Применение математико-статистических методов исследования для анализа мнения жителей об организации городского парковочного пространства на примере города Волгоград
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Научный журнал Моделирование, оптимизация и информационные технологииThe scientific journal Modeling, Optimization and Information Technology
Online media
issn 2310-6018

Application of mathematical and statistical methods of research to analyze the opinion of residents about the organization of urban parking space on the example of the city of Volgograd

Ogar T.P.   Krushel E.G.   Stepanchenko I.V.   Panfilov A.E.   Kharitonov I.M.  

UDC 004.67
DOI: 10.26102/2310-6018/2020.31.4.004

  • Abstract
  • List of references
  • About authors

The article is focused on identifying patterns of formation of parameters of demand for Parking space in the district N of Volgograd, which will improve the organization of parking space in this area. A sociological survey of city residents aimed at identifying public opinion on the use and operation of Parking space in certain areas of the district was conducted. The initial processing of the survey results was carried out, which resulted in the main conclusions on the most important questions of the questionnaire. The analysis of respondents ' responses using mathematical and statistical research methods was carried out. Previously, all data obtained during the survey was normalized. Clustering of respondents ' responses to all questions was performed, which allowed dividing all survey participants into two clusters. To confirm that there is a linear relationship between the various questionnaire questions, a correlation analysis of the data obtained during the survey was performed. The relationship between different pairs of questions was checked by performing regression analysis of the data. Correlation and regression analyses were performed for each of the obtained clusters separately to improve the accuracy of estimating the relationships between regression variables. According to the results of mathematical and statistical analysis, the dependence between the responses of respondents to various questionnaire questions was revealed.

1. Tong C.O., Wong S.C., Leung B.S.Y. Estimation of parking accumulation profiles from survey data. Transportation. 2004;31(2):183–202. DOI: 10.1023/B:PORT.0000016579.36253.a9.

2. Mcshane M., Meyer M.D. Parking policy and urban goals: Linking strategy to needs. Transportation. 1982;11(1):131–152. DOI: 10.1007/BF00167928.

3. T. Lin H. Rivano, F. Le Mouël, A Survey of Smart Parking Solutions. IEEE Transactions on Intelligent Transportation Systems. 2017;18(12):3229-3253. DOI: 10.1109/TITS.2017.2685143.

4. Krushel E.G., Stepanchenko I.V., Panfilov A.E., Lyutaya T.P. Detection of the Patterns in the Daily Route Choices of the Urban Social Transport System Clients Based on the Decoupling of Passengers’ Preferences Between the Levels of Uncertainty. Creativity in Intelligent Technologies and Data Science. 2019;1(1):175-188. DOI: 10.1007/978-3-030-29743-5.

5. Nedumov, Y.R., Turdakov, D.Y., Maiorov, V.D. Ovchinnikov P.E. Automation of data normalization for implementing master data management systems. Program Comput Soft. 2013;39(3):115–123. DOI: 10.1134/S0361768813030055

6. Tyurin A.G., Zuyev I.O. Сluster analysis, methods and algorithms of the clustering. Herald of MSTU MIREA. 2014;2(3):86-97.

7. Berzal, F., Matín, N. Data mining: concepts and techniques by Jiawei Han and Micheline Kamber. ACM SIGMOD Record. 2002;31(1):66-68. DOI: 10.1145/565117.565130.

8. Bain Lee. Applied Regression Analysis. Technometrics. 2012;9(1):182-183. DOI: 10.1080/00401706.1967.10490452.

9. Krushel E.G., Stepanchenko I.V., Panfilov A.E., Berisheva E.D.: An experience of optimization approach application to improve the urban passenger transport structure. CCIS, 2014;466(1):27-39. DOI: 10.1007/978-3-319-11854-3_3.

10. Bai Y., Sun Z., Zeng B., Deng J., Li C.: A multi-pattern deep fusion model for short-term bus passenger flow forecasting. Appl. Soft Comput. 2017;58(1):669–680.

Ogar Tatyana Petrovna

Email: ogar@kti.ru

Kamyshin technological institute (branch) of Volgograd state technical university, Kamyshin

Kamyshin, Russian Federation

Krushel Elena Grigoryevna
Phd In Engineering, Professor
Email: elena-krushel@yandex.ru

Kamyshin technological institute (branch) of Volgograd state technical university

Kamyshin, Russian Federation

Stepanchenko Ilya Viktorovich
Doctor of Technical Science, Associate Professor
Email: stilvi@mail.ru

Kamyshin technological institute (branch of Volgograd state technical university)

Kamyshin, Russian Federation

Panfilov Alexander Eduardovich
Phd In Engineering, Associate Professor
Email: pansanja@yandex.ru

Kamyshin technological institute (branch of Volgograd state technical university)

Kamyshin, Russian Federation

Kharitonov Ivan Michailovich
Phd In Engineering, Associate Professor
Email: wisdom_monk@mail.ru

Kamyshin technological institute (branch of Volgograd state technical university)

Kamyshin, Russian Federation

Keywords: urban parking space, transport, transport system, statistical methods, cluster, correlation, regression analysis

For citation: Ogar T.P. Krushel E.G. Stepanchenko I.V. Panfilov A.E. Kharitonov I.M. Application of mathematical and statistical methods of research to analyze the opinion of residents about the organization of urban parking space on the example of the city of Volgograd. Modeling, Optimization and Information Technology. 2020;8(4). Available from: https://moitvivt.ru/ru/journal/pdf?id=850 DOI: 10.26102/2310-6018/2020.31.4.004 (In Russ).

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