INNOVATSION TEXNOLOGIYALAR
Maxsus son, 2023-yil, dekabr
ISSN 2181-4732
30
In this article, we will review some of the applications of
CNT to model the process of
regulating air traffic with an increase in the intensity of flights. We will focus on three main aspects:
the network topology of the ATM system, the network resilience to disruptions and failures, and the
network optimization for performance improvement [1, 2].
Network topology of the ATM system.
The ATM system can be represented as a network of
nodes and links, where nodes are entities that provide or consume air traffic services, such as airports,
air traffic control (ATC) agencies, or aircraft, and links are connections that enable the exchange of
information or coordination between nodes, such as flight routes, radio frequencies, or data links. The
network topology of the ATM system reflects its structure and organization, as well as its operational
characteristics and constraints.
One example of applying CNT to analyze the network topology of the ATM system is the work
by Zanin A.L. (2013), who studied the European ATM network using data from EUROCONTROL.
They constructed a network where nodes were airports and links were flights between them. They
calculated various network metrics, such as degree distribution, clustering coefficient, average path
length, and assortativity coefficient. They found that the European ATM network had a scale-free
structure, meaning that it followed a power-law degree distribution, where a few nodes had a very
high number of connections (hubs), while most nodes had a low number of connections. They also
found that the network had a high clustering coefficient, meaning that nodes tended to form groups
or communities with dense connections among them. Moreover, they found that the network had a
low average path length, meaning that a small number of hops or intermediate nodes could reach any
two nodes. Finally, they found that the network had a negative assortativity coefficient, meaning that
nodes tended to connect with nodes that had a different degree than them [2, 3].
The analysis of the network topology of the ATM system can provide insights into its
functionality and efficiency. For instance, hubs can facilitate the connectivity and accessibility of the
network, but they can also create congestion and delays. Communities can reflect regional or
operational similarities
or differences among nodes, but they can also create fragmentation or
isolation. Path length can indicate the speed and reliability of information or coordination flows in
the network, but it can also depend on external factors such as weather or regulations. Assortativity
can indicate the diversity or homogeneity of node interactions in the network, but it can also affect
its robustness or vulnerability [3].
Network resilience to disruptions and failures.
The ATM system is subject to various types of
disruptions and failures that can affect its normal operation and performance. These can be caused by
natural phenomena (such as storms or volcanic eruptions), technical malfunctions (such as equipment
breakdowns or cyberattacks), human errors (such as miscommunication or miscoordination), or
intentional actions (such as strikes or terrorism). Disruptions and failures can have different impacts
on different parts of the network depending on their severity, location, duration,
frequency, and
propagation.
One example of applying CNT to assess the network resilience to disruptions and failures is the
work by Wang et al. (2016), who studied the Chinese ATM network using data from CAAC. They
constructed a network where nodes were ATC agencies and links were flight routes between them.
They simulated two types of attacks on the network: random failures, where nodes were removed
randomly from the network; and targeted attacks, where nodes were removed according to their
centrality measures (such as degree centrality or betweenness centrality). They measured the impact
of these attacks on the network using metrics such as size of giant component (the largest connected
subnetwork), average path length (the average number of hops between any two nodes), efficiency
(the inverse of average path length), diameter (the maximum number of hops between any two nodes),
and robustness (the ratio of size of giant component before and after the attack) [4].
They found that the Chinese ATM network was resilient to random failures, meaning that it
could maintain its connectivity and functionality even after a large fraction of nodes were removed.
However, they found that the network was vulnerable to targeted attacks, meaning that it could lose
its connectivity and functionality after a small fraction of nodes were removed. They also found that
INNOVATSION TEXNOLOGIYALAR Maxsus son, 2023-yil, dekabr
ISSN 2181-4732
31
the network had different levels of vulnerability depending on the centrality measure used to select
the nodes for removal. For instance, the network was more vulnerable to attacks based on
betweenness centrality than on degree centrality, because betweenness
centrality captured the
importance of nodes as bridges or bottlenecks in the network.
The evaluation of the network resilience to disruptions and failures can provide guidance for
designing and implementing contingency plans and recovery strategies for the ATM system. For
instance, random failures can be mitigated by increasing the redundancy and diversity of the network,
while targeted attacks can be prevented by enhancing the security and protection of the network.
Moreover, different types of disruptions and failures can require different types of responses
depending on their impact on different parts of the network [4, 5].
Network optimization for performance improvement.
The ATM system aims to achieve high
levels of performance in terms of safety, capacity, efficiency, predictability, environment, and cost-
efficiency. These performance areas are interrelated and often conflicting, meaning that improving
one area can compromise another area. Therefore, there is a need to find optimal solutions that can
balance and harmonize these performance areas according to the objectives and preferences of the
stakeholders involved in the ATM system.
One example of applying CNT to optimize the network performance is the work by Zhang et
al. (2019), who studied the US ATM network using data from FAA. They constructed a network
where nodes were airports and links were flights between them. They proposed a multi-objective
optimization model that considered three performance indicators: flight delay (the difference between
actual and scheduled arrival times), flight distance (the length of flight routes),
and flight fuel
consumption (the amount of fuel consumed by flights). They used a genetic algorithm to find Pareto-
optimal solutions that minimized these indicators simultaneously. They compared these solutions
with the actual flight data and evaluated their potential benefits [5, 6].
They found that their optimization model could reduce flight delay by 19.8%, flight distance
by 2.4%, and flight fuel consumption by 2.6%, compared with the actual flight data. They also found
that their optimization model could generate different trade-offs among these indicators depending
on the weights assigned to them. For instance, they found that reducing flight delay had a positive
effect on reducing flight fuel consumption, but a negative effect on increasing flight distance.
