Volume 21 Issue 5
Nov.  2021
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Article Navigation >  Journal of Traffic and Transportation Engineering  > 2021  >  21(5): 199-213
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
shu

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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