Airline Network Research Articles

Impact of airline network on the global importation risk of mpox, 2022.

From 1 January 2022 to 4 September 2022, a total of 53 996 mpox cases were confirmed globally. Cases are predominantly concentrated in Europe and the Americas, while other regions are also continuously observing imported cases. This study aimed to estimate the potential global risk of mpox importation and consider hypothetical scenarios of travel restrictions by varying passenger volumes (PVs) via airline travel network. PV data for the airline network, and the time of first confirmed mpox case for a total of 1680 airports in 176 countries (and territories) were extracted from publicly available data sources. A survival analysis technique in which the hazard function was a function of effective distance was utilised to estimate the importation risk. The arrival time ranged from 9 to 48 days since the first case was identified in the UK on 6 May 2022. The estimated risk of importation showed that regardless of the geographic region, most locations will have an intensified importation risk by 31 December 2022. Travel restrictions scenarios had a minor impact on the global airline importation risk against mpox, highlighting the importance to enhance local capacities for the identification of mpox and to be prepared to carry out contact tracing and isolation.

Traditional air carriers fare and booking class names as a part of their marketing strategy

This paper focuses on the issue of fare and booking class names of traditional air carriers. Paper discusses the issues of traditional air carriers, history, development and current status, fleets, airline networks, services offered as well as mutual alliances. The article also focuses on the market analysis of the offered class names of the traditional air carriers divided by region. Paper includes an analysis of the data obtained in clear tables regarding preferred names of classes.

Aircraft maintenance facility location planning

This investigation combines the aircraft maintenance routing problem (AMRP) with the aircraft maintenance facility location problem as they are interdependent. Unlike the classical fixed charge location model, aircraft maintenance facility location is not based on static customer demands but on how aircraft move around the airline network while undergoing periodic maintenance checks. Two important factors relevant to this analysis are: (a) aircraft overnight stays at maintenance facilities and airports away from their home bases and (b) the location and cost of maintenance facility construction. There are two possible scenarios concerning the status of maintenance facilities. The first is a tabula rasa case – for a start-up airline with no existing maintenance facilities (AMFLP). The second is an operating airline case with prepositioned maintenance facilities, which we denote as a maintenance facility location update problem (AMFUP). We formulate both as binary integer multicommodity flow problems whose aim is to find the cost-minimizing number, size, and location of the maintenance facilities. Experiments with flight schedules from two regional airlines indicate that total annualized costs are convex in the number of maintenance airports. Though new facility costs tend to dominate, costs associated with noncyclic routes become increasingly important as the number of maintenance airports decreases. Key contributions of this paper are that it (a) demonstrates the superiority of the integration of the aircraft maintenance routing and aircraft maintenance facility location problems, (b) provides a formulation and solution methodology dealing with this significant but under-researched problem, and (c) presents extensive computational experiments, cost and sensitivity analyses for two real life aircraft flight schedules.

Malmquist TFP Yöntemiyle Finansal Etkinlik Analizi: Star Alliance Üyesi Havayolu İşletmelerinde Bir Uygulama

The purpose of this study is to analyze the financial efficiency of 15 airlines that are members of the Star Alliance which is considered the largest international strategic airline network for the period 2016-2019 using the Malmquist Total Factor Efficiency method. In addition, other purposes include the comparison of the change in technical efficiency (TE), technological change (TD) and total factor productivity (TFP) values of airlines that are members of the Star Alliance As a result of the analysis, it was found that the average technological change and total factor productivity values of the airlines in question increased in the period 2016-2017, and the average technical efficiency values decreased. On the other hand, in the period 2017-2018, the opposite situation was observed. In the period 2018-2019, technical efficiency, technological change and total factor productivity values decreased. It was found that the technical efficiency values of the Air Canada and Turkish Airlines increased during the entire period, while the average technical efficiency, technological change and total factor productivity values of the Air New Zealand, Asiana Airlines, Avianca, Lufthansa and Thai Airways decreased.

Comprehensive Evaluation of Chinese Low-Cost Airlines Network Based on Technique for Order Preference by Similarity to an Ideal Solution Approach

Comprehensive Evaluation of Chinese Low-Cost Airlines Network Based on Technique for Order Preference by Similarity to an Ideal Solution Approach

Strategic plan for China's air high-speed rail express freight network and its carbon reduction potential.

The booming development of express freight demand is eager for innovative transportation modes to satisfy the demand for capacity, timeliness, and low carbon emissions. China is working to create an air-HSR fast freight mode based on the continuous expansion of its high-speed rail (HSR) network in an effort to relieve the burden on air freight and lower the emissions associated with it. In this study, a novel freight network design model for the air-HSR planning problem is developed. It combines the centrality of nodes, carbon emissions, and time efficiency to resolve the trade-offs between time efficiency and carbon emissions. This research supports the planning decision and the viability of the air-HSR mode. The hub location decision and initial air-HSR express freight network plan for China are obtained by using the traversal search algorithm and real case data. We then further examine the carbon emission reduction potential that the air-HSR intermodal express network can generate compared with the airline network. However, since the real problem is more complicated and involves many stakeholders, the carbon reduction potential is calculated based on some simplified theoretical assumptions to obtain specific quantitative results. The results indicated that the planned air-HSR network could provide express service nationwide in 24h, while the carbon emission was reduced by 2.08 million tons, which is a 57.35% reduction compared to the original airline network, and saved the entire transportation sector 498.7 million RMB. Additionally, carbon emissions have decreased across each city pair, notably in the central and eastern areas where there is a substantial possibility for carbon emission reduction.

The impact of air travel on the precocity and severity of COVID-19 deaths in sub-national areas across 45 countries

Human travel fed the worldwide spread of COVID-19, but it remains unclear whether the volume of incoming air passengers and the centrality of airports in the global airline network made some regions more vulnerable to earlier and higher mortality. We assess whether the precocity and severity of COVID-19 deaths were contingent on these measures of air travel intensity, adjusting for differences in local non-pharmaceutical interventions and pre-pandemic structural characteristics of 502 sub-national areas on five continents in April–October 2020. Ordinary least squares (OLS) models of precocity (i.e., the timing of the 1st and 10th death outbreaks) reveal that neither airport centrality nor the volume of incoming passengers are impactful once we consider pre-pandemic demographic characteristics of the areas. We assess severity (i.e., the weekly death incidence of COVID-19) through the estimation of a generalized linear mixed model, employing a negative binomial link function. Results suggest that COVID-19 death incidence was insensitive to airport centrality, with no substantial changes over time. Higher air passenger volume tends to coincide with more COVID-19 deaths, but this relation weakened as the pandemic proceeded. Different models prove that either the lack of airports in a region or total travel bans did reduce mortality significantly. We conclude that COVID-19 importation through air travel followed a ‘travel as spark’ principle, whereby the absence of air travel reduced epidemic risk drastically. However, once some travel occurred, its impact on the severity of the pandemic was only in part associated with the number of incoming passengers, and not at all with the position of airports in the global network of airline connections.

A Framework for Predicting Airfare Prices Using Machine Learning

Many academics have concentrated on applying machine learning to retrieve information from databases to enable researchers to perform better. A difficult issue in prediction models is the selection of practical strategies that yield satisfactory forecast accuracy. Traditional software testing techniques have been extended to testing machine learning systems; however, they are insufficient for the latter because of the diversity of problems that machine learning systems create. Hence, the proposed methodologies were used to predict flight prices. A variety of artificial intelligence algorithms are used to attain the required, such as Bayesian modeling techniques such as Stochastic Gradient Descent (SGD), Adaptive boosting (ADA), Decision Trees (DT), K-nearest neighbor (KNN), and Logistic Regression (LR), have been used to identify the parameters that allow for effective price estimation. These approaches were tested on a data set of an extensive Indian airline network. When it came to estimating flight prices, the results demonstrate that the Decision tree method is the best conceivable Algorithm for predicting the price of a flight in our particular situation with 89% accuracy. The SGD method had the lowest accuracy, which was 38 %, while the accuracies of the KNN, NB, ADA, and LR algorithms were 69 %, 45 %, and 43 %, respectively. This study's presented methodologies will allow airline firms to predict flight prices more accurately, enhance air travel, and eliminate delay dispersion. Index Terms— Machine learning, Prediction model, Airline price prediction, Software testing,

Evaluation of Homogenization in Metroplex Operations Based on Multi-Dimensional Indicators

The development of a Multiple Airport Region (MAR) could lead to fierce competition. To evaluate the level of homogenization between airports in a MAR, a homogenization evaluation method based on multi-dimensional indicators was developed. A multi-dimensional indicator system is proposed that takes into account infrastructure, integrated support, operational efficiency and airline networks. Then the Critic method and the Delphi method are used to assign hierarchical weights for each dimension of the indicators. The multi-layer homogenization matrix of the airport pairs within the MAR is derived. For airport pairs with high comprehensive homogenization, suggestions are provided according to analysis of the indicators. This study paper selected three typical MARs internationally to demonstrate the advantage of the proposed approach. The airport pairs with high a homogenous coefficient (greater than 0.5) were selected to analyze the reasons causing high homogeneity. Results show that the multi-dimensional indicators and hierarchical fusion captured the characteristics of the homogenization of MAR. Most airport pairs in New York MAR and in London MAR had strong differentiation of route network layout, airport pairs in Greater Bay MAR had ambiguous division of labor and low homogenization of route network, except CAN and SZX airports. Suggestions are discussed separately to mitigate the homogeneity of the airports in the MAS, thus, to improve the operation performance of the MARs.

Large Aerial Image Tie Point Matching in Real and Difficult Survey Areas via Deep Learning Method

Image tie point matching is an essential task in real aerial photogrammetry, especially for model tie points. In current photogrammetry production, SIFT is still the main matching algorithm because of the high robustness for most aerial image tie points matching. However, when there is a certain number of weak texture images in a surveying area (mountain, grassland, woodland, etc.), these models often lack tie points, resulting in the failure of building an airline network. Some studies have shown that the image matching method based on deep learning is better than the SIFT method and other traditional methods to some extent (even for weak texture images). Unfortunately, these methods are often only used in small images, and they cannot be directly applied to large image tie point matching in real photogrammetry. Considering the actual photogrammetry needs and motivated by the Block-SIFT and SuperGlue, this paper proposes a SuperGlue-based LR-Superglue matching method for large aerial image tie points matching, which makes learned image matching possible in photogrammetry application and promotes the photogrammetry towards artificial intelligence. Experiments on real and difficult aerial surveying areas show that LR-Superglue obtains more model tie points in forward direction (on average, there are 60 more model points in each model) and more image tie points between airline(on average, there are 36 more model points in each adjacent images). Most importantly, the LR-Superglue method requires a certain number of points between each adjacent model, while the Block-SIFT method made a few models have no tie points. At the same time, the relative orientation accuracy of the image tie points matched by the proposed method is significantly better than block-SIFT, which reduced from 3.64 μm to 2.85 μm on average in each model (the camera pixel is 4.6 μm).

Airport location for smart and sustainable living: A model and a case study of rural Missouri, U.S.

• Airports can be relocated to reduce carbon footprint of driving to airport location. • A carbon-footprint formula helps compare emissions from transportation. • Reducing transportation's carbon footprint enables sustainable and smart living. • Real-world results from Missouri, U.S., for smart and sustainable airport location. We consider the problem of determining the location of a regional airport with connections to major hubs within an airline network for the objective of minimizing the carbon footprint of travelers, road congestion, and accident risk. This is aligned with an overarching goal of smart and sustainable living. The overall travel journey is usually composed of a trip from home to the airport, called the connecting trip , and a trip in the air, which is composed of one or more flights. Currently, in the United States, millions of travelers drive in excess of 100 miles to reach overcrowded airports. The connecting trip not only magnifies the driver's carbon footprint, but also creates traffic congestion in cities that have airports—further increasing CO 2 generation in areas surrounding the airports and worsening road safety. The model proposed here identifies a suitable airport location to directly reduce total driving miles. Indirectly, congestion on roads connecting to the airport and the traffic intensity at the airports in the hub-and-spoke network will also be reduced and passengers’ comfort levels will be improved. We will employ a case study from the state of Missouri, United States, to illustrate our model and its concomitant analysis.

Seasonality in European and North American Air Transport Markets: Network Structures and Implications for Airline Performance and Recovery

Abstract This industry note focuses on the seasonal variability in air traffic services and highlights the differences between air service capacity provision in Europe and North America. We explore the manifestations and measurement of seasonality, looking at the trends across airport communities and among airlines in the two continental regions. The relationship between airline network structure and seasonality is also explored in the study. We argue that there are a complex set of drivers of the observed differences in air traffic seasonality and set out a research agenda for further investigation.

Investigating the impact of adding new airline routes on air transportation resilience in China

When building new airports various strategies for initiating new air routes prominently affect the overall system performance. From a perspective of disruption management, however, very little is known regarding the impacts of different strategies exerted to the resilience of airline networks in a multi-layer system configuration. In this paper, we established a multi-layer China airline network (CAN) using route data from 46 airlines in mainland China and proposed four optimization strategies for adding new routes based on service-oriented perspectives. The geographical traits of the multi-layer CAN was first explored and, taking the Daxing International Airport (a new-built airport in Beijing, China) as a demonstrative example, we then conducted both qualitative and quantitative analyses to investigate the impact of the proposed strategies and their effectiveness of improving network resilience. It is revealed that those strategies have heterogeneous effects on the network performance in terms of meeting passenger demands and balancing overall connectivity at a national strategic level, and the overall resilience of the CAN could be significantly improved under the selected strategy by 59.8% on average. Based on the findings, several managerial and policy implications are put forward and discussed. This paper sheds new lights on resilience-building strategies for service carriers and airport managers and is also of value to regional governments for offering incentives for expanding connections to new destinations.

Core-stability over networks with widespread externalities

The Covid-19 epidemic highlighted the significance of externalities: contacts with other people affect the chances of getting infected for our entire network. We study endogenous network formation where not only players or pairs but larger coalitions can, cooperatively change the network. We introduce a model for coalitional network stability for networks with widespread externalities. The network function form generalises the partition function form of cooperative games in allowing the network to be taken into account. The recursive core for network function form games generalises the recursive core for such environments. We present two simple examples to illustrate positive and negative externalities. The first is of a favour network and show that the core is nonempty when players must pay transfers to intermediaries; this simple setting also models economic situations such as airline networks. The second models social contacts during an epidemic and finds social bubbles as the solution.

Autonomous inference of complex network dynamics from incomplete and noisy data.

The availability of empirical data that capture the structure and behaviour of complex networked systems has been greatly increased in recent years; however, a versatile computational toolbox for unveiling a complex system's nodal and interaction dynamics from data remains elusive. Here we develop a two-phase approach for the autonomous inference of complex network dynamics, and its effectiveness is demonstrated by the tests of inferring neuronal, genetic, social and coupled oscillator dynamics on various synthetic and real networks. Importantly, the approach is robust to incompleteness and noises, including low resolution, observational and dynamical noises, missing and spurious links, and dynamical heterogeneity. We apply the two-phase approach to infer the early spreading dynamics of influenza A flu on the worldwide airline network, and the inferred dynamical equation can also capture the spread of severe acute respiratory syndrome and coronavirus disease 2019. These findings together offer an avenue to discover the hidden microscopic mechanisms of a broad array of real networked systems.

Estimating the potential for global dissemination of pandemic pathogens using the global airline network and healthcare development indices

Pandemics have the potential to incur significant health and economic impacts, and can reach a large number of countries from their origin within weeks. Early identification and containment of a newly emerged pandemic within the source country is key for minimising global impact. To identify a country’s potential to control and contain a pathogen with pandemic potential, we compared the quality of a country’s healthcare system against its global airline connectivity. Healthcare development was determined using three multi-factorial indices, while detailed airline passenger data was used to identify the global connectivity of all countries. Proximities of countries to a putative ‘Worst Case Scenario’ (extreme high-connectivity and low-healthcare development) were calculated. We found a positive relationship between a country’s connectivity and healthcare metrics. We also identified countries that potentially pose the greatest risk for pandemic dissemination, notably Dominican Republic, India and Pakistan. China and Mexico, both sources of recent influenza and coronavirus pandemics were also identified as among the highest risk countries. Collectively, lower-middle and upper-middle income countries represented the greatest risk, while high income countries represented the lowest risk. Our analysis represents an alternative approach to identify countries where increased within-country disease surveillance and pandemic preparedness may benefit global health.

Analysis of the Structure and Dynamics of European Flight Networks

We analyze structure and dynamics of flight networks of 50 airlines active in the European airspace in 2017. Our analysis shows that the concentration of the degree of nodes of different flight networks of airlines is markedly heterogeneous among airlines reflecting heterogeneity of the airline business models. We obtain an unsupervised classification of airlines by performing a hierarchical clustering that uses a correlation coefficient computed between the average occurrence profiles of 4-motifs of airline networks as similarity measure. The hierarchical tree is highly informative with respect to properties of the different airlines (for example, the number of main hubs, airline participation to intercontinental flights, regional coverage, nature of commercial, cargo, leisure or rental airline). The 4-motif patterns are therefore distinctive of each airline and reflect information about the main determinants of different airlines. This information is different from what can be found looking at the overlap of directed links.

Hidden city travel and its impact on airfare: The case with competing airlines

Hidden city pricing is an anomaly in airline pricing where a direct flight from A to B is more expensive than a multi-leg flight from A to B and B to C. In this case, customers wanting to travel from A to B may consider purchasing the connecting flight from A to C, deplane in B, and throw away the second leg of the itinerary. This practice is called hidden city ticketing. We study the cause and the impact of hidden city ticketing. The airline industry has perceived this practice harmful to profitability and has taken measures to discourage such practice. We build a stylized model where two airlines compete on a hub-and-spoke airline network and use a game theoretic approach to study airline and consumer decisions. We find that hidden city pricing can arise when an airline lowers the price of its indirect flight to compete with the competitor’s direct flight on the same route. We also find that strategic customers who seek hidden city traveling opportunity can attenuate competition by limiting the airlines’ ability to lower their price for connecting flights. Thus, when customers in the market start practicing hidden city ticketing, airlines face less competition and may be able to charge higher prices. Our results provide future-looking perspective on what may happen to the equilibrium airfare if customers start practicing hidden city ticketing.

Aircraft Maintenance 4.0 in an era of disruptions

Digital transformation in manufacturing is having a distinct impact on a variety of business models, including aircraft maintenance. Airline operators are revamping their Maintenance, Repair and Overhaul (MRO) activities with the use of integrated digital platforms and data mining to achieve greater operational efficiencies in parts usage, downtime, and service costs. The term Aircraft Maintenance 4.0 captures the leveraging of information to achieve greater operational agility and to reduce costs. Consolidating data obtained via wireless connectivity from diverse sources enables effective performance monitoring that can inform MRO operations. The severe restriction of flights worldwide due to the pandemic has disrupted the progress of Aircraft Maintenance 4.0 by altering flight routes and rendering most of the historical data obsolete. In this paper, certain aspects of the disruption that deal with airline network changes and their evolution during the pandemic are studied by using publicly available data of a commercial airline.

Network structure revelation and airport role evaluation under three different COVID-19 pandemic periods: Evidence from a Chinese airline

The continuous spread of coronavirus disease 2019 (COVID-19) has had a substantial impact on China's domestic airline networks. It is important for airlines to identify key airports and airport roles in future network design. In this paper, a k-core algorithm is used to decompose the network layers during different periods of COVID-19 to investigate the network structure and the airport role change. By considering both airport degree and route traffic, network characteristics are analyzed, and the key airports are determined based on network evaluation. The results show that the airline network is robust due to its mixed hub-and-spoke network structure, which is basically dominated by direct flights between airports. However, different operation patterns should be implemented based on airport roles. It is not advisable for airlines to pursue network connectivity at the cost of a low passenger load factor.