Methodology for forecasting demand for freight transportation
https://doi.org/10.46973/0201-727X_2023_4_62
Abstract
The paper provides the description of the effective methodology developed by the authors for forecasting the demand for freight transportation. The study proposed using an integrated approach that includes various forecasting methods, such as time series analysis, econometric models and expert assessments. Time series analysis is a method based on the study of changes in the value of a variable over time. Econometric models, in turn, make it possible to establish the relationship between various economic indicators and the demand for freight transportation. The use of expert assessments considers using the characteristics of a particular industry and the professional opinions. The study confirms that using different forecasting methods can significantly improve forecast accuracy and help logistics companies make more informed decisions. So, the methodology developed by the authors for forecasting the demand for freight transportation allows us to achieve the most accurate results and optimize the activities of logistics companies.
About the Authors
I. S. Vyskrebentsev
Ural State Transport University (URGUPS)
Russian Federation
Russian Federation
Vyskrebentsev Ivan Sergeevich, Chair «Transport Economics», Postgraduate Student
M. B. Petrov
Ural State Transport University (URGUPS); Center for the Development and Placement of Productive Forces of the Institute of Economics of the Ural Branch of the Russian Academy of Sciences
Russian Federation
Russian Federation
Petrov Mikhail Borisovich, Chair «Transport Economics», Doctor of Engineering Sciences Supervisor
References
1. Izotov, O. A. Forecasting cargo transportation / O. A. Izotov // System analysis and logistics. – 2019. – No. 4. – P. 12–19– EDN NDIYRJ.
2. Analysis of the output characteristics of the pneumatic drive system for sand supply of transport vehicles / I. A. Yaitskov, P. Yu. Konovalov, Yu. P. Bulavin, I. V. Volkov // News of Tula State University. Technical science. – Iss. 3. – 2020. – P. 242–253. – EDN HHGRAE.
3. Golovach, Y. N. Pneumoautomatics of locomotives / Y. N. Golovach, I. V. Skogorev, V. O. Kubil. – Novocherkassk : Helikon, 2006. – 276 p. – ISBN 5-901677-02-1.
4. Krakovsky, Yu. M., Dombrovsky, I. A. Forecasting road freight turnover based on statistical and expert information / Yu. M. Krakovsky, I. A. Dombrovsky // Bulletin of DAAD Fellows. – 2013. – No. 1. – P. 48–54. – DOI 10.36724/2072-8735-2023-I7-I-33-41.
5. Krakovsky, Yu. M. Generalized forecasting of indicators of freight transportation by rail based on the scenario approach / Yu. M. Krakovsky, N. N. Popova // Vestnik Rostovskogo Gosudarstvennogo Universiteta Putey Soobshcheniya. – 2020. – No. 3. – P. 132–138. – DOI 10.31063/2073-6517/201.16-4.10.
6. Petrov, M. B. New approaches to forecasting for the purpose of managing the development of large systems of territorial infrastructure / M. B. Petrov, K. B. Kozhov // Innovative transport. – 2017. – No. 2. – P. 3–10. – DOI 10.20291/2311.164Х2017-2-3-10.
7. Application of fuzzy-based support vector regression to forecast of international airport freight volumes / C. H. Yang [et al.] // Mathematics. – 2022. – Vol. 10, No. 14. – P. 2399. – DOI 10.3390/math10142399.
8. SP 131.13330.2020. Construction climatology : Set of Rules : approved and put into effect by the Order of the Ministry of Construction and Housing and Communal Services of the Russian Federation dated December 24, 2020 No 859/pr. – (ed. May 30, 2022).– Moscow : Ministry of Building of the Russian Federation, 2020. – 150 p.
9. Bogachev, T. V. Methodology for analyzing and forecasting indicators of economic regional transport systems using the fuzzy linear regression method / T. V. Bogachev, T. V. Alekseychik, I. A. Kharitonov // Bulletin of the Rostov State Economic University (RINH). – 2020. – No. 1(69). – P. 150–157. – EDN FMLPPP.
10. A system dynamics model for shipbuilding demand forecasting / Y. Wada [et al.] // Journal of Marine Science and Technology. – 2018. – Vol. 23, No. 2. – P. 236–252. – DOI 10.1007/s00773-017-0466-6.
11. Gao, S. Road Traffic Freight Volume Forecast Using Support Vector Machine Combining Forecasting / S. Gao, Z. Zhang, C. Cao // Journal of Software. – 2011. – Vol. 6, No. 9. – P. 1680–1687. – DOI 10.4304/JSW.6.9.1680-1687.
12. Timchenko, V. S. Software complex for simulation modeling of railway transportation processes / V. S. Timchenko // SCIENCE. UNIVERSITY. – 2016. – P. 176–178. – EDN XAQVFJ.
13. Federal State Statistics Service : official website. – URL: https://rosstat.gov.ru (date of access: 11/11/2023).
14. Official statistical methodology for calculating the index of industrial production : approved by order of Rosstat dated January 16, 2020 No. 7. URL: https://rosstat.gov.ru/storage/mediabank/met_indOKVED2.pdf (date of access: 11/11/2023).
15. Vyskrebentsev, I. S. Methodology for assessing the level of development of transport infrastructure in the region / I. S. Vyskrebentsev, M. B. Petrov // Transport of the Urals. – 2023. – No. 3(78). – P. 99–104. – DOI 10.20291/1815-9400-2023-3-99-104.
Review
For citations:
Vyskrebentsev I.S., Petrov M.B. Methodology for forecasting demand for freight transportation. Vestnik Rostovskogo Gosudarstvennogo Universiteta Putej Soobshcheniya. 2023;(4):62-71. (In Russ.) https://doi.org/10.46973/0201-727X_2023_4_62
Views:
8
JATS XML
Email this article 