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Improving Air Transportation by Using the Fuzzy Origin–Destination Matrix

Author

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  • Vladimir Sudakov

    (Department of Data Analysis and Machine Learning, Financial University under the Government of the Russian Federation, 125167 Moscow, Russia)

Abstract

The work is devoted to the development of new methods and algorithms to support decision making when planning air travel using uncertainties in the form of fuzzy numbers. The proposed approach makes it possible to define rational methods of choice: how to change the transport graph to better meet the needs of the population. This is particularly relevant in the context of the reduced demand for air travel caused by the pandemic and the need to switch from large to smaller aircraft types. The problem is solved by restoring the fuzzy origin–destination matrix of current statistics on air traffic between airports. The problem is that we do not know what proportion of passengers moving between the specified points are forced to use large transport hubs as intermediate destinations. To determine the validity of the origin–destination matrix, we build a number of optimization models to determine fuzzy intervals and search for correspondence with the maximum value of the membership function. Algorithmic and software search for the fuzzy origin–destination matrix and fuzzy ranking of potentially promising routes are developed. The perspective of the given approach is shown by an example of a task concerning a choice of new routes between regional airports.

Suggested Citation

  • Vladimir Sudakov, 2021. "Improving Air Transportation by Using the Fuzzy Origin–Destination Matrix," Mathematics, MDPI, vol. 9(11), pages 1-13, May.
    • Handle: RePEc:gam:jmathe:v:9:y:2021:i:11:p:1236-:d:564195
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    References listed on IDEAS

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    1. Foulds, Les R. & do Nascimento, Hugo A.D. & Calixto, Iacer C.A.C. & Hall, Bryon R. & Longo, Humberto, 2013. "A fuzzy set-based approach to origin–destination matrix estimation in urban traffic networks with imprecise data," European Journal of Operational Research, Elsevier, vol. 231(1), pages 190-201.
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    Cited by:

    1. Vladimir Sudakov & Alexander Zhukov, 2023. "Fuzzy Domination Graphs in Decision Support Tasks," Mathematics, MDPI, vol. 11(13), pages 1-16, June.
    2. Sergey Korchagin & Ekaterina Romanova & Petr Nikitin & Denis Serdechnyy & Konstantin V. Bublikov & Irina Bystrenina, 2022. "Mathematical Modeling of Dielectric Permeability and Volt-Ampere Characteristics of a Semiconductor Nanocomposite Conglomerate," Mathematics, MDPI, vol. 10(4), pages 1-7, February.

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