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Understanding Topological and Spatial Attributes of Bus Transportation Networks in Cities of Chongqing and Chengdu

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      Abstract

      It is critical to understand the characteristics of public transportation networks (PTNs). Existing studies have mainly focused on the topological structure of PTNs and have revealed the commonalities of the topological structures of PTNs. However, few studies have examined the differences regarding topological structure characteristics between the PTNs of different cities. In addition, the nature and extent of the influence of specific urban geographic conditions and morphology on PTNs are unclear. This paper focuses on the influence of urban spatial and geographic environments on bus transportation networks (BTNs) by comparatively studying the topological and spatial attributes of two typical BTNs, respectively, from a mountainous city and a plain city in China, from the perspectives of basic statistical properties, types, connection properties, and spatial attributes, by using the complex networks theory and spatial analysis method. The results reveal that the two BTNs have similar statistical properties and they both have scale-free features as well as small-world features. However, these two BTNs are significantly different in the connection properties and spatial attributes. The difference is found closely related to the city’s geographic conditions and spatial morphology. The implications of this study regarding urban traffic planning and land planning are discussed.

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      Collective dynamics of 'small-world' networks.

      Networks of coupled dynamical systems have been used to model biological oscillators, Josephson junction arrays, excitable media, neural networks, spatial games, genetic control networks and many other self-organizing systems. Ordinarily, the connection topology is assumed to be either completely regular or completely random. But many biological, technological and social networks lie somewhere between these two extremes. Here we explore simple models of networks that can be tuned through this middle ground: regular networks 'rewired' to introduce increasing amounts of disorder. We find that these systems can be highly clustered, like regular lattices, yet have small characteristic path lengths, like random graphs. We call them 'small-world' networks, by analogy with the small-world phenomenon (popularly known as six degrees of separation. The neural network of the worm Caenorhabditis elegans, the power grid of the western United States, and the collaboration graph of film actors are shown to be small-world networks. Models of dynamical systems with small-world coupling display enhanced signal-propagation speed, computational power, and synchronizability. In particular, infectious diseases spread more easily in small-world networks than in regular lattices.
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        Emergence of scaling in random networks

        Systems as diverse as genetic networks or the world wide web are best described as networks with complex topology. A common property of many large networks is that the vertex connectivities follow a scale-free power-law distribution. This feature is found to be a consequence of the two generic mechanisms that networks expand continuously by the addition of new vertices, and new vertices attach preferentially to already well connected sites. A model based on these two ingredients reproduces the observed stationary scale-free distributions, indicating that the development of large networks is governed by robust self-organizing phenomena that go beyond the particulars of the individual systems.
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          The structure and function of complex networks

           M. Newman (2003)
          Inspired by empirical studies of networked systems such as the Internet, social networks, and biological networks, researchers have in recent years developed a variety of techniques and models to help us understand or predict the behavior of these systems. Here we review developments in this field, including such concepts as the small-world effect, degree distributions, clustering, network correlations, random graph models, models of network growth and preferential attachment, and dynamical processes taking place on networks.
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            Author and article information

            Affiliations
            [1 ]Chongqing University Planning & Design Institute Co. Ltd, Chongqing University, Chongqing 400030, China
            [2 ]Faculty of Architecture and Urban Planning, Chongqing University, Chongqing 400030, China
            [3 ]Key Laboratory of New Technology for Construction of Cities in Mountain Areas, Chongqing University, Chongqing 400030, China
            [4 ]Beijing Tsinghua Tongheng Urban Planning & Design Institute Co. Ltd, Tsinghua University, Beijing 10085, China
            Journal
            Mathematical Problems in Engineering
            Mathematical Problems in Engineering
            Hindawi Limited
            1024-123X
            1563-5147
            October 17 2018
            October 17 2018
            : 2018
            : 1-14
            10.1155/2018/4137806
            © 2018

            http://creativecommons.org/licenses/by/4.0/

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