Spatio-Temporal Vessel Trajectory Clustering Based on Data Mapping and Density
Spatio-Temporal Vessel Trajectory Clustering Based on Data Mapping and Density
复制标题
基于数据映射和密度的时空血管轨迹聚类
DOI：
10.1109/access.2018.2866364
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发表时间：
2018-01-01
期刊：
影响因子：
3.9
通讯作者：
Xiong, Naixue
中科院分区：
文献类型：
--
作者：
Li, Huanhuan;Liu, Jingxian;Xiong, Naixue
自动识别系统(AISS)是对雷达系统的补充，它们已被安装并广泛应用于舰船上，以基于超高频数据通信方案识别目标并提高航行安全性。此外，当局亦已建立人工智能系统网络，以加强主要港口的交通安全和改善管理。AISS记录了包含丰富的交通流信息的船只轨迹，是识别位置和分析运动特征的基础。然而，冗余信息的加入会降低轨迹聚类的精度，因此，轨迹数据挖掘已成为一个重要的研究方向。为了以较高的精度和较低的计算代价提取有用的信息，本文将轨迹映射和聚类方法相结合，对从AISS获取的大数据进行挖掘。特别是，融合距离(MD)被用来衡量不同轨迹之间的相似性，而多维尺度(MDS)被用来构造一个合适的轨迹间相似性的低维空间表达式。在此基础上，提出了一种改进的基于密度的噪声应用空间聚类(DBSCAN)算法来对空间点进行聚类，以获得最优聚类。MD、MDS和改进的DBSCAN算法的融合可以识别轨迹的轨迹，并获得更好的聚类性能。使用真实的AIS航迹数据库对桥区航道和密西西比河航迹进行了实验，验证了该方法的有效性。实验还表明，新方法比传统的谱聚类、亲和传播聚类等方法具有更高的准确率。
Automatic identification systems (AISs) serve as a complement to radar systems, and they have been installed and widely used onboard ships to identify targets and improve navigational safety based on a very high-frequency data communication scheme. AIS networks have also been constructed to enhance traffic safety and improve management in main harbors. AISs record vessel trajectories, which include rich traffic flow information, and they represent the foundation for identifying locations and analyzing motion features. However, the inclusion of redundant information will reduce the accuracy of trajectory clustering; therefore, trajectory data mining has become an important research direction. To extract useful information with high accuracy and low computational costs, trajectory mapping and clustering methods are combined in this paper to explore big data acquired from AISs. In particular, the merge distance (MD) is used to measure the similarities between different trajectories, and multidimensional scaling (MDS) is adopted to construct a suitable low-dimensional spatial expression of the similarities between trajectories. An improved density-based spatial clustering of applications with noise (DBSCAN) algorithm is then proposed to cluster spatial points to acquire the optimal cluster. A fusion of the MD, MDS, and improved DBSCAN algorithms can identify the course of trajectories and attain a better clustering performance. Experiments are conducted using a real AIS trajectory database for a bridge area waterway and the Mississippi River to verify the effectiveness of the proposed method. The experiments also show that the newly proposed method presents a higher accuracy than classical ones, such as spectral clustering and affinity propagation clustering.