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LIU Xing-long, CHU Xiu-min, MA Feng, LEI Jin-yu. Discriminating method of abnormal dynamic information in AIS messages[J]. Journal of Traffic and Transportation Engineering, 2016, 16(5): 142-150. doi: 10.19818/j.cnki.1671-1637.2016.05.016
Citation: LIU Xing-long, CHU Xiu-min, MA Feng, LEI Jin-yu. Discriminating method of abnormal dynamic information in AIS messages[J]. Journal of Traffic and Transportation Engineering, 2016, 16(5): 142-150. doi: 10.19818/j.cnki.1671-1637.2016.05.016
shu

Discriminating method of abnormal dynamic information in AIS messages

doi: 10.19818/j.cnki.1671-1637.2016.05.016
  • 1.

    National Engineering Research Center for Water Transport Safety, Wuhan University of Technology, Wuhan 430063, Hubei, China

  • 2.

    School of Energy and Power Engineering, Wuhan University of Technology, Wuhan 430063, Hubei, China

More Information
  • Author Bio:

    LIU Xing-long(1987-), male, doctoral student, +86-27-86581899, liuxinglong_its@163.com

    CHU Xiu-min(1969-), male, researcher, PhD, +86-27-86581899, chuxm@whut.edu.cn

  • Received Date: 2016-03-01
  • Publish Date: 2016-10-25
    Abstract
  • Aiming at the abnormal dynamic information in ship automatic identification system(AIS)messages, a recognition approach based on probabilistic inference with four steps including prior knowledge extraction, evidence modeling, evidence combination and weight coefficient optimization was proposed.Likelihood modeling approach was used to transform artificially identified velocity, course angle and track position information included in AIS data to evidence reliability between 0 and 1 that was composed by evidential reasoning(ER)rule.The verified AIS data was regarded as the input, and nonlinear optimization approach was used to modify the weight coefficient of evidence.The AIS data of ferry in Wuhan Tianxingzhou Bridge waters and the cargo ships in Wuqiao waters were used to carry out verification test.Test result shows that the recognition accuracies of correct data, incorrect data and total data for ferry in WuhanTianxingzhou Bridge waters under optimized weight coefficients are 91.67%, 97.62% and 92.63% respectively.When the minimum total deviation is goal, the recognition accuracies of correct data, incorrect data and total data for cargo ships in Wuqiao waters are 91.79%, 89.87% and 91.65% respectively.When the minimum deviation of correct data is goal, the recognition accuracies of correct data, incorrect data and total data for cargo ships in Wuqiao waters are 93.18%, 49.95% and 90.03% respectively.Obviously, the discriminating method of AIS dynamic information based on ER rule can flexibly adjust the weight coefficient of evidence with different optimized goals, and has the accuracy close to the artificial level.

     

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