A Zero-Inflated Negative Binomial Regression Model to Evaluate Ship Sinking Accident Mortalities

Sinking accidents are a seafarer’s nightmare. Using 10 years’ of worldwide sinking accident data, this study aims to develop a mortality count model to evaluate the human life loss resulting from sinking accidents using zero-inflated negative binomial regression approaches. The model results show that the increase of the expected human life loss is the largest when a ship suffers a precedent accident of capsizing, followed by fire/explosion or collisions. Lower human life loss is associated with contact and machinery/hull damage accidents. Consistent with our expectation, cruise ships involved in sinking accidents usually suffer more human life loss than non-cruise ships and there is be a bigger mortality count for sinking accidents that occur far away from the coastal area/harbor/port. Fatalities can be less when the ship is moored or docked. The results of this study are beneficial for policy-makers in proposing efficient strategies to reduce sinking accident mortalities.

• Record URL:
  • https://doi.org/10.1177/0361198118787388
• Record URL:
  • https://doi.org/10.1177/0361198118787388
• Availability:
  • Find a library where document is available. Order URL: http://worldcat.org/issn/03611981
• Authors:
  • Chai, Tian
  • Xiong, De-qi
  • Weng, Jinxian
• Publication Date: 2018

Language

• English

Media Info

• Media Type: Print
• Features: Figures; References; Tables;
• Pagination: pp 65-72
• Serial:
  • Transportation Research Record: Journal of the Transportation Research Board
  • Volume: 2672
  • Issue Number: 11
  • Publisher: Sage Publications, Incorporated
  • ISSN: 0361-1981
  • EISSN: 2169-4052
  • Serial URL: http://journals.sagepub.com/home/trr

Subject/Index Terms

• TRT Terms: Capsizing; Emergencies; Fatalities; Fires; Maritime safety; Water transportation crashes
• Uncontrolled Terms: Sinking (Maritime crashes)
• Subject Areas: Marine Transportation; Safety and Human Factors; Security and Emergencies;

Filing Info

• Accession Number: 01677320
• Record Type: Publication
• Report/Paper Numbers: 18-06154
• Files: TRIS, TRB, ATRI
• Created Date: Aug 1 2018 1:39PM