A complex network approach towards modeling and analysis of the Australian Airport Network

Hossain, Md. Murad; Alam, Sameer

doi:10.1016/j.jairtraman.2016.12.008

Cited by 90 publications

(51 citation statements)

References 24 publications

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“…For the topology of a network, the degree distribution is an important feature. The links between two nodes in a network are assigned randomly have a Poisson degree distribution with many of the nodes having typical degree [15], whether it is a Worldwide Airport Network (WAN), Airport Network of China (ANC) [16], Airport Network of Italy (ANI) [17] or Indian Airport Network (IAN) [7], or Australian Airport Network (AAN) [18]. Degree distribution can be found in all of these.…”

Section: Literature Reviewmentioning

confidence: 99%

“…It's values will be between 0 -1 where 0 means no colonies at all and 1 means densely formed cluster or colonies in which every node (airport) is connected with every other node (airport). If the clustering coefficient is 0.5 as in the case of AAN [18] and if 2 airports were randomly chosen then there is a probability that the neighbors are connected directly is 50% whereas, with the same size small-world and random network the possibility is 63.50% and 9.1% respectively. The high clustering coefficient of AAN confirmed a large amount of concentration and also suggests the high probability of transfer with less connecting flights.…”

Section: Literature Reviewmentioning

confidence: 99%

“…For instance, betweenness centrality will show which airport comes in between from departure to destination whereas closeness centrality will tell which individual airport is near all other individuals in a network. Sameer Alam and Murad Hussain created a table of top 20 cities (airport within cities) of Australia according to their degree, closeness and betweenness [18].…”

mentioning

confidence: 99%

“…1 also illustrates that 10 cities which are at the top are highly connected and plays a vital role in transportation. Apart from Australia, the centrality measures were also performed by others such as Gudia and Maria [18]. They performed betweenness measures for three different period and randomized IAN appropriately.…”

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confidence: 99%

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Analysis of Airport Network in Pakistan Utilizing Complex Network Approach

Malik¹,

Mahmood²,

Hammal³

et al. 2019

ijacsa

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Field of complex network covers different social, technological, biological, scientific collaborative work, communication networks and many others. Among these networks, transportation network is an important indicator to measure the economic growth in any country. In this study different dynamics of Airport Network in Pakistan are analyzed by the complex network methodology. Dataset of air transportation has been collected from Civil Aviation Authority of Pakistan (CAA) and formatted to accomplish the complex network requirements. The network is formed to observe its different properties and compare these with their topological counterparts. In this, network nodes are represented by Airports of Pakistan while flights between them within a week are considered as edges. The behavior of degree distribution is observed as preferential attachment of nodes, which represented that few nodes are controlling overall network which emphasizes that Airport Network in Pakistan (ANP) follows power law. Clustering coefficient displayed the network as a clustered network. Result of short average path length highlights that Airport Network in Pakistan is small-world network. Study also signified the average nearest neighbour degree node, which explained that ANP exhibited disassortative mixing in nature which states that high degree nodes (airports) tend to connect to low degree nodes (airports). Interestingly, is has been observed that it is not necessary that the most connected node is also the most central node in degree centralities.

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Section: Literature Reviewmentioning

confidence: 99%

Section: Literature Reviewmentioning

confidence: 99%

mentioning

confidence: 99%

mentioning

confidence: 99%

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Analysis of Airport Network in Pakistan Utilizing Complex Network Approach

Malik¹,

Mahmood²,

Hammal³

et al. 2019

ijacsa

View full text Add to dashboard Cite

show abstract

“…Recently, the development of complex network theory has led to many studies being published in the traffic field [1], including research on the network structure of high-speed passenger transport systems, such as high-speed rail [2,3] and air transport [4][5][6][7][8][9][10]. As a complex man-made network, the air transport network plays an important role in human social and economic development.…”

Section: Introductionmentioning

confidence: 99%

Evolution and Determinants of an Air Transport Network: A Case Study of the Chinese Main Air Transport Network

Min

Luan

et al. 2019

Sustainability

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The Chinese main air transport network (CMATN) is the framework for air passenger transport in the country. This study uses complex networks and an econometric model to analyze CMATN’s evolution and determinants. In terms of overall network structure, the network has always shown small-world properties, with smaller average path lengths (2.06–2.15) and larger clustering coefficients (0.68–0.77), while its cumulative degree distribution follows an exponential function. City passenger volumes conform to the degree power law function, which means that the more destinations a city connects to, the higher its passenger traffic will be. In major hub cities, such as Beijing, Shanghai, and Guangzhou, control power decreases, while Chengdu, Kunming, Chongqing, Xi’an, Urumqi, and other cities play an increasingly important role in CMATN. In terms of main route passenger volumes and formation, increases in GDP and tourism have had a promoting effect, while high-speed rail (HSR) poses a threat to overlapping routes. CMATN is primarily located in the central and eastern regions, focusing on China’s economy, tourism, and efficient HSR development. Although the competition from HSR affects the overall network structure of CMATN based on its influence on specific routes, we believe that the impact is limited due to the different transport attributes of the two networks. The research results of this study can become an information source for decision makers and provide a reference for air transport to seek sustainable development.

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Tracing the spatial‐temporal evolution dynamics of air traffic systems using graph theories

Xiao

Gao

et al. 2022

Int J of Intelligent Sys

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Air traffic systems are of great significance to our society. However, air traffic systems are extremely complicated since an air traffic system encompasses many components which could evolve over time. It is therefore challenging to analyze the evolution dynamics of air traffic systems. In this paper we propose a graph perspective to trace the spatial-temporal evolutions of air traffic systems. Different to existing studies which are model-driven and only focus on certain properties of an air traffic system, in this paper we propose a data-driven perspective and analyze a couple of properties of an air traffic system. Specifically, we model air traffic systems with both unweighted and weighted graphs with respect to real-world traffic data. We then analyze the evolution dynamics of the constructed graphs in terms of nodal degrees, degree distributions, traffic delays, causality between graph structures and traffic delays, and system resilience under airport failures. To validate the effectiveness of the proposed approach, a case study on the American air traffic systems with respect to 12-month traffic data is carried out. It is found that the structures and traffic mobilities of the American air traffic systems do not evolve significantly over time, which leads to the stable distributions of the traffic delays as evidenced by a causality analysis. It is further found that the American

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A complex network approach towards modeling and analysis of the Australian Airport Network

Cited by 90 publications

References 24 publications

Analysis of Airport Network in Pakistan Utilizing Complex Network Approach

Analysis of Airport Network in Pakistan Utilizing Complex Network Approach

Evolution and Determinants of an Air Transport Network: A Case Study of the Chinese Main Air Transport Network

Tracing the spatial‐temporal evolution dynamics of air traffic systems using graph theories

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