Statistical Laws in Urban Mobility from microscopic GPS data in the area of Florence

TL;DR

This study analyzes GPS data from Florence to uncover statistical laws governing urban mobility, revealing universal patterns and emphasizing the importance of microscopic dynamics for understanding complex social systems.

Contribution

It demonstrates that simple microscopic assumptions can explain macroscopic mobility laws, advancing the application of statistical physics to social systems.

Findings

Identified universal distributions for path lengths and activity downtimes.

Showed microscopic assumptions can reproduce macroscopic statistical laws.

Highlighted the need for dynamic microscopic data to understand transient behaviors.

Abstract

The application of Statistical Physics to social systems is mainly related to the search for macroscopic laws, that can be derived from experimental data averaged in time or space,assuming the system in a steady state. One of the major goals would be to find a connection between the statistical laws to the microscopic properties: for example to understand the nature of the microscopic interactions or to point out the existence of interaction networks. The probability theory suggests the existence of few classes of stationary distributions in the thermodynamics limit, so that the question is if a statistical physics approach could be able to enroll the complex nature of the social systems. We have analyzed a large GPS data base for single vehicle mobility in the Florence urban area, obtaining statistical laws for path lengths, for activity downtimes and for activity degrees. We show also that simple generic assumptions on the microscopic behavior could explain the existence of stationary macroscopic laws, with an universal function describing the distribution. Our conclusion is that understanding the system complexity requires dynamical data-base for the microscopic evolution, that allow to solve both small space and time scales in order to study the transients.