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.

翻译：在现代时代,跟踪和处理移动物体的要求逐渐增加,因为许多应用迅速要求精确移动物体的位置。地图匹配方法被用作一种预处理技术,与相应的道路上移动对象点相匹配。然而,大多数全球定位系统轨道数据集包括停留点不规则,使地图匹配算法与不相关的街道不匹配。因此,在全球定位系统轨道数据集中确定停留点区域的结果是更准确的匹配和更快的方法。在这项工作中,我们用DBSCAN的轨迹数据集将停留点分组,并消除多余的数据,通过降低处理时间来提高地图匹配算法的效率。我们计算了我们拟议方法的性能和精确度,与基于模糊的地图匹配算法相比,地面的真象数据集的性能和准确性。幸运的是,我们的方法将减少27.39%的数据大小和8.9%处理时间缩短,结果与以前的模糊的地图匹配方法相同。