Abstract

We investigate urban street networks as a whole within the frameworks of information physics and statistical physics. Urban street networks are envisaged as evolving social systems subject to a Boltzmann-mesoscopic entropy conservation. For self-organized urban street networks, our paradigm has already allowed us to recover the effectively observed scale-free distribution of roads and to foresee the distribution of junctions. The entropy conservation is interpreted as the conservation of the surprisal of the city-dwellers for their urban street network. In view to extend our investigations to other urban street networks, we consider to perturb our model for self-organized urban street networks by adding an external surprisal drift. We obtain the statistics for slightly drifted self-organized urban street networks. Besides being practical and manageable, this statistics separates the macroscopic evolution scale parameter from the mesoscopic social parameters. This opens the door to observational investigations on the universality of the evolution scale parameter. Ultimately, we argue that the strength of the external surprisal drift might be an indicator for the disengagement of the city-dwellers for their city.

Highlights

  • We seek to understand the statistics of urban street networks

  • Conclusions and future works The primary goal of our investigation is to understand the statistics of urban street networks

  • We present the surprisal statistical physics model that we showed to govern self-organized urban street networks

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Summary

Introduction

We seek to understand the statistics of urban street networks. Such an understanding will help urban designers and decision makers to improve urban policies in general and urban transportation in particular. The valuation function Va assigns to each road or junction of the urban street network a numerical quantity that characterizes its physical state. The weight function w, or more precisely its composition with the valuation function Va as expressed in (2), allows us to assign to each mental picture of the urban street network a numerical quantity that characterizes its perception among the city-dwellers This assignment is the probability distribution Pr of our system. Ideal self-organized urban street networks Coherence based on Boltzmannian mesoscopic surprisals It is time to explicitly invoke Jaynes’s maximum entropy principle for the functional entropy (4) with the first logarithmic moment (5) as single characterizing constraint. A careful implementation written in C language that uses the Levin transformation encoded in the HURRY procedure (Fessler et al (1983), Algo. 602) as implemented in the GNU Scientific Library (Galassi et al 2009) appears efficient in terms of both precision and speed

Conclusions and future works
We have
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