Analysis of statistical data distribution about wagon flows. Part II
https://doi.org/10.51955/2312-1327-2026-2-94
Abstract
The second part of the article examines the estimation of the distribution function of the total volumes of loading and unloading that did not pass the test for normal distribution (rejection of the null hypothesis). Time series are used as the main tool for processing statical data on incoming car traffic and forming predictive models. The dataset of incoming wagon flows data is being evaluated in order to identify the distribution law for further correct construction of predictive models.
In order to take into account, the trends observed in the data, a mixed distribution model is proposed for wagon flows following the Eastern railway polygon destination to the seaports of the Far East. In particular, the gamma-normal distribution, which is a favorable mixed distribution model. The study further improves the statistical data analysis process by introducing the use of the A-D-test in addition to the more traditional K-S-test. In the future, when building predictive models of carriage fluctuations using ARIMA- or GARCH-models, a correctly determined distribution type will accurately describe the model errors. A number of recommendations have been formulated that expand the possibility of using parametric statistics in the process of analyzing data sets on changes in freight flows in railway transport
About the Authors
Anna K. Mozalevskaya
Irkutsk State Transport University, 15 Chernyshevskogo St., Irkutsk, 664074
Russian Federation
Russian Federation
Applicant for a Degree, Senior Lecturer at the Department of Construction of Railways, Bridges and Tunnels»
Ekaterina V. Malovetskaya
Irkutsk State Transport University, 15 Chernyshevskogo St., Irkutsk, 664074
Russian Federation
Russian Federation
Cand. of Sci. (Technology), Associate Professor, Associate Professor at Department of Railway Operations Management
Roman S. Bolshakov
Irkutsk State Transport University, 15 Chernyshevskogo St., Irkutsk, 664074
Russian Federation
Russian Federation
Cand. of Sci. (Technology), Associate Professor, Associate Professor at the Department of Railway Operations Management
References
1. Bychkov I. V., Zharkov M. L., Kazakov A. L. (2023). Application of queuing theory for modeling the Ulaanbaatar railway. Computational technologies. 6(28): pp. 17-36. (In Russian)
2. Company reporting: official website of Russian Railways. Available at: https://company.rzd.ru/ru/9471 (accessed 23 December 2025). (In Russian)
3. Ghosh S, Mitra J. (2020). Importance of Normality Testing, Parametric and Non-Parametric Approach, Association, Correlation and Linear Regression (Multiple & Multivariate) of Data in Food & Bio-Process Engineering. Mathematical and Statistical Applications in Food Engineering. CRC Press. Pp. 112-126.
4. Golts G. A. (1982). Studying the patterns of transport development in the USSR. Problems of forecasting and optimization of transport operations. Moscow: Nauka. Pp. 23-63. (In Russian)
5. Gorelik M. A. (1982). Forecasting promising cargo flows in maritime transport. Problems of forecasting and optimization of transport operations. Moscow: Nauka. Pp. 63-78. (In Russian)
6. Kabanov A. B., Osminin A.T. (2022). Scientific approach to planning rail freight transportation. Railway transport. 18: pp. 12-16. (In Russian)
7. Lapidus B.M. (2023). Tasks of advanced development of Russian railways. Railway transport. 2: pp. 4–14. (In Russian)
8. Makhutov N. A., Lapidus B. M., Gadenin M. M., Titov E. Yu. (2023). Tasks and Prospects for the Development of Scientific Research within the Framework of Cooperation between Russian Railways and the Russian Academy of Sciences. Railway Transport. 7: pp. 6-11. (In Russian)
9. Malovetskaya E. V., Mozalevskaya A. K. (2022). Assessing the Impact of Irregularities on the Transportation Process. Problems of Transport Safety: Proceedings of the XII International Scientific and Practical Conference Dedicated to the 160th Anniversary of the Belarusian Railway. In 2 parts, Gomel, November 24–25. Gomel: Belarusian State University of Transport. 2: pp. 207-209. (In Russian)
10. Malovetskaya E. V., Mozalevskaya A. K. (2023). Possibilities of Improving the Efficiency of the Transportation Process Based on the Construction of Comprehensive Forecast Models of Infrastructure Loading. T-Comm: Telecommunications and Transport. 7(17): pp. 38–46. DOI 10.36724/2072-8735-2023-17-7-38-46. (In Russian)
11. Malovetskaya E. V., Voskresenskaya E. V., Mozalevskaya A. K. (2023). Prospects for comprehensive forecasts when assessing the load of railway transport infrastructure. Finance, Economics, and Industry for Sustainable Development (ESG 2023). Pp. 217-225.
12. Mamaev E. A., Valchuk E. S. (2016). Forecasting container turnover of world seaports. Bulletin of the Rostov State University of Railway Engineering. 2 (62): pp. 94-101. (In Russian)
13. Mozalevskaya, E. V. Malovetskaya, R. S. Bolshakov. (2025). Analysis of the distribution of statistical data on car traffic. Part I. Crede Experto: transport, society, education, language. 3(46): pp. 162-177. DOI 10.51955/2312-1327_2025_3_162. (In Russian)
14. Pravdin I. V. Negrey V. Ya. (1980). Forecasting passenger flows. Moscow: Transport. 224 p. (In Russian)
15. Pravdin N. V., Negrey V. Ya., Shelelyaev A. I. (1980). On the issue of constructing theoretical models of fluctuations in traffic flows. Problems of perspective development of railway stations and junctions. Gomel: Beliizht. Pp. 3-20. (In Russian)
16. Razali N. M, Wah Y. B. (2011). Power comparisons of Shapiro-Wilk, Kolmogorov-Smirnov, Lilliefors and Anderson-Darling tests. Journal of Statistical Modeling and Analytics. 2(1): pp. 21-33.
17. Strategy for the Development of Railway Transport in the Russian Federation until 2030: of June 17, 2008 №877-r: approved by the order of the Government of the Russian Federation. 2008. June 17. Available at: https://mintrans.gov.ru/documents/1/1010 (accessed 30 April 2025). (In Russian)
18. Ugryumov A. K. (1968). The unevenness of train movement. Moscow: Transport, 1968. 112 p. (in Russian)
19. Yap B. W., Sim C. H. (2011). Comparisons of various types of normality tests. Journal of Statistical Computation and Simulation. 81(12): pp. 2141-2155. DOI 10.1080/00949655.2010.520163.
20. Yazici B, Yolacan S. (2007). A comparison of various tests of normality. Journal of Statistical Computation and Simulation. 77(2): pp. 175-183. DOI 10.1080/10629360600678310.
Review
For citations:
Mozalevskaya A.K., Malovetskaya E.V., Bolshakov R.S. Analysis of statistical data distribution about wagon flows. Part II. Crede Experto: transport, society, education, language. 2026;13(2):94-110. (In Russ.) https://doi.org/10.51955/2312-1327-2026-2-94
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