Airline Network Research Articles

Identification of Disruptions and Recoveries in Airline Networks

Identification of Disruptions and Recoveries in Airline Networks

Cooperation and competition to design a point-to-point airline network under regulation for a new entry

Cooperation and competition to design a point-to-point airline network under regulation for a new entry

Global trends and problems of digital transformation in the global aviation services market

In the article, based on an assessment of the evolution, dynamics and modern trends in the development of world aviation, it was substantiated that at the present stage, aviation is one of the most globalized industries, continuing its geographical expansion, withstanding world crises and demonstrating longterm growth and sustainability. The main trends in the modernization of the aviation industry are revealed and the mechanisms of the impact of digitalization on the consumption patterns in the modern world market of aviation services in the context of demographic and technological shifts are assessed. The possibilities of introducing new models of service delivery in the context of digitalization of world aviation have been identified. The main global trends have been formulated that will determine the development of the world aviation services market in the context of its digitalization in the future, in particular: the development of customer focus; accelerated implementation of advanced data analytics; increasing pressure on airlines in the context of increasing environmental sustainability; complication of distribution systems in the context of increased competition with international companies — technology giants; modification of international airline networks; growth of cross-border partnerships and strategic alliances in the industry. We have found that the most important area of modernization of the industry will be the challenges of the development of intermodal and multimodal air transport communications, as well as the optimization of air traffic management at the international level. Finally, the authors believe that regulatory differentiation and heterogeneity in different countries remains a key barrier to digital transformation in the global aviation services market, and besides, national legislation is not yet able to timely adapt to the rapid changes taking place in the market.

Vulnerability of Regional Aviation Networks Based on DBSCAN and Complex Networks

To enhance the accuracy of performance analysis of regional airline network, this study applies complex network theory and Density-Based Spatial Clustering of Applications with Noise (DBSCAN) algorithm to investigate the topology of regional airline network, constructs node importance index system, and clusters 161 airport nodes of regional airline network. Besides, entropy power method and approximating ideal solution method (TOPSIS) is applied to comprehensively evaluate the importance of airport nodes and complete the classification of nodes and identification of key points; adopt network efficiency, maximum connectivity subgraph and network connectivity as vulnerability measurement indexes, and observe the changes of vulnerability indexes of key nodes under deliberate attacks and 137 nodes under random attacks. The results demonstrate that the decreasing trend of the maximum connectivity subgraph indicator is slower and the decreasing trend of the network efficiency and connectivity indicators is faster when the critical nodes of the regional airline network are deliberately attacked. Besides, the decreasing trend of the network efficiency indicator is faster and the decreasing trend of the maximum connectivity subgraph indicator is slower when the nodes of four different categories are randomly attacked. Finally, it is proposed to identify and focus on protecting critical nodes in order to better improve the security level of regional airline system.

Trends in the development of the air transportation market of Ukraine and influencing factors

Introduction. Given the current complex realities of conducting aviation business in a pandemic, the application of large-scale measures to combat its spread, as well as taking into account the development of liberalization of the air market, analysis of the effectiveness of Ukrainian airlines and identify current trends in Ukraine's air transportation market is an important task. The purpose of the paper is to investigate state of air transportation market in Ukraine in dynamics, to identify current trends and status of national airlines. Results. After the recession of 2014-2015, the volume of international passenger traffic by domestic airlines increased during the period 2016-2019. In 2020, it decreased by 76 %. At the same time, passenger traffic of Ukrainian airlines decreased by 65 % compared to 2019. Over the past five years, Ukraine’s air transportation market has evolved from highly concentrated to moderately concentrated. During this period, domestic airlines were more active in carrying out charter flights, while foreign airlines began to dominate in performing regular and scheduled flights with active participation of leading European low-cost airlines. Analysis of the market share of airlines with the largest volumes of passenger air traffic in air market of Ukraine showed that over past three years there has been a rapid decline in market shares of leading network airlines, both domestic and foreign. There was a rapid growth of market shares of foreign airlines – low-cost carriers. Conclusions. The changes identified in the air transportation market of Ukraine can be explained not only by the consequences of the pandemic, but also by the consequences of air transportation market liberalization. The signing of the Agreement on the Common Aviation Area in September 2021 between Ukraine and the EU will have a direct impact on the development of air transportation market of Ukraine, both in terms of supply and demand. Domestic airlines will be able to benefit from market liberalization only if they increase their competitiveness.

Balancing capacity and epidemic spread in the global airline network

The structure of complex networks has long been understood to play a role in transmission and spreading phenomena on a graph. Such networks form an important part of the structure of society, including transportation networks. As society fights to control the COVID-19 pandemic, an important question is how to choose the optimum balance between the full opening of transport networks and the control of epidemic spread. In this work we investigate the interplay between network dismantling and epidemic spread rate as a proxy for the imposition of travel restrictions to control disease spread. For network dismantling we focus on the weighted and unweighted forms of metrics that capture the topological and informational structure of the network. Our results indicate that there is benefit to a directed approach to imposing travel restrictions, but we identify that more detailed models of the transport network are necessary for definitive results.

Pricing carbon in the aviation sector: Evidence from the European emissions trading system

A policy change in the European Union's Emissions Trading Scheme (EU ETS) provides us with a unique opportunity to measure the impact of carbon pricing on aviation, the most climate-intensive mode of transport. We implement a difference-in-differences strategy on a sample based on all flights within Europe from 2010 to 2016 to examine the causal impact of the EU ETS on emissions and supply. We find that the EU ETS reduced emissions by 4.7% in the regulated routes relative to the counterfactual. When we restrict the sample to short-haul flights, routes on which competition from other means of transport may exist (less than 1,000 km), the reduction in emissions is 10.7%. Finally, the reduction in emissions is also high for low-cost airlines (−11%) but it is not statistically significant for network airlines. In sum, the EU ETS has helped to mitigate emission growth by 3 Mt CO2 per year during the period analyzed, but not to reduce absolute emissions in the sector, as needed.

An integrated connection planning and passenger allocation model for low-cost carriers

The aviation industry has recently experienced a fast move toward the hybridization of airline business models. This has remarkably affected airline network structures, moving from a neat contraposition between hub-and-spoke networks (typical of full-service carriers) and point-to-point networks (typical of low-cost carriers) to more hybrid architectures. While literature has widely documented the ongoing restructuring of airline networks, the development of decision support systems in this respect has been rather scant. In this paper, we develop an optimization-based approach—referred to Integrated Connection Planning and Passenger Allocation Model (ICPPAM)—to support low-cost airlines in the early stage of connection planning, encompassing the definition of the most promising transfer airports and the set of optimal connecting itineraries to be rolled out over an existing network of flights. The ICPPAM solves the passenger mix problem on every flight segment, leveraging endogenous demand estimates based on an empirical discrete choice model. We demonstrate the applicability of the proposed approach to the large-scale network of Ryanair in 2019. Results highlight that the proposed approach can significantly aid airlines’ decision-making, leading to significant revenue improvements (up to 4.4%) and shedding insights into network development alternatives.

Airline Network Planning: Mixed-integer non-convex optimization with demand–supply interactions

Airlines routinely use analytics tools to support flight scheduling, fleet assignment, revenue management, crew scheduling, and many other operational decisions. However, decision support systems are less prevalent to support strategic planning. This paper fills that gap with an original mixed-integer non-convex optimization model, named Airline Network Planning with Supply and Demand interactions (ANPSD). The ANPSD optimizes network planning (including route selection, flight frequencies and fleet composition), while capturing interdependencies between airline supply and passenger demand. We first estimate a demand model as a function of flight frequencies and network configuration, using a two-stage least-squares procedure fitted to historical data, and then formalize the ANPSD by integrating the empirical demand function into an optimization model. The model is formulated as a non-convex mixed-integer program. To solve it, we develop an exact cutting plane algorithm, named 2αECP, which iteratively generates hyperplanes to develop an outer approximation of the non-linear demand functions. Computational results show that the 2αECP algorithm outperforms state-of-the-art benchmarks and generates tight solution quality guarantees. A case study based on the network of a major European carrier shows that the ANPSD provides much stronger solutions than baselines that ignore – fully or partially – demand–supply interactions.

Integration of turnaround and aircraft recovery to mitigate delay propagation in airline networks

This article presents a novel approach to incorporate the aircraft turnaround, which has recently been identified as one of the major contributors to airline delay, into existing concepts for integrated aircraft, crew, and passenger recovery. We aim to fill the research gap on how to holistically model network delay propagation as tactical decision support for airline schedule recovery. Our model introduces a heterogeneous vehicle routing problem with time windows for the assignment of aircraft to flight routes and integrates it with an extended version of the resource-constrained project schedule problem for the allocation of scarce resources to turnarounds at the central hub airport, such that we can proactively estimate delay propagation in an airline network. Passenger and crew itineraries are modelled as links between flights, such that needed transfer times influence the stand allocation and resource assignment. These links may only be broken if reserve capacities are available and the related rebooking and compensation costs are more efficient than accepting departure delays to maintain transfers. With this approach, we are able to calculate flight-specific delay cost functions and find substantial dependencies about the time of the day, the number of succeeding flight legs and particular downstream destinations.The integrated recovery model is implemented into a rolling horizon algorithm and applied to a case study setting to analyse its performance in comparison to the individual turnaround and aircraft recovery models. Within different delay scenarios, we find that the incorporation of turnaround recovery options significantly improves the resilience of the airline network. Especially in low and moderate delay situations, we achieve a full recovery of the flight schedule simply by rebooking passengers, reallocating aircraft among stands and accelerating ground operations. Thus, often considered recovery options, such as aircraft swaps and flight cancellations, are not required for delays around 30 min in our case study. This reduces total costs in comparison to the conventional aircraft recovery model by 49%. Despite the lower efficiency of turnaround recovery in medium and high delay scenarios, the combination of flexible aircraft assignments and ground operations still generates additional cost savings of at least 21% and helps to reduce the necessary amount of optimal recovery options.

High-Order Community Detection in the Air Transport Industry: A Comparative Analysis among 10 Major International Airlines

Community detection in a complex network is an ongoing field. While the air transport network has gradually formed as a complex system, the topological and geographical characteristics of airline networks have become crucial in understanding the network dynamics and airports’ roles. This research tackles the highly interconnected parts in weighted codeshare networks. A dataset comprising ten major international airlines is selected to conduct a comparative analysis. The result confirms that the clique percolation method can be used in conjunction with other metrics to shed light on air transport network topology, recognizing patterns of inter- and intra-community connections. Moreover, the topological detection results are interpreted and explained from a transport geographical perspective, with the physical airline network structure. As complex as it may seem, the airline network tends to be a relatively small system with only a few high-order communities, which can be characterized by geographical constraints. This research also contributes to the literature by capturing new insights regarding the topological patterns of the air transport industry. Particularly, it reveals the wide hub-shifting phenomenon and the possibility of airlines with different business models sharing an identical topology profile.

Airline Network Structure: Motifs and Airports’ Role in Cliques

The air transport system can be considered to be a complex network with airports as vertices and direct flights as edges. Research in this area contributes to the optimisation of the airline network and the sustainable development in transportation. This study chose Air China as an example to discover the dynamics of the airline network topologically. Serving as a critical agent of social and economic connections between cities, the airline network structure evolves over time. However, Air China maintains its multicentric and hierarchical structure and forms a mature point-to-point network with codeshare partners. This research also extracts key players at the airport level and investigates the topological structure of highly connected cliques. The results show that the combination of airports in the cliques may be affected by the airline capacity, traffic rights and interline cooperation. Meanwhile, smaller airports appear more often in cliques than hub airports, which can be interpreted and justified with slot limits at mega-airports. The weighted clique percolation method provides new insights to detecting overlapping communities, which can be characterized by geographical constraints. The shared vertices in the combined codeshare network indicate the possible hub shifting in the constantly changing aviation sector.

Airline transportation and arrival time of international disease spread: A case study of Covid-19.

In this era of globalization, airline transportation has greatly increased international trade and travel within the World Airport Network (WAN). Unfortunately, this convenience has expanded the scope of infectious disease spread from a local to a worldwide occurrence. Thus, scholars have proposed several methods to measure the distances between airports and define the relationship between the distances and arrival times of infectious diseases in various countries. However, such studies suffer from the following limitations. (1) Only traditional statistical methods or graphical representations were utilized to show that the effective distance performed better than the geographical distance technique. Researchers seldom use the survival model to quantify the actual differences among arrival times via various distance methods. (2) Although scholars have found that most diseases tend to spread via the random walk rather than the shortest path method, this hypothesis may no longer be true because the network has been severally altered due to recent COVID-related travel reductions. Therefore, we used 2017 IATA (International Air Transport Association) to establish an airline network via various chosen path strategies (random walk and shortest path). Then, we employed these two networks to quantify each model’s predictive performance in order to estimate the importation probability function of COVID-19 into various countries. The effective distance model was found to more accurately predict arrival dates of COVID-19 than the geographical distance model. However, if pre-Covid airline data is included, the path of disease spread might not follow the random walk theory due to recent flight suspensions and travel restrictions during the epidemic. Lastly, when testing effective distance, the inverse distance survival model and the Cox model yielded very similar importation risk estimates. The results can help authorities design more effective international epidemic prevention and control strategies.

Product differentiation and network optimality

This paper studies airline network optimality. We modify existing models (Brueckner and Spiller (1991) and Zhang (1996)) to explain airline network optimality, using heterogeneity and different market sizes. Dropping small destinations from a hub-spoke network may increase overall profits and total welfare. But consumer surplus is always higher with a full network. This leads to a policy dilemma: the airline may want to drop smaller destinations, while the local economy benefits from these destinations. The level of substitution hardly has an effect.

Country distancing increase reveals the effectiveness of travel restrictions in stopping COVID-19 transmission

Despite a number of successful approaches in predicting the spatiotemporal patterns of the novel coronavirus (COVID-19) pandemic and quantifying the effectiveness of non-pharmaceutical interventions starting from data about the initial outbreak location, we lack an intrinsic understanding as outbreak locations shift and evolve. Here, we fill this gap by developing a country distance approach to capture the pandemic’s propagation backbone tree from a complex airline network with multiple and evolving outbreak locations. We apply this approach, which is analogous to the effective resistance in series and parallel circuits, to examine countries’ closeness regarding disease spreading and evaluate the effectiveness of travel restrictions on delaying infections. In particular, we find that 63.2% of travel restrictions implemented as of 1 June 2020 are ineffective. The remaining percentage postponed the disease arrival time by 18.56 days per geographical area and resulted in a total reduction of 13,186,045 infected cases. Our approach enables us to design optimized and coordinated travel restrictions to extend the delay in arrival time and further reduce more infected cases while preserving air travel.

The impact of climate change on the recoverability of airline networks

• We develop a method to assess climate change-imposed costs on airline recovery. • Our models capture the modified aircraft payload-range curves under climate change. • Daily total airline recovery costs increase on average by 15.7% to 49.4%. • Costs increase due to aircraft swaps and cancellations, and disrupted passengers. • We show that airlines should design aircraft and operations in view of climate change. This paper studies the impact of climate change-imposed constraints on the recoverability of airline networks. We first use models that capture the modified payload-range curves for different aircraft types under multiple climate change scenarios, and the associated (reduced) aircraft capacities. We next construct a modeling and algorithmic framework that allows for simultaneous and integrated aircraft and passenger recovery that explicitly capture the above-mentioned capacity changes in aircraft at different times of day. The results, based on the RCP8.5 climate change scenario, suggest that daily total airline recovery costs increase on average by 15.7% to 49.4%; and by 10.6% to 165.0% over individual disrupted days – depending on the original source of disruption, the climate change case of interest and year of interest. Aircraft-related costs are driven by a huge increase in aircraft swaps and cancellations; and passenger-related costs are driven by increases in disrupted (misconnected) passengers who need to be rebooked on the same or a different airline. This work motivates the critical need for airlines to systematically incorporate climate change as a factor in the design of aircraft as well as in the design and operations of airline networks.

Long-Distance Airport Substitution and Air Market Leakage: Empirical Investigations in the U.S. Midwest

Following airline mergers and network reorganizations aimed at reducing operational costs, consolidated air services at large hub airports have encouraged air travelers to forego use of their smaller local airports to access large hub airports offering superior air services farther away. This study investigates airport leakage in areas of Wisconsin and Michigan served by small airports, where air travelers may leak to neighboring large hubs. Using a proximity-based service area definition, three airports experiencing leakage are identified, and a hierarchical logit airport choice model is applied that accounts for air service characteristics and access distance for travelers coming from these airports’ service areas. Results show that a similar mean number of flight legs at both the local and substitute (large hub) airports will encourage leakage at Dane County Regional and Gerald R. Ford International airports, indicating that adding direct flights alone will not be sufficient to combat leakage. Comparable access distances to local and substitute airports have opposite effects on the local markets of Gerald R. Ford International and Milwaukee Mitchell International airports—promoting leakage at the former but discouraging it at the latter. Furthermore, proportional increases in airfares at local airports lead to uneven losses of markets in investigated service areas. Overall, the study provides empirical evidence of long-distance airport leakage in parts of the U.S. Midwest, and how its implications can be used by small airports seeking to further understand and respond to travelers’ airport choices within their local markets.

Analysis of the effect of bundled airport privatization on an airline network

Depopulated regions face declines in traffic demand for airline service. The airline network in Hokkaido, Japan, plays an important role in the regional economy, healthcare, and sightseeing. Toward maintaining this airline network, the “bundled privatization” of airports has been discussed. Under such a system, several airports are privatized as a package for management by a single company. This study proposes a model for analyzing the behaviors of airport operators, airline companies, and passengers when bundled privatization is introduced to an airline network. Under bundled privatization, an airport management company can determine the landing fees of all airports such as to maximize the profits of all its airports, while an airline company can determine its service frequencies and aircraft sizes such as to maximize its profit. As a result of these two profit-maximization behaviors, an equilibrium state is obtained. The proposed model is applied to the airline network in Hokkaido, and the results of numerical calculations are discussed.

Hong Kong's aviation and tourism growth - An empirical investigation

This study investigates the critical link between aviation and tourism growth in Hong Kong, with a focus on the identification of causal relationships between scheduled airline seat capacity and visitor arrivals. This is achieved through the cointegration analysis and Granger-causality test, using monthly data collected from Hong Kong's top 17 tourist source markets during 2008–2018. Our analysis finds clear evidence of an overall two-way causal relationship between airline scheduled capacity and tourist arrivals. The positive externalities between the two crucial sectors to the Hong Kong economy call for coordinated planning and policy design in the tourism and aviation sectors, and justify continued government support. On the other hand, market-specific features are evident in selected markets. Such heterogeneity is likely due to airline network effect and regulation in the international market. Therefore, direct subsidy is not always an efficient support measure. A combination of liberalization policy and industry supports are preferred in views of the benefits to be achieved for the two sectors and the Hong Kong economy.

An integrated reinforced learning and network competition analysis for calibrating airline itinerary choice models with constrained demand

This paper presents a novel methodology to develop itinerary choice models (ICM) for air travelers that addresses the limitations of the traditional utility-maximization approach. The methodology integrates a reinforcement learning algorithm and an airline network competition analysis model. The reinforcement learning algorithm searches for the values of parameters of the itinerary choice model while considering maximizing a reward function. The reward function is measured as the negative of the difference between the estimated and observed system metrics. The airline network competition analysis model is used to calculate the estimated system metrics. It is a simulation model that represents passenger-itinerary assignment. It captures the demand–supply interactions at the network level while considering the competition among all airlines. An ICM system is calibrated using the developed framework considering the global airline network, which includes more than 500,000 airport pairs. Validating the model against ground truth data shows that the developed model adequately captures the travelers’ itinerary choice behavior and replicates the competition pattern among airlines.