Hub Airports Research Articles

Dynamic model of arriving passengers based on dual value drive

In order to optimize the service process of integrated transportation hubs, the dynamic characteristics of the gathering behavior of arriving passengers were analyzed, and the transformation mechanism of the gathering behavior of arriving passengers in the time dimension of inverse gamma distribution, gamma distribution and chi-square distribution was studied. It was revealed that the gathering behavior was jointly affected by the flight operation time and the utility of the transfer mode, and was converted into the time value and utility value of the arriving passengers respectively. On this basis, a dual-value-driven dynamic model of arriving passengers was established based on the three distributions, and the output was the parameter-adjustable distribution of arriving passengers. The results show that the simulation output with adjustable parameters is consistent with the real distribution, which provides a method and basis for the accurate prediction of the distribution of arriving passengers at hub airports.

Utilizing Fuzzy TOPSIS for Sustainable Development: A Case Study in Selection of Airport Location

In recent years, most countries have seen an increased focus on the concept of sustainable development in decisions regarding transportation infrastructure where environmental and social criteria are considered on par with economic criteria. This is particularly important in decisions to build new airports or expand their capacity with long-term implications for economic growth, welfare and regional development processes.The fuzzy TOPSIS (Technique for Order Preference by Similarity to the Ideal Solution) method is distinguished from the other methods of the multi-criteria decision making (MCDM) in that it arranges the available alternatives according to their proximity to the optimal solution. In real life, alternatives are observed in relation to the criteria and the weights of the criteria with fuzzy numbers. In such a case, the crisp traditional methods that solve the problems of multi-criteria decision-making are considered to be of weak effectiveness. The aim of this paper is to solve the problem of choosing a new hub airport for a hypothetical European Union airline, by applying the fuzzy TOPSIS method based on alpha level sets as it was applied to a selected data set from the available alternatives (candidate airports). The criteria for expressing the performance of these airports are defined and evaluated by the decision maker. The objective of the practical application of the studied problem is to show the usefulness of the method used as a tool that helps the decision maker to determine which of the alternatives represents the best solution in an environment with fuzzy data.

A robust optimization approach for designing multi-period airport hub network with uncertain capacity

A robust optimization approach for designing multi-period airport hub network with uncertain capacity

Enhancing airline connectivity: An optimisation approach for flight scheduling in multi-hub networks with bank structures

While one salient characteristic of hub airports lies in connecting passengers, the full-service airlines in North America concentrate their networks spatially over a number of hubs. Having witnessed the emerging multi-hub network, this paper investigated the flight scheduling problem under a multi-hub configuration, taking a well-defined bank structure and airport operational restrictions into account. An integrated non-convex Mixed-Integer Nonlinear Programming (MINLP) approach was proposed to enhance airline connectivity, considering different combinations of traffic flow direction and connecting times. To verify the scalability and effectiveness of the proposed model, a comprehensive case study has been undertaken with real-world scheduling data from Air China, which was solved by a novel problem-specific Selective Simulated Annealing (SSA) algorithm. Substantial improvements were achieved without sacrificing the scheduling efficiency. Precisely, the programme adjusted the flights during a typical operational day in a timely manner. The post-optimisation outcomes have witnessed its effectiveness with a 17.97%, 17.06%, 22.41% and 53.86% increase in airline connectivity at its four major hub airports (Chengdu Shangliu, Beijing Capital, Shanghai Pudong and Hongqiao) in China, respectively. A clear pattern of the bank structure also confirms its positive impact on airline connectivity under the multi-hub network configuration. Lastly, a comparative analysis for the distribution of all feasible connections further highlights the critical challenge concerning the role of the hubs in a multi-hub network. More specifically, Air China’s multi-hub network systematically performs better on Domestic-International routes, due to flight schedule, frequencies, geographical placements and detours. Among the four hub airports, Beijing Capital International Airport stands out as a dominant one, which implies its potential to serve as a robust international hub airport.

Competition in the African air transport market

The air transport market in Africa is regulated and viable for designated carriers. Although there are some sub-regional liberalizations, airlines primarily compete based on the specifications of bilateral air service agreements. However, this competitive landscape has not been thoroughly investigated. This study examines market concentration in the African aviation industry and explores its impact on the output of African airlines. The competition was measured at the airport level using the Herfindahl-Hirschman index. Panel data were drawn from 54 countries, 339 airports, and 221 airline groups for the period 2015–2019. Kernel density estimation shows that the region’s market for air transport services is fragmented, with many small airlines. Strong competition is more prevalent at larger hub airports, airports near tourist destinations, and major airports far from larger African hubs, while markets tend to be concentrated where dominant and protected flag carriers operate. Competition is also low or limited at smaller airports offering limited flights. The results of panel regression analysis reveal a nonlinear relationship between market concentration and airline output, as strong competition may limit airlines’ output, and high concentration may promote a dominant airline or alliance that may hinder other airlines from entering or remaining in the market.

Messaging that resonates with a community during airport construction

Airports throughout the world are remodelling, expanding and upgrading to handle increasing travel demand and Salt Lake International Airport (SLC) is no exception. In 2020, SLC opened the first new hub airport built in the USA in the 21st century. There were many challenges along the way, such as a worldwide pandemic and an earthquake with an epicentre just miles away from the airport. Perhaps the most monumental task was to build a 4 million square foot airport adjacent to an operating one. But beyond navigating construction and operations, building support for a new airport — when the existing airport was so beloved in the community — created additional challenges. SLC’s communication and marketing team was tasked with crafting messages to help the community navigate this change, while also educating, informing and engaging the public. This paper discusses how, in order to provide a roadmap for communicating through the massive construction project, a Strategic Communication and Marketing Plan was developed for The New SLC Redevelopment Program that outlined goals and audiences, messaging, branding, marketing, traditional and social media and video production. The paper presents how the campaign was rooted in research and continually adjusted based on feedback and other evaluation mechanisms. The first step in developing a comprehensive communication plan was to conduct research by surveying SLC’s audiences prior to undertaking a branding programme. An important research component was to identify key audiences and stakeholders, including internal audiences — such as employees and tenants — to help spread the project’s message through grass-roots efforts. Once feedback from the survey was compiled, a brand was developed that resonated with the intended audiences. Just as important was to develop strategic messaging to inform audiences, while also educating and engaging. Next, determining the most effect ways to deliver the message was key, and was done through a combination of advertising, events, press conferences and traditional and social media.

Investigating the impact of spatial dependence and heterogeneity on airport relationships: Empirical evidence from China

This research focused on the relationships among 34 major airports in China, analyzing how these relationships are influenced by spatial dependence and heterogeneity. Using a spatial econometric model, it found that passenger traffic in China’s airports displays a mutually driven aggregation effect. Furthermore, the study revealed that the airport relationship changes from complementarity to competition as the geographic distance between them decreases. The study also classified the main airports in China into three hierarchical levels and found significant complementarity and competition among those within the hub-and-spoke network structure. Specifically, this study identified a complementary relationship among regional trunk and local branch airports, as-well-as significant competition among hub airports. Crucially, the study suggests that the absence of a hub airport weakens the collaborative interactions between different types of airports, emphasizing the critical importance of hub airports for the connectivity and operational efficiency of China’s airport network.

Layout analysis of the RCEP international airline network based on hub identification using improved contribution matrix

The signing of the Regional Comprehensive Economic Partnership agreement brings new opportunities for the development of international air transportation. Faced with fierce competition, it is worth studying how hub airports should enhance competitiveness, and how low-cost carriers and full-service carriers should optimize the RCEP international airline network layout for better development. Aiming at providing suggestions for the development of hub airports, low-cost and full-service carriers in the RCEP international airline network, this paper identifies the hub airports, analyzes the layout of the RCEP international airline network, and the multi-layered characteristics based on an improved contribution matrix using data from 2010 to 2019 collected from the Official Airline Guide (OAG). This method comprehensively considers attributes of hub airports and the multi-layered characteristics of the airports and routes. The layout analysis indicates that the RCEP international transportation market presents a more open environment for competition and cooperation where base carriers are often the biggest supporters of hub construction. The multi-layered characteristics analysis reveals that low-cost carriers contribute more towards opening up new RCEP routes than full-service carriers. It is advised that carriers newly entering the RCEP international aviation transportation market and low-cost carriers dedicate to establishing new routes around their hub airports to monopolize this market and enhance their market share, whilst full-service carriers consolidate existing routes and increase route density to achieve economic benefits.

Hub Airport End-Around Taxiway Construction Planning Development: A Review

Hub airports typically have multiple parallel runways, requiring aircraft to transfer between them. This increases the risk of runway incursions. End-around taxiways (EATs) mitigate such risk by enabling bypassing without runway crossings. This review summarizes 15 EAT layouts worldwide and presents two classification methods: by configuration (N-type, M-type, Large N-type) and by operational relationship with runways (back-around, runway end-around, start-around). The key benefits of EATs were manifold, including the reduction of runway incursions, controller workload, and delays while improving communication safety, capacity, and efficiency. However, drawbacks such as increased land use, construction costs, and potentially longer taxi times and distances should be considered. Therefore, the optimization of the EAT layout is essential. In pursuit of optimal design, considerations should encompass limiting obstacle surfaces, flight procedures, navigation/lighting facilities, jet blast, and visual obstructions from end-around aircraft to departing aircraft. Notably, challenges arise in reducing distances between EATs and thresholds/ends. The given solutions include displacing thresholds, creating “sunken” lowered elevation EATs, and utilizing terrain shielding instead of metallic visual screens. A case study was introduced to demonstrate the successful improvement of the EAT via sunken EATs and terrain shielding at Guiyang Airport. The conclusion encourages further optimization of EAT layouts that balance safety and efficiency.

The effect of United States commercial airport rate-setting methods on airport bond ratings

PurposeA major source of external funding for US airports comes from issuing municipal bonds. Credit rating agencies evaluate the bonds using multiple factors, but the judgments behind the ratings are not well understood. This paper examines if airport rate-setting methods affect the bond ratings of US airports.Design/methodology/approachUsing a set of unbalanced panel data for 58 hub airports from 2010 to 2019, we examine the effect of the rate-setting methods and other airport characteristics on Fitch’s airport bond rating.FindingsWe find that compensatory airports consistently receive a very high bond rating from Fitch. The probability of getting a very high Fitch rating increases by ∼28 percentage points for a compensatory airport. Additionally, the probability of getting a very high rating is about 33 percentage points higher for a legacy hub.Research limitations/implicationsThe study uses Fitch bond ratings. Future studies could examine if S&P’s and Moody’s ratings are also influenced by airport rate-setting methods and legacy hub status.Practical implicationsThe results uncover the linkage between bond ratings and their determinants for US airports. This information is important for investors when assessing airport creditworthiness and for airport operators as they manage capital project financing.Originality/valueThis is the first study to evaluate the effects of rate-setting methods on airport bond rating and also the first to document a statistically significant relationship between airports’ legacy hub status and bond ratings.

Joint runway–gate assignment based on the Branch-and-Price algorithm

Airport runway assignment and gate assignment are both crucial problems for large multirunway airports. A cooperative schedule for the runway and gate can have a significant effect on airport ground operation efficiency and safety. This paper focuses on the joint assignment problem of runways and gates considering parking time differences and airline preferences. A quadratic model is established to reduce taxiing fuel consumption loss and increase the robustness of gate assignment. An improved branch-and-price algorithm is proposed by utilizing an improved pulse algorithm and various acceleration strategies to improve the solution performance. The actual flight data at an international hub airport are utilized to validate the proposed model. The results indicate that the joint assignment model can effectively reduce ground taxiing loss, optimize gate assignment robustness, and increase the utilization rate of contact gates. The proposed algorithm also achieves better computational accuracy and efficiency.

Quantifying the energy flexibility potential of a centralized air-conditioning system: A field test study of hub airports

Centralized air-conditioning systems are widely considered a major energy consumer with high energy flexibility, contributing to renewable penetration and power system regulation. Nevertheless, a lack of understanding of their system components' abilities limits the utilization of their full potential. Hereby, we took the centralized air-conditioning systems of two hub airports as typical examples and quantified their energy flexibility potential by cooling load reduction potential (qmax), cooling energy storage capacity (Q), based on field test. Cooling plants had the highest potential (qmax = 70–110 Wc/m2; Q = 500∼1500 Whc/m2), followed by terminal devices with building thermal mass (qmax = 2–11 Wc/m2; Q = 10–40 Whc/m2) and transmission & distribution networks (qmax = 50–200 Wc/m2; Q = 10–20 Whc/m2). Various air-conditioning terminal devices utilize building thermal mass to different degrees. During the investigation, the conventional all-air system used 7.8 %∼18.7 % of the building's thermal capacity, whereas that of the radiant floor system significantly increased to 38.9 %∼48.3 %. Consequently, the systems of the hub airports can participate in demand response programs with a cooling load reduction of 2∼15 MWe, for 6–20 h using different operating strategies. These findings shed light on demand-side flexibility characterization and exploitation to support a decarbonized energy system.

Selection of Secondary Hub Airport Location Based on Connectivity and Green Airport Solutions

Selection of Secondary Hub Airport Location Based on Connectivity and Green Airport Solutions

The impact of Airport business practices and governance forms on excess capacity

Excess capacity poses a persistent challenge for airports as they often encounter lumpy capacity problems. The excess capacity is primarily influenced by factors such as uncertain demand, airline competition, airport pricing strategies, and the market structure of the airport industry. However, airport business practices also account for this issue. Using data from 59 U.S. airports between 2009 and 2019, we found that residual airports have less unused capacity than compensatory airports. This can be explained by the control of signatory airlines over the residual airports’ investment decisions and the lack of retained earnings under the residual agreement. We did not detect a significant difference in excess capacity between single-purpose and multipurpose governance forms. However, it is noteworthy that large hub airports exhibit higher capacity utilization compared to medium hub airports. Airports dominated by LCCs have higher capacity utilization rates, while the unused capacity remains consistent between cargo and non-cargo airports.

Quantifying energy flexibility potential of ground electric vehicles in an airport with real behavior data

Towards sustainable airports, vehicle electrification can not only reduce direct carbon emissions, but also provide great energy flexibility to integrate renewable energy. However, it remains challenging to quantify their potential, due to a lack of understanding of various vehicles’ complicated behavior in an airport. Hereby, a large-scale investigation into the behavior and energy consumption of all types of vehicles was performed in a Chinese hub airport. From the overall perspective of an airport, a behavior data-based simulation was conducted to evaluate their accessible storage (EAS) under different conditions, including charging infrastructure and flight schedule. For the full-potential case with a normal flight schedule, the airside vehicles have the largest share of EAS (60 %), followed by the landside vehicles in the ground transportation center (30 %) and the staff parking lots (10 %). The overall EAS is almost as large as the energy storage demand to realize a fully solar-supplied airport. Nevertheless, the current capacity is limited to only 15 % of the full potential by electrification rate and charging behavior. This study fills the gap of lacking behavior data of the vehicles in airports. The findings shed light on the role of vehicle electrification in airport decarbonization and provide guidelines for future practice.

Comparison of Taxi-in and Taxi-out Times by Airport Hub Classification and Number of Hot Spots

Comparison of Taxi-in and Taxi-out Times by Airport Hub Classification and Number of Hot Spots

Transformer based day-ahead cooling load forecasting of hub airport air-conditioning systems with thermal energy storage

The air conditioning system constitutes more than half of the total energy demand in hub airport buildings. To enhance the energy efficiency and to enable intelligent energy management, it is vital to build an accurate cooling load prediction model. However, the current models face challenges in dealing with dispersed load patterns and lack interpretability when black box approaches are adopted. To tackle these challenges, we propose a novel k-means-Temporal Fusion Transformer (TFT) based hybrid load prediction model. Specifically, the daily load patterns are grouped using an improved k-means clustering method that considers both input feature weights and dynamic time warping (DTW) distances. Additionally, the statistical features of the clustering output are inputted into the TFT. By further incorporating context information, the integration of data between different schema categories is achieved, thus reducing errors that may occur during the transition process. As a result, the prediction performance and interpretability are significantly improved. The Chongqing Jiangbei Airport T3A terminal is used as a case study, and experiments are conducted using cooling data from the No.1 energy station, as well as the airport traffic data and the meteorological station data. Results are compared with other mainstream models, confirming that the proposed day-ahead load forecasting model achieves improvements in several performance indicators, including MAE, MAPE, CV-RMSE, and R2, which are 384 kW, 3%, 5%, and 0.058 respectively.

Air traffic inefficiencies and predictability evaluation using route mapping—the Tokyo International Airport case

Air traffic inefficiencies lead to excess fuel burn, emissions and air traffic controller (ATCo) workload. Various stakeholders have developed metrics to assess the operation performance. Most metrics compare the actual trajectories to some benchmark ones to calculate excess time or distance. This research is inspired by cellular automata (CA) and develops a combined time-distance lateral inefficiency and predictability metric using discrete space and time mapping on published flight routes. The analysis is focused on Tokyo International Airport, but uses only track data and published routes, which makes it easily applicable to any other hub airport worldwide. The mapping and velocity analyses are used to investigate when and where ATCos are most likely to intervene to provide save separation. A metric which can be adjusted to evaluate both traffic flow predictability and efficiency is proposed. This metric can be applied to better understand current traffic and enable future improvements towards seamless air traffic flow management.

Electric Aircraft Operations: An Interisland Mobility Case Study

This study focuses on the feasibility of electric aircraft operations between the Caribbean islands of Aruba, Bonaire, and Curaçao. It explores the technical characteristics of two different future electric aircraft types (i.e., Alice and ES-19) and compares their operational requirements with those of three conventional types currently in operation in the region. Flight operations are investigated from the standpoint of battery performance, capacity, and consumption, while their operational viability is verified. In addition, the CO2 emissions of electric operations are calculated based on the present energy mix, revealing moderate improvements. The payload and capacity are also studied, revealing a feasible transition to the new types. The impact of the local climate is discussed for several critical components, while the required legislation for safe operations is explored. Moreover, the maintenance requirements and costs of electric aircraft are explored per component, while charging infrastructure in the hub airport of Aruba is proposed and discussed. Overall, this study offers a thorough overview of the opportunities and challenges that electric aircraft operations can offer within the context of this specific islandic topology.

Bi-Level Optimization for De-Icing Position Allocation and Unmanned De-Icing Vehicle Fleet Routing Problem.

Aircraft icing due to severe cold and local factors increases the risk of flight delays and safety issues. Therefore, this study focuses on optimizing de-icing allocation and adapting to dynamic flight schedules at medium to large airports. Moreover, it aims to establish a centralized de-icing methodology employing unmanned de-icing vehicles to achieve the dual objectives of minimizing flight delay times and enhancing airport de-icing efficiency. To achieve these goals, a mixed-integer bi-level programming model is formulated, where the upper-level planning guides the allocation of de-icing positions and the lower-level planning addresses the collaborative scheduling of the multiple unmanned de-icing vehicles. In addition, a two-stage algorithm is introduced, encompassing a Mixed Variable Neighborhood Search Genetic Algorithm (MVNS-GA) as well as a Multi-Strategy Enhanced Heuristic Greedy Algorithm (MSEH-GA). Both algorithms are rigorously assessed through horizontal comparisons. This demonstrates the effectiveness and competitiveness of these algorithms. Finally, a model simulation is conducted at a major northwestern hub airport in China, providing empirical evidence of the proposed approach's efficiency. The results show that research offers a practical solution for optimizing the use of multiple unmanned de-icing vehicles in aircraft de-icing tasks at medium to large airports. Therefore, delays are mitigated, and de-icing operations are improved.