Micro-interventions in urban transportation from pattern discovery on the flow of passengers and on the bus network

In this paper, we describe a case study in a large metropolis in which, based on data collected by digital sensors, we sought to understand mobility patterns of people using buses and how this can generate knowledge to suggest interventions to be applied incrementally to the transportation network in use. We first estimated an Origin-Destination matrix of bus users based on datasets about ticket validation and GPS positioning of buses. Then we represented 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, apply community discovery techniques in order to identify clusters in the service supply infrastructure. Finally, by superimposing the flow of people represented in the Origin-Destination matrix in the supply network, we exemplify how micro-interventions can be prospected, by means of an example of introducing express routes.