  The studies carried out with the objective of minimizing the effects of
congestion, delay and environment problems on the transportation network have
gained increasing importance in the last years. Among these studies, short-term
traffic flow and average vehicle speed forecasting methods have come into
prominence due to their easy implementations, efficient usage on different
areas and cost-effectiveness. A large number of studies have reported that
these methods, in which the expected future values of link flows and average
speeds are forecasted in desired points, can reduce the traffic congestion by
anticipating the problems in traffic management. In this paper, a
spatio-temporal approach accounted for historical traffic characteristics data
collected from a large number of points is presented for average speed
forecasts in a given link. The proposed approach includes an algorithm that
enables to take into account the most informative data in an input set by
determining them for each stage. It is aimed to increase the forecasting
accuracy by using sparse matrices in the algorithm while decreasing the
calculation times significantly compared to the similar methods presented in
the literature.
