Airline itinerary choice modeling using machine learning

Understanding how customers choose between different itineraries when searching for flights is very important for the travel industry. This knowledge can help travel providers, either airlines or travel agents, to better adapt their offer to market conditions and customer needs. This has a particular importance for pricing and ranking suggestions to travelers when searching for flights. This problem has been historically handled using Multinomial Logit (MNL) models. While MNL models offer the dual advantage of simplicity and readability, they lack flexibility to handle collinear attributes and correlations between alternatives. Additionally, they require expert knowledge to introduce non-linearity in the effect of alternatives' attributes and to model individual heterogeneity. In this work, we present an alternative modeling approach based on non-parametric machine learning (ML) that is able to automatically segment the travelers and to take into account non-linear relationships within attributes of alternatives and characteristics of the decision maker. We test the models on a dataset consisting of flight searches and bookings on European markets. The experiments show our approach outperforming the standard and the latent class Multinomial Logit model in terms of accuracy and computation time, with less modeling effort.