World-wide Airport Network Research Articles

Towards analyzing the robustness of the Integrated Global Transportation Network Abstraction (IGTNA)

The well-functioning of our transportation systems is essential for ensuring the mobility of people and goods. According to the Sustainable Developmental Goals (SDG) adopted by the United Nations General Assembly in 2015, it is of utmost importance to build and maintain a resilient infrastructure. In terms of transportation systems, a resilient infrastructure guarantees seamless connections and robustness against failures and targeted attacks. Existing studies on small-scale road, subway, or air transportation networks have revealed interesting patterns and phenomena for different parts of the world. These studies, however, are usually either spatially or modal-wise constrained. Most notably this holds for the worldwide airport network, which has frequently been analyzed in the literature. Transportation interactions on the ground are usually considered through aggregation of nodes into either cities or multiple airport regions. This conceptual simplification leaves room for the analysis of an Integrated Global Transportation Network Abstraction (IGTNA), covering aviation and ground transportation on our planet. We believe that it is crucial for planners and analysts to include all infrastructure, assets, networks, modes, and users as a single system. IGTNA enables to model all aspects of interconnectivity at a high-level of details, encoding points of interests as nodes and potential transitions between points of interests as links. Our aggregated network representation of the global transportation system consists of 5.3 million nodes and more than 8.6 million links. We propose scalable data extraction and network dismantling techniques. This study is, to the best of our knowledge, the first to report on the robustness of the IGTNA. We believe that our study enables decision-makers to achieve effective decision-making on future transportation investment through a holistic understanding of the impacts from negative shocks of disruptive events.

Post-COVID-19 Scenarios of Global Airline Traffic until 2040 That Reflect Airport Capacity Constraints and Mitigation Strategies

Although there has been an unprecedented decline in traffic volume due to the COVID-19 crisis, robust growth in global demand for air transport services in the past means that air traffic is expected to recover in the long term. While capacity constraints are currently not a major topic at airports due to the extremely low levels of traffic, there is growing evidence to suggest that important nodes of the worldwide airport network will struggle to deal with capacity constraints after the recovery. The objectives of this research were therefore as follows: to elaborate long-term global passenger and flight volume scenarios in a post-COVID-19 world; to conduct an empirical and model-based analysis of the impact of limited airport capacity on the future development of air traffic in these scenarios; and to derive general strategies for mitigating capacity constraints at certain international airports. Thus, the main aim of this paper is to present a model-based scenario analysis of the long-term impact of the COVID-19 crisis on the capacity situation for airports. Our results indicate that once the pandemic is over, the capacity crunch will remain on the airports’ agenda for some time.

A reduced model for complex network analysis of public transportation systems

Public transportation networks (PTNs) are represented as complex networks in order to analyze their robustness regarding node and link failures, to classify them into different theoretical network models, and to identify the characteristics of the underlying network. Usually, PTNs have a large amount of 1- and 2- degree nodes that blur the analysis and their characterization as complex networks. Subway and train-based transport networks present long single lines that connect central stations to far destinations differently from airport networks that usually have few large airports (hubs) connecting a significant number of small airports. By focusing on relevant network nodes and links and allowing comparisons between PTNs of different transportation modes, this paper proposes the Reduced Model as a simple method of network reduction that preserves the network skeleton (backbone structure) by properly removing 2-degree nodes of weighted and unweighted network representations. Different from other proposed methods, its simple formulation leads to mathematical expressions that show how the reduced model affects fundamental network metrics (degree, path length, and clustering coefficient distributions). The Reduced model is applied to four large real-world PTNs: (i) two Brazilian cities with bus-based transport; (ii) the Seoul metro network; (iii) a worldwide airport network. The results reveal a hub-based hierarchical structure when a large number of intermediary stops are present and small-world properties that emphasizes hub–hub connections after applying the Reduced model. Therefore, the reduced model emphasizes characteristics of the networks that could be difficult to identify without reduction.

Finding multiple core-periphery pairs in networks.

With a core-periphery structure of networks, core nodes are densely interconnected, peripheral nodes are connected to core nodes to different extents, and peripheral nodes are sparsely interconnected. Core-periphery structure composed of a single core and periphery has been identified for various networks. However, analogous to the observation that many empirical networks are composed of densely interconnected groups of nodes, i.e., communities, a network may be better regarded as a collection of multiple cores and peripheries. We propose a scalable algorithm to detect multiple nonoverlapping groups of core-periphery structure in a network. We illustrate our algorithm using synthesized and empirical networks. For example, we find distinct core-periphery pairs with different political leanings in a network of political blogs and separation between international and domestic subnetworks of airports in some single countries in a worldwide airport network.

Robustness analysis metrics for worldwide airport network: A comprehensive study

Robustness of transportation networks is one of the major challenges of the 21st century. This paper investigates the resilience of global air transportation from a complex network point of view, with focus on attacking strategies in the airport network, i.e., to remove airports from the system and see what could affect the air traffic system from a passenger’s perspective. Specifically, we identify commonalities and differences between several robustness measures and attacking strategies, proposing a novel notion of functional robustness: unaffected passengers with rerouting. We apply twelve attacking strategies to the worldwide airport network with three weights, and evaluate three robustness measures. We find that degree and Bonacich based attacks harm passenger weighted network most. Our evaluation is geared toward a unified view on air transportation network attack and serves as a foundation on how to develop effective mitigation strategies.

A complex network approach towards modeling and analysis of the Australian Airport Network

An airport network forms the backbone of any air transportation system. The relationships among the origins and destinations of flights result in a complex network of routes which can be complemented with information associated with the routes themselves, for instance, traffic load and distance. In this paper, we modeled the Australia's civil domestic airport infrastructure as a network and analyzed the resulting network structure and its features using complex network tool. This case study identifies and investigates complex network measures, such as the degree distribution, characteristics path length, clustering coefficient and centrality measure as well as the correlations among them to understand the topology of an airport network. This analysis of the Australian Airport Network (AAN) indicates that it has a cumulative degree distribution described by the power-law function. As it has an average path length of 2.90, it is considered to have small-world properties. It is also found that it has a clustering coefficient of 0.50 which is higher than that of a random network of the same size which indicates that the transitivity and cohesiveness of AAN is different from a random network. In contrast to the World-wide Airport Network (WAN), the AAN is found to have disassortative mixing similar to the airport networks of China and India.

Concentric network symmetry

Quantification of symmetries in complex networks is typically done globally in terms of automorphisms. Extending previous methods to locally assess the symmetry of nodes is not straightforward. Here we present a new framework to quantify the symmetries around nodes, which we call connectivity patterns. We develop two topological transformations that allow a concise characterization of the different types of symmetry appearing on networks and apply these concepts to six network models, namely the Erdős–Rényi, Barabási–Albert, random geometric graph, Waxman, Voronoi and rewired Voronoi. Real-world networks, namely the scientific areas of Wikipedia, the world-wide airport network and the street networks of Oldenburg and San Joaquin, are also analyzed in terms of the proposed symmetry measurements. Several interesting results emerge from this analysis, including the high symmetry exhibited by the Erdős–Rényi model. Additionally, we found that the proposed measurements present low correlation with other traditional metrics, such as node degree and betweenness centrality. Principal component analysis is used to combine all the results, revealing that the concepts presented here have substantial potential to also characterize networks at a global scale. We also provide a real-world application to the financial market network.

Hub competition and travel times in the world-wide airport network

The aim of this work is to measure the competition between airport hubs based on an analysis of travel times in the world-wide airport network. By considering the minimum travel time required to connect each pair of airports, it is possible to create new measures of hub competition, separating the effects of hub position and temporal coordination. This analysis was carried out at the global level, considering all 232 airports with more than 3 million seats yearly offered in departure flights in 2008, and also in relevant geographic markets. The results show a high level of competition among the most important world airports, but the major airports of Europe have a geographical advantage in relation to world markets over the major American and Asian airports. We also show that airports located in different continents often compete for the same origin–destination markets. Geographical position appears to be the most important variable explaining hub performance. Secondary hubs show a higher degree of specialization towards specific markets.

Analysis of the airport network of India as a complex weighted network

Transportation infrastructure of a country is one of the most important indicators of its economic growth. Here we study the Airport Network of India (ANI) which represents India’s domestic civil aviation infrastructure as a complex network. We find that ANI, a network of domestic airports connected by air links, is a small-world network characterized by a truncated power-law degree distribution and has a signature of hierarchy. We investigate ANI as a weighted network to explore its various properties and compare them with their topological counterparts. The traffic in ANI, as in the World-wide Airport Network (WAN), is found to be accumulated on interconnected groups of airports and is concentrated between large airports. In contrast to WAN, ANI is found to be having disassortative mixing which is offset by the traffic dynamics. The analysis indicates possible mechanism of formation of a national transportation network, which is different from that on a global scale.

Global disease spread: Statistics and estimation of arrival times

We study metapopulation models for the spread of epidemics in which different subpopulations (cities) are connected by fluxes of individuals (travelers). This framework allows one to describe the spread of a disease on a large scale and we focus here on the computation of the arrival time of a disease as a function of the properties of the seed of the epidemics and of the characteristics of the network connecting the various subpopulations. Using analytical and numerical arguments, we introduce an easily computable quantity which approximates this average arrival time. We show on the example of a disease spread on the world-wide airport network that this quantity predicts with a good accuracy the order of arrival of the disease in the various subpopulations in each realization of epidemic scenario, and not only for an average over realizations. Finally, this quantity might be useful in the identification of the dominant paths of the disease spread.

Optimal paths in complex networks with correlated weights: The worldwide airport network

We study complex networks with weights w(ij) associated with each link connecting node i and j. The weights are chosen to be correlated with the network topology in the form found in two real world examples: (a) the worldwide airport network and (b) the E. Coli metabolic network. Here w(ij) approximately equals x(ij)(k(i)k(j))alpha, where k(i) and k(j) are the degrees of nodes i and j , x(ij) is a random number, and alpha represents the strength of the correlations. The case alpha >0 represents correlation between weights and degree, while alpha< 0 represents anticorrelation and the case alpha=0 reduces to the case of no correlations. We study the scaling of the lengths of the optimal paths, l(opt), with the system size N in strong disorder for scale-free networks for different alpha. We find two different universality classes for l(opt), in strong disorder depending on alpha: (i) if alpha >0 , then for lambda >2 the scaling law l(opt) approximately equals N(1/3), where lambda is the power-law exponent of the degree distribution of scale-free networks, and (ii) if alpha< or =0 , then l(opt) approximately equals N((nu)(opt)) with nu(opt) identical to its value for the uncorrelated case alpha=0. We calculate the robustness of correlated scale-free networks with different alpha and find the networks with alpha< 0 to be the most robust networks when compared to the other values of alpha. We propose an analytical method to study percolation phenomena on networks with this kind of correlation, and our numerical results suggest that for scale-free networks with alpha< 0 , the percolation threshold p(c) is finite for lambda >3, which belongs to the same universality class as alpha=0 . We compare our simulation results with the real worldwide airport network, and we find good agreement.

Vulnerability of weighted networks

In real networks complex topological features are often associated with a diversity ofinteractions as measured by the weights of the links. Moreover, spatial constraints may alsoplay an important role, resulting in a complex interplay between topology, weight, andgeography. In order to study the vulnerability of such networks to intentional attacks, theseattributes must therefore be considered along with the topological quantities. In order totackle this issue, we consider the case of the worldwide airport network, which is a weightedheterogeneous network whose evolution and structure are influenced by traffic andgeographical constraints. We first characterize relevant topological and weighted centralitymeasures and then use these quantities as selection criteria for the removal ofvertices. We consider different attack strategies and different measures of thedamage achieved in the network. The analysis of weighted properties shows thatcentrality driven attacks are capable of shattering the network’s communication ortransport properties even at a very low level of damage in the connectivity pattern.The inclusion of weight and traffic therefore provides evidence for the extremevulnerability of complex networks to any targeted strategy and the need for them to beconsidered as key features in the finding and development of defensive strategies.

Modeling the world-wide airport network

Recently, we have presented the first exhaustive analysis of the world-wide airport network. Two important results of that study are that: (i) the world-wide airport network is a small-world network with power-law decaying degree and betweenness centrality distributions; (ii) the most connected cities (largest degree) are typically not the most central cities (largest betweenness centrality). This second finding is particularly significant because of results demonstrating that nodes with high betweenness tend to play a more important role in keeping networks connected than those with high degree. Here, we investigate if current network models can explain this finding and we show that they cannot. Thus, we propose a new model that explains this behavior in terms of the geo-political constraints that affect the growth of the airport network. We further hypothesize that in other infrastructures, affected by similar geo-political constraints, critical locations might not coincide with highly-connected hubs.