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基于UWB定位的邮轮乘员伴随关系发现算法

严思迅 吴兵 商蕾 吕洁印 汪洋

严思迅, 吴兵, 商蕾, 吕洁印, 汪洋. 基于UWB定位的邮轮乘员伴随关系发现算法[J]. 交通信息与安全, 2021, 39(6): 54-62, 99. doi: 10.3963/j.jssn.1674-4861.2021.06.007
引用本文: 严思迅, 吴兵, 商蕾, 吕洁印, 汪洋. 基于UWB定位的邮轮乘员伴随关系发现算法[J]. 交通信息与安全, 2021, 39(6): 54-62, 99. doi: 10.3963/j.jssn.1674-4861.2021.06.007
YAN Sixun, WU Bing, SHANG Lei, LYU Jieyin, WANG Yang. A Detection Algorithm for Discovering Accompanying Relationship of Cruise Passengers Based on UWB Positioning[J]. Journal of Transport Information and Safety, 2021, 39(6): 54-62, 99. doi: 10.3963/j.jssn.1674-4861.2021.06.007
Citation: YAN Sixun, WU Bing, SHANG Lei, LYU Jieyin, WANG Yang. A Detection Algorithm for Discovering Accompanying Relationship of Cruise Passengers Based on UWB Positioning[J]. Journal of Transport Information and Safety, 2021, 39(6): 54-62, 99. doi: 10.3963/j.jssn.1674-4861.2021.06.007

基于UWB定位的邮轮乘员伴随关系发现算法

doi: 10.3963/j.jssn.1674-4861.2021.06.007
基金项目: 

国家自然科学基金青年项目 51809206

工信部高技术船舶科研项目 G18473CZ06

深圳市科技创新委员会项目 CJGJZD20200617102602006

详细信息
    作者简介:

    严思迅(1997—), 硕士研究生.研究方向: 交通信息与安全.E-mail: yansixun123@163.com

    通讯作者:

    汪洋(1976—)博士, 副研究员.研究方向: 水上交通安全、事故干预与应急决策.E-mail: wangyang.itsc@whut.edu.cn

  • 中图分类号: U695.1

A Detection Algorithm for Discovering Accompanying Relationship of Cruise Passengers Based on UWB Positioning

  • 摘要: 准确发现邮轮内部空间乘客之间的伴随关系, 在室内环境安装UWB定位设备开展室内人员定位实验。根据UWB定位的位置数据特点, 提出结合室内位置语义的Hausdorff-DBSCAN算法以聚类邮轮乘员轨迹, 并利用LSTM神经网络对疑似伴随关系对象进行相似度变化趋势的预测。传统的Hausdorff算法在计算轨迹相似度时未考虑轨迹时序一致的问题, 引入位置语义序列能够较好地解决这个问题。改进后的Hausdorff-DBSCAN算法的输入为乘员轨迹数据集, 根据轨迹整体相似度阈值选定聚类半径, 输出具有伴随关系的乘员轨迹聚类结果; LSTM神经网络以定长时间窗口的点邻近度序列为输入, 预测后1个时刻点邻近度值, 结合轨迹相似度阈值和预测结果分析乘员伴随关系的时序变化。利用Anylogic建模单层邮轮室内环境进行乘员仿真得到的轨迹数据验证算法的有效性。改进的Hausdorff-DBSCAN算法的准确率为0.920, 召回率为0.950, F1值为0.934, 准确率高出对比算法至少5.7%, 召回率高出对比算法至少8.0%, F1值高出对比算法至少6.7%。同时LSTM在预测邮轮乘员之间相似度变化时, 收敛后的误差值能保持在3%~4%左右, 预测结果具有较高的准确性。

     

  • 图  1  邮轮室内空间轨迹序列

    Figure  1.  Trajectory sequence in the indoor space of the cruise

    图  2  基于改进Hausdorff-DBSCAN的算法流程

    Figure  2.  Work flow of improved Hausdorff-DBSCAN Algorithm

    图  3  RNN的结构及隐藏层展开

    Figure  3.  Structure of RNN and its unfolded hidden layer

    图  4  LSTM细胞结构

    Figure  4.  Cell structure of LSTM

    图  5  聚类结果可视化

    Figure  5.  Visualization of clustering results

    图  6  “海洋量子”号邮轮第4层模型

    Figure  6.  The 4thfloor model of Quantum of the Seas

    图  7  类簇0, 7和9中的轨迹可视化

    Figure  7.  Visualization of trajectories in Cluster 0, 7 and 9

    图  8  非伴随关系的2条轨迹

    Figure  8.  Non-accompanying relationship of two trajectories

    图  9  非伴随乘员轨迹训练误差值变化

    Figure  9.  Training loss variation of the non-accompanying trajectories

    图  10  LSTM模型输出与真值对比

    Figure  10.  Comparison of the output and the true value of LSTM model

    图  11  具有伴随关系的2条轨迹

    Figure  11.  Accompanying relationship of two trajectories

    图  12  伴随乘员轨迹训练误差值值变化

    Figure  12.  Training loss variation of the accompanying trajectories

    图  13  LSTM模型输出与真值对比

    Figure  13.  Comparison of the output and the true value of LSTM model

    图  14  伴随乘员轨迹点邻近度的预测结果

    Figure  14.  Points similarity prediction of accompanying crews

    表  1  t1时刻各乘员位置记录

    Table  1.   Position records of each passenger at time t1

    编号 横向距离 纵向距离 区域
    1 x1 y1 R1t1
    2 x2 y2 R2t1
    z xz yz Rzt1
    下载: 导出CSV

    表  2  行人轨迹数据样本

    Table  2.   Samples of pedestrian trajectory data

    编号 时间点 横向距离 纵向距离 区域
    1 12 652.51 69.59 R1t12
    5 30 640.82 49.74 R5t30
    10 70 562.57 120.60 R10t70
    28 80 554.84 125.36 R28t80
    下载: 导出CSV

    表  3  轨迹间归一化相似度

    Table  3.   Normalized similarity between trajectories

    编号 0 1 2 197 198 199
    0 1 0.28 0.37 0.23
    1 0.38 1 0.81 0.89 0.77
    2 0.78 1 0.15 0.16 0.01
    198 0.37 0.89 0.16 1
    199 0.23 0.77 0.01 1
    下载: 导出CSV

    表  4  部分聚类簇及其簇间元素

    Table  4.   Partial clusters and the elements among them

    簇编号 簇中乘员ID
    0 (0, 6, 53, 67, 94, 96, 119, 136, 141, 150)
    4 (4, 13, 27, 42, 77, 84, 90, 92, 124, 143, 149, 166, 172, 173, 178)
    6 (7, 11, 14, 39, 45, 46, 48, 75, 83, 106, 144, 147, 153, 171, 182, 186, 191, 196)
    7 (8, 9, 12, 17, 29, 32, 35, 36, 49, 51, 54, 58, 62, 74, 99, 100, 103, 113, 122, 133, 142, 148, 152, 185)
    9 (16, 30, 43, 81, 95, 104, 107, 115, 157)
    下载: 导出CSV

    表  5  不同聚类算法性能对比

    Table  5.   Performance comparison of different clustering algorithms

    方法 precision recall F1
    Grid-based 0.850 0.780 0.813
    DTW-DBSCAN 0.820 0.750 0.794
    improved-ANGES 0.870 0.880 0.875
    Hausdorff-DBSCAN 0.920 0.950 0.934
    下载: 导出CSV
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  • 收稿日期:  2021-07-31
  • 网络出版日期:  2022-01-12

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