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We investigate the relationship between airline network structure and airport congestion. More specifically, we study the ways in which airlines adjust capacity to delays depending on the network type they operate. We find some evidence suggesting that airlines operating hub-and-spoke structures react less to delays than airlines operating fully connected configurations. In particular, network airlines have incentives to keep frequency high even if this is at the expense of a greater congestion at their hub airports. We also show that airlines in slot-constrained airports seem to react to higher levels of congestion by using bigger aircraft at lower frequencies; thus, we conclude that conditioning the number of available slots on the levels of delays at the airport seems an effective measure that creates the right incentives for airlines to reduce the congestion they generate.

This paper aims to embark a mathematical model based on investigation and comparison of airport pricing policies under various types of competition, considering both per-passenger and per-flight charges at congested airports.

In this model, four-game theoretic strategies are assessed and closed-form formulas have been proved for each of the mentioned strategies. Numerical examples and graphical representations of the optimal solutions are provided to illustrate the models.

The rectitude of the presented formulas is evaluated with sensitivity analysis and numerical examples have been put forward. Finally, managerial implications are suggested by means of the proposed analysis.

The represented model is inherently limited to investigate all the available and influential factors in the field of congestion pricing. With this regard, several studies can be implemented as the future research of this study. The applications of other game theoretic approaches such as Cartel games and its combination with the four mentioned games seem to be worthwhile. Moreover, it is recommended to investigate the effectiveness of the proposed model and formulations with a large-scale database.

The authors formulate a novel strategy that put forwards a four-game theoretic strategy, which helps managers to select the best suitable ones for their specific airline and/or air traveling companies. The authors find that by means of the proposed model, the application of Stackelberg–Bertrand behavior in the field of airport congestion pricing will rebound to a more profitable strategy in contrast with the other three represented methods.

Airline travel is composed of business and nonbusiness travelers, each with different preferences that give rise to differences in demand elasticities and substitution not only across airlines but also airports. In this study, we develop and estimate a model of airline wherein consumers choose which airports and airline to use that allows for unobserved differences between travelers (e.g., business and nonbusiness travelers). The results point to the role that airports themselves play in the ultimate selection of a flight, and that there are strong interactive effects between the airlines’ networks and the consumers’ preferences across airports.

An important and challenging question for air transportation regulators and airport operators is the definition and specification of airport capacity. Annual capacity is used for long-term planning purposes as a degree of available service volume, but it poses several inefficiencies when measuring the true throughput of the system because of seasonal and daily variations of traffic. Instead, airport throughput is calculated or estimated for a short period of time, usually one hour. This brings about a mismatch: air traffic forecasts typically yield annual volumes, whereas capacity is measured on hourly figures. To manage the right balance between airport capacity and demand, annual traffic volumes must be converted into design hour volumes, so that they can be compared with the true throughput of the system. This comparison is a cornerstone in planning new airport infrastructures, as design-period parameters are important for airport planners in anticipating where and when congestion occurs. Although the design hour for airport traffic has historically had a number of definitions, it is necessary to improve the way air traffic design hours are selected. This study aims to provide an empirical analysis of airport capacity and demand, specifically focusing on insights related to air traffic design hours and the relationship between capacity and delay.

By reviewing the empirical relationships between hourly and annual air traffic volumes and between practical capacity and delay at 50 European airports during the period 2004–2021, this paper discusses the problem of defining a suitable peak hour for capacity evaluation purposes. The authors use information from several data sources, including EUROCONTROL, ACI and OAG. This study provides functional links between design hours and annual volumes for different airport clusters. Additionally, the authors appraise different daily traffic distribution patterns and their variation by hour of the day.

The clustering of airports with respect to their capacity, operational and traffic characteristics allows us to discover functional relationships between annual traffic and the percentage of traffic in the design hour. These relationships help the authors to propose empirical methods to derive expected traffic in design hours from annual volumes. The main conclusion is that the percentage of total annual traffic that is concentrated at the design hour maintains a predictable behavior through a “potential” adjustment with respect to the volume of annual traffic. Moreover, the authors provide an experimental link between capacity and delay so that peak hour figures can be related to factors that describe the quality of traffic operations.

The functional relationships between hourly and annual air traffic volumes and between capacity and delay, can be used to properly assess airport expansion projects or to optimize resource allocation tasks. This study offers new evidence on the nature of airport capacity and the dynamics of air traffic design hours and delay.

Over the past 50 years, air travel in the United States has increased from approximately 33 million passengers in 1960 to over 607 million passengers in 2007 (National Transportation Statistics, 2011, Table 1–40). This is over an 18-fold increase in air travel in the past five decades. Over that same time period, the number of airports increased modestly, from 15,161 in 1980 to 19,750 in 2009. The number of those airports serving public commercial traffic is even smaller, and has declined from 730 airports in 1980 to 559 in 2009 (National Transportation Statistics, 2011, Table 1–3). Together, these two facts point to phenomenal growth among airports (measured by the number of passenger trips).

Weather events have a significant impact on airport arrival performance and may cause delays in operations and/or constraints in airport capacity. In Europe, almost half of all regulated airport traffic delay is due to adverse weather conditions. Moreover, the closer airports operate to their maximum capacity, the more severe is the impact of a capacity loss due to external events such as weather. Various weather uncertainties occurring during airport operations can significantly delay some arrival processes and cause network-wide effects on the overall air traffic management (ATM) system. Quantifying the impact of weather is, therefore, a key feature to improve the decision-making process that enhances airport performance. It would allow airport operators to identify the relevant weather information needed, and help them decide on the appropriate actions to mitigate the consequences of adverse weather events. Therefore, this research aims to understand and quantify the impact of weather conditions on airport arrival processes, so it can be properly predicted and managed.

This study presents a methodology to evaluate the impact of adverse weather events on airport arrival performance (delay and throughput) and to define operational thresholds for significant weather conditions. This study uses a Bayesian Network approach to relate weather data from meteorological reports and airport arrival performance data with scheduled and actual movements, as well as arrival delays. This allows us to understand the relationships between weather phenomena and their impacts on arrival delay and throughput. The proposed model also provides us with the values of the explanatory variables (weather events) that lead to certain operational thresholds in the target variables (arrival delay and throughput). This study then presents a quantification of the airport performance with regard to an aggregated weather-performance metric. Specific weather phenomena are categorized through a synthetic index, which aims to quantify weather conditions at a given airport, based on aviation routine meteorological reports. This helps us to manage uncertainty at airport arrival operations by relating index levels with airport performance results.

The results are computed from a data set of over 750,000 flights on a major European hub and from local weather data during the period 2015–2018. This study combines delay and capacity metrics at different airport operational stages for the arrival process (final approach, taxi-in and in-block). Therefore, the spatial boundary of this study is not only the airport but also its surrounding airspace, to take both the arrival sequencing and metering area and potential holding patterns into consideration.

This study introduces a new approach for modeling causal relationships between airport arrival performance indicators and meteorological events, which can be used to quantify the impact of weather in airport arrival conditions, predict the evolution of airport operational scenarios and support airport decision-making processes.