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Comparison of Forecasting Methods for Intercity Passenger Flows for Various Modes of Transport

https://doi.org/10.30932/1992-3252-2020-18-74-92

Abstract

The article is devoted to methodological features of forecasting intercity passenger flows under the conditions of transformation of the transport system of Russia, namely, the emergence of a new type of rail transport which is high-speed rail. The objective of the article is to present the authors’ methodology for forecasting passenger flows and to prove its higher efficiency relative to the methods used in Russia today. The article considers the historical aspect of forecasting passenger flows, analyzes strengths and weaknesses of existing approaches to forecasting and modelling passenger flows. The authors argue that it is impossible to simulate the number of trips with changes in transportation parameters only on the basis of patterns identified by retrospective data series (the most common approach to forecasting passenger flows in Russia).
The article proposes an alternative methodology based on the calculation of passenger’s total costs of a trip, which depend on cost of travel, loss of time, frequency of departure of vehicles and their comfort, as well as considering the dynamics of key social-economic indicators. The technique allows minimizing measurement errors arising from the lack of primary information about some types of passenger transport, as well as calculating the induced demand for trips arising as a result of improved transportation characteristics. The authors identified and expressed in quantitative terms the main factors of redistribution of passenger flows to newly introduced types of transport.
The article discusses the experience of forecasting passenger flow according to the proposed method at the example of four itineraries where movement of high-speed trains of Lastochka type started. The forecasted results are compared with the actual volumes of transportation, on the basis of which conclusions are drawn about the effectiveness of the forecasting method and its applicability in modern realities of the Russian transport system. The advantages and disadvantages of the proposed approach to forecasting passenger traffic, as well as the possibilities of its implementation and further development in Russia are identified.

About the Authors

N. A. Makutsky
LLC Center for Economics of Infrastructure
Russian Federation

Makutsky, Nikita A. – Leading Expert 

Moscow



M. S. Fadeev
LLC Center for Economics of Infrastructure
Russian Federation

Fadeev, Maxim S. – Director 

Moscow



P. A. Chistyakov
LLC Center for Economics of Infrastructure
Russian Federation

Chistyakov, Pavel A. – Vice-president 

Moscow



References

1. Cervero, R. Induced Travel Demand: Research Design, Empirical Evidence, and Normative Policies. Journal of Planning Literature, 2002, Vol. 17 (3), pp. 3–20. [Electronic resource]: http://jpl.sagepub.com/cgi/content/abstract/17/1/3. Last accessed 27.02.2020. DOI: 10.1177/088122017001001.

2. Osetrov, E. S. Mathematical models, methods and algorithms to forecast passenger transportation. Ph.D. (Physics and Mathematics) thesis [Matematicheskie modeli, metody i algoritmy dlya prognozirovaniya passazhirskikh perevozok. Diss. na soiskanie uchenoi stepeni kand. fiz.-mat. nauk]. Dubna, 2018. [Electronic resource] : https://wwwinfo.jinr.ru/dissertation/Osetrov_disser.pdf.Last accessed 27.02.2020.

3. McNally, M. G. The Four Step Model. In: Handbook of Transport Modelling. Ed. D. A. Hensher, and K. J. Button, 2000, pp. 35–52. [Electronic resource]: https://pdfs.semanticscholar.org/c91e/e47992495bd9c9fe36ed4dbe85dc3c21aecf.pdf. Last accessed 27.02.2020. https://doi.org/10.1108/9780857245670–003.

4. Bonsall, P. W., Champernowne, A. F., Wilson, A. G., and Mason, A. C. Transport modelling: sensitivity analysis and policy testing (Original title) [Russian edition title: Modelling of passenger flows in the transport system (Modelirovanie passazhiropotokov v transportnoy systeme)]. Transl. from English by E. M. Schlafstein. Moscow, Transport publ., 1982, 207 p.

5. Box, G. E. P., Jenkins, G. M. Time-Series Analysis, Forecasting and Control. Holden-Day, San Francisco, 1970. [Electronic resource]: https://www.scirp.org/reference/ReferencesPapers.aspx? ReferenceID=2106713.Last accessed 27.02.2020.

6. Sasrtono. Models for Train Passenger Forecasting of Java and Sumatra. Journal of Physics: Conference Series, Vol. 824:012032, 3rd International Conference on Mathematics, Science and Education 2016, 3–4 September 2016, Semarang, Indonesia. DOI: 10.1088/1742–6596/824/1/012032.

7. Nagel, K., Wagner, R., Woesler, R. Still flowing: Approaches to traffic flow and traffic jam modeling. Operations Research, January 2, 2003, Vol. 51, pp. 681–710. Corpus ID: 3616360. DOI: 10.1287/opre.51.5.681.16755.

8. Guo, Xin. Passenger capacity prediction model based on LOGIT and system dynamics for passenger dedicated line. Transaction of Beijing Institute of Technology, Iss. 1, pp. 31–34.

9. Yan, Xi; Li, Jing. Analysis on predict model of railway passenger travel factors judgment with softcomputing methods. Journal of Industrial Engineering and Management (JIEM), OmniaScience, Barcelona, 2014, Vol. 7, Iss. 1, pp. 100–114. [Electronic resource]: https://www.econstor.eu/bitstream/10419/188593/1/v07-i01-p0100_940-6029-2-PB.pdf. Last accessed 26.02.2020. DOI: http://dx.doi.org/10.3926/jiem.940


Review

For citations:


Makutsky N.A., Fadeev M.S., Chistyakov P.A. Comparison of Forecasting Methods for Intercity Passenger Flows for Various Modes of Transport. World of Transport and Transportation. 2020;18(1):74-92. https://doi.org/10.30932/1992-3252-2020-18-74-92

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ISSN 1992-3252 (Print)