Air Passenger and Freight Demand Forecast for Ireland

Verma, Nivriti (2023) Air Passenger and Freight Demand Forecast for Ireland. Masters thesis, Dublin, National College of Ireland.

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Abstract

This study addresses the dynamic challenge of predicting variable air transport demand in Ireland and its partner nations. Investigating the repercussions of the COVID-19 pandemic on freight and passenger movement within Ireland, the research underscores the critical need for precise predictions to enhance operational efficiency and strategic foresight. Utilizing a comprehensive approach, the paper employs four distinct time series forecasting models, such as ARIMA/SARIMA, Simple Exponential Smoothing, Double Exponential Smoothing (Holt’s Linear), and Triple Exponential Smoothing (Holt-Winters) and meticulously evaluates their efficacy in forecasting both passenger and freight demand. Notably, the findings indicate the superior performance of the Holt Linear model in predicting passenger demand, while the SARIMA model stands out for its accuracy in freight demand forecasting. This study provides valuable insights into forecasting methodologies within the aviation industry, establishing the foundation for technological advancements in capacity optimization and informed decision-making.

Item Type:	Thesis (Masters)
Supervisors:	Name Email Rifai, Hicham UNSPECIFIED
Subjects:	Q Science > QA Mathematics > Electronic computers. Computer science T Technology > T Technology (General) > Information Technology > Electronic computers. Computer science H Social Sciences > HD Industries. Land use. Labor > Specific Industries > Aviation Industry D History General and Old World > DA Great Britain > Ireland Q Science > Q Science (General) > Self-organizing systems. Conscious automata > Machine learning
Divisions:	School of Computing > Master of Science in Data Analytics
Depositing User:	Ciara O'Brien
Date Deposited:	26 May 2025 08:30
Last Modified:	26 May 2025 08:30
URI:	https://norma.ncirl.ie/id/eprint/7637

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