International Research Journal of Innovations in Engineering and Technology - IRJIET International Open Access, Monthly, Peer Reviewed, Reputed Journal ISSN (online): 2581-3048

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DOI Prefix: 10.47001/IRJIET

Forecasting the Demand of Domestic Sectors that Contribute 80% of SAUDIA Domestic Capacity

Abdulrahman AlakilIndustrial Engineering Department, King Abdul-Aziz University, Jeddah, Saudi Arabia

Vol 4 No 4 (2020): Volume 4, Issue 4, April 2020 | Pages: 45-49

International Research Journal of Innovations in Engineering and Technology

OPEN ACCESS | Research Article | Published Date: 28-04-2020

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Abstract

Domestic Air Travel demand in KSA has dramatically grown in recent years after the introduction of 3 new carriers resulting in the renewal of domestic air transportation. Air Travel demand forecast is a key element for an airline company to plan its short term or long-term business plan. Historical demand from 2013 to 2018 was studied to forecast future demand. Trend Analysis (Linear - Exponential Growth - Quadratic), Winter (Additive - Multiplicative), and Exponential Smoothing (Single - Double) and multi regression were used to forecast demand from 2020 to 2025.

Keywords

Forecasting, Time Series, Regression Analysis, future demand, Air Travel Demand


Citation of this Article

Abdulrahman Alakil, “Forecasting the Demand of Domestic Sectors that Contribute 80% of SAUDIA Domestic Capacity” Published in International Research Journal of Innovations in Engineering and Technology - IRJIET, Volume 4, Issue 4, pp 45-49, April 2020. https://doi.org/10.47001/IRJIET/2020.404007

Licence Copyright (c) 2026 International Research Journal of Innovations in Engineering and Technology creative common licence This work is licensed under Creative common Attribution Non Commercial 4.0 Internation Licence
References
  1. S. Chopra and P. Meindl, “Supply Chain Management Strategy, Planning, and Operation,” Springer, 2010, pp. 265–275.
  2. S.M.T. Fatemi Ghomi and K. Forghani, Airline passenger forecasting using neural networks and Box–Jenkins, January 25-26, 2016.
  3. Nam, Kyungdoo; Schaefer, Thomas. Logistics and Transportation Review; Vancouver, Vol. 31, Iss. 3, (Sep 1995).
  4. Rico Merkerta, Tony Webber, How to manage seasonality in service industries – The case of price and seat factor management in airlines.
  5. Orhan Sivrikaya and Enar Tunç,The Open Transportation Journal, 2013, 7, 20-26, Demand Forecasting for Domestic Air Transportation in Turkey.
  6. María Rosa Nieto, Rafael Bernardo Carmona-Benítez, School of Business and Economics, Universidad Anáhuac México, Huixquilucan, 52786, Estado de México, Mexico, ARIMA + GARCH + Bootstrap forecasting method applied to the airline industry.

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