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Multi-scale collision risk estimation for maritime traffic in complex port waters

Author

Listed:
  • Xin, Xuri
  • Liu, Kezhong
  • Loughney, Sean
  • Wang, Jin
  • Li, Huanhuan
  • Ekere, Nduka
  • Yang, Zaili

Abstract

Ship collision risk estimation is an essential component of intelligent maritime surveillance systems. Traditional risk estimation approaches, which can only analyze traffic risk in one specific scale, reveal a significant challenge in quantifying the collision risk of a traffic scenario from different spatial scales. This is detrimental to understanding the traffic situations and supporting effective anti-collision decision-making, particularly as maritime traffic complexity grows and autonomous ships emerge. In this study, a systematic multi-scale collision risk estimation approach is newly developed to capture traffic conflict patterns under different spatial scales. It extends the application of the complex network theory and a node deletion method to quantify the interactions and dependencies among multiple ships within encounter scenarios, enabling collision risk to be evaluated at any spatial scale. Meanwhile, an advanced graph-based clustering framework is introduced to search for the optimal spatial scales for risk evaluation. Extensive numerical experiments based on AIS data in Ningbo_Zhoushan Port are implemented to evaluate the model performance. Experimental results reveal that the proposed approach can strengthen maritime situational awareness, identify high-risk areas and support strategic maritime safety management. This work therefore sheds light on improving the intelligent levels of maritime surveillance and promoting maritime traffic automation.

Suggested Citation

  • Xin, Xuri & Liu, Kezhong & Loughney, Sean & Wang, Jin & Li, Huanhuan & Ekere, Nduka & Yang, Zaili, 2023. "Multi-scale collision risk estimation for maritime traffic in complex port waters," Reliability Engineering and System Safety, Elsevier, vol. 240(C).
  • Handle: RePEc:eee:reensy:v:240:y:2023:i:c:s0951832023004684
    DOI: 10.1016/j.ress.2023.109554
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