Demand Analysis Models for Passenger Air Transportation
https://doi.org/10.30932/1992-3252-2020-18-134-144
Abstract
Based on the specific requirements of the airline or in relation to a specific airline, an individual demand forecasting model can be developed. Such a model is an extension or a combination of various qualitative and quantitative methods for forecasting demand. The task of developing a custom model is often iterative, highly detailed, and driven by expert knowledge and can be accomplished by introducing suitable demand management software.
The task stated in the article is not a staging task for building a model, but only offers to study the available theoretical material for the analysis of demand for air transportation based on the most famous models for forecasting demand for transportation.
The method of scientific research of the problem posed in the article is the method of scientific analysis of existing models. Offer and demand for air transport services are reciprocal but asymmetric. Although the realized demand for transportation cannot take place without an appropriate level of supply, an air transport service can exist without appropriate demand. This is often found in projects that are developed with a margin that meets the expected level of demand, which may or may not be realized, or it may take several years to be realized. Regular air transport services form a supply that exists even if demand is insufficient. Several models presented in the article emphasize the conditions in which there is supply saturation, and on the other hand, the models in which demand is formed due to the mutual attractiveness of the entities that form demand are considered.
About the Author
V. E. ZhukovRussian Federation
Zhukov, Vasily E. – Ph.D. (Eng), Associate Professor
St. Petersburg
References
1. Transport in Russia. 2018: Stat. col. Rosstat [Transport v Rossii]. 2018: Stat. Sb. Rosstat]. Vol. 65. Moscow, 2018, 101 p.
2. Rodriguez, J.-P. Geography of transport systems. 5th edition. New York: Routledge, 2020, 456 p.
3. Actual issues of economic sciences [Aktualnie voprosy ekonomicheskikh nauk]: Proceedings of 3rd international scientific conference. Ufa, Leto publ., 2014, 172 p.
4. Sivakumar, A. Modelling Transport: A Synthesis of Transport Modelling Methodologies. Imperial College, London, 2017, 32 p. [Electronic resource]: https://pdfs. semanticscholar.org/b5ec/260c7b2e885a2f228bd9cd5f68ed 6fc101cf.pdf?_ga=2.259271945.2025433840. 1588192665– 2114981230.1588192665. Last accessed 19.12.2019.
5. EURO Journal on Transportation and Logistics, 2012, Vol. 1, pp. 135–155. [Electronic resource]: https://link.springer.com/article/10.1007/s13676-012-0006-9. Last accessed 19.12.2019.
6. Hendry, D. F. Economic Forecasting. Nuffield College, University of Oxford. July 18, 2000, 70 p. [Electronic resource]: https://folk.uio.no/rnymoen/DFHForc.pdf. Last accessed 19.12.2019.
7. Introduction to the mathematical modelling of transport flows: Study guide [Vvedenie v matematicheskoe modelirovanie transportnykh potokov]. Ed. 2nd, rev. and enl. Ed. by A. V. Gasnikov. Moscow, MCNMO publ., 2013, 428 p.
8. Least Squares Method: Methodological instructions [Metod naimenshikh kvadratov: Metod. ukazaniya]. Comp.: L. V. Kolomiyets, N. Yu. Ponikarova. Samara, Publishing house of Samara University, 2017, 32 p.
9. Sazhenkova, T. V., Ponomarev, I. V., Pron, S. P. Methods of analysis of time series: Educational-methodical manual [Metody analiza vremennykh ryadov: Uchebnometodicheskoe posobie]. Barnaul, Publishing house of Altai University, 2020, 60 p.
10. Casella, G., Berger, R., Santana, D. Statistical Inference. 2nd edition, Duxbury Advanced Series, Pacific Grove, CA, 2002, 210 p. [Electronic resource]: https://www. coursehero.com/file/27287478/Statistical-Inference-2ndEdition-by-G-Casella-and-R-Berger-Solutionspdf/. Last accessed 19.12.2019.
11. Miller, S. J. The Method of Least Squares. Mathematics Department, Brown University, Providence, RI 02912, 2019, pp. 1–7. [Electronic resource]: https://www. coursehero. com/file/36451365/MethodLeastSquarespdf/. Last accessed 19.12.2019.
12. Forecasting of socio- economic processes: Study guide [Prognozirovanie sotsialno-ekonomicheskikh protsessov: Uchebno- metodicheskoe posobie]. Compiled by O. V. Kapitanova. Nizhny Novgorod, Nizhny Novgorod State University, 2016, 74 p.
13. Antokhonova, I. V. Methods of forecasting socioeconomic processes: a textbook for universities [Metody prognozirovaniya sotsialno-ekonomicheskikh protsessov: Uchebnoe posobie dlya vuzov]. 2nd ed., rev. and enl. Moscow, Yurayt Publishing House, 2019, 213 p. [Electronic resource]: https://biblio-online.ru/bcode/444126. Last accessed 19.12.2019.
14. Crescenzo, Di A., Spina, S. Analysis of a growth model inspired by Gompertz and Korf laws, and an analogous birth-death process. Mathematical Biosciences, 2016, Vol. 282, pp. 121–134. [Electronic resource]: https://arxiv.org/pdf/1610.09297.pdf. Last accessed 19.12.2019.
15. Sovetov, B. Ya., Sikerin, A. V. Gravity and entropy models of flows in territorial planning of development of the transport system [Gravitatsionnaya i entropiinaya modeli potokov pri territorialnom planirovanii razvitiya transportnoi sistemy]. Informatika i komp’yuternie tekhnologii. Proceedings of St. Petersburg Electrotechnical University «LETI», 2016, Iss. 8, pp. 21–24.
16. Vlasov, A. A. Theory of transport flows: Monograph [Teoriya transportnykh potokov: Monografiya]. Penza, PGUAS publ., 2014, 124 p
Review
For citations:
Zhukov V.E. Demand Analysis Models for Passenger Air Transportation. World of Transport and Transportation. 2020;18(1):134-144. https://doi.org/10.30932/1992-3252-2020-18-134-144