Community Structure Detection in the Evolution of the United States Airport Network

Gegov, Emil; Postorino, Maria Nadia; Atherton, Mark; Gobet, Fernand

doi:10.1142/s0219525913500033

Cited by 16 publications

(8 citation statements)

References 32 publications

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“…In the former structure, travelers need to transfer at an intermediary airport (i.e., hub) to reach their destinations, whereas in the latter one, only non-stop flights exist. Similar structures have also emerged in air transport networks in other geographies, such as the US and Europe [6,7]. Importantly, however, "real-hubbing" in terms of a connection-based wave structure, has been relatively stable and occurs at only four airports: Beijing (PEK), Shanghai (PVG), Guangzhou (CAN) and Kunming (KMG).…”

Section: Introductionmentioning

confidence: 78%

Modeling the Evolutionary Mechanism of China’s Domestic Air Transport Network

Zhang

Tang

et al. 2020

Sustainability

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With the rapid expansion of China’s domestic air transport network (CATN), it is fundamental to model which factors and mechanisms impact this development. This paper investigates how the combined endogenous and exogenous factors influencing the evolution of CATN based on longitudinal data by utilizing a more all-encompassing methodology of stochastic actor based-modeling (SABM). Endogenous variables include a density effect, a betweenness effect, a transitivity closure effect, and a ‘number of distances-two’ effect. Exogenous variables incorporate airport hierarchy, a distance effect, presence or absence of low-cost carriers (LCCs) and high-speed rail (HSR). The systematic classification of Chinese airports into more than the typical two or three tiers allows the impacts of the four endogenous covariates to be revealed. Overall, the CATN has tended to evolve into a more compacted and non-concentrated network structure through the creation of non-stop routes and closed triads. The integrated inclusion of low-cost carrier and high-speed rail effects highlights the importance of market presence to the initiation of new routes at initial stages, cultivating potential demand and increasing accessibility. In addition, the construction of HSR to one primary airport within a multi-airport system can raise “shadow effects” for other airports. Our findings provide policy suggestions for airport operators in terms of developing accurate positions in the hierarchy and strengthening transfer ability.

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Section: Introductionmentioning

confidence: 78%

Modeling the Evolutionary Mechanism of China’s Domestic Air Transport Network

Zhang

Tang

et al. 2020

Sustainability

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“…Particularly, regional airports are often close to each other and are faced with either cooperation/integration or competition strategies among them. Hubs or community airports too are not exempt from this challenge and the evolution of airport networks is also an indirect effect of different strategies [18,19].…”

Section: Airport Roles and Clustering Criteriamentioning

confidence: 99%

A Geometric Fuzzy-Based Approach for Airport Clustering

Postorino

Versaci

2014

Advances in Fuzzy Systems

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Airport classification is a common need in the air transport field due to several purposes—such as resource allocation, identification of crucial nodes, and real-time identification of substitute nodes—which also depend on the involved actors’ expectations. In this paper a fuzzy-based procedure has been proposed to cluster airports by using a fuzzy geometric point of view according to the concept of unit-hypercube. By representing each airport as a point in the given reference metric space, the geometric distance among airports—which corresponds to a measure of similarity—has in fact an intrinsic fuzzy nature due to the airport specific characteristics. The proposed procedure has been applied to a test case concerning the Italian airport network and the obtained results are in line with expectations.

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“…Therefore, we propose to resort to community detection algorithms to determine weakly interacting clusters of flights. The use of complex network methods in air traffic management has been used in several contexts [19][20][21][22][23][24]. For example, community detection algorithms have been applied to the networks of airports, sectors, or navigation points to investigate their structure [22,23].…”

Section: Introductionmentioning

confidence: 99%

“…The use of complex network methods in air traffic management has been used in several contexts [19][20][21][22][23][24]. For example, community detection algorithms have been applied to the networks of airports, sectors, or navigation points to investigate their structure [22,23]. can be built, where two flights are linked if they are likely to influence each other in an optimisation process.…”

Section: Introductionmentioning

confidence: 99%

Community Detection for Air Traffic Networks and Its Application in Strategic Flight Planning

2021

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An environmentally and economically sustainable air traffic management system must rely on fast models to assess and compare various alternatives and decisions at the different flight planning levels. Due to the numerous interactions between flights, mathematical models to manage the traffic can be computationally time-consuming when considering a large number of flights to be optimised at the same time. Focusing on demand–capacity imbalances, this paper proposes an approach that permits to quickly obtain an approximate but acceptable solution of this problem. The approach consists in partitioning flights into subgroups that influence each other only weakly, solving the problem independently in each subgroup, and then aggregating the solutions. The core of the approach is a method to build a network representing the interactions among flights, and several options for the definition of an interaction are tested. The network is then partitioned with existing community detection algorithms. The results show that applying a strategic flight planning optimisation algorithm on each subgroup independently reduces significantly the computational time with respect to its application on the entire European air traffic network, at the cost of few and small violations of sector capacity constraints, much smaller than those actually observed on the day of operations.

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Community Structure Detection in the Evolution of the United States Airport Network

Cited by 16 publications

References 32 publications

Modeling the Evolutionary Mechanism of China’s Domestic Air Transport Network

Modeling the Evolutionary Mechanism of China’s Domestic Air Transport Network

A Geometric Fuzzy-Based Approach for Airport Clustering

Community Detection for Air Traffic Networks and Its Application in Strategic Flight Planning

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