Is this project an undergraduate, graduate, or faculty project?
Undergraduate
Project Type
group
Campus
Daytona Beach
Authors' Class Standing
Noa Teed, Senior Michael Leitelt, Senior
Lead Presenter's Name
Noa Teed
Lead Presenter's College
DB College of Engineering
Faculty Mentor Name
Mihhail Berezovski
Abstract
Accurately predicting the demand for aviation is a complex problem that is essential for the success of the private aviation industries. Factors such as seasonality and location affect the demand for private flights, but high-demand events and holidays introduce additional and often unexpected influences on these services. Flexjet Inc., a renowned global provider of private aviation services, operates extensively in European destinations, where travel is heavily characterized by high-demand events and holidays. This research utilizes detailed characterization data provided by Flexjet Inc. containing over 1.1 million private flights between 2,016 locations from 2018 and 2019. Leveraging advanced data analysis techniques, this project constructs a spatio-temporal forecasting model to accurately predict the demand for private jet travel during high-demand events and holidays in European destinations. This research delivers valuable insights to providers of private aviation, enabling them to proactively respond to market fluctuations and optimize their operational strategies.
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?
Yes, Spark Grant
Event-Driven Demand Modeling of European Private Aviation Travel
Accurately predicting the demand for aviation is a complex problem that is essential for the success of the private aviation industries. Factors such as seasonality and location affect the demand for private flights, but high-demand events and holidays introduce additional and often unexpected influences on these services. Flexjet Inc., a renowned global provider of private aviation services, operates extensively in European destinations, where travel is heavily characterized by high-demand events and holidays. This research utilizes detailed characterization data provided by Flexjet Inc. containing over 1.1 million private flights between 2,016 locations from 2018 and 2019. Leveraging advanced data analysis techniques, this project constructs a spatio-temporal forecasting model to accurately predict the demand for private jet travel during high-demand events and holidays in European destinations. This research delivers valuable insights to providers of private aviation, enabling them to proactively respond to market fluctuations and optimize their operational strategies.