Control and Optimization of Air Traffic Networks

Abstract

The air transportation system connects the world through the transport of goods and people. However, operational inefficiencies such as flight delays and cancellations are prevalent, resulting in economic and environmental impacts. In the first part of this article, we review recent advances in using network analysis techniques to model the interdependencies observed in the air transportation system and to understand the role of airports in connecting populations, serving air traffic demand, and spreading delays. In the second part, we present some of our recent work on using operational data to build dynamical system models of air traffic delay networks. We show that Markov jump linear system models capture many of the salient characteristics of these networked systems. We illustrate how these models can be validated and then used to analyze system properties such as stability and to design optimal control strategies that limit the propagation of disruptions in air traffic networks. Expected final online publication date for the Annual Review of Control, Robotics, and Autonomous Systems, Volume 4 is May 2021. Please see http://www.annualreviews.org/page/journal/pubdates for revised estimates.

Information Entropy Based on Propagation Feature of Node for Identifying the Influential Nodes

“…Many activities can be described as spreading in nature [8][9][10][11][12][13]. In recent years, many research studies focused on the spreading process due to its theoretical meaning and practical value [14,15], including rumor controlling [16][17][18], information diffusion [19][20][21][22], air traffic controlling [23][24][25], and viral marketing [26][27][28]. Among them, research on identification of the influential nodes in complex networks is a hotspot.…”

“…Figure 1: (Color online) e sample network in the figure has 16 nodes as well as25 edges. e SIR model can be used to simulate the nodes' real spreading influence, that is, the amount of the infected nodes can be considered as spreading influence.…”

Air delay propagation patterns in Europe from 2015 to 2018: an information processing perspective

“…Additionally, 1 minute of ground delay implies between 1 to 4 kg of fuel consumption, one order of magnitude higher in the case of airborne delay [21]. Delays have thus extensively been studied, usually by constructing large-scale simulations, as for instance in [22][23][24][25][26][27][28][29][30], or by relying on statistical and machine learning techniques [31][32][33][34][35].…”

From data to complex network control of airline flight delays

“…Air traffic is an essential part of human mobility and global trade, but now it is outstripping the capacity and becoming frequently congested. Passenger airline flight delays and cancellations are prevalent and have socio-economic and environmental consequences 6 – 8 . Hence, these delays have caught the attention of decision makers and researchers from different fields 9 , 10 .…”

“…Among those with such focus the most relevant is Ref. 8 whose approach models air traffic delay dynamics as topology transitions among a discrete modes of airport networks. Each such mode is associated with one characteristic airport topology.…”