Global Air Transportation Network Research Articles

Prevalence and scalable control of localized networks

The ability to control network dynamics is essential for ensuring desirable functionality of many technological, biological, and social systems. Such systems often consist of a large number of network elements, and controlling large-scale networks remains challenging because the computation and communication requirements increase prohibitively fast with network size. Here, we introduce a notion of network locality that can be exploited to make the control of networks scalable, even when the dynamics are nonlinear. We show that network locality is captured by an information metric and is almost universally observed across real and model networks. In localized networks, the optimal control actions and system responses are both shown to be necessarily concentrated in small neighborhoods induced by the information metric. This allows us to develop localized algorithms for determining network controllability and optimizing the placement of driver nodes. This also allows us to develop a localized algorithm for designing local feedback controllers that approach the performance of the corresponding best global controllers, while incurring a computational cost orders-of-magnitude lower. We validate the locality, performance, and efficiency of the algorithms in Kuramoto oscillator networks, as well as three large empirical networks: synchronization dynamics in the Eastern US power grid, epidemic spreading mediated by the global air-transportation network, and Alzheimer's disease dynamics in a human brain network. Taken together, our results establish that large networks can be controlled with computation and communication costs comparable to those for small networks.

Investigating and Identifying Critical Airports for Controlling Infectious Diseases Outbreaks

The enormous economic impact of air transportation networks on a local, national, and international level has created an interest for further investments and increased the complexity of the global air transport network. The increased number of worldwide airport passengers and of the aircraft movements and also of the international airport cargo shipments is evident. Therefore, the functionality of this complex air transport network is very important and requires to be investigated and evaluated for identifying the airports that appear to be critical to this network. However, besides the economic benefits that air transport network is offering in local, national and international level through their services (shipments of goods and transport of passengers) play an ever-increasing role in safeguarding global health security. This paper investigated the global air transport network for identifying the airports that may constitute a public health event of international concern from infectious diseases (e.g. COVID-19). In detail, critical airports, in terms of centrality measures (e.g. Closeness, Degree, Betweenness, and Page Rank Centrality) were identified and addressed by pointing them to global authorities for suggesting and implementing routine prevention and control measures for possible future disease outbreaks.

Global expansion of COVID-19 pandemic is driven by population size and airport connections

The pandemic state of COVID-19 caused by the SARS CoV-2 put the world in quarantine, led to hundreds of thousands of deaths and is causing an unprecedented economic crisis. However, COVID-19 is spreading in different rates at different countries. Here, we tested the effect of three classes of predictors, i.e., socioeconomic, climatic and transport, on the rate of daily increase of COVID-19 on its exponential phase. We found that population size and global connections, represented by countries’ importance in the global air transportation network, are the main explanations for the early growth rate of COVID-19 in different countries. Climate and socioeconomics had no significant effect in this big picture analysis. Our results indicate that the current claims that the growth rate of COVID-19 may be lower in warmer and humid countries should be taken very carefully, risking to disturb well-established and effective policy of social isolation that may help to avoid higher mortality rates due to the collapse of national health systems.

Predicting dengue importation into Europe, using machine learning and model-agnostic methods

The geographical spread of dengue is a global public health concern. This is largely mediated by the importation of dengue from endemic to non-endemic areas via the increasing connectivity of the global air transport network. The dynamic nature and intrinsic heterogeneity of the air transport network make it challenging to predict dengue importation. Here, we explore the capabilities of state-of-the-art machine learning algorithms to predict dengue importation. We trained four machine learning classifiers algorithms, using a 6-year historical dengue importation data for 21 countries in Europe and connectivity indices mediating importation and air transport network centrality measures. Predictive performance for the classifiers was evaluated using the area under the receiving operating characteristic curve, sensitivity, and specificity measures. Finally, we applied practical model-agnostic methods, to provide an in-depth explanation of our optimal model’s predictions on a global and local scale. Our best performing model achieved high predictive accuracy, with an area under the receiver operating characteristic score of 0.94 and a maximized sensitivity score of 0.88. The predictor variables identified as most important were the source country’s dengue incidence rate, population size, and volume of air passengers. Network centrality measures, describing the positioning of European countries within the air travel network, were also influential to the predictions. We demonstrated the high predictive performance of a machine learning model in predicting dengue importation and the utility of the model-agnostic methods to offer a comprehensive understanding of the reasons behind the predictions. Similar approaches can be utilized in the development of an operational early warning surveillance system for dengue importation.

The evolution of aviation network: Global airport connectivity index 2006–2016

This paper studies the evolution of global air transport network over the period 2006–2016. A Global Airport Connectivity Index (GACI) is proposed, combining degree, closeness and eigenvector topological indicators and two new volumetric indicators. We find faster passenger growth for larger airports that have improved in GACI. While America and North Asia focused on regional hub developments, serving short- to medium-haul routes, West Europe, South East Asia and Middle East focused on developing their major airports into global hubs. Our analysis shows that increasing GACI improves an airport’s competitive position and gives rise to increased influence over other airports.

Does Spatial Dispersal Continue Post-Financial Crisis? Analysis of Global Air Transport Network 2011-2015

Economic performance of a city (or region) and its air travel capability are deemed as interlocked in the modern age. It is thus important for planners and stakeholders to understand the changes of the global air transport network. This paper uses a mixed approach, by utilizing both the topological metrics and volume-embedded tools, to examine the global aviation network between 2011 and 2015. With sophisticated demarcation of the concentration concept, the research reconfirms the trend in recent literature that suggested a dispersal pattern in the global aviation network. Furthermore, with the development of an “airport centrality index”, the paper provides evidence consistent with the bypassing of traditional hubs, especially among the mega-hubs. Competition for connecting passengers among mega- and major-hubs is also discussed.

Robustness of the air transport network

This paper presents a methodology for the detection of critical airports (those whose isolation would cause the largest losses in network connectivity) in the global air transport network (ATN), based on simulating an attack on selected ATN airports using different adaptive selection criteria. The performances of several node selection criteria are compared, together with a new criterion based on Bonacich power centrality. The results show that most critical airports can be detected with an adaptive strategy based on betweenness centrality. The detection of such airports may help the development of contingency plans to develop an appropriate response to any airport closure.

Capacity Discipline and the Consolidation of Airport Connectivity in the United States

Airlines reduced available domestic capacity at airports across the United States from 2007 to 2012 in response to a global economic downturn and high and volatile fuel prices. More recently, despite an economic recovery and more stable fuel prices, airlines have continued to keep capacity low relative to historical levels in a strategy that has been referred to as “capacity discipline.” The effects of these shifts in capacity on airport connectivity to the global air transportation network remain unclear. This paper introduces an intuitive index to compute airport connectivity as a function of both the quantity and quality of scheduled nonstop and connecting service. Connectivity scores were computed for 462 U.S. airports; medium-hub and small-hub airports were found to have lost more connectivity on average than large-hub airports from 2007 to 2012. In multiairport regions, losses in connectivity at secondary and tertiary airports outpaced losses at primary airports. With lower levels of connectivity, smaller airports in these regions will need to employ creative strategies to prevent passengers from leaking to primary hubs. Whether these airports can recover from connectivity consolidation will also depend on how long the capacity discipline equilibrium remains in effect among U.S. airlines.

Nighttime aircraft noise exposure: flying towards arterial disease

This editorial refers to ‘Effect of nighttime aircraft noise exposure on endothelial function and stress hormone release in healthy adults’, by F.P. Schmidt et al. doi:10.1093/eurheartj/eht269 Over the last four decades there has been a steady increase in air travel worldwide. The global air transport network has contributed to the growth of economies by providing better access to national and international resources, increasing tourism, encouraging investment and innovation, and improving business operations and efficiency. Air transport also offers a vital lifeline to remote communities and rapid worldwide support during emergency situations. However, along with these benefits, detrimental effects linked to the expanding air network have been recognized. There are increasing data to suggest that air pollution and noise from airplanes have significant environmental and cardiovascular implications. Air pollutants have been associated with climate changes and increased hospitalization and mortality due to cardiovascular disease in high risk populations.1 In parallel, auditory impairment as a result of the direct effects of sound energy has been well recognized. As a result, protective legislation requiring hearing protectors to be worn by those working in close proximity is currently in place.2 However, the indirect effect of noise on human health remains an area of active research. Evidence for an adverse effect on public health for aircraft noise exposure, in particular during night-time, has strengthened recently as results from epidemiological community studies have become available.3 At night, cerebral function recovers from the rigours of daytime activities and key brain processes are restored. Aircraft noise has been associated with sleep fragmentation and an increase in stress hormone levels, and concerns have been …

Spatio-temporal fluctuations in the global airport hierarchies

The global air transportation network is responsible for moving millions of domestic and international passengers each year. Not surprisingly, relationships between airports vary widely, due to a myriad of geographic, economic, political and historical determinants. Further, given the dynamic nature of the many influences acting on the air transportation system, inter-airport relationships and the structure of the global air network as a whole are also constantly changing. The purpose of this paper is to explore such spatio-temporal variations in the structure of the global airport hierarchies. Here, we show how the concept of nodal regions can be applied to measure the extent of these variations. To facilitate this analysis, a database of nearly 900 airline carrier schedules and 4650 worldwide origins and destinations, representing a nearly complete record of commercial air travel over a six-year period, is examined. Given this dataset, nodal regions are derived for all airports represented. In general, results suggest that regions associated with individual airports are often relatively dynamic at the yearly as well as quarterly level. Los Angeles International Airport (LAX) is utilized as a local case-study to provide a detailed examination of these dynamics.

Spatiotemporal dynamics of complex ecological networks: Power-law scaling and implications for conservation planning

AbstractSpatial constraints on the topology of complex networks are just beginning to be appreciated, both theoretically and in concrete examples like the Internet and global air transportation network. Ecological networks, composed of habitat patches connected by species dispersal, are intrinsically spatial and show promise as a tool for conservation planning; but while habitat-loss effects on ecological networks have been simulated, such effects have not been directly measured in ecological networks varying over time. In this study, I used satellite remote sensing to study ecological networks composed of wetland habitat in the Prairie Pothole Region (PPR) of North America. I find power-law scaling of important topological properties as a function of dispersal ability and as wetland density varies with climate. Prairie wetland networks are 'meso-worlds' with mean topological distance increasing faster with network size than small-world networks, but slower than regular lattices. While similar dynamics have been shown in random spatial networks, these results emphasize the importance of processes determining locations of nodes in a spatial network, with possible implications in other areas like wireless communication networks or disease transmission networks. Wetland networks establish a climate envelope for landscape connectivity in the PPR, and I show that wetland-dependent species face a 'crisis of connectivity' with climate change. The global biodiversity crisis requires that conservation planners act quickly over large areas using limited resources; a network-based approach to coarse-filter conservation planning in dynamic landscapes should be broadly applicable to this problem.

Spatiotemporal dynamics of complex ecological networks: Power-law scaling and implications for conservation planning

AbstractSpatial constraints on the topology of complex networks are just beginning to be appreciated, both theoretically^1-3^ and in concrete examples like the Internet^4^ and global air transportation network^5^. Ecological networks, composed of habitat patches connected by species dispersal, are intrinsically spatial and show promise as a tool for conservation planning; but while habitat-loss effects on ecological networks have been simulated, such effects have not been directly measured in ecological networks varying over time^6-8^. In this study, I used satellite remote sensing to study ecological networks composed of wetland habitat in the Prairie Pothole Region (PPR) of North America. I find power-law scaling of important topological properties as a function of dispersal ability and as wetland density varies with climate. Prairie wetland networks are 'meso-worlds' with mean topological distance increasing faster with network size than small-world networks^9^, but slower than regular lattices^10^. While similar dynamics have been shown in random spatial networks^1,3^, these results emphasize the importance of processes determining locations of nodes in a spatial network, with possible implications in other areas like wireless communication networks^11^ or disease transmission networks^12^. Wetland networks establish a climate envelope for landscape connectivity in the PPR, and I show that wetland-dependent species face a 'crisis of connectivity' with climate change. The global biodiversity crisis requires that conservation planners act quickly over large areas using limited resources^13,14^; a network-based approach to coarse-filter conservation planning in dynamic landscapes should be broadly applicable to this problem.