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Predicting the demand for future flights accurately is highly important in order to ensure the survival of any airline. In order to reliably forecast the number of potential passengers, revenueÌýmanagement forecasting methods need error-free true demand data.Ìý

The true demand for a booking class is the number of passengers willing to purchase the ticket assuming the booking class is always available for purchase. However, due to aircraft's physical capacity limitations and historicalÌýrevenueÌýmanagement decisions, the collected booking data seldom represents true demand.Ìý

Adding to the true demand modelling complexity, airline customers' preferences and experiences vary significantly. Not accountingÌýfor these phenomena,Ìýthe true demand may result in erroneous predictions of the true demand. In addition, with similar options offered by various airlines,Ìýthe substitution patterns manifest strongly in observed booking numbers.Ìý

To account for the varying preferences of passengers and for correlations between products,Ìýwe introduce a novel Segment Specific Cross Nested LogitÌýModelÌýwithÌýbrandÌýloyalty. The model explicitly handles substitution patterns between all the market options, allows tracking changing customers' preferences and accounts for theirÌýpast experience.Ìý

Using data from aÌýstatedÌýpreference experiment conducted among 360 Australians,Ìýwe showed that our discrete choice model constantly outperformed the more simplistic methods by 0.04 to 0.061 in terms of the adjusted R2, attaining results on par with more sophisticated models. At the same time our model was only marginally more computationally expensive than the simpler models.Ìý

In this thesis we also propose a novel unconstraining model. The Market Spill RecaptureÌýModelÌýdelivers true demand estimates for all the booking classes offered by all the airlines in a specific market. Using anÌýagent-basedÌýbooking simulator developed for this research, with discrete choice models from theÌýstatedÌýpreference experiment driving virtualÌýpassenger'sÌýdecisions, we simulated booking data for six virtual airlines.Ìý

The Segment Specific Cross Nested LogitÌýModelÌýwithÌýbrandÌýloyalty was used to model demand interactions in our unconstraining model. For the simulated data our model delivered true demand estimates that were betweenÌýoneÌýperÌýcent to 10 perÌýcent more accurate than theÌýMulti-FlightÌýRecaptureÌýMethod.

Category: Aviation consumers

Researcher: Dr TomaszÌýDrabas

Supervisor: Dr Cheng-Lung (Richard) Wu

Level: PhD

Status: Completed

In order to improve the revenue from airport retailing, airport operators and retailers require thorough understanding of the consumption behaviours of air passengers when they are at airport terminals.Ìý

There is little research on passengers’ consumption behaviour at airports and reported results by various studies in the literature often had conflictingÌýconclusions. In particular, the causal and psychological effects of limited time for passengers to spend while in a terminal are less studied.

The exact choice behaviour of passengers among various (categories of) goods in a terminal has been neglected, resulting inÌýmuch ‘guess work’ by many airport operators when confronted by retail business planning.Ìý

This project is intended to gain insights to better understand air passenger consumption behaviour in an airport environment. Results of this study will assist the development of airport retail business, the effectiveness of airport retail marketing strategiesÌýand improving the future design of airport terminals that explicitly considers the role of terminal layout in non-aeronautical revenues.Ìý

From the perspective of increasing airport retail revenues,Ìýthere are some research questions that will be further examined in this project.

Category: Aviation consumersÌý

Researcher:ÌýWen-Chun Tseng

Supervisor:ÌýDr Cheng-Lung (Richard) Wu

Level:ÌýPhD

Status: Completed