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多特征融合的船舶轨迹相异度模型分析与优选

刘磊 张永 张明阳 王永明 陈静

刘磊, 张永, 张明阳, 王永明, 陈静. 多特征融合的船舶轨迹相异度模型分析与优选[J]. 交通运输工程学报, 2021, 21(5): 199-213. doi: 10.19818/j.cnki.1671-1637.2021.05.017
引用本文: 刘磊, 张永, 张明阳, 王永明, 陈静. 多特征融合的船舶轨迹相异度模型分析与优选[J]. 交通运输工程学报, 2021, 21(5): 199-213. doi: 10.19818/j.cnki.1671-1637.2021.05.017
LIU Lei, ZHANG Yong, ZHANG Ming-yang, WANG Yong-ming, CHEN Jing. Analysis and optimization of ship trajectory dissimilarity models based on multi-feature fusion[J]. Journal of Traffic and Transportation Engineering, 2021, 21(5): 199-213. doi: 10.19818/j.cnki.1671-1637.2021.05.017
Citation: LIU Lei, ZHANG Yong, ZHANG Ming-yang, WANG Yong-ming, CHEN Jing. Analysis and optimization of ship trajectory dissimilarity models based on multi-feature fusion[J]. Journal of Traffic and Transportation Engineering, 2021, 21(5): 199-213. doi: 10.19818/j.cnki.1671-1637.2021.05.017

多特征融合的船舶轨迹相异度模型分析与优选

doi: 10.19818/j.cnki.1671-1637.2021.05.017
基金项目: 

国家自然科学基金项目 72071041

江苏省交通运输科技项目 2018Y02

详细信息
    作者简介:

    刘磊(1992-),男,安徽安庆人,东南大学工学博士研究生,从事航运分析和调度优化研究

    张永(1976-),男,浙江嵊州人,东南大学教授,工学博士

  • 中图分类号: U666.1

Analysis and optimization of ship trajectory dissimilarity models based on multi-feature fusion

Funds: 

National Natural Science Foundation of China 72071041

Transportation Science and Technology Project of Jiangsu Province 2018Y02

More Information
  • 摘要: 基于船舶自动识别系统轨迹,构建了船舶轨迹静态相异度模型、动态相异度模型以及组合相异度模型,包括轨迹起点和终点相异度模型、轨迹长度相异度模型、轨迹空间分布相异度模型、轨迹航速均值相异度模型、轨迹航向均值相异度模型、轨迹航速标准差相异度模型和轨迹航向标准差相异度模型;采用KNN分类算法进行轨迹分类,分析了单个相异度模型的有效性和时效性,对比了单个相异度模型和组合相异度模型下轨迹分类效果,研究了组合相异度模型中相异度模型的类别和权重对轨迹分类的影响;分别以内河航道和港口水域船舶轨迹进行试验。试验结果显示:在采用单个相异度的情况下,就分类效果而言,轨迹起点和终点相异度模型和轨迹航向均值相异度模型在内河航道和港口水域船舶轨迹分类效果均优于其他模型,而基于轨迹航速均值相异度模型和轨迹航速标准差相异度模型的轨迹分类效果最低,就分类效率而言,基于航速、航向均值和标准差的相异度模型耗时明显低于其他3个相异度模型;采用组合相异度进行轨迹分类,内河航道和港口水域船舶轨迹分类结果的基于精确率和召回率的宏平均值和微平均值均能接近99%;将组合相异度中相异度类别数由4个增加到7个,轨迹分类评估结果进一步得到提高。因此,单个相异度模型中以轨迹起点和终点相异度模型、轨迹航向均值相异度模型以及轨迹空间分布相异度模型分类效果最优且稳定,而轨迹空间分布相异度模型和轨迹长度相异度模型耗时明显高于其他方式,各相异度模型在不同场景中的适应性基本相似,通过增加组合相异度中相异度类别能够提高轨迹识别效果。

     

  • 图  1  船舶轨迹相异度有效性研究流程

    Figure  1.  Flow of effectiveness research of ship trajectory dissimilarity

    图  2  船舶轨迹示意

    Figure  2.  Schematic of ship trajectory

    图  3  船舶轨迹静态相异度

    Figure  3.  Static dissimilarities of ship trajectories

    图  4  船舶轨迹动态相异度

    Figure  4.  Dynamic dissimilarities of ship trajectories

    图  5  船舶原始轨迹分布

    Figure  5.  Distributions of original ship trajectories

    图  6  类型A~F和a~f船舶轨迹特征分布

    Figure  6.  Feature distributions of ship trajectories A-F and a-f

    图  7  武汉段水域内各类船舶中心轨迹

    Figure  7.  Central trajectories of various ships in Wuhan waterway

    图  8  湛江港水域内各类船舶中心轨迹

    Figure  8.  Central trajectories of various ships in Zhanjiang Port

    图  9  武汉段水域k值选取试验结果

    Figure  9.  Experimental results of k value selection in Wuhan waterway

    图  10  湛江港水域k值选取试验结果

    Figure  10.  Experimental results of k value selection in Zhanjiang Port

    图  11  单个相异度模型KNN分类试验评估结果

    Figure  11.  Evaluation results of KNN classification experiments based on single dissimilarity model

    图  12  武汉段水域内分类错误轨迹特征分布及实例

    Figure  12.  Feature distribution and example of wrong classification trajectories in Wuhan waterway

    表  1  船舶原始轨迹类别与数目

    Table  1.   Categories and numbers of ship original trajectories

    区域 武汉段水域船舶轨迹 湛江港水域船舶轨迹
    类型 A B C D E F a(L) b(U) c(L) d(U) e f
    数目 110 1 825 100 28 52 27 706 465 513 125 104 111
    下载: 导出CSV

    表  2  基于单个相异度模型的KNN分类耗时统计

    Table  2.   Time-consuming statistics of KNN classification based on single dissimilarity model

    相异度模型 D1 D2 D3 D4 D5 D6 D7
    时间/s 武汉段水域 4.571 104.961 1 101.140 1.059 1.138 1.260 3.054
    湛江港水域 3.311 24.695 228.054 0.854 0.991 1.089 1.318
    下载: 导出CSV

    表  3  相异度模型组合下轨迹分类评估结果

    Table  3.   Evaluation results of KNN classification experiments based on combined dissimilarity models

    组合相异度 Mi Ma 耗时/s
    武汉段水域 湛江港水域 武汉段水域 湛江港水域 武汉段水域 湛江港水域
    Dc, 1 0.993 0.991 0.980 0.991 2.057×103 304.180
    Dc, 2 0.990 0.997 0.998 0.996 2.058×103 357.749
    Dc, 3 0.996 0.996 0.993 0.995 2.006×103 371.963
    下载: 导出CSV
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  • 收稿日期:  2021-04-15
  • 网络出版日期:  2021-11-13
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