Volume 21 Issue 5
Nov.  2021
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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
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Analysis and optimization of ship trajectory dissimilarity models based on multi-feature fusion

doi: 10.19818/j.cnki.1671-1637.2021.05.017
  • 1.

    School of Transportation, Southeast University, Nanjing 211100, Jiangsu, China

  • 2.

    School of Mechanical Engineering, Aalto University, Espoo 02210, Finland

  • 3.

    School of Navigation, Dalian Maritime University, Dalian 116026, Liaoning, China

Funds:

National Natural Science Foundation of China 72071041

Transportation Science and Technology Project of Jiangsu Province 2018Y02

More Information
  • Author Bio:

    LIU Lei(1992-), male, doctoral student, lei1992@seu.edu.cn

    ZHANG Yong(1976-), male, professor, PhD, zhangyong@seu.edu.cn

  • Received Date: 2021-04-15
    Available Online: 2021-11-13
  • Publish Date: 2021-10-01
    Abstract
  • Based on ship automatic identification system (AIS) trajectories, static dissimilarity models, dynamic dissimilarity models, and a combined dissimilarity model of ship trajectories were constructed, including the following dissimilarity models: trajectory departure and destination, trajectory length, trajectory spatial distribution, trajectory mean speed, trajectory mean course, trajectory speed standard deviation, and trajectory course standard deviation. Trajectories were classified using the KNN classification algorithm, the effectivenesses and efficiencies of each single dissimilarity model were analyzed, the effect of trajectory classification under different unique dissimilarity models and the combined dissimilarity model were compared, and the influence of the categories and weights of dissimilarity models on trajectory classification in the combined dissimilarity model was studied. Experiments were conducted using ship trajectories in inland waterways and port waters. Experimental results show that under the condition of adopting a single dissimilarity, in terms of the classification effect, the ship trajectory classification based on the dissimilarity model of trajectory departure and destination and the dissimilarity model of trajectory mean course is better than that using other dissimilarity models in inland waterways and port waters, whereas the trajectory classification effect based on the dissimilarity model of trajectory mean speed and the dissimilarity model of trajectory speed standard deviation is worse. In terms of classification efficiency, the time consumed by the dissimilarity models based on mean value and standard deviation is significantly lower than that of the other dissimilarity models. Through the analysis and optimization of trajectory dissimilarity models based on the trajectory classification results of the KNN classification algorithm, when the trajectory classification is conducted using the combined dissimilarity model, macro and micro averages based on accuracy and recall of ship trajectory classification results in the inland waterway and port waters can both reach 99%; moreover, by increasing the number of dissimilarity categories in the combined dissimilarity from 4 to 7, the evaluation result of trajectory classification is further improved. Therefore, in the single dissimilarity model, the classification effects of the dissimilarity model of trajectory departure and destination, the dissimilarity model of trajectory mean course, and the dissimilarity model of trajectory spatial distribution are optimal and stable, whereas the time consumption of the dissimilarity model of trajectory spatial distribution and the dissimilarity model of trajectory length are significantly higher than those of other models. The adaptabilities of each dissimilarity are similar in different scenarios. By increasing the dissimilarity category in the combined dissimilarity model, the trajectory recognition effect can be improved. 3 tabs, 12 figs, 30 refs.

     

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    • References(30)
  • [1]
    MA Feng, CHU Xiu-min, YAN Xin-ping. Short message characteristics of AIS base stations[J]. Journal of Traffic and Transportation Engineering, 2012, 12(6): 111-118. (in Chinese) http://jtysgcxb.xml-journal.net/article/id/201206017
    [2]
    ZHANG Li-ye, MENG Qiang, XIAO Zhe, et al. A novel ship trajectory reconstruction approach using AIS data[J]. Ocean Engineering, 2018, 159: 165-174. doi: 10.1016/j.oceaneng.2018.03.085
    [3]
    LIU Xing-long, CHU Xiu-min, MA Feng, et al. Discriminating method of abnormal dynamic information in AIS messages[J]. Journal of Traffic and Transportation Engineering, 2016, 16(5): 142-150. (in Chinese) http://jtysgcxb.xml-journal.net/article/id/201605016
    [4]
    LEI Jin-yu, CHU Xiu-min, HE Wei, et al. Visual analytic system of vessel traffic in bridge waterway[J]. Journal of Shanghai Jiaotong University, 2017, 51(7): 840-845. (in Chinese) https://www.cnki.com.cn/Article/CJFDTOTAL-SHJT201707011.htm
    [5]
    SVANBERG M, SANTÉN V, HÖRTEBORN A, et al. AIS in maritime research[J]. Marine Policy, 2019, 106: 103520. doi: 10.1016/j.marpol.2019.103520
    [6]
    XIANG Zhe, HU Qin-you, SHI Chao-jian, et al. Computation method of ship domains in restricted waters based on AIS data[J]. Journal of Traffic and Transportation Engineering, 2015, 15(5): 110-117. (in Chinese) http://jtysgcxb.xml-journal.net/article/id/201505014
    [7]
    LEI Jin-yu, LIU Lei, CHU Xiu-min, et al. Automatic identification system data-driven model for analysis of ship domain near bridge-waters[J]. Journal of Navigation, 2021, DOI: 10.1017/S0373463321000461.
    [8]
    YANG Dong, WU Ling-xiao, WANG Shuai-an, et al. How big data enriches maritime research—a critical review of automatic identification system (AIS) data applications[J]. Transport Reviews, 2019, 39(6): 755-773. doi: 10.1080/01441647.2019.1649315
    [9]
    ZHANG Li-ye, MENG Qiang, FWA T F. Big AIS data based spatial-temporal analyses of ship traffic in Singapore port waters[J]. Transportation Research Part E: Logistics and Transportation Review, 2019, 129: 287-304. doi: 10.1016/j.tre.2017.07.011
    [10]
    MOU Jun-min, ZHANG Xin-sheng, YAO Xin, et al. Emission inventory of ship based on navigation data in Arctic region[J]. Journal of Traffic and Transportation Engineering, 2019, 19(5): 116-124. (in Chinese) http://jtysgcxb.xml-journal.net/article/id/201905012
    [11]
    BORKOWSKI P. The ship movement trajectory prediction algorithm using navigational data fusion[J]. Sensors, 2017, DOI: 10.3390/s17061432.
    [12]
    ZHEN Rong, JIN Yong-xing, HU Qin-you, et al. Maritime anomaly detection within coastal waters based on vessel trajectory clustering and Naïve Bayes classifier[J]. Journal of Navigation, 2017, 70(3): 648-670. doi: 10.1017/S0373463316000850
    [13]
    HE Zheng-wei, YANG Fan, LIU Li-rong. Ship safe navigation depth reference map based on AIS data[J]. Journal of Traffic and Transportation Engineering, 2018, 18(4): 171-181. (in Chinese) http://jtysgcxb.xml-journal.net/article/id/201804018
    [14]
    XU Yao, LI Zhuo-ran, MENG Jin-long, et al. Extraction method of marine lane boundary from exploiting trajectory big data[J]. Journal of Computer Application, 2019, 39(1): 105-112. (in Chinese) https://www.cnki.com.cn/Article/CJFDTOTAL-JSJY201901021.htm
    [15]
    ZHANG Shu-kai, SHI Guo-you, LIU Zheng-jiang, et al. Data-driven based automatic maritime routing from massive AIS trajectories in the face of disparity[J]. Ocean Engineering, 2018, 155: 240-250. doi: 10.1016/j.oceaneng.2018.02.060
    [16]
    YAN Zhao-jin, XIAO Yi-jia, CHENG Liang, et al. Exploring AIS data for intelligent maritime routes extraction[J]. Applied Ocean Research, 2020, DOI: 10.1016/j.apor.2020.102271.
    [17]
    FILIPIAK D, W$ \mathop {\rm{E}}\limits_{\rm{c}} $CEL K, STRÓŻYNA M, et al. Extracting maritime traffic networks from AIS data using evolutionary algorithm[J]. Business and Information Systems Engineering, 2020, 62(5): 435-450. doi: 10.1007/s12599-020-00661-0
    [18]
    ZHAO Liang-bin, SHI Guo-you, YANG Jia-xuan. Adaptive hierarchical clustering of ship trajectory with DBSCAN algorithm[J]. Navigation of China, 2018, 41(3): 53-58. (in Chinese) https://www.cnki.com.cn/Article/CJFDTOTAL-ZGHH201803011.htm
    [19]
    MOU Jun-min, CHEN Peng-fei, HE Yi-xiong, et al. Fast self-tuning spectral clustering algorithm for AIS ship trajectory[J]. Journal of Harbin Engineering University, 2018, 39(3): 428-432. (in Chinese) https://www.cnki.com.cn/Article/CJFDTOTAL-HEBG201803005.htm
    [20]
    LIU Lei, CHU Xiu-min, JIANG Zhong-lian, et al. Ship trajectory classification algorithm based on KNN[J]. Journal of Dalian Maritime University, 2018, 44(3): 15-21. (in Chinese) https://www.cnki.com.cn/Article/CJFDTOTAL-DLHS201803005.htm
    [21]
    SHENG Kai, LIU Zhong, ZHOU De-chao, et al. Research on ship classification based on trajectory features[J]. The Journal of Navigation, 2018, 71(1): 100-116. doi: 10.1017/S0373463317000546
    [22]
    LEE J, HAN J, WHANG K. Trajectory clustering: a partition-and-group framework[C]//ACM. 2007 ACM SIGMOD International Conference on Management of Data. New York: ACM Press, 2007: 593-604.
    [23]
    JIANG Yu-ling, XIONG Zhen-nan, TANG Ji-hong. Ship trajectory clustering algorithm based on DBSCAN[J]. Navigation of China, 2019, 42(3): 1-5. (in Chinese) https://www.cnki.com.cn/Article/CJFDTOTAL-ZGHH201903001.htm
    [24]
    SHENG Pan, YIN Jing-bo. Extracting shipping route patterns by trajectory clustering model based on automatic identification system data[J]. Sustainability, 2018, 10(7): 2327. doi: 10.3390/su10072327
    [25]
    LI Huan-huan, LIU Jing-xian, LIU R W, et al. A dimensionality reduction-based multi-step clustering method for robust vessel trajectory analysis[J]. Sensors, 2017, DOI: 10.3390/s17081792.
    [26]
    PENG Xiang-wen, GAO Shu, CHU Xiu-min, et al. Clustering method of ship's navigation trajectory set based on spark[J]. Navigation of China, 2017, 40(3): 49-53. (in Chinese) https://www.cnki.com.cn/Article/CJFDTOTAL-ZGHH201703011.htm
    [27]
    FU Pei-guo, WANG Hao-zhou, LIU Kui-en, et al. Finding abnormal vessel trajectories using feature learning[J]. IEEE Access, 2017, 5: 7898-7909. doi: 10.1109/ACCESS.2017.2698208
    [28]
    ZHAO Liang-bin, SHI Guo-you. A trajectory clustering method based on Douglas-Peucker compression and density for marine traffic pattern recognition[J]. Ocean Engineering, 2019, 172: 456-467. doi: 10.1016/j.oceaneng.2018.12.019
    [29]
    GAO Miao, SHI Guo-you, LI Wei-feng. Online compression algorithm of AIS trajectory data based on improved sliding window[J]. Journal of Traffic and Transportation Engineering, 2018, 18(3): 218-227. (in Chinese) http://jtysgcxb.xml-journal.net/article/id/201803022
    [30]
    ZHENG Zu-duo, SU Dong-cai. Short-term traffic volume forecasting: a k-nearest neighbor approach enhanced by constrained linearly sewing principle component algorithm[J]. Transportation Research Part C: Emerging Technologies, 2014, 43: 143-157. doi: 10.1016/j.trc.2014.02.009
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