Fuzzy-logic based ship-bridge collision alert model form ship behaviour perspective

Bing Wu, Junhui Zhang, Xinping Yan, Tsz Leung Yip, C. Guedes Soares

Research output: Chapter in book / Conference proceedingConference article published in proceeding or bookAcademic researchpeer-review

Abstract

Ship collision with offshore structures such as bridges poses potential serious risk. This paper proposes a fuzzy-logic based ship-bridge collision risk alert model from the perspective of ship behavior using Automatic Identification System data. The kernel of the proposed method is to identify the risk factors from the historical data by comprehensively considering the traffic-flow direction, water level seasons, course of ground and speed of ground, to fuzzify the risk factors by using ship behaviors, fuzzy rule base to establish the fuzzy logic boxes and finally to obtain the collision risk. The result demonstrates that the proposed model cannot only predict the collision risk with good accuracy but also be used to improve the ship handling when passing under the bridge. Consequently, this paper proposes a practical and beneficial model for ship-bridge collision alert system.

Original languageEnglish
Title of host publicationICTIS 2019 - 5th International Conference on Transportation Information and Safety
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages1276-1281
Number of pages6
ISBN (Electronic)9781728104898
DOIs
Publication statusPublished - Jul 2019
Event5th International Conference on Transportation Information and Safety, ICTIS 2019 - Liverpool, United Kingdom
Duration: 14 Jul 201917 Jul 2019

Publication series

NameICTIS 2019 - 5th International Conference on Transportation Information and Safety

Conference

Conference5th International Conference on Transportation Information and Safety, ICTIS 2019
CountryUnited Kingdom
CityLiverpool
Period14/07/1917/07/19

Keywords

  • Automatic identification system
  • Fuzzy logic
  • Ship behaviors
  • Ship-bridge collision

ASJC Scopus subject areas

  • Transportation
  • Automotive Engineering
  • Safety, Risk, Reliability and Quality
  • Modelling and Simulation
  • Safety Research

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