On the Metropolis Algorithm for Urban Street Networks

TL;DR

This paper adapts the Metropolis algorithm to optimize urban street networks by minimizing surprisal in their information representation, providing a novel approach to understanding and designing complex city layouts.

Contribution

It introduces a new application of the Metropolis algorithm to urban street networks, focusing on minimizing surprisal in their information structure.

Findings

The algorithm effectively reduces surprisal in modeled networks.

Urban street networks can be optimized using information-theoretic principles.

The approach offers insights into the organization of complex urban layouts.

Abstract

The complexity of urban street networks is well accepted to reside in the information space where roads map to nodes and junctions to links between nodes. Assuming that information networks preserve their amount of surprisal on average leads us to adapt the single-flip Metropolis algorithm to compute information networks with minimal amounts of surprisal.