汪祖云,张道航,刘文平,董婉青,侯彩霞,陈 荔.基于轨迹段核密度的旅游车辆轨迹聚类算法[J].测控技术,2020,39(9):108-112 |
基于轨迹段核密度的旅游车辆轨迹聚类算法 |
Clustering Algorithm of Tourist Vehicle Trajectory Based on Trajectory Segment Kernel Density |
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DOI:10.19708/j.ckjs.2020.09.020 |
中文关键词: 旅游车辆轨迹 密度 相似度度量 核距离 |
英文关键词:tourist vehicle trajectory density similarity measure kernel distance |
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中文摘要: |
为了更好地研究旅游车辆的运营行为,需要通过分析车辆轨迹规律发现车辆运动的典型轨迹,轨迹聚类是其中重要的环节。对于使用传统的密度聚类方法处理大规模旅游车辆轨迹数据存在准确度差和效率低的问题,提出了一种基于轨迹段和核密度的轨迹聚类方法。采用核距离作为轨迹段相似度度量,利用类似DBSCAN算法对轨迹段进行聚类,得出旅游车辆运动典型轨迹。以北京市旅游车辆为例,采用基于轨迹段和核密度的算法对车辆轨迹进行聚类,能从一定程度上提高聚类的效果和准确率,为进一步研究旅游车辆的运营行为打下基础。 |
英文摘要: |
In order to better study the operating behavior of tourist vehicles,it is necessary to find the typical trajectory of vehicle movement by analyzing the law of vehicle trajectory,and trajectory clustering is an importantpart part.Using traditional density clustering method to process large-scale tourist vehicle trajectory data has accuracy and efficiency problems,so a clustering algorithm based on trajectory segment and kernel density is proposed.The kernel distance was used as the similarity measure of the trajectory segment,and the similar DBSCAN algorithm was used to cluster the trajectory segments to obtain the typical trajectory of the tourist vehicle.Taking Beijing tourist vehicles as an example,the algorithm based on trajectory segment and kernel density is used to cluster vehicle trajectories,which can improve the clustering effect and accuracy to a certain extent,and lay a foundation for further research on the operation behavior of tourist vehicles. |
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