime safety.

In 2019, the International Maritime Organization (IMO) prescribed guidelines to standardize the user interface (UI) of navigation equipment such as radio detection and ranging (RADAR) and the electronic chart display and information system (ECDIS), which mandate the unification of various terminologies and icons to improve usability. However, comprehensive studies on the operability and standardization of global positioning system (GPS) plotters, which are auxiliary equipment installed across various domestic and commercial vessels for navigational purposes, have not been conducted. Therefore, we performed a case study to identify the user requirements for standardizing the UI of GPS plotters in South Korea. Additionally, we conducted a survey to ascertain the frequency of use of the navigational functions in a GPS plotter, in which, several deck officers and captains participated. The frequently used navigational functions that were subsequently identified confirmed the need for standardization of the UI. Furthermore, the users requested the standardization of the menus of these functions using Korean terminologies. Hence, we attempted to derive standardized items that can be applied to both the functions of the GPS plotters and the navigation information obtained, which can contribute to the improvement of the UI across the different types of plotters.

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.

Maritime Autonomous Surface Ships (MASSs) raise safety and regulatory challenges that extend beyond technical reliability. This study builds on a published system-theoretic process analysis (STPA) of degraded operations that identified 92 loss scenarios. These scenarios were reformulated into a two-round Delphi survey with 20 experts from academic, industry, seafaring, and regulatory backgrounds. Panelists rated each scenario on severity, likelihood, and detectability. To avoid rank reversal, common in the Risk Priority Number, an adjusted index was applied. Initial concordance was low (Kendall’s W = 0.07), reflecting diverse perspectives. After feedback, Round 2 reached substantial agreement (W = 0.693, χ² = 3265.42, df = 91, p < 0.001) and produced a stable Top 10. High-priority items involved propulsion and machinery, communication links, sensing, integrated control, and human–machine interaction. These risks are further exacerbated by oceanographic conditions, such as strong currents, wave-induced motions, and biofouling, which can impair propulsion efficiency and sensor accuracy. This highlights the importance of environmental resilience in MASS safety. These clusters were translated into five action bundles that addressed fallback procedures, link assurance, sensor fusion, control chain verification, and alarm governance. The findings show that Remote Operator competence and oversight are central to MASS safety. At the same time, MASSs rely on artificial intelligence systems that can fail in degraded states, for example, through reduced explainability in decision making, vulnerabilities in sensor fusion, or adversarial conditions such as fog-obscured cameras. Recognizing these AI-specific challenges highlights the need for both human oversight and resilient algorithmic design. They support explicit inclusion of Remote Operators in the STCW convention, along with watchkeeping and fatigue rules for Remote Operation Centers. This study provides a consensus-based baseline for regulatory debate, while future work should extend these insights through quantitative system modeling.