Modeling Airline Frequency Competition for Airport Congestion Mitigation

Abstract

Demand often exceeds capacity at the congested airports. Airline frequency competition is partially responsible for the growing demand for airport resources. We propose a game-theoretic model for airline frequency competition under slot constraints. The model is solved to obtain a Nash equilibrium using a successive optimizations approach, wherein individual optimizations are performed using a dynamic programming-based technique. The model predictions are validated against actual frequency data, with the results indicating a close fit to reality. We use the model to evaluate different strategic slot allocation schemes from the perspectives of the airlines and the passengers. The most significant result of this research shows that a small reduction in the total number of allocated slots translates into a substantial reduction in flight and passenger delays, and also a considerable improvement in airlines' profits.

Integrated Airline Scheduling: Considering Competition Effects and the Entry of the High Speed Rail

“…These enhanced models, however, do not account for changes in passenger demand resulting from competition. Studies that have considered the impact of competition on travel demand include, for example, Hansen (1990); Hong and Harker (1992); Yan, Tang, and Lee (2007); Wei and Hansen (2007) ;Pita, Barnhart, and Antunes (2013); and Vaze and Barnhart (2012a). These studies, however, do not consider multimodal competition and do not differentiate between different types of airlines, i.e., legacy airlines (LAs) and low-cost airlines (LCAs).…”

“…This is especially true in markets where the competitor fares are different from each other and the competing airlines are different from the perspectives of the passengers in other ways (Vaze and Barnhart 2012a). There are other attributes, such as fares and travel times, that can significantly affect passengers' airline choice (Behrens and Pels 2012;Román, Espino, and Martín 2007).…”

“…Many studies have modeled market share as a function of the logarithms of the attributes such as frequency, price, and travel time (Wei and Hansen 2005;Vaze and Barnhart 2012a). The relationship in (1) may be extended and rewritten as a discrete choice multinomial logit model (see Equation (2)), where the choice probability is proportional to the exponential of the systematic utility (v(a | w)) of each airline a in market w v(a | w) log asc a + α log f w a + β log p w a + γ log tt w a ,…”

“…However, because of demand uncertainty and the effects of revenue management, the airlines are rarely able to sell all of the seats on an aircraft. Therefore, we will assume a maximum allowable load factor for every flight leg (Vaze and Barnhart 2012a). Our optimization formulation allows for practical constraints that ensure that certain aspects of an existing flight schedule are included in the new schedule.…”

“…Early theoretical development and empirical evidence that higher-frequency shares are associated with disproportionately higher market shares was provided in the 1970s before deregulation (Simpson 1970). After deregulation, there exist a number of references to the S-curve in the aviation literature (Wei and Hansen 2005;Vaze 2011;Vaze and Barnhart 2012a;Belobaba 2015). The most commonly used mathematical expression for the S-curve relationship is given by…”