  The requirement to trace and process moving objects in the contemporary era
gradually increases since numerous applications quickly demand precise moving
object locations. The Map-matching method is employed as a preprocessing
technique, which matches a moving object point on a corresponding road.
However, most of the GPS trajectory datasets include stay-points irregularity,
which makes map-matching algorithms mismatch trajectories to irrelevant
streets. Therefore, determining the stay-point region in GPS trajectory
datasets results in better accurate matching and more rapid approaches. In this
work, we cluster stay-points in a trajectory dataset with DBSCAN and eliminate
redundant data to improve the efficiency of the map-matching algorithm by
lowering processing time. We reckoned our proposed method's performance and
exactness with a ground truth dataset compared to a fuzzy-logic based
map-matching algorithm. Fortunately, our approach yields 27.39% data size
reduction and 8.9% processing time reduction with the same accurate results as
the previous fuzzy-logic based map-matching approach.
