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A trajectory simplification method based on the clustering of vessel speed and course segments

机译:基于血管速度和课程段聚类的轨迹简化方法

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Automatic Identification Systems (AIS) can provide massive ship trajectory data that is valuable for mining information in water traffic. However, it is also easy to cause the data redundancy, adding difficulty to the data processing. To address this problem, this paper proposes a trajectory simplification method based on the segmental clustering of the ship's speed and course. Firstly, the maneuver points of a trajectory are determined by the speed and course threshold. The trajectory is divided into segments in accordance with the maneuver points. Then, the feature points of each segment are extracted based on K-medoids clustering method. The maneuver points and feature points of every trajectory segments are combined as trajectory features s to achieve the purpose of simplifying the ship's trajectory. The experiment results show that the method can well maintain the feature of original trajectory and eliminate the redundant track points in AIS trajectory. Compared with tradition Douglas-Peuker (DP) algorithm, the distortion rate could be reduced 0.2% with the same simplification.
机译:自动识别系统(AIS)可以提供大量船舶轨迹数据,这对于水交通中的采矿信息有价值。但是,它也很容易导致数据冗余,增加数据处理的难度。为了解决这个问题,本文提出了一种基于船舶速度和课程的分段集群的轨迹简化方法。首先,轨迹的操纵点由速度和课程阈值确定。轨迹按照机动点分成段。然后,基于K-METOIDS聚类方法提取每个段的特征点。每个轨迹段的机动点和特征点组合为轨迹功能S,以达到简化船舶轨迹的目的。实验结果表明,该方法可以很好地维持原始轨迹的特征,并消除AIS轨迹中的冗余轨道点。与传统Douglas-Peuker(DP)算法相比,失真率可以减小0.2%,简化相同。

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