Airline Network Research Articles

The determinants of airline networks in the island regions of Japan

This study aims to show the determinants of the airline’s networks in Japanese island regions. Transportation systems are generally needed for residents in island regions to maintain their livelihood. Meanwhile, it is also necessary for people who live in outside regions such as tourists. Therefore, the transportation systems would be significant to maintain both tourism industries and the social situations in many of islands in Japan. This study, first, applied the network analysis to verify the nexus of connections among the transportation network in island regions in Japan. Degree centrality was calculated, and the result shows that some islands which is well-known as the tourism destinations located in southern part of Japan obtained many of connections to other places including other islands, as well as the mainland. Then regression model with the result of network analysis was composed and estimated by maximum likelihood method. The outcome shows that the situations of island society such as aged problem and economy in the islands would be influenced to maintain the airlines, as well as the amount of tourism demand. From these results, this paper concluded that both internal situations of island society and the tourism demand would be important to maintain island’s transportation system.

Airline operational disruptions and loss-reduction investment

Airlines have experienced disruptions from random shocks such as extreme weather conditions and airport congestion. Airlines thus have invested in improving their flight and scheduling system to minimize the losses from shocks. This paper analyzes the effects of the airline network structure on the incentives of airlines to invest in their operations system. The analysis shows that under reasonable conditions, the level of investment is higher under the hub-and-spoke network than under the fully-connected (or point-to-point) network, and airlines invest less than the efficient level that maximizes social welfare under the fully-connected network. In addition, flight frequency regulations decrease investment under either network.

Reshaping China's urban networks and their determinants: High-speed rail vs. air networks

As high-speed rail (HSR) and air transportation developed rapidly in the last decade, their coopetition and interactional relationship has reshaped China's urban networks. Based on HSR and flight schedule data from 2009 to 2019, this paper constructs a weighted network to compare China's urban networks and their evolution, and employs machine learning to investigate the potential determinants. The results indicated that both networks tend toward polarization in the overall distribution and that the structure of urban networks under HSR networks is more hierarchical. That of HSR networks gradually forms a corridor structure along the trunk lines, while that of airline networks mainly shows a diamond spatial structure with Beijing, Shanghai, Guangzhou, and Chengdu as the cores. As for the evolution of urban networks, geographical factors, per capita GDP, and the tourism function of cities have more important impacts on that of HSR networks, while the network's topological structure and education resources have a greater impact on that of airline networks. Some socioeconomic attributes, such as urban administrative level, population, and the proportion of tertiary industry, have similar and limited influences on the two networks.

PPChain: A Blockchain for Pandemic Prevention and Control Assisted by Federated Learning.

Taking COVID-19 as an example, we know that a pandemic can have a huge impact on normal human life and the economy. Meanwhile, the population flow between countries and regions is the main factor affecting the changes in a pandemic, which is determined by the airline network. Therefore, realizing the overall control of airports is an effective way to control a pandemic. However, this is restricted by the differences in prevention and control policies in different areas and privacy issues, such as how a patient's personal data from a medical center cannot be effectively combined with their passenger personal data. This prevents more precise airport control decisions from being made. To address this, this paper designed a novel data-sharing framework (i.e., PPChain) based on blockchain and federated learning. The experiment uses a CPU i7-12800HX and uses Docker to simulate multiple virtual nodes. The model is deployed to run on an NVIDIA GeForce GTX 3090Ti GPU. The experiment shows that the relationship between a pandemic and aircraft transport can be effectively explored by PPChain without sharing raw data. This approach does not require centralized trust and improves the security of the sharing process. The scheme can help formulate more scientific and rational prevention and control policies for the control of airports. Additionally, it can use aerial data to predict pandemics more accurately.

An Approach for Quick Method of Estimating Passenger And Aircraft Demand for Feeder Air Services in India

The recent and the proposed entry of new airlines both in the ‘no-frills’ and in the "full service" categories has completely changed the aviation scenario in India. Obviously this explosive increase in capacity will have a profound effect on the economy of the airlines both in the short term as well as in the long-term period. A growth of over 25% per annum in passenger traffic may not be sustainable for long; thus galloping increase in capacity may result in mergers and strategic alliances of airlines on one side and closing down of some of the airlines on the other side. A point, which is to be noted is that, most of the new entrants also operate in the major trunk routes and serve the metros and the major cities. What appears to have not been tapped is the equally large potential in the regional and short haul sectors, which serve major, and small city pairs and also act as feeders to the major hubs.Realizing the importance of this, the government in its new civil aviation policy has given thrust to regional/ short-haul air travel. In this paper, an attempt is made to review the hierarchy of airline networks in India with respect to trunk, major and feeder routes. The air passenger traffic carried in terms of revenue passenger kilometers (RPK) is compared with upper class rail traffic. The present status of feeder airline services in India is outlined. It is pertinent to study the passenger traffic demand in these feeder routes as compared to upper class rail traffic as some percentage of upper class rail passengers are willing to shift to air travel due to marginal difference between airfare and upper class rail fare and time factor. The future RPKs are forecasted based on the actual RPKs data and the estimated RPKs of earlier market survey. A suitable aircraft size for airline operations in these feeder routes is arrived at based on existing air traffic data by a simplified approach from the basic principles of airline business. A macroscopic measure is used to determine the aircraft demand by assuming average block speed and utilization for each aircraft type. A sensitivity analysis was carried out for various levels of traffic growth keeping in view the sharp increase of air travel in recent years and further growth in air traffic fuelled by feeder sector as the hinterland opens up industry, services and tourism.

Identifying key nodes in complex networks based on an improved gravity model

The identification of key nodes in complex networks is a hot topic. Therefore, it attracts increasing attention from different fields, like airline networks and social networks. To identify the key nodes in complex network, we suggest an improved gravity model method that takes propagation features into account. Relevant experiments were carried out in four actual airline networks based on the Susceptible Infected Recovered (SIR) model. First, we analyze the correlation between the proposed method and other benchmark methods.Then, Kendall’s correlation coefficient and the imprecision function were used as evaluation metrics to analyze and validate the proposed method. Empirical results reveal that the suggested method outperforms previous benchmark methods in terms of precision and effectiveness for identifying key nodes, especially in the US air network, where Kendall’s tau achieves a 107% improvement compared to the gravity centrality method.

Low-Cost Carriers route network development

Low-Cost Carriers achieve economies of scale by deploying large, single-model fleets across an expanding set of regional bases. The addition of new bases and routes is bound by profitability and operational aspects, both posing limitations to the travel demand that can be served. We explored the evolution of Ryanair's network and discussed the factors that influence the airline's choices. We used highly detailed information on the evolution of the fleet and routes and calculated suitable indicators for the quantification of the trends. Based on these observations, we explored the implications for travel and tourism. The analysis was supported by interviews with industry experts. The main conclusion is that there is bi-directional causality between airline network evolution and travel demand.

Dispersal patterns and influence of air travel during the global expansion of SARS-CoV-2 variants of concern

The Alpha, Beta, and Gamma SARS-CoV-2 variants of concern (VOCs) co-circulated globally during 2020 and 2021, fueling waves of infections. They were displaced by Delta during a third wave worldwide in 2021, which, in turn, was displaced by Omicron in late 2021. In this study, we use phylogenetic and phylogeographic methods to reconstruct the dispersal patterns of VOCs worldwide. We find that source-sink dynamics varied substantially by VOC and identify countries that acted as global and regional hubs of dissemination. We demonstrate the declining role of presumed origin countries of VOCs in their global dispersal, estimating that India contributed <15% of Delta exports and South Africa <1%-2% of Omicron dispersal. We estimate that >80 countries had received introductions of Omicron within 100days of its emergence, associated with accelerated passenger air travel and higher transmissibility. Our study highlights the rapid dispersal of highly transmissible variants, with implications for genomic surveillance along the hierarchical airline network.

Airline Network Planning Considering Climate Impact: Assessing New Operational Improvements

The aviation industry has set an ambitious goal of reducing its climate impacts. Accordingly, airlines must balance their plans according to this goal with financial considerations. We developed a multi-objective framework to facilitate climate-aware network design by incorporating the objective to minimise the flight average temperature response (ATR) when optimising the airline network. We also assessed the operational improvements (OIs) which are introduced to improve sustainability in airline operations. In particular, we considered intermediate stop-overs (ISOs) and lower flight altitudes as OIs in our case studies. We analysed the impact of considering the climate impact in the planning of operations of three different airline types: one main-hub-and-spoke (KLM), one smaller multi-hub airline (TAP), and one low-cost carrier (EasyJet). The results show that airlines could also lower their environmental impact by 10–36% when considering the ATR as an objective. However, this would require an 8–20% reduction in profits. Adopting lower-altitude flying with ISO could mitigate their climate impact by 27–49% while reducing profits by approximately 6%. Our study highlights the importance of considering the airline network as a whole and demonstrates the potential benefits of operational improvements from a network perspective.

An optimization approach to financial and operational viabilities of spare aircraft

PurposeThe airlines cancel their flights frequently because of factors that they do not have any control over. Spare aircraft can potentially address some of the issues caused by cancelled flights. This paper aims to offer an exploratory study into the financial and operational viabilities of spare aircraft for airlines.Design/methodology/approachMathematical models are proposed to evaluate the financial and operational metrics under different scenarios. The models are applied to Delta, Spirit and Southwest Airlines with different business models. All data are extracted from US Bureau of Transport Statistics, Cirium Diio Mi and CAPA databases. The IBM Cplex solver was used to execute the binary linear program models.FindingsThe research revealed that factors such as airline network size, hub and spoke structure and average weekly flight cancellations are crucial in establishing the need for spare aircraft. For the number of weekly cancellations, there exist break-even values that reasonably justify spare aircraft.Practical implicationsModels can be customized and applied to other modes of transportations.Originality/valueThis study is the first to consider the use of spare aircraft in airlines from both financial and operational perspectives within the scope of the mathematical model. The analyses identify financial break-even points for a number of spare aircraft and their home base locations for three airlines. Operational utilization of spare aircraft is studied and contrasted with financial metrics.

Double power-law distribution in spatial network induced by cost constraints

We investigate the scaling laws of the degree distribution in an evolving spatial network where the long-range links of a node are subject to a cost constraint. The constraint can cause a discontinuous reduction in the length of the links to be attached to the node once the node reaches some critical degree k c . We show that this effect can result in an abrupt change in the attachment probability and consequently induces a double power-law degree distribution. We derive the distribution analytically for the homogeneous constraint and demonstrate a consistent result for the heterogeneous one. Our model finds a robust connection between the double power law and the spatial constraint and offers a plausible explanation of the common occurrence of the distribution in airline networks.

Network science approach for identifying disruptive elements of an airline

Currently, flight delays are common and they propagate from an originating flight to connecting flights, leading to large disruptions in the overall schedule. These disruptions cause massive economic losses, affect airlines’ reputations, waste passengers’ time and money, and directly impact the environment. This study adopts a network science approach for solving the delay propagation problem by modeling and analyzing the flight schedules and historical operational data of an airline. We aim to determine the most disruptive airports, flights, flight-connections, and connection types in an airline network. Disruptive elements are influential or critical entities in an airline network. They are the elements that can either cause (airline schedules) or have caused (historical data) the largest disturbances in the network. An airline can improve its operations by avoiding delays caused by the most disruptive elements. The proposed network science approach for disruptive element analysis was validated using a case study of an operating airline. The analysis indicates that potential disruptive elements in a schedule of an airline are also actual disruptive elements in the historical data and they should be considered to improve operations. The airline network exhibits small-world effects and delays can propagate to any part of the network with a minimum of four delayed flights. Finally, we observed that passenger connections between flights are the most disruptive connection type. Therefore, the proposed methodology provides a tool for airlines to build robust flight schedules that reduce delays and propagation.

Scenarios for a post-COVID-19 world airline network.

The airline industry was severely hit by the COVID-19 crisis with an average demand decrease of about 64 % (IATA, April 2020), which triggered already several bankruptcies of airline companies all over the world. While the robustness of the world airline network (WAN) was mostly studied as a homogeneous network, we introduce a new tool for analyzing the impact of a company failure: the "airline company network" where two airlines are connected if they share at least one route segment. Using this tool, we observe that the failure of companies well connected with others has the largest impact on the connectivity of the WAN. We then explore how the global demand reduction affects airlines differently and provide an analysis of different scenarios if it stays low and does not come back to its pre-crisis level. Using traffic data from the Official Aviation Guide and simple assumptions about customer's airline choice strategies, we find that the local effective demand can be much lower than the average one, especially for companies that are not monopolistic and share their segments with larger companies. Even if the average demand comes back to 60 % of the total capacity, we find that between 46 % and 59 % of the companies could experience a reduction of more than 50 % of their traffic, depending on the type of competitive advantage that drives customer's airline choice. These results highlight how the complex competitive structure of the WAN weakens its robustness when facing such a large crisis.

Probabilistic model for the impact of fear of flying on airline network structures

Many passengers experience nervousness and/or anxiety when they fly and, on average, they make fewer trips than those who are not afraid. This might reduce airline revenues and profits. In this paper, a new mathematical model of profitability of different airline network choices in a liberalized market is proposed in terms of two particular personal features of passengers: the loyalty to a preferred airline and the fear of flying, which are assumed to be random. A detailed analysis of different network-type structures is presented and optimal solutions depending on the impact of these two random attributes for each of the networks are characterized in terms of the model parameters. The main finding is that the symmetric networks (hub-and-spoke or point-to-point) may not always be the optimal configuration, but a hybrid network configuration may be favoured in some cases. It is also shown that under certain conditions, the point-to-point network structure provides a higher total number of flights due to the fear of flying, which pushes the airlines to reduce the number of indirect routes.

Identifying influential airports in airline network based on failure risk factors with TOPSIS

As a kind of complex networks, airline network has been studied by more and more scholars. How to identify influential airports in airline network is an open issue, which has become a far-reaching direction in its research and development. Many centrality measures have been improved and proposed to address it. However, the majority of studies only focused on one centrality measure, and each centrality measure has its own drawbacks and restrictions. In order to address this issue, learning to the evaluation and analysis principle of failure mode and effects analysis (FMEA), we think that the influence of airports can be measured by their possible effects after failure in the airline network. In this paper, a novel method for identifying influential airports is proposed. Firstly, three risk factors of airport’s failure mode are defined based on complex network theory and the transportation mechanism of airports in airline network flight flow. Then, the effects analyses of three failure risk factors of each airport are made. Finally, the technique for order preference by similarity to ideal solution (TOPSIS) algorithm is utilized to fuse the effects of the three defined failure risk factors to determine the comprehensive influence of airport in airline network. To evaluate the performance of the proposed method, the Susceptible–Infected (SI) model is applied to examine the spreading influence of the airports ranked by various centrality measures. The comparison experimental conducted on four airline networks illustrate the effectiveness of the proposed method.

Multiple scaling law in networks with dynamic spatial constraint

We study a network model where the spatial constraint evolves with the network growth and demonstrate that the dynamic constraint can generally cause a non-stationary multiple scaling law in degree distribution. In specific, we show that the different power-law segments emerge successively during the network evolution, from a single power law at the beginning to a double one and to more scaling laws due to the particular model setting. Our findings indicate a plausible geometric origin of the double power-law distribution and predict the non-stationary nature accompanied by its emergence, both of which are supported by our empirical study on the Chinese airline network.

Evaluating Accessibility Based on the Combined High-Speed Rail and Air Transportation Network in China

Both high-speed rail (HSR) and air transportation networks have important impacts on accessibility to cities. However, few attempts have been made to analyze the combined modal accessibility of HSR and air, especially in the case of China. To fill this gap, this study aims to measure accessibility under a combined HSR and air transportation network in China. Average travel time (ATT), weighted average travel time (WATT), and potential accessibility (PA) are employed as indicators of accessibility. The results show that inter-urban accessibility under the combined network shows a decreasing trend from the central to the surrounding region, with the level of combined network urban accessibility significantly higher than that of either individual network. Under the ATT, WATT, and PA indicators, the combined network outperforms the HSR network by 46.53%, 47.69%, and 49.23%, respectively, and the airline network by 15.49%, 13.17%, and 75.45%, respectively. In addition, the combined network shows a more equitable distribution of accessibility than that of the HSR network.

A Method to Optimize Routing Paths for City-Pair Airlines on Three-Layer Air Transport Networks

Air transportation is a huge, complex system with emergence and self-organization, which makes it important for it to be modelled. In this paper, to model the air transportation system with more accuracy, from physical facilities to traffic applications, three-layer networks, including the air route network, the city-pair airline network, and flight operation network, are built, where the air route network is regarded as the physical layer, and city-pair airline network and flight operation network are the application layers. Furthermore, a powerful tool, complex network theory, is applied to discuss the topology characteristics of the three-layer networks. Moreover, considering the path diversity of city-pair airlines, a simulated annealing-based framework is proposed to optimize the routing paths on an air route network for each city-pair airline, such that the traffic congestion of the air route network can be alleviated, where an elaborated method for perturbing solutions, named Selection after Remove (SAR), is adopted. Experimental results show that, compared with the default routing paths, the shortest routing paths, and the random routing paths, the proposed routing-path optimization strategy can reduce the maximum traffic flows of the air route network by 2.4%, 4.6%, and 4.8%, respectively, which shows that the proposed optimization method performs well in alleviating the traffic congestion of the air route network.

Airport dominance, route network design and flight delays

Airlines with a dominant position at the destination airports have little competitive pressure to reduce delays, but they might care about the negative effects that delays generate on their own flights. Using detailed daily flight data for six Spanish airports in 2017–2018 including very precise information on the external factors that generate flight delays, we find that flights operated by network airlines (i.e., airlines that operate a hub-and-spoke network) with a large presence at the destination airports have less delays than flights operated by other airlines. This finding is in line with the literature on congestion internalization, which predicts a negative relationship between airlines’ dominance at the destination airports and delays. We also show that flights operated by low-cost airlines (i.e. airlines that operate a point-to-point network) with a dominant presence at destination airports are more likely to exhibit delays. This result could be explained by the route configuration of low-cost airlines and by their relative low number of connecting passengers.

Vision 2030 and prospects for network airlines in Saudi Arabia

The air transport industry in Saudi Arabia is experiencing rapid growth of late, fuelled in large part by the Kingdom's Vision 2030 aspirations and national development plan. This ambitious national vision seeks to diversify the Kingdom's economy, and in the process, develop and expand a wide range of industry and public sectors. Central to these plans is air transport, including not only the building of new airports and related transport infrastructure, along with expanding the wider travel and hospitality sectors, but also the launch of a new network airline in 2025 – Riyadh Air. This new airline will join current national flag carrier Saudia, and together they will play central roles in growing both the domestic and international air markets in the Kingdom, mainly out of the two main hubs Riyadh Airport (RUH) and Jeddah Airport (JED). The paper here aims to assess this coming network carrier duopoly, including the future prospects for both airlines as they seek to compete in a region, and indeed globally, against well-established legacy and flag carriers. Evidently, the future of network airlines in KSA could very well shape and determine prospects for the industry more generally for decades to come.