  Studies on public transportation in Europe suggest that European inhabitants
use buses in ca. 56% of all public transport travels. One of the critical
factors affecting such a percentage and more, in general, the demand for public
transport services, with an increasing reluctance to use them, is their
quality. End-users can perceive quality from various perspectives, including
the availability of information, i.e., the access to details about the transit
and the provided services. The approach proposed in this paper, using
innovative methodologies resorting on data mining and machine learning
techniques, aims to make available the unavailable data about public transport.
In particular, by mining GPS traces, we manage to reconstruct the complete
transit graph of public transport. The approach has been successfully validated
on a real dataset collected from the local bus system of the city of L'Aquila
(Italy). The experimental results demonstrate that the proposed approach and
implemented framework are both effective and efficient, thus being ready for
deployment.
