2010Unpublished venueRequires access

Analysis of Nanjing public transport network based on complex network

Wei Yu, Xue Liang, Zhang Yonghui, Huang Yin-di

Open publisher page 3 citations

Abstract

The topology model of Nanjing public transport network is established. Combined with the thought of complex network, the bus stations are looked as the nodes, and the connection between sites are as the sides. On the basis of a large number of statistical data, the characteristics of complex network in Nanjing are researched through Space L and Space P methods. The results show that the degree distribution of the public transport network is in line with the rate of distribution of power and has scale-free properties. And it has a smaller average distance and high clustering coefficient, which is the typical property of small-world.

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What this paper is about

The topology model of Nanjing public transport network is established. Combined with the thought of complex network, the bus stations are looked as the nodes, and the connection between sites are as the sides. On the basis of a large number of statistical data, the characteristics of complex network in Nanjing are researched through Space L and Space P methods. The results show that the degree distribution of the public transport network is in line with the rate of distribution of power and has scale-free properties. And it has a smaller average distance and high clustering coefficient, which is the typical property of small-world.

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Available abstract

The topology model of Nanjing public transport network is established. Combined with the thought of complex network, the bus stations are looked as the nodes, and the connection between sites are as the sides. On the basis of a large number of statistical data, the characteristics of complex network in Nanjing are researched through Space L and Space P methods. The results show that the degree distribution of the public transport network is in line with the rate of distribution of power and has scale-free properties. And it has a smaller average distance and high clustering coefficient, which is the typical property of small-world.

Key concepts: Clustering coefficient, Degree distribution, Complex network, Public transport, Average path length, Topology (electrical circuits), Computer science, Network topology

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