Journal ArticleDOI
Robustness analysis of the European air traffic network
TLDR
The Relative Area Index (RAI) as mentioned in this paper quantifies the importance of an individual node relevant to the performance of the entire network when it suffers from capacity reduction at a local scale.Abstract:
The European air traffic network (ATN), consisting of a set of airports and area control centres, is highly complex. The current indicator of its performance, air traffic flow management delays, is insufficient for planning and management purposes. Topological analysis of ATNs of this kind has highlighted betweenness centrality (BC) as an indicator of network robustness, although such an indicator assumes no knowledge of actual traffic flows and the network's operational characteristics. This paper conducts topological and operational analyses of the European ATN in order to derive a more relevant and appropriate indicator of robustness. By applying a flow maximisation model to the network influenced by a range of capacity reductions at the local level, we propose a new index called the Relative Area Index (RAI). The RAI quantifies the importance of an individual node relevant to the performance of the entire network when it suffers from capacity reduction at a local scale. Air traffic data from three typ...read more
Citations
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Machine learning
TL;DR: Machine learning addresses many of the same research questions as the fields of statistics, data mining, and psychology, but with differences of emphasis.
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Efficiency and robustness of weighted air transport networks
TL;DR: A novel efficiency metric for weighted air transport networks (ATNs), where the weight represents the connection strength between two airports concerning the number of routes, the frequency of flights, or thenumber of passenger seats is proposed.
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Robustness Assessment of Urban Road Network with Consideration of Multiple Hazard Events.
TL;DR: A Monte Carlo method and a heuristic algorithm to handle different scenarios with multiple hazard events, which is proved to be quite efficient and can be applied to conduct risk analysis of other systems where multiple failures or disruptions exist.
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Study on evolution characteristics of air traffic situation complexity based on complex network theory
TL;DR: Results show that the complexity vector helps to describe the structural characteristics of air traffic situations and identify the evolutionary characteristics of different traffic situations, and indicates that the air traffic situation is evolving stably on the whole, but will probably runs into severe change.
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Modeling the resilience of an airline cargo transport network affected by a large scale disruptive event
TL;DR: The results have indicated that the network’s resilience has been affected differently regarding the particular indicators of performance as FOMs, and it has been the least affected regarding ‘value of time’ and the most regarding ’inventory cost’ of the air cargo shipments.
References
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Centrality in social networks conceptual clarification
TL;DR: In this article, three distinct intuitive notions of centrality are uncovered and existing measures are refined to embody these conceptions, and the implications of these measures for the experimental study of small groups are examined.
Journal ArticleDOI
Machine learning
TL;DR: Machine learning addresses many of the same research questions as the fields of statistics, data mining, and psychology, but with differences of emphasis.
Book ChapterDOI
Neural Networks for Pattern Recognition
Suresh Kothari,Heekuck Oh +1 more
TL;DR: The chapter discusses two important directions of research to improve learning algorithms: the dynamic node generation, which is used by the cascade correlation algorithm; and designing learning algorithms where the choice of parameters is not an issue.
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Social Network Analysis
TL;DR: In this article, the development of social network analysis, tracing its origins in classical sociology and its more recent formulation in social scientific and mathematical work, is described and discussed. But it is argued that the analysis of social networks is not a purely static process.
MonographDOI
What is social network analysis
TL;DR: Social networks operate on many levels, from families up to the level of nations, and play a critical role in determining the way problems are solved, organizations are run, and the degree to which individuals achieve their goals.