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arXiv 2209.06751physics.soc-ph
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城市交通网络与基于最优运输的基础设施:相似性与规模经济

Urban transportation networks and optimal transport-based infrastructures: similarity and economy of scale

Daniela Leite, Caterina De Bacco

AI总结:

本研究受最优运输理论与规模经济启发,提出仅需少量起讫点即可生成类公共交通网络结构的方法,可模拟多种交通网络、提出改进建议,并提供网络相似性的定量度量。

摘要:

设计并优化城市交通网络结构是一项具有挑战性的任务。本研究中,我们提出了一种受最优运输理论与规模经济原则启发的方法,该方法仅需输入少量信息,即可生成与公共交通网络结构相似的网络。与标准方法不同,该方法无需预先假设任何初始骨干网络基础设施,而是仅利用少量起讫点,直接从连续空间中提取该基础设施。通过分析一组城市火车、有轨电车与地铁网络,我们发现在多个研究案例中,模拟基础设施与真实基础设施之间存在显著的相似性。通过调整一个参数,我们的方法可以模拟一系列不同的地铁、有轨电车与火车网络,这些模拟网络可进一步用于就相关交通特性提出可能的改进建议。我们的算法输出自然提供了一种有理论依据的两网络间相似性定量度量,可用于自动化筛选相似的模拟网络。

英文摘要:

Designing and optimizing the structure of urban transportation networks is a challenging task. In this study, we propose a method inspired by optimal transport theory and the principle of economy of scale that uses little information in input to generate structures that are similar to those of public transportation networks. Contrarily to standard approaches, it does not assume any initial backbone network infrastructure, but rather extracts this directly from a continuous space using only a few origin and destination points. Analyzing a set of urban train, tram and subway networks, we find a noteworthy degree of similarity in several of the studied cases between simulated and real infrastructures. By tuning one parameter, our method can simulate a range of different subway, tram and train networks that can be further used to suggest possible improvements in terms of relevant transportation properties. Outputs of our algorithm provide naturally a principled quantitative measure of similarity between two networks that can be used to automatize the selection of similar simulated networks.

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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