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Article Navigation >  Journal of Traffic and Transportation Engineering  > 2017  >  17(5): 149-158
MA Wen-yao, WU Zhao-lin, LI Wei-feng. Conformal detection algorithm of anomalous behaviors of vessel[J]. Journal of Traffic and Transportation Engineering, 2017, 17(5): 149-158.
Citation: MA Wen-yao, WU Zhao-lin, LI Wei-feng. Conformal detection algorithm of anomalous behaviors of vessel[J]. Journal of Traffic and Transportation Engineering, 2017, 17(5): 149-158.
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Conformal detection algorithm of anomalous behaviors of vessel

  • 1.

    Navigation College, Dalian Maritime University, Dalian 116026, Liaoning, China

  • 2.

    Navigation College, Guangdong Ocean University, Zhanjiang 524088, Guangdong, China

More Information
  • Author Bio:

    MA Wen-yao(1980-), male, associate professor, doctoral student, wenyaoma1980@ 163.com

    WU Zhao-lin(1947-), male, professor, wuzl@dlmu.edu.cn

  • Corresponding author: LI Wei-feng(1983-), male, lecturer, sddmlwf@163.com
  • Received Date: 2017-04-22
  • Publish Date: 2017-10-25
    Abstract
  • In order to accurately detect the anomalous maneuvering behaviors of ship and reduce the false alarm rate of anomalous behaviors, a conformal detection algorithm of anomalous behaviors was proposed.Some characteristics was introduced into the trajectory points of ship to reflect the maneuvering modes, and the similarity of maneuvering behavior was measured through altering course behavior and speed-changing behavior.The integrated behavior similarity of ship was defined by combining the spatial location similarity and the maneuvering behavior similarity, the nearest neighbor feature points of single track points on training trajectory sequence were calculated, and the conformal detection sample sequence was constructed.In order to overcome the sample overlapping situation of class distribution, the singular value measure of conformal detection algorithm was improved, the nonconformance score between the samples was calculated by the integrated behavior similarity, and the randomness test value of single track point wasused to determine the distribution conformance of trajectory point and sample sequence.The real AIS data of Qiongzhou Strait were taken as the normal data, the random anomalous trajectories were simulated by the computer, the artificial abnormal maneuvering behaviors were defined, and the abnormal detection tests were carried out.Experimental result shows that the detecting accuracy rate of random anomalous behaviors is 100%, but a part of normal trajectories in the set of evaluation trajectories are divided into anomalous trajectories by mistake.When the confidence levels are 99.0% and 99.7%, respectively, the false alarm rates are 0.6% and 0.2%, respectively, and less than the significance levels of 0.01 and 0.003, respectively.Therefore, the algorithm can effectively detect the random abnormal trajectory generated by the computer, strictly control the false alarm rate by using designated significance level, and detect artificial changing speed and altering course abnormal behaviors, and the test result changes with the change of ship behavior.

     

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