摘要
通过挖掘海量AIS数据,提出了一种新的航道水深信息获取方法,即构建船舶安全航行水深参考图;采用数据预处理的方法对历史与在线的AIS数据进行清洗和修补,生成船舶运动轨迹;选定船舶航行区域的时间与经纬度,采用K-means聚类算法对船舶航行过程中的吃水数据进行聚类分析,得到不同安全航行区域的船舶分类,运用BP神经网络模型预测并补齐AIS数据中缺失的船舶最大吃水信息;分割船舶历史轨迹,当子轨迹的时间间隔在10~20min时,采用Spline插值方法对船舶轨迹中的丢失数据进行插值;采用凸包构建同类船舶的安全航行水深区域图,将不同吃水类型船舶的安全航行水深区域图合并,得到船舶安全航行水深合并图;将不同吃水类型的船舶安全航行水深合并图与航道图叠加,得到船舶安全航行水深参考图。试验结果表明:当聚类算法参数设置为4时,聚类后得到4类船舶,对应的船舶最大吃水范围分别为0.1~4.8、4.8~6.6、6.6~10.0、10.0~13.0m,对应的至少可通航船舶吃水分别为1.8、2.4、3.3、5.0m,说明船舶最大吃水与至少可通航船舶吃水呈正相关关系;构建的船舶安全航行水深参考图在电子航道图中覆盖了86%的航道,并与航道图的深水部分重合率为80%,因此,构建的船舶安全航行水深参考图能反映航道水深的真实情况,满足不同类别船舶的导航需求。
A new method of obtaining channel depth information was proposed by mining massive AIS data,which was used to construct ship safe navigation depth reference map.The historical and online AIS data were cleaned and mended by data preprocessing method,and ship motion trajectories were generated.The time,longitudes and latitudes of ship navigation areas were selected,and K-means clustering algorithm was used to cluster and analyze the draft data during ship navigation process,then the ship classifications in different safe navigation areas were obtained.The BP neural network model was applied to predict and complete the missing maximum ship draft information from the AIS data.The historical trajectory of the ship wassegmented,and when the time interval of the sub-trajectory was 10-20 min,the spline interpolation method was used to interpolate the missing data in the ship trajectories.The safe navigation depth area maps for similar types of ships were constructed by using convex hulls,and the maps with different draft types were combined to obtain a combined safe navigation depth map.The combined safe navigation depth map was superimposed on the channel chart to obtain a safe navigation depth reference map.Experimental result shows that when the clustering algorithm parameter is 4,four ship types are obtained by clustering.The corresponding maximum draft ranges of the ships are 0.1-4.8,4.8-6.6,6.6-10.0,and 10.0-13.0 m,and the corresponding least navigable ship drafts are 1.8,2.4,3.3,and 5.0 m,respectively.Thus,the maximum drafts of the ships are positively correlated with the least navigable drafts.The constructed ship safe navigation depth reference map covers 86% of the target channels in the electronic channel chart,and the overlap rate with the deep-water areas in the channel chart is80%.Therefore,the constructed ship safe navigation depth reference map can reflect the true conditions of the channel depths,and meet the navigation needs of different types of ships.
作者
何正伟
杨帆
刘力荣
HE Zheng-wei;YANG Fan;LIU Li-rong(School of Navigation,Wuhan University of Technology,Wuhan 430063,Huhei,China;Key Laboratory ofHubei Province for Inland Navigation Technology,Wuhan University of Technology,Wuhan 430063,Hubei,China;National Engineering Research Center for Water Transportation Safety,Wuhan University ofTechnology,Wuhan 430063,Hubei,China)
出处
《交通运输工程学报》
EI
CSCD
北大核心
2018年第4期171-181,共11页
Journal of Traffic and Transportation Engineering
基金
国家自然科学基金项目(51479157)
中央高校基本科研业务费专项资金资助项目(2018Ⅲ064GX
2018-zy-127)
关键词
交通信息工程
水深参考图
BP神经网络
船舶分类
轨迹分割
凸包理论
traffic information engineering
depth reference map
BP neural network
ship classification
trajectory segmentation
convex hull theory
