Рис. 1. Общая схема организации искусственной дорожной сети.Важной характеристикой агента-ТС является его личное пространство, сжимаемое и расширяемое в зависимости от плотности дорожного потока относительно данного агента. Если значение плотности, то есть количество транспортных средств, расположенных вблизи агента-ТС превышает некоторый пороговый уровень (различный для ОТС и БТС), то радиус личного пространства агента резко увеличивается (из-за паники водителя) и стремления избежать аварии, вызывая эффекты «турбулентности» и «давки» (т.е. дорожные заторы) (рис. 1).На рисунке 2 представлена визуализация правил принятия решений агентом-БТС, предложенных в данной модели, при наличии дорожных заторов.

Actual problems of modeling of ecologic-economic systems on the example of the Republic of Armenia (RA) are considered. Based on the methods of agent modeling and system dynamics, the simulation model of ecological-economic system which has allowed constructing the RA Ecological Map was created. The important purpose of the suggested approach is search of scenarios of rational modernization of the agent-enterprises, which are the main sources of emissions with simultaneous definition of effective strategy of the government regulation. The bi-criterial optimization problem for the ecological-economic system of RA is formulated and solved with the help of the developed genetic algorithm.

This article continues the description of the control system for ground unmanned vehicles as part of the integration of a phenomenological approach to modeling the behavior of agents and methods of fuzzy clustering in order to improve the quality of decisions. As a result, adaptive fuzzy clustering methods provide support for adaptive ground unmanned vehicles control, which minimizes the risks of accidents (emergencies involving ground unmanned vehicles) and maximizes traffic (total output stream) in conditions of heavy traffic. The second part is devoted to the description of the developed fuzzy clustering algorithm, software implementation and experiments. As a result, within the framework of the developed model of ground unmanned vehicles movement, fuzzy clustering methods are used to ensure the procedure for choosing the most preferable (least dense) lane in conditions of heavy traffic and to support continuous information exchange between ground unmanned vehicles. The software implementation of the developed simulation model in the AnyLogic environment was performed and numerical experiments aimed at analyzing scenarios of the development of the road situation with the participation of the ground unmanned vehicles ensemble were carried out. Various behavioral scenarios of the developed ground unmanned vehicles control system were investigated, and agent clustering was performed for each scenario under consideration. As a result of numerical experiments, the effectiveness of using the proposed fuzzy clustering procedure to assess the density of the road flow and adaptive control and maneuvering of the ground unmanned vehicles is confirmed.

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