English

From one-way streets to percolation on random mixed graphs

Statistical Mechanics 2021-04-21 v1 Disordered Systems and Neural Networks Physics and Society

Abstract

In most studies, street networks are considered as undirected graphs while one-way streets and their effect on shortest paths are usually ignored. Here, we first study the empirical effect of one-way streets in about 140140 cities in the world. Their presence induces a detour that persists over a wide range of distances and characterized by a non-universal exponent. The effect of one-ways on the pattern of shortest paths is then twofold: they mitigate local traffic in certain areas but create bottlenecks elsewhere. This empirical study leads naturally to consider a mixed graph model of 2d regular lattices with both undirected links and a diluted variable fraction pp of randomly directed links which mimics the presence of one-ways in a street network. We study the size of the strongly connected component (SCC) versus pp and demonstrate the existence of a threshold pcp_c above which the SCC size is zero. We show numerically that this transition is non-trivial for lattices with degree less than 44 and provide some analytical argument. We compute numerically the critical exponents for this transition and confirm previous results showing that they define a new universality class different from both the directed and standard percolation. Finally, we show that the transition on real-world graphs can be understood with random perturbations of regular lattices. The impact of one-ways on the graph properties were already the subject of a few mathematical studies, and our results show that this problem has also interesting connections with percolation, a classical model in statistical physics.

Keywords

Cite

@article{arxiv.2103.10062,
  title  = {From one-way streets to percolation on random mixed graphs},
  author = {Vincent Verbavatz and Marc Barthelemy},
  journal= {arXiv preprint arXiv:2103.10062},
  year   = {2021}
}

Comments

11 pages, 3 tables, 11 figures

R2 v1 2026-06-24T00:18:14.079Z