Universal Model of Urban Street Networks
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arXiv
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| Main Authors: | , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866908560610295808 |
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| author | Barthelemy, Marc Boeing, Geoff |
| author_facet | Barthelemy, Marc Boeing, Geoff |
| contents | Analyzing 9,000 urban areas' street networks, we identify properties, including extreme betweenness centrality heterogeneity, that typical spatial network models fail to explain. Accordingly we propose a universal, parsimonious, generative model based on a two-step mechanism that begins with a spanning tree as a backbone then iteratively adds edges to match empirical degree distributions. Controlled by a single parameter representing lattice-equivalent node density, it accurately reproduces key universal properties to bridge the gap between empirical observations and generative models. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2509_21931 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Universal Model of Urban Street Networks Barthelemy, Marc Boeing, Geoff Physics and Society Disordered Systems and Neural Networks Statistical Mechanics Analyzing 9,000 urban areas' street networks, we identify properties, including extreme betweenness centrality heterogeneity, that typical spatial network models fail to explain. Accordingly we propose a universal, parsimonious, generative model based on a two-step mechanism that begins with a spanning tree as a backbone then iteratively adds edges to match empirical degree distributions. Controlled by a single parameter representing lattice-equivalent node density, it accurately reproduces key universal properties to bridge the gap between empirical observations and generative models. |
| title | Universal Model of Urban Street Networks |
| topic | Physics and Society Disordered Systems and Neural Networks Statistical Mechanics |
| url | https://arxiv.org/abs/2509.21931 |