Towards a Statistical Physics of Human Mobility

TL;DR

This paper applies statistical physics principles to analyze human mobility patterns using GPS data, uncovering potential universal laws and proposing models to explain observed empirical distributions.

Contribution

It introduces a physics-inspired framework to identify universal mobility laws and models trip length, time, and visitation distributions based on GPS data.

Findings

Trip length distribution follows a specific empirical law.

Elapsed times between trips exhibit Benford's law.

Visitation frequency ranks reveal organizational patterns.

Abstract

In this paper, we extend some ideas of statistical physics to describe the properties of human mobility. From a physical point of view, we consider the statistical empirical laws of private cars mobility, taking advantage of a GPS database which contains a sampling of the individual trajectories of 2% of the whole vehicle population in an Italian region. Our aim is to discover possible "universal laws" that can be related to the dynamical cognitive features of individuals. Analyzing the empirical trip length distribution we study if the travel time can be used as universal cost function in a mesoscopic model of mobility. We discuss the implications of the elapsed times distribution between successive trips that shows an underlying Benford's law, and we study the rank distribution of the average visitation frequency to understand how people organize their daily agenda. We also propose simple stochastic models to suggest possible explanations of the empirical observations and we compare our results with analogous results on statistical properties of human mobility presented in the literature.