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Structure and robustness of S\~ao Paulo public transport network

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      Abstract

      Public Transport Networks plays a central role in cities devolopment. In big cities such system may be represented by a complex network and understand its properties is of great interest for managers and scholars. In this work, the urban public transport system of S\~ao Paulo is reinterpreted as a coupled (bus, subway and train) network, bypassing operational details and focusing on connectivity. Using a empirically generated graph, a statistical characterization is made by network metrics. Nearby bus stops and rail transport stations (subway and train) may or may not be considered as a single vertex in the network representation of the transport system, depending on how much an user is willing to walk to shift from one stop/station to another. This distance radius is then used to group nearby stops/stations as a single vertex in the network representation of the urban public transport system and then its properties are studied as a function of this radius. This radius is used as proxy of the user's willingness to walk until the nearest point to access transportation. The variation of the measure \( \rho \) leads to changes in the perception of the topology of the public transport network as shown in this work. An interesting result was that the network is assortative. Another aspect investigated was the degree distribution of the network. It was not possible to distinguish between power-law or a log-normal distribution. An exploratory model is used to test the robustness of the network by randomly, deterministically and preferentially targeting the stops and service lines. According to the grouping radius, aka willingness, different fragmentation values were obtained under attack simulations. We showed that increasing this willingness generates great reduction in the number of necessary jumps between buses, subway and trains lines to achieve all the network destinations.

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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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        Complex networks: Structure and dynamics

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          Is the Boston subway a small-world network ?

          The mathematical study of the small-world concept has fostered quite some interest, showing that small-world features can be identified for some abstract classes of networks. However, passing to real complex systems, as for instance transportation networks, shows a number of new problems that make current analysis impossible. In this paper we show how a more refined kind of analysis, relying on transportation efficiency, can in fact be used to overcome such problems, and to give precious insights on the general characteristics of real transportation networks, eventually providing a picture where the small-world comes back as underlying construction principle.
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            Author and article information

            Journal
            22 August 2018
            1808.08117

            http://arxiv.org/licenses/nonexclusive-distrib/1.0/

            Custom metadata
            21 pages, 9 figures
            physics.soc-ph

            General physics

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