  In this paper, we describe a case study in a big metropolis, in which from
data collected by digital sensors, we tried to understand mobility patterns of
persons using buses and how this can generate knowledge to suggest
interventions that are applied incrementally into the transportation network in
use. We have first estimated an Origin-Destination matrix of buses users from
datasets about the ticket validation and GPS positioning of buses. Then we
represent the supply of buses with their routes through bus stops as a complex
network, which allowed us to understand the bottlenecks of the current scenario
and, in particular, applying community discovery techniques, to identify
clusters that the service supply infrastructure has. Finally, from the
superimposing of the flow of people represented in the OriginDestination matrix
in the supply network, we exemplify how micro-interventions can be prospected
by means of an example of the introduction of express routes.
