Airline Network Research Articles

Towards a maturity model for big data analytics in airline network planning

The evaluation, acquisition and use of newly available big data sources has become a major strategic and organizational challenge for airline network planners. We address this challenge by developing a maturity model for big data readiness for airline network planning. The development of the maturity model is grounded in literature, expert interviews and case study research involving nine airlines. Four airline business models are represented, namely full-service carriers, low-cost airlines, scheduled charter airlines and cargo airlines. The maturity model has been well received with seven change requests in the model development phase. The revised version has been evaluated as exhaustive and useful by airline network planners. The self-assessment of airlines revealed low to medium maturity for most domains. Organizational factors show the lowest average maturity, IT architecture the highest. Full-service carriers seem to be more mature than airlines with different business models.

A subset selection based approach to structural reducibility of complex networks

Most of the real world networks such as the internet network, collaboration networks, brain networks, citation networks, powerline and airline networks are very large and to study their structure and dynamics one often requires working with large connectivity (adjacency) matrices. However, it is almost always true that a few or sometimes most of the nodes and their connections are not very crucial for network functioning or that the network is robust to a failure of certain nodes and their connections to the rest of the network. In the present work, we aim to extract the size reduced representation of complex networks such that new representation has the most relevant network nodes and connections and retains its spectral properties. To achieve this, we use the Subset Selection (SS) procedure. The SS method, in general, is used to retrieve maximum information (based on Frobenius norm) from a matrix in terms of its most informative columns. The retrieved matrix, typically known as subset has columns of an original matrix that have the least linear dependency. We present the application of SS procedure to many adjacency matrices of real-world networks and model network types to extract their subset. The subset owing to its small size can play a crucial role in analyzing spectral properties of large complex networks where space and time complexity of analyzing full adjacency matrices are too expensive. The adjacency matrix constructed from the obtained subset has a smaller size and represents the most important network structure. We observed that the subset network which is almost half the size of the original network has better information flow efficiency than the original network. Also, we found that the contribution to the Inverse Participation ratio of the network comes almost entirely from nodes that are there in the subset. This implies that the SS procedure can extract the top most influential nodes without the need for analyzing the full adjacency matrix.

Network Structural Vulnerability: A Multiobjective Attacker Perspective

In this paper, we provide a novel framework to assess the vulnerability/robustness of a network with respect to pair-wise nodes' connectivity. In particular, we consider attackers that aim, at the same time, at dealing the maximum possible damage to the network in terms of the residual connectivity after the attack and at keeping the cost of the attack (e.g., the number of attacked nodes) at a minimum. Differently from the previous literature, we consider the attacker perspective using a multiobjective formulation and, rather than making hypotheses on the mindset of the attacker in terms of a particular tradeoff between the objectives, we consider the entire Pareto front of nondominated solutions. Based on that, we define novel global and local robustness/vulnerability indicators and we show that such indices can be the base for the implementation of effective protection strategies. Specifically, we propose two different problem formulations and we assess their performances. We conclude this paper by analyzing, as case studies, the IEEE118 power network and the U.S. Airline Network as it was in 1997, comparing the proposed approach against centrality measures.

Analysis on Chinese Airline Network Invulnerability

AbstractAir transportation systems are often subject to failures or attacks induced by unexpected abominable weather or temporal airspace occupation, while complex networks have been springing up as a convenient yet efficient tool to represent and analyze various realistic complex systems such as realistic airline system. In terms of Chinese airline network formed during the spring festival timespan, structural empirical research and invulnerability simulation analysis against various deliberate attack strategies were made using complex network theory, where nodes and edges denotes domestic airports and direct flights between them respectively. The analysis results indicate: The presented airline network is a small net-work with scale-free characteristics, and correlation shows remarkable hierarchical structure and obvious assortative characteristics; The network shows obvious invulnerability under deliberate node attack, while shows partly robustness under edge attack even with obvious attack effects against various attack strategies.

A Network Modelling Approach to Flight Delay Propagation: Some Empirical Evidence from China

This paper examines flight delay propagation in air transport networks. Delays add to additional costs, inefficiencies, and unsustainable development. An integrated flight-based susceptible-infected-susceptible (FSIS) model was developed to analyse the flight delay process from a network-based perspective. The probability of flight delay propagation was determined using a translog model. The model was applied to an airline network consisting of thirty-three routes involving three airlines. The results show that the propagation probability is network-related and varies across different routes. The variation is related to the flight frequencies at airports, route distances, scheduled buffer times, and the propagated delay time. Whereas buffer time has a greater impact on smaller airports, flight movement has a greater impact on larger airports. Having a better understanding of how delays happen can help the development of strategies to avoid them. This will lead to less costs, higher efficiencies, and more sustainable airport and airline development.

Determine the cost of denying boarding to passengers: An optimization-based approach

We present an optimization-based method to evaluate the financial impact of denying boarding to the lowest-fare customers under airline network overbooking. Airlines are demanding quantitative support to evaluate the impact on revenue when facing the decision to deny boarding to customers. Although overbooking is a well-regulated operation for decades, it has drawn considerable criticism due to, at least partially, disagreement regarding compensation. Usually, airlines deny boarding to the lowest-fare customers to assure seat availability for customers in higher-paying booking classes. We model such an assurance by chance-constrained optimization, and we gain managerial implications for overbooking practices, for low-cost airlines in particular. We conclude that low-cost airlines are vulnerable regarding revenue growth under network overbooking in comparison to other airlines. We explore possible revenue-improving suggestions, which will ease the effects of network booking for airlines both analytically and numerically.

ASSESSING THE STOCK MARKET PERFORMANCES OF EU LOW-COST AIRLINES

The top seven Low-Cost Airlines in the EU, as ranked by 2018 passenger travel, are evaluated over a four year time frame using stock market performance in order to ascertain which airlines provide superior performance in relation to the others in the group and in relation to the European stock market. Several topics are discussed relating to this group of airlines including business models, operating strategies, airline networks, regional airports, and standardized fleets. Three airlines emerge with superior stock market performance: Jet2, WizzAir, and Ryanair.Keywords: EU; Low-Cost Airlines; Stock Market PerformanceJEL classifications: G11; L93; R40DOI: https://doi.org/10.32479/ijefi.8160

STOCK MARKET REACTIONS TO REGULATORY EVENTS ON EMISSION TRADING: EVIDENCE FROM THE EUROPEAN AIRLINE INDUSTRY

The inclusion of international aviation emissions into the European Union Emission Trading Scheme starting from 2012 is expected to bring additional costs to flights having either origins or destinations at a European Union airport. In turn, this should bring new costs to airlines. The aim of this paper is to analyze how financial markets reacted to the official announcements on this regulatory change. Our findings, using an event-study methodology and employing a sample of 20 European airlines’ stocks, reveal that the stock markets tend not to take these regulatory changes into account in the valuation of the European airlines’ stocks. We also document that the magnitude of the reaction of low-cost airlines is not statistically significant than that of full-service network airlines’ reaction.

Study on Structural Characteristics of China’s Passenger Airline Network Based on Network Motifs Analysis

The air passenger transport network system is an important agent of social and economic connections between cities. Studying on the airline network structure and providing optimization strategies can improve the airline industry sustainability evolution. As basic building blocks of broad networks, the concept of network motifs is cited in this paper to apply to the structural characteristic analysis of the passenger airline network. The ENUMERATE SUBGRAPHS (G, k) algorithm is used to identify the motifs and anti-motifs of the passenger airline network in China. A total of 37 airline companies are subjected to motif identification and exploring the structural and functional characteristics of the airline networks corresponding to different motifs. These 37 airline companies are classified according to the motif concentration curves into three development stages, which include mono-centric divergence companies at the low-level development stage, transitional companies at the intermediate development stage, and multi-centric and hierarchical companies at the advanced development stage. Finally, we found that adjusting the number of proper network motifs is useful to optimize the overall structure of airline networks, which is profitable for air transport sustainable development.

An Integrated Problem of p-Hub Location and Revenue Management with Multiple Capacity Levels under Disruptions

This paper considers an integrated hub location and revenue management problem in which a set of capacities is available from which one can be chosen for each hub and the disruption is considered in a star-star shaped airline network. We propose a two-stage stochastic programming model to maximize the profit of the network in which the cost of installing the hubs at different levels of capacities, the transportation cost, and the revenue obtained by selling airline tickets are considered. To provide flexible solutions, a hybrid two-stage stochastic programming-robust optimization model is developed by putting relative emphasis on a weighted sum of profit maximization. Furthermore, a sample average approximation approach is used for solving the stochastic programming formulation and a genetic algorithm approach is applied for both formulations. Numerical experiments are conducted to verify the mathematical formulations and compare the performance of the used approaches.

Assessing diversity in multiplex networks

Diversity, understood as the variety of different elements or configurations that an extensive system has, is a crucial property that allows maintaining the system’s functionality in a changing environment, where failures, random events or malicious attacks are often unavoidable. Despite the relevance of preserving diversity in the context of ecology, biology, transport, finances, etc., the elements or configurations that more contribute to the diversity are often unknown, and thus, they can not be protected against failures or environmental crises. This is due to the fact that there is no generic framework that allows identifying which elements or configurations have crucial roles in preserving the diversity of the system. Existing methods treat the level of heterogeneity of a system as a measure of its diversity, being unsuitable when systems are composed of a large number of elements with different attributes and types of interactions. Besides, with limited resources, one needs to find the best preservation policy, i.e., one needs to solve an optimization problem. Here we aim to bridge this gap by developing a metric between labeled graphs to compute the diversity of the system, which allows identifying the most relevant components, based on their contribution to a global diversity value. The proposed framework is suitable for large multiplex structures, which are constituted by a set of elements represented as nodes, which have different types of interactions, represented as layers. The proposed method allows us to find, in a genetic network (HIV-1), the elements with the highest diversity values, while in a European airline network, we systematically identify the companies that maximize (and those that less compromise) the variety of options for routes connecting different airports.

A Combined Average-Case and Worst-Case Analysis for an Integrated Hub Location and Revenue Management Problem

This paper investigates joint decisions on airline network design and capacity allocation by integrating an uncapacitated single allocation p-hub median location problem into a revenue management problem. For the situation in which uncertain demand can be captured by a finite set of scenarios, we extend this integrated problem with average profit maximization to a combined average-case and worst-case analysis of this integration. We formulate this problem as a two-stage stochastic programming framework to maximize the profit, including the cost of installing the hubs and a weighted sum of average and worst case transportation cost and the revenue from tickets over all scenarios. This model can give flexible decisions by putting the emphasis on the importance of average and worst case profits. To solve this problem, a genetic algorithm is applied. Computational results demonstrate the outperformance of the proposed formulation.

A multiobjective hub-airport location problem for an airline network design

This paper presents a biobjective multiple allocation p-hub median problem, discusses the properties of the Pareto frontier and proposes exact and heuristic algorithms for finding the Pareto frontier. Our motivation emanates from airline networks and their new hub investment strategies. The first objective minimizes the total transportation cost of the network, while the second one minimizes 2-stop journeys in order to improve customer satisfaction, which is negatively affected by the multiple-transit routes of airlines. Although using hubs reduces operating costs in networks, a cost-effective hub network may not imply minimum individual travel times for passengers, or happy passengers. It is well-known that airline customers prefer flights with fewer stops. However, reducing 2-stop routes increases the number of arcs, non-stop and 1-stop routes, and thus the total cost in the network. We analyzed the tradeoff between these objective functions. We performed experiments on well-known data sets from the literature. We were able to find the Pareto frontier exactly for small/medium size instances. A variable neighborhood search (VNS) heuristic is presented to approximate the Pareto frontier of large size instances. We also performed an application on the current Turkish aeronautics network. The results are presented and discussed.

Alternative risk-averse approaches for airline network revenue management

In this study, seat inventory control is considered for airline networks. Alternative optimization models are proposed for risk-averse decision makers by incorporating the following measure: lower-semideviation of revenue from a given threshold level or expected revenue. Performance of the proposed models is tested in a simulation model for a sample network under different scenarios by using a nesting heuristic and simulating arrival pattern of the airline demand.

Determining the antecedents and consequences of the airline brand personality

Purpose: This study investigates air passengers’ perceptions of the brand personalities of airlines and explores the relationships between the brand personality and its antecedent factor, the country-of-origin of the airlines, and the consequent factor which is the purchase intentions to the corresponding airlines.Design/methodology/approach: The study uses a questionnaire survey containing air passengers’ trip characteristics and demographics and three main scales: country-of-origin, brand personality, and purchase intentions to collect the data of 490 respondents flying from Kaohsiung to Macao. The passengers are from three types of airlines: an international airline with a global network, a regional airline, and a low-cost carrier. The analysis of variance (ANOVA) was conducted to investigate the differences in the perceptions of brand personalities of airlines and several models were developed to determine the antecedent and the consequent factors of airline brand personality.Findings: The air passengers’ perceptions regarding the brand personalities of different types of airlines were significantly different. The country-of-origin of airlines was proved as a significant factor for the brand personality. The brand personality also significantly influenced passengers’ loyalty to the corresponding airlines. Moreover, some demographic and trip characteristics such as personal monthly income, memberships in the airline’s frequent flyer program, and experiences with the airlines were also determined as the significant variables to impact passengers’ loyalties.Originality/value: The concept of brand personality has been explored by various researches but few related to airline management and identified as a key element to brand marketing. Particularly, low-cost carriers always implement different marketing strategies to create a distinctive position in their target customers’ mind and legacy airlines are also doing different things to retain their customers. Therefore, investigating passengers’ perceptions of airlines from the perspective of brand personality is also an important issue for airline management. Not only does this study prove the differences in passengers’ cognitions of brand personalities of a global network airline, a regional network airline, and a low-cost airline but conclude the determinants and influences of the brand personality.

Exploring network effects of point-to-point networks: An investigation of the spatial patterns of Southwest Airlines’ network

This paper explores network effects in Point-to-Point airline networks by examining the spatial patterns of Southwest airlines' route network during Southwest's major network expansionary period for completing continental US geographic coverage between 1990 and 2006. Estimation results from a spatial probit model reveal clear spatial dependence in profitability across different routes served by the carrier. Detailed investigation suggests two main sources of network effects, namely: (1) airport and regional presence, and (2) substitutability between airport-pair markets. Findings of the paper suggest also that the network effects embedded in Southwest's Point-to-Point network have many distinguishing features as compared to those identified in a typical Hub-and-Spoke network. The results of this study on Southwest's network expansionary period help us to predict how emerging LCC networks in other large aviation market, such as China, are likely to be developed over time if regulators give carriers reasonable freedom to develop their service network efficiently.

Network Revenue Management with Cancellations and No‐Shows

Customer cancellations and no‐shows have an important impact on the performance of an airline's revenue management system. In this study, we study airline network revenue management problems with customer cancellations and no‐shows, considering two types of demand models: independent‐demand and choice‐based‐demand models. In independent‐demand models, the booking request rates for different products are independent of the assortment of products offered by the airline (exogenous), whereas in choice‐based‐demand models they depend on the airline's assortment (endogenous). The stochastic problems of optimizing the booking policies for the two demand models are not tractable because of the high‐dimensional state space. Therefore, for each model, we use the corresponding deterministic, continuous‐time, and continuous‐state model, known as the fluid model. A booking policy based on the fluid solution can be implemented for the original stochastic, discrete model. This fluid policy is shown to be asymptotically optimal when the arrival rates become high and the seat capacities become large. In the independent‐demand setting, an optimal fluid solution can be computed by solving a convex optimization problem for which efficient algorithms exist, whereas for some choice‐based‐demand models, the fluid control problems are known to be intractable. We also develop mixed fluid policies for the independent‐demand setting, taking advantage of the real‐time booking state, to improve the performance over the fluid policy. In a factorial experiment of 20 small‐sized single‐leg problems, the revenue loss under one of the mixed fluid policies is demonstrated to be less than 1% of the optimal value for all problems.

High-Fidelity Design-Allocation Optimization of a Commercial Aircraft Maximizing Airline Profit

Traditionally, computational design optimization of commercial aircraft is performed by considering a small number of representative operating conditions. These conditions are based on the design Mach number, altitude, payload, and range for which the aircraft will be flown. However, the design also influences which routes and mission parameters are optimal, and so there is coupling that is ignored when using the traditional approach. Here, the aircraft design, mission profiles, and the allocation of aircraft to routes in an airline network are simultaneously optimized. This is a mixed-integer nonlinear programming problem that is reformulated as a nonlinear programming problem because of the large number of design variables. The reformulated problem is solved using a gradient-based optimization approach with a parallel computational framework that facilitates the multidisciplinary analysis and the derivative computation. A surrogate model is used for the computational fluid dynamics analysis that is retrained in each optimization iteration given the new set of shape design variables. The resulting optimization problem contains over 4,000 design variables and close to 14,000 constraints. The optimization results show a 2% increase in airline profit compared with the traditional multipoint optimization approach. The wing area increases to the upper bound, enabling a higher cruise altitude that improves propulsive efficiency. This study finds that simultaneously optimizing the allocation, mission, and design to maximize airline profit results in a different optimized wing design from that resulting from the multipoint optimization approach.

Exploring the mechanisms behind the country-specific time of Zika virusimportation.

The international spread of Zika virus (ZIKV) began in Brazil in 2015. To estimate the riskof observing imported ZIKV cases, we calculated effective distance, typically an excellent predictorof arrival time, from airline network data. However, we eventually concluded that, for ZIKV, effectivedistance alone is not an adequate predictor of arrival time, which we partly attributed to the difficultyof diagnosing and ascertaining ZIKV infections. Herein, we explored the mechanisms behind theobserved time delay of ZIKV importation by country, statistically decomposing the delay into twoparts: the actual time to importation from Brazil and the reporting delay. The latter was modeled as afunction of the gross domestic product (GDP) and other variables that influence underlying diagnosticcapacity in a given country. We showed that a high GDP per capita is a good predictor of shortreporting delay. ZIKV infection is generally mild and, without substantial laboratory capacity, casescan be underestimated. This study successfully demonstrates this phenomenon and emphasizes theimportance of accounting for reporting delays as part of the data generating process for estimatingtime to importation.

The evolution of air transport networks and impacts on shortest travel times between NUTS-3 regions – a case study for intra-European trips originating in Germany

The European air transport market has evolved over the past decades at an amazing pace. Key developments include the rise of the low cost carriers (LCCs), orginally at regional and military conversion airports and now also at the larger secondary airports and hubs, the withdrawal of network carriers from non-hub routes and a widely dissapperance of classical regional air services with turboprops. We hypothesize that all these developments have had impacts on travel times between European regions and hence of the achievement of the European Commission’s Flightpath 2050 goal that 90% of trips within Europe can be completed within 4 hours. In this conference presentation, we present a methodology to analyse shortest car and air travel times including a combination of both modes for trips between European regions. We then apply our model to three test cases for travel from Germany to Europe on the level of NUTS-3 regions (districts and larger cities) in order to address the following questions: First, we assess for which region pairs and distances combinations of car and scheduled air transport offer travel time savings over trips by car only. Second, we evaluate how the evolution of the European air transport network has changed shortest travel times for trips from each German NUTS-3 region to Europe between 2000 and 2017. Third, we analyse to what extent full interlining between any airline - including low cost carriers - could help to reduce shortest travel times. The latter topic is related to the current trend of low cost carriers offering transfer connections and/or acting as feeders for traditional network airlines.