Assessment of Aircraft Fuel Efficiency in Domestic Flights using Multiple Regression Analysis

Abstract

The aviation industry has significantly evolved over the past century, playing a crucial role in global transportation, trade, and tourism. However, its reliance on fossil fuels has raised environmental concerns, necessitating sustainable practices to mitigate carbon emissions. This study examines the relationship between fuel consumption and various operational parameters for the Airbus A321 aircraft, utilizing multiple linear regression analysis to develop a predictive model for fuel efficiency. The dataset, comprising 110 flight records from Istanbul Airport, includes independent variables such as the number of passengers, flight level, flight distance, average wind speed, airspeed, flight duration, aircraft takeoff weight, and total fuel load. Statistical tests, including normality checks, correlation analysis, and multicollinearity assessments, were conducted to ensure the validity of the model. Findings indicate that flight duration, aircraft takeoff weight, and total fuel load significantly influence fuel consumption, while variables such as flight level and wind speed have negligible effects. The study highlights the importance of optimizing flight planning, weight management, and fuel policies to enhance operational efficiency and reduce environmental impact. The results provide valuable insights for the aviation industry, supporting data-driven decision-making in fuel efficiency and sustainability efforts. By integrating advanced statistical modeling and strategic operational planning, airlines can achieve cost optimization while promoting environmentally responsible practices. This research contributes to aviation sustainability by offering a data-driven approach to fuel efficiency analysis, which can inform future innovations in aircraft design, air traffic management, and alternative fuel utilization.

Keywords

• Fuel Efficiency
• Multiple Linear Regression
• Aviation Sustainability
• Operational Optimization
• Environmental Impact

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