A preliminary framework for managing airport capacity and demand from an economic perspective

Purpose>Airport capacity constraints lead to operational congestion and delays, which have become major threats to the aviation industry. They impose large costs on airlines and their passengers. Uncertainty in demand or unexpected events can cause a mismatch between capacity and demand, resulting in either capacity oversupply, with a decrease in efficiency, or airport congestion over an extended period. Moreover, airport capacity is rather difficult to define due to its multifaceted and dynamic nature, and it depends both on the available infrastructure and on operating procedures. Additionally, traditional capacity management methods do not consider relevant behavioral economic challenges to conventional analysis, particularly failure of the expected utility hypotheses and dependence of valuations on reference points. This study aims to develop a preliminary framework to include economic concepts when evaluating expansions of airport capacity.Design/methodology/approach>This paper reviews major opportunities in airport demand and capacity management from an economic perspective while appraising the challenges involved in airport capacity expansion processes that have not been fully completely in past studies. Although welfare economics provides the conceptual foundations for demand/capacity analyses, the authors integrate the findings regarding capacity definition, uncertainty management and behavioral economics into standard economics to guide the measurement of the airport capacity expansion problem.Findings>The authors obtain several insights regarding airport capacity and demand management. First, airport capacity is a complex metric when evaluating airport expansion, and it depends both on the available infrastructure and on operating procedures. Furthermore, airport throughput is highly conditioned by factors that shape capacity and delay and shows significant variability when these factors are modified. Second, a marginal change in capacity at congested airports may have a great impact on demand distribution, airline competition, aircraft types, fares, operating revenues, route map and other characteristics of a given airport. Behavior after capacity expansion is highly reliant on the slot allocation models. Additionally, overall social welfare is usually affected after changes in infrastructure in terms of increased connectivity, economic benefits and negative externalities, including noise and local pollution. Third, on-time performance is clearly nonlinear, and thus sensitive to variations in demand and capacity. Finally, airport capacity and demand management involve a trade-off between mitigating congestion and maximizing capacity utilization, so decision-making tools are required to support and enhance policy and managerial choices. Three main challenges arise when developing new methods for evaluating airport expansions: the definition of capacity, the management of uncertainty in demand and the need to consider economic concepts.Originality/value>This paper explores and produces an in-depth understanding of the problem of airport capacity and demand balance. The authors propose a preliminary framework that considers the challenges that have been previously identified and that, particularly, provides an economic perspective for airport capacity expansion processes. This framework is completed with a theoretical model to help policymakers and airport operators when faced with a capacity development decision.

Visualizing the historical COVID-19 shock in the US airline industry: A Data Mining approach for dynamic market surveillance.

One of the purposes of Artificial Intelligence tools is to ease the analysis of large amounts of data. In order to support the strategic decision-making process of the airlines, this paper proposes a Data Mining approach (focused on visualization) with the objective of extracting market knowledge from any database of industry players or competitors. The method combines two clustering techniques (Self-Organizing Maps, SOMs, and K-means) via unsupervised learning with promising dynamic applications in different sectors. As a case study, 30-year data from 18 diverse US passenger airlines is used to showcase the capabilities of this tool including the identification and assessment of market trends, M&A events or the COVID-19 consequences.

The Constrained Air Transport Energy Paradigm in 2021

The increasing relevance of air transport as a contributor to climate change requires the development of emissions reduction technologies in a socio-economic and cultural context, where demand and air traffic have traditionally held sustained growth rates. However, the irruption of COVID-19 in 2020 has had an enormous negative impact on air travel demand and traffic volumes. Coincidentally, during 2020, new technology proposals for emissions reduction based on use of hydrogen and synthetic fuels have emerged from the aviation stake holders. By following a novel approach connecting the analysis of expectations of technology developments and their deployment into the fleet to market constraints, this study discusses how, even considering the new technology proposals and even if the COVID-19 has led to a completely different scenario in tourism and aviation, the air transport energy paradigm will remain unchanged in the upcoming decades as a consequence of market constraints, aircraft complexity, compliance with safety requirements, and extended life cycles. In this frame, aviation needs to keep on pursuing the abatement of its emissions while managing social expectations in a realistic manner and leaning on compensation schemes to achieve emissions contention while new technologies become serviceable in the longer term.