Maritime accidents remain a significant concern for the shipping industry, despite recent technological developments. In the Republic of Korea, the leading cause of maritime accidents is navigator error, particularly in collisions and groundings; this cause has led to 79% of maritime accidents, according to a recent assessment. The reduction of navigator error is crucial for accident prevention; however, the lack of objective measures to monitor navigator error remains a challenge. The purpose of this study was to develop an objective classification of navigation behaviors in a simulated environment. The statistical model of classification of lookout activity was developed by collecting participants’ lookout behavior using a Kinect sensor within a given scenario. This classification model was validated in non-scenario experiments. The results showed that seven standard lookout activities during a lookout routine were accurately classified in both the model development and validation phases. The proposed model classification of lookout activity using an optical sensor is expected to provide a better understanding of how navigators behave to help prevent maritime accidents in practice.

In order to prevent ship collisions, it is important to understand the behavior of navigators that leads to these collisions. The main cause of marine accidents in the Republic of Korea is attributed to navigator error, particularly in collisions. Hence, reducing navigator error is a key issue that needs to be addressed to prevent accidents. However, the lack of objective measure to quantify navigator error remains a challenge. The purpose of this study is to develop an objective identification of a navigator’s behavior in a collision encountering situation. Two behavior models for the success and failure of collision avoidance are developed by collecting participants’ actions, using a ship maneuvering simulator within a given scenario. These maneuvering behavior models are validated in terms of their discrimination powers. The results show that maneuvering behavior is clearly identified in the data processing and model development phases. The proposed behavior models are expected to provide a better understanding of how navigators behave to help reduce collision accidents.

A very significant number of marine accidents occur because of human errors. This study aimed to prevent ship collisions by identifying types of navigators’ errors. Based on Reason’s classification theory, the possible human errors are classified into skill-based slips (SBSs) (errors caused by the lack of skills), rule-based mistakes (RBMs) (errors caused by the misapplication of rules), and knowledge-based mistakes (KBMs) (errors caused by the lack of navigator’s knowledge). For this study, a scenario-based experiment using a ship-handling simulator was conducted with 50 recruited student navigators. The results revealed two primary human errors of accidents, namely lack of knowledge and misapplication of rules. The results suggest that a collision can be minimized when a navigator has sufficient knowledge of an appropriate course of action and a deep understanding of safety rules. Accidents cannot be prevented by identifying errors, but steps can be taken to narrow the knowledge gap. Based on the results, we proposed a simulation training on navigator error in an unfamiliar situation. The results are expected to reduce errors in the maritime sector using a human-centric work system.