Abstract: We consider the task of developing algorithms for cyber-physical systems (CPS) for proactively managing the state of unstable systems with a chaotically evolving state vector. Examples of such processes are changes in the state of gas- and hydrodynamic environments, stock price evolution, thermal phenomena, and so on. The main problem of this type of CPS is creating forecasts that would allow us to compare the efficiency of different feasible control actions. The presence of a chaotic element in the state dynamics of unstable systems does not allow to build of control CPS based on conventional statistical extrapolation algorithms. Hence, in the current chapter, we consider forecasting algorithms built upon machine learning and instance-based data analysis. In the conditions of chaotic influences, which are common in unstable immersion environments, obtaining an accurate forecast is highly complicated. Within the conducted computational experiment that employed direct averaging by three after-effects of analog windows, the average forecast accuracy oscillates between 15 and 20%. Effective forecasting of a chaotic process of a complicated inertia-less nature based on the considered computational schemes has not been achieved yet. This means that additional research, based on multidimensional statistical measures, is required.
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Article "Discrete-event modelling of the capacity of the toll collection exit point and the formation of congestion" was published by the International Journal of Simulation and Process Modelling. Congratulations to co-authors Talavirya A. and Laskin M!
Abstract: The study addresses the issue of assessing toll plaza (TP) throughput capacity and traffic congestion during toll road operations. To demonstrate a methodology for transport micro-modelling, the western high speed diameter (WHSD) toll road TP in St. Petersburg, Russia, was used as a case study. The discrete-event simulation model of the TP at the toll road exit was developed using AnyLogic software. We accounted for the specifics of traffic composition and user behaviour in the selected urban district. The operation of the TP at low and high traffic flow rates was analysed using the developed simulation model. The empirical data parameters for service time distribution were obtained for each situation. When a TP operates above the threshold throughput capacity, a mathematical method for determining the parameters of the traffic congestion (queue length and time, number of vehicles in the queue) was proposed.
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