Examining nonlinear and interaction effects of multiple determinants on airline travel satisfaction
Artikel i vetenskaplig tidskrift, 2021

Improving passengers’ satisfaction is crucial for airline industry and requires in-depth understandings regarding the complex effects of various factors. This study investigates the importance, complex nonlinear effects and interaction effects of various factors (including passenger characteristics and service attributes) on airline travel satisfaction in data-driven manners leveraging machine-learning (ML) approaches. The results show that ML algorithms such as Random Forest have superiority in modeling airline travel satisfaction as compared to conventional logistic regressions. The quantitative importance of various factors is estimated and compared to reveal key determinants of passengers’ satisfaction using permutation-based importance and accumulated local effect analysis. More importantly, results suggest that the main effects of service attributes present piecewise nonlinear patterns. There are piecewise interaction effects between passenger characteristics and service attributes and among service attributes on airline travel satisfaction. Practical implications on efficient and cost-effective measures of promoting satisfaction are derived and discussed based on the findings.

Nonlinear effect

Data-driven approaches

Travel satisfaction

Machine learning



Kun Gao

Chalmers, Arkitektur och samhällsbyggnadsteknik, Geologi och geoteknik

Ying Yang

Australian Catholic University

Xiaobo Qu

Chalmers, Arkitektur och samhällsbyggnadsteknik, Geologi och geoteknik

Transportation Research Part D: Transport and Environment

1361-9209 (ISSN)

Vol. 97 102957


Transportteknik och logistik

Övrig annan teknik

Datavetenskap (datalogi)



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