摘要
为解决挖掘船舶自动识别系统(Automatic Identification System,AIS)数据时需手动选择研究海域或数据及数据量较大的问题,提出结合航迹聚类和概率密度估计的交通流区域自动识别方法。通过分析航迹结构特征,构建航向航程模型;采用Pearson相关系数度量航向航程模型的相似性,并进行航迹聚类。运用核密度估计(Kernel Density Estimation,KDE)推算聚类航迹的概率密度,自动识别交通流区域,并进一步提取通航分道区域。以渤海海峡为例进行验证,识别效果良好。
To study a particular region or traffic flow with Automatic Identification System（AIS） data which are normally picked up manually and is inconvenient due to the scale of the database,a method for automatically identifying the intersectional region of the traffic flow is proposed based on track cluster and probability density estimation.The features of the track structure are analyzed and the course-route model of the track is built;the structural similarity of the course-route models is measured with the Pearson Correlation Coefficient and grouped into track clusters.The probability density of track clusters is calculated by means of Kernel Density Estimation（KDE）,and the traffic flow region and traffic lanes are identified.The method is verified with the data of the Bohai Strait.
出处
《中国航海》
CSCD
北大核心
2016年第4期87-90,132,共5页
Navigation of China
基金
国家高技术研究发展计划("八六三"计划)子课题(2009AA045003)
