2022. No. 4 Vol.16

      This article presents an approach to simulation of the rates of road traffic accidents involving unmanned ground vehicles within a multi-agent intelligent transportation system for the ‘smart city.’ A new simulation model of an intelligent transport system has been developed which makes it possible to significantly reduce the number of potential road traffic accidents (TAs) and implements the concept of socially-centered management of the urban economy. The software implementation of such a large-scale agent-based model was carried out using the FLAME GPU framework, which allows us to effectively parallelize the agents’ behaviour logic and consider their individual decision-making systems when modelling the spacial dynamics of an ensemble of unmanned ground vehicles (UGVs) interacting with other road users: the usual manned ground vehicles (MGVs), unexpected obstacles (e.g., pedestrians, etc.). Various scenarios of such agents’ behaviour in an intelligent transportation system are studied, including the occurrence of an accident under certain conditions (e.g., under the high speed and traffic intensity of UGVs, etc.) and various configurations of the digital road network (DRN). We determine the parameter values that provide for the individual decision-making system of UGVs remaining stable with respect to the characteristics of the external environment (including in extreme situations), ensuring the safety of other road users on the scale of the ‘smart city.’

      The digital transformation of enterprises and organizations in modern conditions is carried out through the development and implementation of new business models based on various digital technologies which are collectively accumulated as part of digital business platforms. Insufficient development of methods and means of choosing adequate business models for the functioning of network enterprises at the present time, depending on the competitive strategy used, production technologies, digital maturity, and security policy, determines the relevance of this study. The aim of the work is to develop a method to justify the rational choice of the type of business model of digital transformation of a network enterprise under the conditions of multi-criteria evaluation of various factors of obtaining network effects, digital maturity and ensuring economic and information security. To achieve the goal, methodological approaches are used as approaches to solve the problem: to formalize the business model based on the St. Gallen framework, to classify business models of the working group on business models Industry 4.0 to build a knowledge-based system using fuzzy sets of production rules. A method is proposed for classifying the types of business models of a network enterprise depending on the competitive strategy applied, the stage of the life cycle of products and services provided, the type of production and the method of using digital business platforms. In accordance with the classification of the working group on business models of Industry 4.0, network effects are determined for the main roles of participants in network interaction for each type of business model. A conceptual multi-criteria model for choosing the type of business model has been developed, implemented in the form of sets of production rules of a knowledge-based system which include an assessment of network effects, digital maturity, commercial risks and information security risks.

      This paper deals with the problem of quantitative description and improvement of the quality of the service provided by taxi aggregator companies working on the Russian market. This problem seems to be insufficiently addressed in open research publications due to its high specificity, though some research aiming at searching the quality metrics have been conducted for some companies worldwide. The goal of the current research is mathematical formalization of a rating system assessing the driver service quality that allows one to design a parametrically tunable model. The proposed mathematical model of the rating system is described by means of a state graph where the transition from a vertex to another happens when the explicitly written conditions are satisfied. We show that the rating evaluation for a driver remaining in the active can be carried out by means of linear filtration performed as digital signal processing of the time series consisting of the scores which are given to the driver by their passengers. The type and waveform of the filter impulse response is suggested. The A/B-test conducted for the group of drivers working with a taxi aggregator proved the fact that the integral metric of service quality is sensitive to changes in the parameters of the proposed rating system; this eventually led to a decrease in the rate of taxi rides accompanied with a negative client experience. The rating system model developed can be utilized to increase the quality of the service provided by the taxi aggregator by means of more effective differentiation of the drivers, while the subsequent optimization of the rating system parameters can serve as a tool for achieving indicators supporting the strategic goals of the company.

      In the period 2020–2022, the Russian economy has been facing the new, unprecedented challenges of coronavirus and sanctions. In order to analyze the current state of affairs, we are offering an econometric study of Russia’s macroeconomic production function for 1990–2022 and an estimation of the marginal rate of technical substitution under internal and external restrictions associated with the spread of the Wuhan coronavirus (SARS-CoV-2) and the conduct of Russia’s special military operation in Ukraine, accompanied by increased sanctions pressure on the Russian economy. We have obtained several significant results. In the years 1991–1996 the marginal rate of technical substitution was increasing, and in 1997–2020 it was decreasing except for 2008–2009 and 2015. In the context of the Wuhan coronavirus pandemic, the main reasons for the Russian economy’s decline in 2020 and growth in 2021 were, first of all, fluctuations in the world crude oil price, and not the Wuhan coronavirus pandemic as such. We did not find any evidence that the decline in the world crude oil price in 2020 was caused by a decrease in demand from China, since Russian oil exports to China increased. Contrary to many negative forecasts, the results of our forecasting of Russia’s GDP for 2022 show that under sharply increased sanctions pressure, with the world price of Urals oil at $60 per barrel, the average growth rate will be 0%, while at $70 it will be 4%, and at $80 it will be 7%. Under the reduced demand for Russian gas and the shutdown of the Nord Stream 1 gas pipeline, the forecast volumes of gross natural gas production by Gazprom (excluding Gazprom Neft) in the Tyumen Region for 2022, based on the exponential production function studied by econometric methods, range from 364 to 392 billion cubic meters. Using the example of Great Britain, where in 2021 the average actual export prices for Russian oil and gas were the lowest compared to other Western European countries, we discuss the economic inexpediency of setting marginal prices for Russian energy products by Western consumers.