Regional socio-economic parameters modeling and system analysis by means of programming and computing suite

Ketova, Karolina; Rusyak, I. G.; Saburova, E. A.; Vavilova, Daiana

doi:10.1088/1757-899x/862/5/052044

Cited by 5 publications

(1 citation statement)

References 4 publications

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“…Экономические системы с построенной системой справедливого распределения финансовых средств способны обеспечить своим гражданам высокие жизненные стандарты и социальные гарантии [1]. Для оптимального планирования и построения систем распределения финансовых средств используются методы моделирования и системного анализа региональных социально-экономических параметров [2]. Для анализа качества жизни, оценки степени дифференциации населения по уровню доходов на практике используют статистические данные денежных доходов и расходов населения, труда и заработной платы, социального обеспечения, статистические данные здравоохранения, образования и др.…”

Section: Introductionunclassified

Analysis and modeling of incomes of the population of the socio-economic system Russian Federation

Ketova¹,

Rusyak²

2020

TDSE

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Section: Introductionunclassified

Analysis and modeling of incomes of the population of the socio-economic system Russian Federation

Ketova¹,

Rusyak²

2020

TDSE

View full text Add to dashboard Cite

Computer simulation of traffic flows

Kasatkina

Vavilova

2020

J. Phys.: Conf. Ser.

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The paper is presented to the simulation of traffic flows in conditions of traffic light regulation. A simulation multi-agent model of traffic flows was developed, which includes three agents: a car, a traffic light, a generator. A computational algorithm for the movement of vehicles for imitating the movement of vehicles along the driving lanes and its behavior at the intersection was developed. The simulation multi-agent model and the computational algorithm are implemented in the form of an intelligent analytical system that includes a database designed in the MS SQL DBMS environment, as well as a visualization module. During the simulation of traffic flows in the system, the required output parameters of the model are fixed. By simulation tools one of the problematic road sections of the city of Izhevsk was built, including a network of intersections of 10 years of October Street, Udmurtskaya Street, Pushkinskaya Street. The developed simulation model allows calculating the indicators of the average queue length at different times of the day, taking into account the intensity of input traffic flows. Thus, graphs of the dynamics of changes in the average queue length can be obtained and, on their basis, the time periods of congestion occurrence are determined. For example, during the simulation of the operation of a section of the transport network of the city of Izhevsk, it was established that there are two time periods for the occurrence of traffic congestion: from 7:00-9:00 AM and 5:00-7:00 PM, in which the indicator of the maximum queue length reaches 40 cars.

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Modelling a human capital of an economic system with neural networks

Ketova

Vavilova

2020

J. Phys.: Conf. Ser.

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In the paper, a problem of modelling size, structure and dynamics of a human capital is solved. The research object is an economic system. The subject is a practice of applying neural networks to socio-economic parameters modelling, specifically a human capital. The objective of the paper is to build an adapted neural network algorithm with the purpose of modelling the parameter being studied. Two human capital components are estimated; these are its quantitative and qualitative properties. The key element of a quantitative property (namely, the population reproduction) has a bearing on stability of a human capital development. The quantitative property is multifold: its aspects are healthcare, culture, education and science. To estimate a human capital structure, a population is being divided onto social clusters on the basis of these aspects. As a part of the study, it was found that such mathematical modelling instrument as neural networks is very suitable for conducting a cluster analysis of a given social system. Neural networks are effective means to solve poorly formalized problems; they are tolerant to frequent changes of an environment and can be used to process a vast set of contradictive or incomplete data. The data base comprises demographic data, volume of investments into qualitative human capital properties, and socio-economic development indicators of a given economic system. A gradation of demographic elements of the society based on physical condition and cultural and educational level is built, according to which a statistical data is gathered to solve the clusterization problem. A volume of investment into a human capital is defined by budgetary costs and private investments of the people. Modelling human capital investment dynamics is performed with neural networks being applied as well. The neural network model used herein is a multilayer perceptron with sigmoid logistic activation function. Neural network modelling of predicted values of investment volumes has proved its effectiveness. An estimation of a human capital for the period of 2000-2019, as well as its forecast for years 2020-2025, is exemplified by Russian economic system. Calculations showed that the indicator being studied has been demonstrating the biggest growth rates since 2013, with an ongoing growth to be expected. Evaluated results correlate with a Russian human capital index dynamic pattern, which is defined by UN specialists, qualitatively. A proposed method of a human capital prognosis and estimation can be used furthermore to compare and estimate socio-economic state of Russia’s regions.

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Regional socio-economic parameters modeling and system analysis by means of programming and computing suite

Cited by 5 publications

References 4 publications

Analysis and modeling of incomes of the population of the socio-economic system Russian Federation

Analysis and modeling of incomes of the population of the socio-economic system Russian Federation

Computer simulation of traffic flows

Modelling a human capital of an economic system with neural networks

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