Spatial Aggregation of Worldwide Air Transportation Networks

CICTP 2017 ◽  
2018 ◽  
Author(s):  
Xiaoqian Sun ◽  
Sebastian Wandelt
2021 ◽  
Author(s):  
Max Li ◽  
Karthik Gopalakrishnan ◽  
Hamsa Balakrishnan ◽  
Sang Hyun Shin ◽  
Darsh Jalan ◽  
...  

Author(s):  
Yong Yang ◽  
Kai-Jun Xu ◽  
Chen Hong

Air transportation networks play important roles in human mobility. In this paper, from the perspective of multilayer network mechanism, the dynamics of the Chinese air transportation network are extensively investigated. A multilayer-based passengers re-scheduling model is introduced, and a multilayer cooperation (MC) approach is proposed to improve the efficiency of network traffic under random failures. We use two metrics: the success rate and the extra transfer number, to evaluate the efficiency of re-scheduling. It is found that a higher success rate of passengers re-scheduling can be obtained by MC, and MC is stronger for resisting the instability of the capacity of links. Furthermore, the explosion of the number of extra transfer can be well restrained by MC. Our work will highlight a better understanding of the dynamics and robustness of the Chinese air transportation network.


2019 ◽  
Vol 4 (1) ◽  
Author(s):  
George Cybenko ◽  
Steve Huntsman

AbstractDirected contact networks (DCNs) are temporal networks that are useful for analyzing and modeling phenomena in transportation, communications, epidemiology and social networking. Specific sequences of contacts can underlie higher-level behaviors such as flows that aggregate contacts based on some notion of semantic and temporal proximity. We describe a simple inhomogeneous Markov model to infer flows and taint bounds associated with such higher-level behaviors, and also discuss how to aggregate contacts within DCNs and/or dynamically cluster their vertices. We provide examples of these constructions in the contexts of information transfers within computer and air transportation networks, thereby indicating how they can be used for data reduction and anomaly detection.


2012 ◽  
Vol 23 (06) ◽  
pp. 1250044 ◽  
Author(s):  
ZHUO SUN ◽  
JIANFENG ZHENG ◽  
HONGTAO HU

This study explores the community structure in spatial maritime shipping networks. As compared with air transportation networks and urban road networks, ports in spatial maritime shipping networks have smaller connections due to the physical confinement. A new divisive algorithm is proposed for detecting community structure in spatial maritime shipping networks. At each iteration for modularity optimization, the length of each edge is successively updated, instead of edge removal used in the conventional divisive method. Finally, numerical experiments based on the global maritime shipping network are carried out to account for the properties of community structure in spatial maritime shipping networks.


1973 ◽  
Vol 7 (3) ◽  
pp. 207-222 ◽  
Author(s):  
Steven Gordon ◽  
Richard de Neufville

2014 ◽  
Vol 513-517 ◽  
pp. 4020-4024
Author(s):  
Zhi Jing Xu ◽  
Zheng Hu Zu ◽  
Tao Zheng ◽  
Qing Xu ◽  
Wen Dou Zhang ◽  
...  

Many studies suggest that air-transportation networks contribute a lot to the spatiotemporal dynamics of infectious diseases. The mobility of individuals over the networks has greatly speeded up the spreading processes of the epidemics and pushed the population in non-epidemic areas into the risk of infection. To figure out the underlying interactions between the air-transportation networks and the transmission of the epidemics, we have (i) analyzed the air-routes and the statistical data on the passenger throughput of the civil aviation of China and (ii) carried out a computer simulation based on the assumption that a novel influenza outbreaks in Southeast Asia. The results show that the topology of the air-transportations networks has a typical structure of heterogeneities. We also find that the epidemics will soon strike China after the initial outbreaks and rapidly spread throughout the whole networks without air-travel restrictions even the reproductive number () is small.


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