  In the era of the proliferation of Geo-Spatial Data, induced by the diffusion
of GPS devices, the map matching problem still represents an important and
valuable challenge. The process of associating a segment of the underlying road
network to a GPS point gives us the chance to enrich raw data with the semantic
layer provided by the roadmap, with all contextual information associated to
it, e.g. the presence of speed limits, attraction points, changes in elevation,
etc. Most state-of-art solutions for this classical problem simply look for the
shortest or fastest path connecting any pair of consecutive points in a trip.
While in some contexts that is reasonable, in this work we argue that the
shortest/fastest path assumption can be in general erroneous. Indeed, we show
that such approaches can yield travel times that are significantly incoherent
with the real ones, and propose a Time-Aware Map matching process that tries to
improve the state-of-art by taking into account also such temporal aspect. Our
algorithm results to be very efficient, effective on low- sampling data and to
outperform existing solutions, as proved by experiments on large datasets of
real GPS trajectories. Moreover, our algorithm is parameter-free and does not
depend on specific characteristics of the GPS localization error and of the
road network (e.g. density of roads, road network topology, etc.).
