  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.
