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Air lines network modelling algorithm

https://doi.org/10.17747/2078-8886-2017-6-22-29

Abstract

This analysis is dedicated to find out methods for setting of route networks where new aircraft can be effectively put into service. The conception of this analysis is based on the idea of so called connectivity principle for airports connected by passenger traffic with each other.
For the passenger traffic analysis the author took passenger traffic data by federal districts starting from the Far East. Then consequently the data for Siberian, Ural, Wolga, Northwestern, Central, Southern and North Caucasian federal districts were analyzed. Passenger traffic to the Crimea was treated separately. Detailed specifications of passenger traffics were provided in order to determine the connections between airports both within federal districts and beyond them and with neighboring areas in western direction. Query of routes was done based on limitations for non-stop flight range and on minimum and maximum (for significant traffics) flight frequencies.
The analysis approach lets us concentrate attention on those airlines which at best fit for putting into service of chosen aircraft. Also this method permits to determine the routes with currently insufficient or low traffics but where there’s a definite growth potential. When analysis data are combined with traffic data and tariffs, then it becomes possible to determine the most profitable routes for introduction of new aircraft. Traffic volume, actual figures and forecast, consolidated characteristics of chosen airlines, list of airlines for further studies of efficiency and competitiveness of introduced aircraft are determined.

About the Author

A. B. Manvelidze
FGBOU VO “MGTU “STANKIN”
Russian Federation

Ph.D. in Economics, assistant professor of the Financial Management Department at the FGBOU VO “MGTU “STANKIN”. Research interests: transport economics



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Review

For citations:


Manvelidze A.B. Air lines network modelling algorithm. Strategic decisions and risk management. 2017;(6):22-29. (In Russ.) https://doi.org/10.17747/2078-8886-2017-6-22-29

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ISSN 2618-947X (Print)
ISSN 2618-9984 (Online)