arXivDaily arXiv每日学术速递 周一至周五更新
arXiv 0709.3203physics.soc-ph
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城市公共交通建模

Modeling Metropolis Public Transport

C. von Ferber, T. Holovatch, Yu. Holovatch, V. Palchykov

摘要:

我们目前的调查结果的公共交通网络(PTNs)的选择14个主要城市的世界PTN的大小范围在2000和46000站,并开发这些网络的演化模型。这些PTNs的结构揭示了一组邻里关系的路线和车站。以这种方式定义的网络由于嵌入的2D地理空间和城市结构的约束而显示出独特的属性。除了复杂网络的标准特征,如最近邻数、平均路径长度和聚类,我们还观察到PTNs特有的特征。虽然其他具有现实世界链接的网络,如嵌入二维或三维的电缆或神经元,通常会显示出类似的行为,但在我们目前的案例中可以详细研究这些行为。路线的地理数据揭示了令人惊讶的自我回避步行属性,我们与表面覆盖的优化。我们提出并模拟了一个进化的增长模型的基础上有效地相互作用的自我避免行走,再现PTN的关键功能。

英文摘要:

We present results of a survey of public transport networks (PTNs) of selected 14 major cities of the world with PTN sizes ranging between 2000 and 46000 stations and develop an evolutionary model of these networks. The structure of these PTNs is revealed in terms of a set of neighbourhood relations both for the routes and the stations. The networks defined in this way display distinguishing properties due to the constraints of the embedding 2D geographical space and the structure of the cities. In addition to the standard characteristics of complex networks like the number of nearest neighbours, mean path length, and clustering we observe features specific to PTNs. While other networks with real-world links like cables or neurons embedded in two or three dimensions often show similar behavior, these can be studied in detail in our present case. Geographical data for the routes reveal surprising self-avoiding walk properties that we relate to the optimization of surface coverage. We propose and simulate an evolutionary growth model based on effectively interacting self-avoiding walks that reproduces the key features of PTN.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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