Air ticket pricing and passengers’ choice behavior: A case study in China

Abstract Air ticket pricing is one of the most complicated pricing strategies, known as “yield-management”. Basically, airlines adjust ticket prices over time prior to the flight departure attempting to gain the maximum profit. A variety of influential factors on ticket pricing have been reported in the literature, such as the type of air route, flight time, airline’s market power on the air routes, historical price and the number of unsold seats (Donovan, 2005; Zhang et al , 2014). The method based on these factors has the advantage that the only source of dynamic information is available in the booking system; however, it does not reflect the impact of ticket pricing on passengers’ choice behavior. Passenger choice behavior on air ticket purchase has been ignored for long in such market-based approaches. In reality, however, it seems important for airlines to consider explicitly behavioral responses of passengers to ticket pricing. Existing literatures have shown that passengers’ air travel behavior is influenced by information related to air ticket price, air route, flight, access service, trip purpose and social demography (Pels, 2003; Basar & Bhat, 2004; Hess et al., 2007; Lian & Ronnevik, 2011). It is probable that passengers already make a choice on a series of decisions, i.e., airline, airport and ticket, prior to buying a ticket. This indicates that airlines should not ignore the influential factors on passenger air travel choice behavior in order to maximize profit. It is thus highly necessary for airlines to incorporate passengers’ choice behavior in the determination of ticket pricing. This paper therefore aims at proposing an air ticket pricing model which incorporates passengers’ travel choice behavior. The modeling framework of ticket pricing is established using the Markov chain framework incorporating a Q-learning algorithm. In the Markov process of price updating, a dynamic mechanism reflecting passenger air travel behavior is incorporated. Passenger air travel behavior is modeled using an “airline-airport” joint choice model based on the nested logit modeling framework. A stated choice experiment is specifically designed to collect data in the airport of Dalian city, China. The stated choice experiment is designed in a nested sequential scheme where the top node considers the choice behavior on whether passengers choose or not choose air travel. This measures the choice decision at a general level. In case air travel is chosen, a mode choice process is then implemented considering air plane, high speed train, normal train and car. Within the choice of air travel, a combined choice of airport and airline is conducted. This is mainly attributed to the complexity regarding the sequence of airport and airline (Hess and Polak, 2006; Escobari & Mellado, 2013; Hihara, 2014) and the incorporation of competitiveness in multi-airport regions. Our proposal model is then applied in the multi-airport region with competition. The price strategy is calculated in the context that passengers switch from one airport to another because of the improved airport access service. Results are compared with those of traditional models which do not take into account “airport-airline” joint choice behavior. By comparing the results, we find that when we use the traditional model, the increased utility caused by the improved access service will  be discounted by the quickly increasing air ticket price; however, when we use our proposal model, the air ticket price does not increase immediately because of knowing the reason that leads to the increase of purchase, the price strategy keep the increased passenger volume attracted by the improved access service for the airport and simultaneously improve the load factor of the flight for the airlines which will lead to a higher profit. This indicates that by knowing the choice behavior of air ticket purchase and changes of other factors influencing the choice behavior but not controlled by the airlines, policy decisions regarding price and service can be made in a more flexible way to in order to maximize profit. Reference Basar, G., and C. 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