Modeling airline passenger choice : passenger preference for schedule in the passenger origin-destination simulator (PODS)

Abstract

This thesis examines how to model the choice of individual travelers among various possible travel alternatives in the airline industry. A review of the models used to represent that choice situation in the Passenger Origin-Destination Simulator (PODS) was undertaken for two reasons. First, the development of computational capabilities has lead to advancements in consumer choice theory that enabled the implementation of more flexible models like mixed logit models. Second, the increasing competition of low-cost new entrant airlines has put great pressure on pricing practices of traditional network carriers. This increasing competition has also compelled these carriers to focus on their strengths, for example, schedule coverage. In this thesis, after a comparison between the PODS Passenger Choice Model and the literature on consumer choice theory, we will then focus on how to model passenger preference for schedule. The review of the literature on air traveler choice reveals that most authors have used discrete choice models, like standard logit or nested logit models, to represent the choice of individual passengers among travel alternatives. However, the logit model has two limitations in the air traveler choice problem: it can accommodate neither random taste variation in some elements of the passenger utility function nor the complex substitution patterns across travel alternatives modeled in PODS. However, we show that the highly flexible mixed logit model brings a solution to these limitations and the choice process modeled in PODS can be approximated by a set of mixed logit models. In the second part of the thesis, we focus on how passenger preference for schedule is modeled in PODS. In the current model, a constant replanning disutility is added to the cost of all paths that are not convenient to the passenger. However, the current approach does not differentiate among paths based on their level of schedule inconvenience and this leads to distortions in the valuation of the revenue advantage of the carrier offering the best schedule. We propose in this thesis an alternative approach called the variable replanning disutility model. In this model, the replanning disutility added to the cost of paths depends on the time location of the path and its level of schedule inconvenience. PODS simulation results show that the variable replanning disutility model leads to a more realistic valuation of the revenue advantage associated with a better schedule coverage.