The adverse impact of flight delays on passenger satisfaction: An innovative prediction model utilizing wide & deep learning

Abstract

This article addresses the substantial negative influence of flight delays on passenger satisfaction and aims to bridge the research gap in understanding passenger satisfaction during delayed flights. We present a passenger satisfaction prediction model leveraging a real dataset from Kaggle. Through an examination of the interplay between individual in-flight services and passenger characteristics using the Pearson correlation coefficient and PCA-K-means clustering methods, we introduce a novel satisfaction prediction model built upon the deep learning Wide & Deep algorithm. Additionally, we employ the DeepLIFT algorithm to interpret the deep learning model and elucidate the salient features impacting passenger satisfaction, as revealed through feature importance analysis. Our findings demonstrate that the prediction model outperforms benchmark models such as MLP, SVM, and Random Forest, achieving higher accuracy. This study contributes to an enhanced comprehension of the multifaceted factors influencing passenger satisfaction following flight delays, and it offers valuable insights and recommendations for the enhancement of service quality among airline companies.