Individual mobility deep insight using mobile phones data

The data sets provided by Information and Communication Technologies have been extensively used to study the human mobility in the framework of complex systems. The possibility of detecting the behavior of individuals performing the urban mobility may offer the possibility of understanding how to realize a transition to a sustainable mobility in future smart cities. The Statistical Physics approach considers the statistical distributions of human mobility to discover universal features. Under this point of view the power laws distributions has been extensively studied to propose model of human mobility. In this paper we show that using a GPS data set containing the displacements of mobile devices in an area around the city Rimini (Italy), it is possible to reconstruct a sample of mobility paths and to study the statistical properties of urban mobility. Applying a fuzzy c-means clustering algorithm, we succeed to detect different mobility types that highlight the multilayer structure of the road network. The disaggregation into homogeneous mobility classes explains the power law distributions for the path lengths and the travel times as an overlapping of exponential distributions, that are consistent with a maximum entropy Principle. Under this point of view it is not possible to infer other dynamical properties on the individual mobility, except for the average values of the different classes. We also study the role of the mobility types, when one restricts the analysis to the an origin-destination framework, by analyzing the daily evolution of the mobility flows.

Automated summary

This study uses GPS data and statistical physics methods to examine the statistical properties of urban mobility. By employing a clustering algorithm, different mobility types are detected, and the power law distributions for path lengths and travel times are explained as an overlapping of exponential distributions, revealing the multilayer structure of the road network.