  With the increasing adoption of intelligent transportation systems and the
upcoming era of autonomous vehicles, vehicular services (such as, remote
driving, cooperative awareness, and hazard warning) will face an ever changing
and dynamic environment. Traffic flows on the roads is a critical condition for
these services and, therefore, it is of paramount importance to forecast how
they will evolve over time. By knowing future events (such as, traffic jams),
vehicular services can be dimensioned in an on-demand fashion in order to
minimize Service Level Agreements (SLAs) violations, thus reducing the chances
of car accidents. This research departs from an evaluation of traditional
time-series techniques with recent Machine Learning (ML)-based solutions to
forecast traffic flows in the roads of Torino (Italy). Given the accuracy of
the selected forecasting techniques, a forecast-based scaling algorithm is
proposed and evaluated over a set of dimensioning experiments of three distinct
vehicular services with strict latency requirements. Results show that the
proposed scaling algorithm enables resource savings of up to a 5% at the cost
of incurring in an increase of less than 0.4% of latency violations.
