论文标题
通过轨迹停留点检测改进基于模糊的地图匹配方法
Improving Fuzzy-Logic based Map-Matching Method with Trajectory Stay-Point Detection
论文作者
论文摘要
追踪和处理当代时代的对象的要求逐渐增加，因为许多应用程序迅速需要精确的移动对象位置。地图匹配方法被用作预处理技术，该技术与相应道路上的移动对象点相匹配。但是，大多数GPS轨迹数据集都包括静置的不规则性，这使地图匹配算法不匹配轨迹与无关紧要的街道。因此，确定GPS轨迹数据集中的停留点区域会导致更好的准确匹配和更快的方法。在这项工作中，我们将留下点集中在带有DBSCAN的轨迹数据集中，并消除了冗余数据，以通过降低处理时间来提高地图匹配算法的效率。与基于模糊逻辑的地图匹配算法相比，我们认为我们提出的方法的性能和精确性。幸运的是，我们的方法可产生27.39％的数据尺寸减少和8.9％的处理时间缩短，其准确的结果与以前的基于模糊的MAP匹配方法相同。
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.
