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The requirement to trace and process moving objects in the contemporary eragradually increases since numerous applications quickly demand precise movingobject locations. The Map-matching method is employed as a preprocessingtechnique, 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 irrelevantstreets. Therefore, determining the stay-point region in GPS trajectorydatasets results in better accurate matching and more rapid approaches. In thiswork, we cluster stay-points in a trajectory dataset with DBSCAN and eliminateredundant data to improve the efficiency of the map-matching algorithm bylowering processing time. We reckoned our proposed method's performance andexactness with a ground truth dataset compared to a fuzzy-logic basedmap-matching algorithm. Fortunately, our approach yields 27.39% data sizereduction and 8.9% processing time reduction with the same accurate results asthe previous fuzzy-logic based map-matching approach.

Improving Fuzzy-Logic based Map-Matching Method with Trajectory Stay-Point Detection