Hub Airports Research Articles

Global–Local Spatial Platform for Economic Geography: Mechanism for Sustaining Bifurcation

This paper develops a spatial platform for economic agglomerations that can represent a hierarchical structure of cities, towns, and so on. A global system models geographical distribution of a system of cities, while population size and local geography of each city are modeled by an individual square lattice network. The mechanism of economic agglomeration is described by economic geography models with the replicator dynamics. As a major theoretical contribution of this paper, we elucidate the bifurcation mechanism of the mono-centric distribution in a square domain. Such bifurcation expresses how satellite places appear around a large city. This bifurcation behavior, called sustaining bifurcation, is different from symmetry-breaking bifurcation studied in nonlinear mathematics, and is not given much attention in economic geography, despite its importance. An air-network of cities with the seven largest hub airports in USA is employed as a realistic example, and is modeled as a global–local system comprising a series of local square lattices with different sizes connected by an equidistant economy. This system for an economic geography model displays successive sustaining bifurcations occurring many times leading to gradual emergence of satellite places around a large city.

Assessment of hub airports' connectivity and Self-Connection Potentials

This study utilizes the global flight schedules over a period of one week to identify and evaluate all possible connections, including online, interline and passenger self-connections and assess their feasibility and practicality in terms of total travel time between two cities, transit time, flying distances, connection types, airport accessibility, airline services, and origin/destination markets. Using the feasible connections, this research then ranked and assessed Top 50 airports by connectivity potential and self-connection potential. It showed the conditions under which an airline should make strategic partnerships with other airlines and/or with the connecting airports to offer more transfer opportunities that have not been possible by the traditional interline agreements. The framework developed in this research can help airlines and airports identify and optimize their connectivity potentials and develop strategic partnerships.

FACTORS INFLUENCING THE AIRPORT'S COMPETITIVE STRATEGY FORMATION

The article is dedicated to the selection of key factors that influence the airport’s competitive strategy formation. The aspects of the "regional airport" concept definition, based on the priority of serving short and medium range routes as well as the point to point destinations, are considered. The benefits of a Hub airport model operation during the formation of the competitive development strategy are determined. The classifications of the airports proposed by the Airports Council International and the US Federal Aviation Administration were review since they are important to determine the airport’s competitive advantages. The key obstacles to the implementation of the Hub airport competitive strategy are outlined. The variations between key prototypes of hub models - Hourglass Hub and a Catchment Area Hub are described. The classification of airports competition is considered in order to improve the understanding of the factors that are important for the formation of their competitive strategy.

Air Transport Centrality as a Driver of Sustainable Regional Growth: A Case of Vietnam

With fast-growing aviation markets, many developing countries are showing remarkable economic development in global terms. As significant growth of air transportation is crucially interrelated with regional growth, it is essential to identify relevant criteria to ensure effective allocation of investments in this regard. This study aimed to investigate airport centrality using social network analysis to detect the key hubs and examine the interrelationship between airport centrality and regional economy indicators in Vietnamese regions. The results revealed that the cities of Tan Son Nhat, Noi Bai, and Da Nang were the key regional hub airports in the air transport network and the development of these leading cities had played a significant role in promoting the improvement of the entire domestic air network. Moreover, the results showed a strong positive correlation between airport centrality and regional growth features. Therefore, policymakers can optimize their decision-making processes in relation to sustainable regional development by considering air transport mobility and network in addition to conventional socioeconomic criteria.

Synthetic control methods for policy analysis: Evaluating the effect of the European Emission Trading System on aviation supply

In this paper, we provide a novel application of synthetic control methods by offering two major modifications to the existing methodological framework. We provide the first complete ex-post evaluation of the causal impact of carbon pricing on aviation supply, expressed in terms of airline output (i.e., number of seats supplied at the airline-route level). We investigated the policy change in the European Union Emission Trading System (EU ETS), the first large greenhouse gas emissions trading scheme in the world. We distinguish between low-cost, regional and full service airlines, short and medium/long-haul routes, routes towards (or from) hub airports versus non-hub airports, monopolistic versus non-monopolistic routes. The analysis shows that the EU ETS does not have a substantial impact on the average aircraft size, while it has caused a reduction of total airline seat capacity and flight frequency, with the percentage of airline seat capacity reduction reaching above 20% at its peak. The overall effect of the policy has a remarkable impact on low-cost and regional airlines, short-haul routes, spoke–spoke markets and monopolistic routes. Our results are the first empirical confirmation to the theoretical prediction in the aviation literature that emission charges will reduce flight frequency and increase load factors while having no effect on aircraft size.

The Influence of the Resident Subsidy on Regional Carrier Economies and the Environment in the Canary Interisland Air Traffic Network

The existence of aviation subsidies can exacerbate congestion in airports that operate near their maximum threshold. One of the main operational issues in airports, that is, aircraft taxiing operations, could become more difficult and, therefore, produce negative effects on airport capacity management. Air carrier economies, touristic mobility (leisure travel), and the environment could also be negatively affected. Greenhouse gas emissions, passenger travel time, and air carriers’ fuel costs, for example, might rise due to aircrafts’ increased taxiing time. This paper seeks to analyse the impact of regional aviation subsidies for regional hub airports in the Canary Island air traffic network; specifically, the effect it has on airports’ capacity in terms of LTO (landing and taking off) cycle delays and its repercussions for passenger travel time, air carrier economics, and the environment. Therefore, to quantify the extent that the increased traffic from subsidies cause airport ground operations to delay flights, the disturbance between aircraft categories in landing and taking off operations must be considered. The disturbance between aircrafts will be evaluated by employing a simple model of landing intervals considering two aircraft categories (i.e., ATRs-72, ATRs-42, and B737/A320 aircraft families). The above-mentioned methodology provides an approximation of the average processing rate for take-off using the ultimate capacity concept for a variety of aircrafts landing on a single runway of the airport. As a result, it must be noted that there is a potential economic and environmental impact associated with the implementation of the subsidy increase for residents’ interisland travel and from the Canary Islands to the mainland. There is also a negative effect on runways for both airports due to the capacity constraints. These impacts, however, have to be balanced with the social and economic benefits that each regional inhabitant derives from the subsidy in terms of improvement of their mobility for any purpose.

An examination of the potential impact of 5G on air travel in the U.S.

The rollout of 5G wireless service has been much anticipated by consumers across the U.S. since it will bring faster data download speeds and expanded service; however, the impact to aviation and air travel are as a major issue and the potential impact to aviation has not been estimated or assessed. This paper fills that gap and provides the first analysis of the potential number of flights impacted by 5G at ten of the busiest airports in the U.S. Using detailed historical weather and flight data in 2019 at 10 large hub airports, we find that nearly 15,700 flight arrivals could be directly impacted annually, at the ten airports considered. The geographical and seasonal impact varies significantly with more flights affected by the potential for 5G interference at airports in Seattle and Dallas Fort-Worth and relatively few flights affected at airports in Phoenix and Las Vegas. A case study examining ten years of data at Chicago O’Hare International, a hub for two major airlines, suggests that over 2,400 flights every year could be impacted by 5G interference. Fortunately, there has been increasing cooperation between the cell phone industry and aviation stakeholders to ensure a safe rollout of 5G C-band; this paper provides a significant contribution by documenting the issues and context for aviation operations.

Strategical district cooling system operation in hub airport terminals, a research focusing on COVID-19 pandemic impact

Part load ratio is often observed in real operations of airport terminal cooling system. This phenomenon is more obvious during the COVID-19 pandemic, as sudden flight restrictions impacting cooling demand are widely adopted in hub airport terminals. This research aims to propose optimal strategies of multi-chiller in airport terminals based on cooling load characteristics modeling, to tackle the aforementioned issues. Numerical experiments based on a real-world Chinese airport terminal are conducted to validate the proposed method. The results show that an average cooling load drop of 30% is observed from scenario of normal flight before COVID-19 to scenario of COVID-19 Period flight, and the average cooling load drop reaches to 44% from scenario of busy flight before COVID-19 to scenario of COVID-19 Period flight. The results also reflect that cooling load presents synchronous trend with passenger flow, but presents asynchronous trend with outdoor temperature. The influence of outdoor temperature on cooling demand delays due to building envelops. It indicates that simple superimposition according to passenger flow change for chiller operation number is reliable, efficient and effective, but is not suitable for outdoor temperature change. The findings are helpful to develop optimal strategies for further real-time control of multi-chiller.

Airline market exit after a shock event: Insights from the COVID-19 pandemic

During the COVID-19 pandemic, passenger demand for air transportation declined drastically. In the Unites States (U.S.), the Coronavirus Aid, Relief, and Economic Security (CARES) Act provided financial assistance. In return, commercial passenger airlines were given minimum service obligations, which allowed airlines to remove markets (flights between origin and destination airport pairs) from their networks as long as they continued operating in all cities that they serviced pre-pandemic. A binary logit methodology is used to model airline-market level decisions to continue operating in a market or to exit it. Two time periods are modeled: during normal operating conditions (before the pandemic) and after a major shock event (after the beginning of the pandemic). Results show that after the pandemic, 8.4 times more airline markets are exited as compared to before. Interestingly, the probability of exit is found to vary widely across markets, airports, and airlines. Some market characteristics have a high probability of exit both before and after the pandemic, including low passenger revenue per available seat mile, low flight frequencies, and flights to/from multi-airport cities. In contrast, other market characteristics impact airlines’ market exit decisions in only one time period rather than both. For example, during normal operating conditions, airport size does not impact market exit. However, after the pandemic, the probability of exit is 1.8 to 2.2 times higher for the larger hub airports as compared to the smallest airports (non-hubs), a result that is explained within the context of the CARES Act minimum service obligations.

Passenger congestion alleviation in large hub airport ground-access system based on queueing theory

Airport public transport systems are plagued by passenger queue congestion, imposing a substandard travel experience and unexpected delays. To address this issue, this paper proposes a bi-level programming for optimizing queueing network in airport access. Firstly, we derive queueing network for airport public transport system, which include the taxi, bus, and subway. Then, we propose a bi-level programming model for optimizing queueing network. The lower level subprogram is designed to solve the passenger transport mode choice, while the upper level subprogram is designed to minimize the number of passengers waiting to be served with queue tolls. Finally, we develop the successive weighted averages (MSWA) method to solve the lower subprogram's passenger share rates and the ant lion optimization (ALO) method to solve the bi-level program's queue toll scheme. The simulation results highlight that our strategy can alleviate queue congestion for daytime and evening scenarios and effectively improve evacuation efficiency.

Managing the Gate Assignment Problem in the Hub Airport with Satellite Halls: A Transfer Demand-Oriented Approach.

This study focuses on managing the gate assignment in the hub airport with both main terminal and satellite halls. We first formulate the gate assignment problem (GAP) as a binary linear programming model with multi-objective functions, where the practical constraints, e.g., gate time conflict and gate compatibility, are considered. Then, we incorporate the impact of gate assignment on transfer passengers and formulate the transfer demand-oriented gate assignment problem (TGAP) as a nonlinear model. A linearization approach and a heuristic approach are designed to solve the TGAP model. A case study is conducted based on the practical data of the Shanghai Pudong International Airport, where a comparison between the results of GAP and TGAP by the two proposed approaches is demonstrated. It shows that the proposed TGAP model and solution approaches can not only enhance the service for transfer passengers but also improve the gate utilization efficiency in the hub airport.

Research on Optimal Allocation of Flight Slots Based on Wake Interval Constraints

Abstract The limited airspace of civil aviation cannot meet the demand for airspace due to the rapid increase in flight volume. This is one of the main reasons for flight delays. This forces us to carry out flight flow management, and the main means of flow management is the resource of flight slots. Optimize the configuration to relieve the moments of “congestion” and the moments of “digging” that are not fully utilized. According to the feature that different types of aircraft need to be equipped with different wake intervals, the article establishes a flight schedule optimization model and develops a flight schedule optimization system. Taking a double runway hub airport as an example, experiments show that the proposed method can effectively alleviate flight delay and reduce flight delay rate.

Multiobjective Location Problems with Variable Domination Structures and an Application to Select a New Hub Airport

Background: Nowadays, variable domination structure is instrumental in studying multiobjective decision making problems. We investigate multiobjective location problems with respect to variable domination structure and its applications in supply chain management. Methods: We formulate practical problems in supply chain management as an optimization problem with a variable domination structure. Moreover, we present the mathematical methods to solve such problems. We investigate two kinds of solutions derived from the concept of minimal and nondominated solutions from vector optimization problems with respect to variable domination structure. Furthermore, we explore how these solution concepts are characterized in practical problems. Results: We ex- pose how those solutions are beneficial in practical problems. However, these results hold true for multiobjective decision making problems with a continuous feasible set; we present a practical problem in the case of a finite set of feasible locations. Conclusions: In many multiobjective location problems, each location’s characterizations, preferences, and restrictions are involved in the decision making process. This study investigates the decision making problems, where different preferences of objective functions at each location are assumed. Moreover, we present a numerical experiment for selecting a new hub airport.

Changes in the volume of air traffic and specialization of segments of the Moscow Aviation Hub from 2014 to 2021

The object of this study is the changes in the development of the Moscow aviation hub in the second decade of the XXI century. The subject of the study is a statistical analysis of changes in the volume of air transportation of passengers, cargo and mail at the Moscow Aviation Hub from 2014 to 2021, both in general and for individual airports in comparison. The change in the share of Sheremetyevo, Domodedovo and Vnukovo in the total volume of the listed types of air transportation is analyzed. The dynamics of the ratio of domestic and international air transportation is investigated. The share of the Moscow aviation hub in the total volume of passenger traffic, cargo turnover and postal air transportation in Russia is considered. The research is based on statistical, historical-geographical, problem-analytical methods, as well as the method of system-structural analysis. This made it possible to conduct an objective, scientifically-based analysis of the processes of changing the volume of air traffic in the Moscow aviation hub. The main conclusions of the study are: air transportation of passengers at the Moscow Aviation hub decreased from 2014 to 2021, while air transportation of cargo and mail increased during this period. The share of Sheremetyevo Airport in all respects increased, the share of Domodedovo decreased, the share of Vnukovo either grew or remained unchanged. The main contribution of the authors to the study of the topic is to consider changes in the statistical parameters of the Moscow aviation hub airports in the context of socio-economic and geopolitical processes, in historical and geographical conditionality; in identifying the key factors of the current dynamics. The novelty of the study is to identify the main trends in the development of the volume and structure of air transportation of the Moscow aviation hub from 2014 to 2021 under the conditions of sanctions pressure and restrictions associated with the COVID-19 pandemic.

Benchmarking the recovery of air travel demands for US airports during the COVID-19 Pandemic

The aviation industry has gone through numerous ups and downs in the past decades. Despite the devastating damage caused by the COVID-19 Pandemic, the aviation industry worldwide still manages to bounce back from the abyss of Q2, 2020, though the speed of recovery is less than satisfactory for most regions. Being aware of the existing literature on air travel demands published since March 2020, this study aims to provide US Primary Hub airports with benchmarks that can help airports predict the recovery of air travel demand during the COVID-19 Pandemic. This study uses the passenger numbers going through airport security checkpoints as the input data and the k-shape clustering algorithm to group airports by their travel demand recovery patterns. The clustering analysis results are presented in a circular dendrogram so that any of the 118 subject airports can quickly locate their benchmarking airports. In this process, the geographic location and hub category of an airport are found to play important roles in determining how local outbound traffic recovers during the Pandemic. We also test if state political preference in the 2020 Presidential Election affects local airport traffic but cannot find any convincing results. The method used by this study can be fed with up-to-date data to produce more timely and reliable results to guide airports and other stakeholders through the recovery journey.

The impact of the COVID-19 pandemic on O-D flow and airport networks in the origin country and in Northeast Asia

The ongoing COVID-19 pandemic has posed a global threat to human health. In order to prevent the spread of this virus, many countries have imposed travel restrictions. This difficult situation has dramatically affected the airline industry by reducing the passenger volume, number of flights, airline flow patterns, and even has changed the entire airport network, especially in Northeast Asia (because it includes the original disease seed). However, although most scholars have used conventional statistical analysis to describe the changes in passenger volume before and during the COVID-19 outbreak, very few of them have applied statistical assessment or time series analysis, and have not even examined how the impact may be different from place to place. Therefore, the purpose of this study was to identify the impact of COVID-19 on the airline industry and affected areas (including the origin-destination flow and the airport network). First, a Clustering Large Applications (CLARA) algorithm was used to group numerous origin-destination (O-D) flow patterns based on their characteristics and to determine if these characteristics have changed the severity of the impact of each cluster during the COVID-19 outbreak. Second, two statistical tests (the paired t-test and the Wilcoxon signed-rank test) were utilized to determine if the entire airport network and the top 30 hub airports changed during COVID-19. Four centrality measurement indices (degree, closeness, eigenvector, and betweenness centrality) of the airports were used to assess the entire network and ranking of individual hub airports. The study data, provided by The Official Aviation Guide (OAG) from December 2019 to April 2020, indicated that during the COVID-19 outbreak, there was a decrease in passenger volume (60%–98.4%) as well as the number of flights (1.5%–82.6%). However, there were no such significant changes regarding the popularity ranking of most airports during the outbreak. Before this occurred (December 2019), most hub airports were in China (April 2020), and this trend remain similar during the COVID-19 outbreak. However, the values of the centrality measurement decreased significantly for most hub airports due to travel restrictions issued by the government.

Output Targeting and Runway Utilization of Major International Airports: A Comparative Analysis Using DEA

The aviation industry is a prominent contributor to economic development. The existence of an airport hub that provides a worldwide transportation network generates economic growth, creates jobs, and facilitates international trade and tourism. This industry also helps in connecting different continents, countries, and cultures. This study utilizes the Data Envelopment Analysis models Charnes, Cooper, and Rhodes (CCR), Banker, Charnes, and Cooper (BCC), Slacks-Based Measure (SBM), and Epsilon Based Measure (EBM) in analyzing and evaluating the operational performance of the 21 major airports runway design during the years of 2016–2019 using the data of the International Civil Aviation Organization (ICAO) air transport statistics. The objective of this paper is to assess the efficiency of various airport runway configurations based on input factors such as number of runways, dimension of runways, airport area, and output factors such as annual number of flights and annual number of passengers. In the four applied models, the results indicated London Heathrow Airport (LHR) and Munich International Airport (MUC) are efficient in utilizing the runway during the considered periods. Surprisingly, airports in the Asian continent with a parallel runway design are more efficient than in North America and Europe. This study can be a valuable reference for operation managers in evaluating and benchmarking the performance of an airport with different types of runway configurations. Moreover, it can be used by decision-makers, investors, stakeholders, policymakers, private companies, and government agencies as a guideline suitable for an airport.

Study on the Hierarchical Structure of the “Belt and Road” Aviation Network Based on K-Core Analysis

Since the “Belt and Road” Initiative, air transport has become the second largest mode of transport after ocean shipping and has contributed more and more to international trade between the countries. To explore the hierarchical characteristics of the aviation network of the “Belt and Road,” analyze the relationship between levels, and identify the core layer of the network, k-core analysis based on the “degree” value was designed and performed; the data of airports and air routes were collected to construct the “Belt and Road” aviation network model; then, k-core decomposition was conducted to reduce the size of nodes layer by layer from the outside to the inside, and a network structure model with 19 levels was obtained; the relationship between the coreness and centrality of nodes in the network was investigated, and the changes in the attribute of networks at different levels and the connection between networks were explored; according to the structural characteristics of the network, the “Belt and Road” aviation network was divided into three categories: the core layer, the middle layer, and the detail layer. The results showed that the highest-level network obtained by k-core decomposition was used as the core layer of the “Belt and Road” aviation network, including 53 airports in 37 countries, all of which have high degree centrality and eigenvector centrality; the core layer network exhibits strong “small world” characteristics; there is little difference between the degree values of nodes, and the network has high stability. The status of China’s airports in the core layer network is not prominent; the four regional sectors such as East Asia, Southeast Asia, Central Asia, and the Middle East are closely connected regarding air transport. Europe is relatively weakly connected with three sectors: East Asia, Southeast Asia, and Central Asia; geographical factors are still the dominant factors determining the status of hub airports along the “Belt and Road.”

Strategy for 19-seat hybrid-electric short haul air transportation

This paper addresses a way to implement greener aviation technologies, such as hybrid-electric propulsion, into the air transportation network to respond to the increasing environmental challenges posed by growing air traffic. New routes could be established between small airports to ensure better air connectivity in Europe while also connecting disadvantaged areas and relieve congestion at hub airports. Such routes could, for example, be served by micro feeder or 19-seat hybrid-electric aircraft, which produce low or no emissions, have lower operating costs, and are more applicable to environmental constraints. To achieve this and overcome the various challenges posed by the new hybrid-electric technologies, a new strategic roadmap for short-haul air transport is needed to optimize network services with small hybrid-electric aircraft.

Characteristics of Unmanned Aircraft System (UAS) Sightings and Airport Safety

This paper presents the characteristics of unmanned aircraft system (UAS) sightings and discusses these characteristics in the context of airport safety using airport operational data. An unmanned aircraft in the vicinity of a commercial airport may be a potential threat to aircraft operations, and may result in operational and economic impacts if it causes an airport delay or shutdown. To ensure a record of UAS activity, the Federal Aviation Administration collects and publishes a UAS Sightings Report. This study analyzed UAS sightings and found that they vary by time of year and time of day, with more sightings in the summer and during the daytime. While all kinds of aircraft may be affected, pilots of general aviation aircraft report more UAS sightings, although overall only 3.3 percent of sightings required the pilot to take evasive action. Only 9.8 percent of UAS sightings occurred at or below 400 feet and 57 percent occurred within five miles of an airport, and sightings are more likely to be reported close to large and medium hub airports. States with a large population (such as California and Florida) and airports in big cities have more UAS sightings. The airports with the most UAS sightings are LaGuardia (LGA), Los Angeles (LAX), John F. Kennedy (JFK), and O’Hare (ORD).