Structural Properties of US Flight Network

Chi, Liping; Wang, Ru; Su, Hang; Xu, Xiaolong; Jun, Zhao; Li, Wei; Cai, X.

doi:10.1088/0256-307x/20/8/362

Cited by 117 publications

(33 citation statements)

References 11 publications

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“…In the recent literature it is possible to find many examples where network science has been applied to the air transportation system (for a review, see [1,2]). Many studies have focused on the topological aspect of the airport network [3][4][5][6][7][8][9][10][11][12][13][14], but network science techniques can also be used to study topics more related to air traffic management [15][16][17][18][19][20][21][22][23][24][25]. In particular, one can consider different elements of the airspace like sectors and navigation points and build networks which are informative about the air traffic management [26].…”

Section: Introductionmentioning

confidence: 99%

Statistical characterization of deviations from planned flight trajectories in air traffic management

Bongiorno

Gurtner

Lillo

et al. 2017

Journal of Air Transport Management

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Understanding the relation between planned and realized flight trajectories and the determinants of flight deviations is of great importance in air traffic management. In this paper we perform an in-depth investigation of the statistical properties of planned and realized air traffic on the German airspace during a 28 day periods, corresponding to an AIRAC cycle. We find that realized trajectories are on average shorter than planned ones and this effect is stronger during night-time than day-time. Flights are more frequently deviated close to the departure airport and at a relatively large angle-to-destination. Moreover, the probability of a deviation is higher in low traffic phases. All these evidences indicate that deviations are mostly used by controllers to give directs to flights when traffic conditions allow it. Finally we introduce a new metric, termed di-fork, which is able to characterize navigation points according to the likelihood that a deviation occurs there. Di-fork allows to identify in a statistically rigorous way navigation point pairs where deviations are more (less) frequent than expected under a null hypothesis of randomness that takes into account the heterogeneity of the navigation points. Such pairs can therefore be seen as sources of flexibility (stability) of controllers traffic management while conjugating safety and efficiency.

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Section: Introductionmentioning

confidence: 99%

Statistical characterization of deviations from planned flight trajectories in air traffic management

Bongiorno

Gurtner

Lillo

et al. 2017

Journal of Air Transport Management

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“…Graphs representing many different real world systems such as for example, author citations relations [2], biological networks [3], [4] or flight connections [5] present many similar topological properties that significantly distinguish them danielg@if.ufrgs.br fromËrdos-Rénny random graphs. Some of these common characteristics, often claimed to be ubiquitous in real world networks, are the presence of hubs (a few highly connected nodes), the so called small world property [6], scale-freeness or self-similarity as a consequence of the network's nodes degree distribution often be similar to a power-law function (p(k) ∝ k −γ ) [7], [8], [9], high clusterization and hierarchical organization [10].…”

Section: Introductionmentioning

confidence: 99%

A comprehensive statistical study of metabolic and protein–protein interaction network properties

Gamermann

Triana-Dopico

Jaime

2019

Physica A: Statistical Mechanics and its Applications

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“…Examples of systems studied within this approach are social networks [1], author citations [2], flights connections [3], metabolic models [4,5], protein-protein interactions [6,7], electrical grids [8,9], among others.…”

Section: Introductionmentioning

confidence: 99%

Master equation for the degree distribution of a Duplication and Divergence network

Sudbrack

Brunnet

Almeida

et al. 2018

Physica A: Statistical Mechanics and its Applications

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Network growth as described by the Duplication-Divergence model proposes a simple general idea for the evolution dynamics of natural networks. In particular it is an alternative to the well known Barabási-Albert model when applied to protein-protein interaction networks. In this work we derive a master equation for the node degree distribution of networks growing via Duplication and Divergence and we obtain an expression for the total number of links and for the degree distribution as a function of the number of nodes. Using algebra tools we investigate the degree distribution asymptotic behavior. Analytic results show that the network nodes average degree converges if the total mutation rate is greater than 0.5 and diverges otherwise. Treating original and duplicated node mutation rates as independent parameters has no effect on this result. However, difference in these parameters results in a slower rate of convergence and in different degree distributions. The more different these parameters are, the denser the tail of the distribution. We compare the solutions obtained with simulated networks. These results are in good agreement with the expected values from the derived expressions. The method developed is a robust tool to investigate other models for network growing dynamics.

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Structural Properties of US Flight Network

Cited by 117 publications

References 11 publications

Statistical characterization of deviations from planned flight trajectories in air traffic management

Statistical characterization of deviations from planned flight trajectories in air traffic management

A comprehensive statistical study of metabolic and protein–protein interaction network properties

Master equation for the degree distribution of a Duplication and Divergence network

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