Modeling joint row- and column-wise correlation in air passenger seat selection: A cross-nested logit approach

Advance seat selection (ASS) fees have gradually become an important revenue generator for the carriers worldwide. In this study, we investigate air passenger seat selection behavior in the ASS service while quantifying spatial correlation structure among seats, which is novel to the literature. Specifically, we employ a Cross-Nested Logit (CNL) model that captures the joint row-and column-wise correlation among seats. The CNL model, together with the Multinomial Logit and Nested Logit models involved as benchmarks, are applied to a rich dataset of 321670 ASS records, extracted from the database of a Chinese regional carrier. The empirical results suggest that the correlation among seats arises from their similarities in both row and column dimensions, with a stronger correlation in the column dimension. And modeling such joint correlation can not only enhance the in-sample model fit, but also facilitate more accurate out-of-sample revenue prediction. Moreover, the utility parameter estimates reveal that passengers exhibit non-linear price sensitivity, representable by a power function with a rational exponent of 0.37. Additionally, passengers display heterogeneous willingness to pay influenced by their flight, booking, and personal characteristics. And seat preferences can be affected by participating in loyalty programs.

Automated summary

Advance seat selection fees are an important revenue source for airlines worldwide. This study examines passenger seat selection behavior and quantifies the spatial correlation structure among seats. The results show that the correlation among seats arises from similarities in both row and column dimensions, with a stronger correlation in the column dimension. Passengers exhibit non-linear price sensitivity and their willingness to pay is influenced by flight, booking, and personal characteristics. Participation in loyalty programs can also impact seat preferences.