SUPPORT VECTOR MACHINES FOR FORECASTING NON-SCHEDULED PASSENGER AIR TRANSPORTATION

Abstract

Forecasting non-scheduled passenger air transportation demand is essential for effective operational planning and decision-making. In this abstract, we explore the use of Gaussian Support Vector Machines (SVM) methods in forecasting nonscheduled passenger air transportation processes. SVM is a type of supervised machine learning algorithm that can be applied to various domains, including nonscheduled passenger air transportation. In classification and regression tasks, SVMs are considered especially useful. SVMs can be used to forecast passenger demand for specific routes or flights. By analysing historical data, including factors such as time of day, day of the week, etc., SVMs can help airlines estimate future passenger demand. This method is crucial for optimising ticket pricing and managing seat inventory. This research proposes the implementation of different Gaussian SVM methods for the forecasting of non-scheduled passenger air transportation.