Context. The article discusses approaches to anticipation identification being an essential part of the decision-making process done by the operator by using the example of a sea captain in ergatic systems of critical infrastructures in the sea transport management. The mentioned above aspect of anticipation of operators can be regarded as being a complex form of human-machine interaction and, certainly, claims for further elaboration of information and tools to be used. Objective. The way to approach development is taken as being based on an information analysis of the full range of trajectories of decision-making by operators at the time of performing complex multi-stage actions. These items are rooting out of their adopted strategy of human-machine interaction. Besides, it leads to the formation of a metric being able to algorithmically represent the enormous number of variants. It can be done taking into account conditions of combinatorial representation in terms of the geometric theory of groups on the Cayley graph. Method. Being a part of the approach elaboration the having been obtained during the analysis of the database of navigation simulators mathematical model of experimental data collecting and processing succeeded to be constructed. To confirm the formalalgorithmic approach a simulation was challenged to be carried out helping to form the trajectory of the operator's decision making in critical situations. It was felicitously performed basing on the three-factor ERO-AEA-EAPI model. Thus, the algebraic and software representation of the metric decision space is noticed to uncover approximate complex human-machine interactions in uncertain environments. As a result, the converting process of data of the main subject of critical infrastructure (i.e. the operator) into knowledge is able to be coped with. In addition, factors possible to be gauged in the proposed metric are able to be uncovered. Results. In order to carry out the feasibility assessment of the developed approach as well as formal-algorithmic ones, an experiment was performed by using the Navi Trainer 5000 navigation simulator (NTPRO 5000). During having one of the most troublesome operations i.e. mooring we wanted the server data to be analyzed. As a result, data about anticipation being shaped as triangular constructs in the quasi-isometric space of Cayley graph is reported to have been obtained. The automated neural networks being used for result obtaining led to delivering of the possibility to get multiple data regression and to analyze the relationships of many independent variables. It is considered to be clear evidence due to having found out results of scattering and reliability diagrams. Conclusions. The having been presented in the investigations formal-algorithmic approach together with the developed software tools and the approaches of converting data into knowledge about operator anticipation are said to embrace the possibility to classify and to identify individual decision-making strategies when managing a vessel and to pre...

Taking into account current trends in the development of ergatic maritime transport systems, the factors of the navigator’s influence on vessel control processes were determined. Within the framework of the research hypothesis, to improve navigation safety, it is necessary to apply predictive data mining models and automated vessel control. The paper proposes a diagram of the ergatic vessel control system and a model for identifying the influence of the navigator “human factor” during navigation. Within the framework of the model based on the principles of navigator decision trees, prediction by data mining means is applied, taking into account the identifiers of the occurrence of a critical situation. Based on the prediction results, a method for optimal vessel control in critical situations was developed, which is triggered at the nodes of the navigator decision tree, which reduces the likelihood of a critical impact on vessel control. The proposed approaches were tested in the research laboratory “Development of decision support systems, ergatic and automated vessel control systems”. The use of the Navi Trainer 5,000 navigation simulator (Wärtsilä Corporation, Finland) and simulation of the navigation safety control system for critical situations have confirmed its effectiveness. As a result of testing, it was determined that the activation of the system allowed reducing the likelihood of critical situations by 18–54 %. In 11 % of cases, the system switched the vessel control processes to automatic mode and, as a result, reduced the risk of emergencies. The use of automated data mining tools made it possible to neutralize the negative influence of the “human factor” of the navigator and to reduce the average maneuvering time during vessel navigation to 23 %

On the basis of empirical experimental data, relationships were identified indicating the influence of navigators' response to such vessel control indicators as maneuverability and safety. This formed a hypothesis about a non-random connection between the navigator's actions, response and parameters of maritime transport management. Within the framework of this hypothesis, logical-formal approaches were proposed that allow using server data of both maritime simulators and operating vessels in order to timely identify the occurrence of a critical situation with possible catastrophic consequences. A method for processing navigation data based on the analysis of temporal zones is proposed, which made it possible to prevent manifestations of reduced efficiency of maritime transport management by 22.5 %. Based on cluster analysis and automated neural networks, it was possible to identify temporary vessel control fragments and classify them by the level of danger. At the same time, the neural network test error was only 3.1 %, and the learning error was 3.8 %, which ensures the high quality of simulation results. The proposed approaches were tested using the Navi Trainer 5000 navigation simulator (Wärtsilä Corporation, Finland). The simulation of the system for identifying critical situations in maritime transport management made it possible to reduce the probability of catastrophic situations by 13.5 %. The use of automated artificial neural networks allowed defining critical situations in real time from the database of maritime transport management on the captain's bridge for an individual navigator.