Spatiotemporal Graph Indicators for Air Traffic Complexity Analysis

Abstract

There has been extensive research in formalising air traffic complexity, but existing works focus mainly on a metric to tie down the peak air traffic controllers workload rather than a dynamic approach to complexity that could guide both strategical, pre-tactical and tactical actions for a smooth flow of aircraft. In this paper, aircraft interdependencies are formalized using graph theory and four complexity indicators are described, which combine spatiotemporal topological information with the severity of the interdependencies. These indicators can be used to predict the dynamic evolution of complexity, by not giving one single score, but measuring complexity in a time window. Results show that these indicators can capture complex spatiotemporal areas in a sector and give a detailed and nuanced view of sector complexity.

Evolution of the Tactical Separation System to support the Integrated Mission Management System in the COAST project

“…The specification of a severity level for the considered clusters is needed in order to allow the IMMS trajectory generation system (indicated as Path Change System in Figure 3) optimizing the trajectory up to the destination airport, in case of needed flight plan modification, while taking into account the overall traffic conditions. In order to assign to each cluster a severity level, the Evo-TSS implements selected criteria, which are recognized as relevant in literature, as indicated in the reference work [28]. Such criteria include: interdependency, strength, and neighbourhood.…”

“…Indeed, in literature, it is also considered a further criterion for traffic clusters severity evaluation. Such further criterion is the so called "clustering coefficient" [28]. The clustering coefficient measures the local cohesiveness of the aircraft inside the defined cluster; therefore, it refers to the measure of the "stability" of the cluster.…”

COVID-19 spread algorithm in the international airport network-DetArpds

“…Finally, Isufaj, Koca & Piera (2021) conducted another study using graph theory for modeling air traffic, with the main objective of simplifying air traffic management. The results were encouraging, as method generated indicators that could measure several structural properties of air traffic, including edge density (graph size), strength (severity of interdependencies), clustering coefficient (proximity of each aircraft), and degree of proximity.…”

Analysis of Worldwide Time-Series Data Reveals Some Universal Patterns of Evolution of the SARS-CoV-2 Pandemic

“…Before selecting the threshold, a filtering procedure was applied to the complete correlation matrix to enhance the positive correlations of interest in this work (see Methods). The applied methodology was proved useful in quantifying correlations of time series in diverse type of data [45,[47][48][49][50][51][52]. Figure 2A shows probability distributions of filtered correlations coefficients for the outbreak and immunisation period.…”

“…Previous studies, based on the empirical data regarding the dynamics of interacting units in many complex systems, provided valuable information about the related stochastic processes. Some striking examples across different spatial and temporal scales include the influence of the world financial index dynamics on different countries [45,46], traffic jamming [47,48], brain-to-brain coordination dynamics [49,50], and the cooperative gene expressions along different phases of the cell cycle [51,52]. Similarly, the collected data of SARS-CoV-2 spreading enable a possibility to investigate the infection dynamics in various details and more appropriate modelling of the emergent behaviours.…”