The article discusses different models used to predict air travel demand, including binary logit, multinomial logit, nested logit, and mixed logit models. These models help researchers understand how people make choices about their travel itineraries. The researchers also explore network GEV models to better capture the complexity of air travel decisions. Overall, the study provides insights into how travelers choose their flights and offers directions for future research in this area.