Source-linked AI summary
Modeling urban street patterns
Marc Barthelemy, Alessandro Flammini
TL;DR
Urban street networks exhibit empirical patterns that simple regular-lattice models do not explain. The paper models simultaneous center and road growth through local optimization and finds statistical properties in good agreement with observed city patterns, supporting a simple candidate mechanism for street-pattern evolution.
Problem
Urban street networks show heterogeneous cell-area distributions and other properties that simple regular-lattice models do not account for.
Method
The model grows randomly located centers and their connecting roads simultaneously, using a local optimization rule that maximizes cumulative-distance reduction.
Results
The model reproduces observed network statistics, including a cell form-factor distribution supported around 0.4 < φ < 0.7 and an area-distribution exponent of 1.9 ± 0.05.
Takeaways & Limitations
Simple local optimization is a candidate process driving city street-pattern evolution, while the spatial distribution of centers remains crucial.
Takeaways & Limitations
The model assumes that centers are independently located, with their locations chosen exogenously.
Abstract
from arXiv · showhide
Urban streets patterns form planar networks whose empirical properties cannot be accounted for by simple models such as regular grids or Voronoi tesselations. Striking statistical regularities across different cities have been recently empirically found, suggesting that a general and details-independent mechanism may be in action. We propose a simple model based on a local optimization process combined with ideas previously proposed in studies of leaf pattern formation. The statistical properties of this model are in good agreement with the observed empirical patterns. Our results thus suggests that in the absence of a global design strategy, the evolution of many different transportation networks indeed follow a simple universal mechanism.