Abstract
One of the ways to prevent accidents at sea is to detect risks caused by humans and to counteract them. These tasks can be executed through an analysis of ship maneuvers and the identification of behavior considered to be potentially dangerous, e.g., based on data obtained online from the automatic identification system (AIS). As a result, additional measures or actions can be taken, e.g., passing at a distance greater than previously planned. The detection of risks at sea requires a prior definition of behavior patterns and the criteria assigned to them. Each pattern represents a specific navigator’s safety profile. The criteria assigned to each pattern for the identification of the navigator’s safety profile were determined from previously recorded AIS data. Due to a large amount of data and their complex relationships, these authors have proposed to use data mining tools. This work continues previous research on this subject. The conducted analysis covered data recorded in simulation tests done by navigators. Typical ship encounter situations were included. Based on additional simulation data, the patterns of behavior were verified for the determination of a navigator’s safety profile. An example of using the presented method is given.
Subject
Ocean Engineering,Water Science and Technology,Civil and Structural Engineering
Reference20 articles.
1. Modeling the Type of Car Driver;Augustynowicz,2008
2. Real-Time Statistical-Based Test Environment for Transmission Control Unit of Passenger Cars;Aliefendioglu;SAE Trans.,1999
3. Human Factor Modelling in the Risk Assessment of Port Manoeuvers
Cited by
8 articles.
订阅此论文施引文献
订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献