DocumentCode
723840
Title
Bias and self-similarity in Dongguan highway network
Author
Wei Li ; Wenhong Wei
Author_Institution
Sch. of Comput., Dongguan Univ. of Technol., Dongguan, China
fYear
2015
fDate
23-25 May 2015
Firstpage
5730
Lastpage
5735
Abstract
The construction of highway network is closely related to the geographical environment, population distribution, social and economic development and it has both natural and social attributes. On the surface, it seems similar to two-dimensional GRID. This paper gives an in-depth analysis on Dongguan highway network (DGN) from the perspective of complex network and compares it with GRID. It is found that the average shortest path of the DGN stays between the small world network and GRID. The DGN has a small number of long edges which are much longer than the average length of the DGN edges and with little association between each other. The degree distribution the DGN is similar to a Normal distribution in which mu is 3 and sigma is 0.8. Compared with the GRID with the shape of a real square, all these mean that the structure of DGN is biased. However, the DGN also shows a characteristic of self-similarity. This even distribution of the bias seems to owing to the social attribute of the DGN, but in fact it is inseparable from the laws of nature. Accordingly, we proposed an evolving model, which group the network which is formed by mixed social and natural factors into a contradiction: people and nature. The model shows same properties with the empirical results.
Keywords
complex networks; normal distribution; roads; DGN; Dongguan highway network; complex network; natural factors; normal distribution; small world network; social factors; Cities and towns; Complex networks; Euclidean distance; Planning; Roads; Sociology; Evolution model; Highway network; Statistical properties; grid;
fLanguage
English
Publisher
ieee
Conference_Titel
Control and Decision Conference (CCDC), 2015 27th Chinese
Conference_Location
Qingdao
Print_ISBN
978-1-4799-7016-2
Type
conf
DOI
10.1109/CCDC.2015.7161826
Filename
7161826
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