Predicting Airline Additional Services Consumption Willingness Based on High-Dimensional Incomplete Data

Abstract

Prediction of the purchase willingness of passengers has great benefits for airlines to promote auxiliary services, however, the datasets stored in passenger travel information systems are often high-dimensional and incomplete. This study develops a prediction method of airline additional service consumption willingness based on high-dimensional and incomplete datasets with a triple-layer hybrid PSO-XGBoost model, which consists of an incomplete data processing layer, a high-dimensional data processing layer, and a predicting layer. The raw dataset is converted into a complete and low-dimensional dataset through the first two layers and inputted into the predicting layer to train and optimize the XGBoost model together with the PSO algorithm and 10-fold cross-validation. The experimental results show that the proposed method outperforms other traditional machine learning models, presenting the highest prediction score with 0.9879 in terms of AUC. The findings help predict airline additional services consumption intentions of passengers and are beneficial to efficient and low-cost precise marketing for airlines.

Applications of Artificial Intelligence in the Air Transport Industry: A Bibliometric and Systematic Literature Review

“…The remaining studies within this cluster cover a range of topics. For example, Chen et al (2022) examine purchase willingness, while Miskolczi et al (2021) explore the attractiveness levels of airports that adopt modern technologies. Azzolina et al (2021) delve into the issue of price discrimination, studying how airline companies utilise customer data for discriminatory pricing and assessing the impact of such practices on social welfare.…”