Exploring the network structure and nodal centrality of China’s air transport network: A complex network approach

2011 ◽  
Vol 19 (4) ◽  
pp. 712-721 ◽  
Author(s):  
Jiaoe Wang ◽  
Huihui Mo ◽  
Fahui Wang ◽  
Fengjun Jin
Keyword(s):  
Complex Network ◽  
Network Structure ◽  
Transport Network ◽  
Air Transport ◽  
Network Approach

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

2019 ◽  
Vol 11 (14) ◽  
pp. 3933 ◽  
Author(s):  
Min Su ◽  
Weixin Luan ◽  
Zeyang Li ◽  
Shulin Wan ◽  
Zhenchao Zhang
Keyword(s):  
Network Structure ◽  
High Speed ◽  
Information Source ◽  
Small World ◽  
Transport Network ◽  
Air Transport ◽  
Promoting Effect ◽  
High Speed Rail ◽  
Path Lengths ◽  
The Impact

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.

scholarly journals ANALYSIS OF POSSIBILITY OF USING NEURAL NETWORK TO FORECAST PASSENGER TRAFFIC FLOWS IN RUSSIA

Aviation ◽  
2007 ◽  
Vol 11 (1) ◽  
pp. 28-34 ◽  
Author(s):  
Tatiana O. Blinova
Keyword(s):  
Neural Network ◽  
Transport Network ◽  
Air Transport ◽  
Investment Efficiency ◽  
Traffic Flows ◽  
Network Approach ◽  
Passenger Transport ◽  
Neural Network Approach ◽  
Passenger Traffic

At present the problem of forecasting passenger transport demand is of immense importance for air transport producers as well as for investors since investment efficiency is greatly affected by the accuracy and adequacy of the estimation performed. The aim of the present research is to analyze the possibility of using a neural network approach to forecast the expansion of the air‐transport network in Russia.

scholarly journals Complex network approach for detecting tropical cyclones

2021 ◽  
Author(s):  
Shraddha Gupta ◽  
Niklas Boers ◽  
Florian Pappenberger ◽  
Jürgen Kurths
Keyword(s):  
Sea Level ◽  
Tropical Cyclones ◽  
Complex Network ◽  
Time Scales ◽  
Southern Oscillation ◽  
Volcanic Eruptions ◽  
Network Approach ◽  
Sea Level Pressure ◽  
Level Pressure ◽  
Climate Networks

AbstractTropical cyclones (TCs) are one of the most destructive natural hazards that pose a serious threat to society, particularly to those in the coastal regions. In this work, we study the temporal evolution of the regional weather conditions in relation to the occurrence of TCs using climate networks. Climate networks encode the interactions among climate variables at different locations on the Earth’s surface, and in particular, time-evolving climate networks have been successfully applied to study different climate phenomena at comparably long time scales, such as the El Niño Southern Oscillation, different monsoon systems, or the climatic impacts of volcanic eruptions. Here, we develop and apply a complex network approach suitable for the investigation of the relatively short-lived TCs. We show that our proposed methodology has the potential to identify TCs and their tracks from mean sea level pressure (MSLP) data. We use the ERA5 reanalysis MSLP data to construct successive networks of overlapping, short-length time windows for the regions under consideration, where we focus on the north Indian Ocean and the tropical north Atlantic Ocean. We compare the spatial features of various topological properties of the network, and the spatial scales involved, in the absence and presence of a cyclone. We find that network measures such as degree and clustering exhibit significant signatures of TCs and have striking similarities with their tracks. The study of the network topology over time scales relevant to TCs allows us to obtain crucial insights into the effects of TCs on the spatial connectivity structure of sea-level pressure fields.

Complex network structure of flocks in the Vicsek Model with Vectorial Noise

2014 ◽  
Vol 25 (03) ◽  
pp. 1350095 ◽  
Author(s):  
Gabriel Baglietto ◽  
Ezequiel V. Albano ◽  
Julián Candia
Keyword(s):  
Phase Transition ◽  
Complex Network ◽  
Network Structure ◽  
Time Step ◽  
Vicsek Model ◽  
Disorder Phase Transition ◽  
First Order ◽  
Direction Of Movement ◽  
Long Range Ordering ◽  
Influence Of Noise

In the Vicsek Model (VM), self-driven individuals try to adopt the direction of movement of their neighbors under the influence of noise, thus leading to a noise-driven order–disorder phase transition. By implementing the so-called Vectorial Noise (VN) variant of the VM (i.e. the VM-VN model), this phase transition has been shown to be discontinuous (first-order). In this paper, we perform an extensive complex network study of VM-VN flocks and show that their topology can be described as highly clustered, assortative, and nonhierarchical. We also study the behavior of the VM-VN model in the case of "frozen flocks" in which, after the flocks are formed using the full dynamics, particle displacements are suppressed (i.e. only rotations are allowed). Under this kind of restricted dynamics, we show that VM-VN flocks are unable to support the ordered phase. Therefore, we conclude that the particle displacements at every time-step in the VM-VN dynamics are a key element needed to sustain long-range ordering throughout.

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