Modeling Demographic and Unobserved Heterogeneity in Air Passengers' Sensitivity to Service Attributes in Itinerary Choice

Abstract

Modeling passengers' flight choice behavior is valuable to understanding the increasingly competitive airline market and predicting air travel demands. This paper estimates standard and mixed multinomial logit models of itinerary choice for business travel, based on a stated preference survey conducted in 2001.The results suggest that observed demographic and trip related differences get incorrectly manifested as unobserved heterogeneity in a random coefficients mixed logit model that ignores demographic and trip-related characteristics of travelers. Among demographics, gender and income level have the most noticeable effects on sensitivity to service attributes in itinerary choice behavior, but frequent flyer membership, employment status, travel frequency, and group travel also emerge as important determinants. However, there is significant residual heterogeneity due to unobserved factors even after accommodating sensitivity variations due to demographic and trip-related factors. Consequently, substitution rates for each service attribute show substantial variations in the willingness-to-pay among observationally identical business passengers.

Dynamic airline scheduling: An analysis of the potentials of refleeting and retiming

“…Parker and Walker (2005) find that business passengers are twice as sensitive to departure time difference as non-business passengers. Adler et al (2005) experience that business passengers are six times as sensitive to arrival time difference as non-business passengers (arrival time being comparable to departure time in an origin-destination environment), and Warburg et al (2006) find that the difference is three-fold. Based on these studies, a ± 1 hour departure period is assigned to Business class passengers and a ± 3 hours departure period to Economy class passengers.…”

“…The concept of Dynamic Airline Scheduling is tested at different days before departure and Jiang's (2006) code is extended to model the problem as real-life as possible. We improve the passenger assignment part of the model significantly by using passenger behavioral results from Adler et al (2005) and Warburg et al (2006). Actual data from Scandinavian Airlines (SAS) is used which includes: Real costs, schedules, passenger bookings, fleet information, and passenger forecasts for 26 different days before departure for all 7 days in a chosen focus week in September, 2006.…”

Measuring the Benefit of Offering Auxiliary Services: Do Bag‐Checkers Differ in Their Sensitivities to Airline Itinerary Attributes?

“…Similarly, the goal of Warburg et al. () was to understand how itinerary preferences varied along a wide range of socioeconomic and trip characteristics so it should not be surprising that a large number of interaction terms were included in their model.…”

The User Costs of Air Travel Delay Variability

“…4 The shadow costs of mean arrival delay are given by , the shadow costs of arriving earlier than the preferred arrival time are given by β, and the shadow costs of arriving later than the preferred arrival time are given by . Empirical research for other travel modes usually finds that arriving late is more costly than arriving early, meaning that , although we may add that the evidence for air travelers is mixed (Warburg et al, 2006;Lijesen, 2006;Hess et al 2007 (1)…”

“…This may be typical for business travelers who usually prefer to arrive in the morning (Warburg et al, 2006). Second, it is assumed that the probability is increasing in time-of-day, meaning that a larger proportion 20 of travelers prefer to arrive in the evening.…”