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The Airline Group of the International Federation of Operations Research (AGIFORS) held a conference in October 2020 that included keynote addresses from KLM Royal Dutch Airlines and Airbus, as well as three panels that included representatives from 11 airlines throughout the world that focused on how COVID-19 is impacting and reshaping the airline industry. This paper presents key themes that emerged from these discussions, including the impact of border closures on airline operations and demand forecasts; the shift in development priorities within revenue management departments; and outlooks for how passenger preferences, booking curves, and fare product restrictions may change after the COVID-19 pandemic. [ABSTRACT FROM AUTHOR]

Network planning models, which forecast the profitability of airline schedules, support many critical decisions, including equipment purchase decisions. Network planning models include an itinerary choice model that is used to allocate air total demand in a city pair to different itineraries. Multinomial logit (MNL) models are commonly used in practice and capture how individuals make trade-offs among different itinerary attributes; however, none that we are aware of account for price endogeneity. This study formulates an itinerary choice model that is consistent with those used by industry and corrects for price endogeneity using a control function that uses several types of instrumental variables. We estimate our model using a database of more than 10 million passenger trips provided by the Airlines Reporting Corporation. Results based on Continental U.S. markets for May 2013 departures show that models that fail to account for price endogeneity overestimate customers’ value of time and result in biased price estimates and incorrect pricing recommendations. The size and comprehensiveness of our database allows us to estimate highly refined departure time of day preference curves that account for distance, direction of travel, number of time zones traversed, departure day of week and itinerary type (outbound, inbound or one-way). These time of day preference curves can be used by airlines, researchers, and government organizations in the evaluation of different policies such as congestion pricing. [ABSTRACT FROM AUTHOR]

When firms evaluate their service system design choices, there is typically more uncertainty surrounding the value that a particular auxiliary service provides than there is on the cost of providing that service. To help inform this decision, we propose an approach where we compare the relative value of the segment of passengers who use an auxiliary service to the relative value of the segment that does not use it. We demonstrate this approach for a typical auxiliary service common to the airline industry. In 2008, most US airlines implemented checked baggage fee policies to decrease their costs by reducing the number of customer service agents needed in the check-in and baggage handling processes. The success of this change has led to a current debate at many of these airlines on whether to make further staffing cuts in these areas, essentially making it even less attractive for passengers to check their baggage. Our proposed methodology helps answer whether passengers who continue to check bags in today's baggage-fee era are more or less valuable than passengers who do not check bags. We explore this question empirically by examining, through a stated preference survey, if a history of checking or not checking bags can be used to segment passengers based on how their itinerary choices are influenced by common airline service attributes (on-time performance, itinerary time, number of connections, airfare, and schedule delay). Contrary to the opinions of some top airline executives, we find that the passengers who continue to check bags at airlines that charge baggage fees are generally less sensitive to differences in three of these important service attributes and are less likely to switch airlines when a competing airline improves its offerings along these dimensions. Thus, airlines that charge for checked bags should consider improving the customer experience for their bag-checking passengers, as they represent a potentially more valuable segment class to the airline. [ABSTRACT FROM AUTHOR]

We investigate factors that influence airline customers’ purchases of premium coach seats using a database of online prices and seat map displays collected from JetBlue’s website. Results show that multiple factors influence purchasing behavior; these factors include the amount of the seat fee, how far in advance the ticket is purchased, the number of passengers traveling together, and load factors (as revealed through seat map displays). We find that customers are between 2 and 3.3 times more likely to purchase premium coach seats (with extra legroom and early boarding privileges) when there are no regular coach window or aisle seats that can be reserved for free. In addition, we find that customers who purchase tickets closer to the departure date are less price-sensitive and are willing to pay higher seat fees. We use these model results to show that JetBlue’s seat fees are currently underpriced in many markets; an optimal static fee would increase revenues by 8% whereas optimal time-dependent fees would increase revenues by 10.2%. In addition, if JetBlue were to leave their seat fees unchanged and instead reserve certain rows of seats for premier customers, they could potentially increase revenues by 12.8%. [ABSTRACT FROM AUTHOR]

Abstract: Historically, peaked schedules have been used with hub-and-spoke networks to maximize passenger connection opportunities. Although peaked schedules can generate more attractive connecting itineraries and revenue for an airline, they are costly to operate because additional manpower and equipment resources are needed to serve the peak periods. Several airlines experimented with depeaking their hubs as a way to reduce costs and improve operations in the 2000s. Prior studies have quantified operational improvements and cost savings associated with depeaking; however, none have quantified revenue impacts. We use difference-in-differences methods to quantify revenue and operational impacts associated with depeaking for five U.S. hubs. Results show that depeaking tends to improve operations, but may negatively impact revenue per available seat mile (RASM). In some cases, revenue losses exceed reported cost savings. [Copyright &y& Elsevier]

Abstract: This paper reviews product debundling trends that have occurred in the US airline industry. Multiple sources of ancillary fees related to ticketing refunds and exchanges, checked baggage, on-board pets, preferred and/or advanced seating assignments, frequent flyer ticket redemptions, and day of departure standby policies are reviewed. Despite the fact that both low cost and network carriers stress the importance of future ancillary fees in their investor reports, our assessment suggests that these fees will be more broadly adopted by low cost carriers. We anticipate that many network carriers will eliminate ancillary fees, particularly as they begin to recognize how these fees can impact other system performance objectives such as minimizing the number of misconnecting passengers. We estimate that the debundling phenomenon has diluted revenues to the US Airport and Airways Trust Fund by at least 5%. [Copyright &y& Elsevier]

Abstract: There has been an increase in the number of involuntary denied airline passenger boardings in the US. A denied boarding is influenced by multiple factors other than compensation amounts, including the accuracy, magnitude, and variability associated with no-show forecasts, load factors, and carriers’ day of departure operating policies. We describe how these and other factors influence denied boardings, why they differ across US carriers, and why carriers have strong internal incentives to reduce denied boardings. [Copyright &y& Elsevier]

This paper summarizes the author's opinions as to how the Internet will influence the next decade of business and research directions in the airline industry. Potential impacts on the Internet on revenue management, pricing, itinerary choice, and new product development are reviewed.Journal of Revenue and Pricing Management (2009) 8, 247–254. doi:10.1057/rpm.2008.55; published online 16 January 2009 [ABSTRACT FROM AUTHOR]

Abstract: This study explores the use of discrete choice methods for airline passenger cancellation behavior. A discrete time proportional odds model with a prospective time scale is estimated based on the occurrence of cancellations (defined as refund and exchange events) in a sample of tickets provided by the Airline Reporting Corporation. Empirical results based on 2004 data from eight domestic US markets indicate that the intensity of the cancellation process is strongly influenced by both the time from ticket purchase and the time before flight departure. Higher cancellations rates are generally observed for recently purchased tickets, and for tickets whose associated flight departure dates are near. Cancellations rates are influenced by several other covariates, including departure day of week, market, and group size. [Copyright &y& Elsevier]

This study models airline passengers' no-show and standby behaviour using passenger and out-bound and inbound itinerary information. It describes factors that influence no-show and standby behaviour in continental US markets using multinomial and nested logit models. The model accounts for early standby opportunities on other itineraries flown by the carrier of interest, ie it incorporates information about booking levels on the carrier's itineraries departing on the same day. Empirical and validation analyses highlight the importance of distinguishing between outbound and inbound itineraries. It concludes by describing a framework carriers can use to implement the no-show model. [ABSTRACT FROM AUTHOR]

Abstract: We investigate how customers respond to an opaque airline product offered by a European carrier. In this opaque product design, customers are randomly assigned to travel to one of approximately ten destinations; however, for a fee they may exclude one or more destinations from the choice set (or a particular package design) prior to learning which destination they will travel to. We use a multidimensional binary logit model to predict the probability that one or more alternatives will be chosen by a customer. Results show that customers are more likely to pay to exclude destinations located close to the origin airport and destinations that speak the same language as the origin airport. Length of stay, cost of living at the destination, and measures of destination attractiveness are also found to be significant predictors for some package designs. Based on these findings, we offer general recommendations for how to design opaque packages for airline customers. [Copyright &y& Elsevier]