The technology breakthrough that Electronic Chart Display and Information System (ECDIS) has brought to modern navigation has the capability to improve the safety of navigation. This could be achieved only when the capabilities of the system are known by an end-user. Cross Track Limit (XTL) is an ECDIS safety parameter, set by the navigator, which enhances the navigational task automation in the function of workload reduction. Determination of factors affecting the value of XTL safety parameter, with special consideration to chart data reliability, is elaborated in this paper. Chart data reliability depends on the quality of chart survey data, which in many cases are outdated and unreliable. Analysis of past research on this subject is used to define the factors affecting XTL. Practices of different shipping companies with regards to XTL are analyzed and compared in order to confirm if there is a uniform practice between them. Nevertheless, shipping companies have a different or no practice of obtaining XTL, which allows the navigator to define safety parameters by a subjective opinion. In this paper, method of XTL determination for a specific vessel is suggested, considering previously defined factors. Finally, crucial influence of survey data to the safety of navigation is presented in this study.

A near-miss management system (NMMS) is a tool used for improving safety at sea if adequately implemented. Valuable knowledge to improve safety management might be gained by investigating and analysing reported events. Therefore, it is of the utmost importance to report each observed near-miss event. Because tankers are generally considered dangerous, but at the same time safe due to stringent requirements, near-miss reports and NMMS policy were collected from one oil tanker ship. Data were pre-processed and analysed. Variables used during analysis were near-miss type, risk level, ship position, and onboard location of near-miss occurrence. Analysis of policy and reports revealed that most near misses occurred on the deck area, but higher-risk-level events were reported in the engine room and navigating bridge. Housekeeping, equipment failure, use of personal protective equipment (PPE), and process-/procedure-related events were most common and generally related to lower risk levels. The most frequent corrective actions recorded were implementing safe working practices and PPE. In addition, higher-risk-level events were related to less effective corrective actions. Based on the findings, suggestions for improvements include promoting safe behaviour and adequate PPE usage through additional training, ensuring proper housekeeping, regular maintenance of shipboard equipment and spare parts management, and toolbox meetings and risk assessments that include conclusions of near-miss investigations and analysis.

The International Regulations for Preventing Collisions at Sea 1972 (COLREGs) have been the cornerstone of maritime navigation since their introduction. Knowledge and implementation of these rules are paramount in collision avoidance at sea. However, terms found in these rules are sometimes imprecise or fuzzy, as they are written by humans for humans, giving them some freedom in interpretation. The term Very Large Ship used in Rule 7 of the COLREGs is, by its nature, fuzzy. While human navigators understand this term’s meaning, it could be challenging for machines or autonomous ships to understand such an unprecise expression. Fuzzy sets could easily describe unprecise terms used in maritime navigation. A fuzzy set consists of elements with degrees of membership in a set, making them perfect for interpreting some terms where boundaries are unclear. This research was conducted among 220 navigational experts to describe linguistic variables used in maritime regulations. This research consists of an internationally distributed questionnaire. Membership data were collected with the adapted horizontal method, and the results were statistically analyzed, followed by regression analyses to describe the range and shape of membership functions. A conceptual model of the implementation of linguistic variables is presented. The novelty of this study derives from the data collecting, modeling, and quantification of the important but neglected linguistic term Very Large Ship based on a large number of navigational experts. The same quantification method could be easily used for other COLREGs linguistic variables, which could easily lift barriers to advances in intelligent solutions based on fuzzy sets. The obtained quantified fuzzy sets can be used in decision support or control systems used by conventional or autonomous ships in the future.

Lifeboats are life-saving equipment used when it is necessary to abandon a ship or, in some ships, for man-overboard situations (to collect persons from water). Every seafarer onboard a ship has a task related to lifeboat operation in an emergency. In order to master and practise the assigned tasks, be ready to react at any moment, and efficiently use life-saving equipment and appliances, seafarers on ships perform drills at prescribed intervals. Effective drill performance is of paramount importance, as it improves safety and enables crew members to practise lifeboat operations. However, although their primary role is life-saving, lifeboat drills have resulted in numerous accidents, causing injuries and fatalities, besides equipment damage. Therefore, it is necessary to prevent such unwanted events and discover their root causes. As the human factor is considered a significant cause of marine accidents, this paper aims to quantify human error probability (HEP) during lifeboat drills. In addition, because lifeboat drill accident data are scarce, this study adopted the Success Likelihood Index Method (SLIM) for human reliability analysis (HRA). Based on expert judgments, the tasks with the highest probability of human error and factors significantly influencing human performance during lifeboat drills are identified. According to the study results, the recovery of the lifeboat is the most hazardous phase with the highest HEP. In addition, the BN-SLIM is adopted to estimate the probability of human error during the recovery of the lifeboat. The task with the largest HEP is confirming the release lever is properly rested and hooks locked (HEP = 4.5%). Furthermore, the design and condition of equipment and Crew Competence are identified as the most important Performance-Shaping Factors (PSFs) that affect crew members’ performance. The BN-SLIM model was verified utilising a sensitivity analysis and validated by analysing real-life lifeboat drill accidents that occurred during lifeboat recovery. The results confirmed that the model could be used to analyse lifeboat accidents and for proactive preventive measures because most influencing factors are recognised, and acting on them can significantly reduce the HEP of the overall task, improve lifeboat safety, and save lives at sea.