Is this project an undergraduate, graduate, or faculty project?
Undergraduate
Project Type
group
Campus
Daytona Beach
Authors' Class Standing
Brooklyn Gossett, Senior Ana Yu Wen
Lead Presenter's Name
Brooklyn Gossett
Lead Presenter's College
DB College of Business
Faculty Mentor Name
Dr. Youngran Choi
Abstract
The aviation industry relies heavily on accurate demand forecasting to guide critical decisions regarding route planning, capacity management, and pricing strategy. Misjudging passenger demand can result in significant revenue loss and operational inefficiency, making it essential for airlines and analysts to identify the key drivers of flight patronage. This study investigates the factors that most significantly predict the number of passengers on domestic flights departing from Hartsfield-Jackson Atlanta International Airport (ATL) during the 2024 calendar year. Using passenger and route data sourced from the Bureau of Transportation Statistics (BTS) and the U.S. Department of Transportation (DOT), a multiple regression analysis was conducted to assess the relationship between passenger volume and four independent variables: average fare by route, fare class types, flight distance, and number of carriers operating the route. The findings indicate that fare levels, market competition, and route distance each play a measurable role in shaping passenger demand across Atlanta's domestic flight network. These results offer actionable insights for airline decision-makers seeking to optimize route networks and refine pricing models based on empirically supported demand predictors. This project contributes to the broader field of aviation demand analytics by demonstrating how publicly available transportation data can be leveraged to build meaningful predictive models at the individual airport level.
Did this research project receive funding support (Spark, SURF, Research Abroad, Student Internal Grants, Collaborative, Climbing, or Ignite Grants) from the Office of Undergraduate Research?
No
Included in
Applied Statistics Commons, Business Analytics Commons, Management and Operations Commons
Predicting Passenger Demand on National Flights Departing from Hartsfield-Jackson Atlanta International Airport (ATL) in 2024
The aviation industry relies heavily on accurate demand forecasting to guide critical decisions regarding route planning, capacity management, and pricing strategy. Misjudging passenger demand can result in significant revenue loss and operational inefficiency, making it essential for airlines and analysts to identify the key drivers of flight patronage. This study investigates the factors that most significantly predict the number of passengers on domestic flights departing from Hartsfield-Jackson Atlanta International Airport (ATL) during the 2024 calendar year. Using passenger and route data sourced from the Bureau of Transportation Statistics (BTS) and the U.S. Department of Transportation (DOT), a multiple regression analysis was conducted to assess the relationship between passenger volume and four independent variables: average fare by route, fare class types, flight distance, and number of carriers operating the route. The findings indicate that fare levels, market competition, and route distance each play a measurable role in shaping passenger demand across Atlanta's domestic flight network. These results offer actionable insights for airline decision-makers seeking to optimize route networks and refine pricing models based on empirically supported demand predictors. This project contributes to the broader field of aviation demand analytics by demonstrating how publicly available transportation data can be leveraged to build meaningful predictive models at the individual airport level.