https://doi.org/10.1140/epjds/s13688-023-00431-4
Regular Article
Individual mobility deep insight using mobile phones data
1
Laboratory of City Science, Physics and Astronomy Department, University of Bologna, Bologna, Italy
2
INFN—sezione di Bologna, Bologna, Italy
3
Telecom-Italia SPA, Rome, Italy
Received:
11
January
2023
Accepted:
25
November
2023
Published online:
8
December
2023
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.
Key words: Human mobility / Mobile phone data / Path length and duration distributions / Multiscale road network
Supplementary Information The online version contains supplementary material available at https://doi.org/10.1140/epjds/s13688-023-00431-4.
© The Author(s) 2023
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