A Classification Tree Application to Predict Total Ship Loss

Ship accidents frequently result in total ship loss, an outcome with severe economic and human life consequences. Predicting the total loss of a ship when an accident occurs can provide vital information for ship owners, ship managers, classification societies, underwriters, brokers, and national authorities in terms of risk assessment. This paper investigates the use of classification trees to predict this type of loss. It uses a set of predictor variables that correspond to a number of factors identified as the most relevant to the total loss of a ship and sample data generated from a large database of recorded ship accidents worldwide. Through extensive tests of induction algorithms, Exhaustive CHAID was found to be more efficient in classifying the total loss accident cases. The predictive ability of the resulting classification tree structure can be utilized for risk assessment reporting.

Record URL:
Availability:
- Find a library where document is available. Order URL: http://worldcat.org/oclc/37387952
Authors:
- Kokotos, Dimitris X
- Smirlis, Yiannis G
Publication Date: 2005

Language

English

Media Info

Media Type: Print
Features: Figures; References; Tables;
Pagination: pp 31-42
Serial:
- Journal of Transportation and Statistics
- Volume: 8
- Issue Number: 2
- Publisher: Research and Innovative Technology Administration
- ISSN: 1094-8848
- Serial URL: http://www.bts.gov/publications/journal_of_transportation_and_statistics/

Subject/Index Terms

TRT Terms: Algorithms; Classification; Crash analysis; Crash causes; Crash data; Maritime safety; Mathematical prediction; Risk assessment; Ships; Trees (Mathematics); Water transportation crashes
Uncontrolled Terms: Total ship loss
Subject Areas: Data and Information Technology; Marine Transportation; Safety and Human Factors; Vehicles and Equipment;

Filing Info

Accession Number: 01022783
Record Type: Publication
Files: TRIS
Created Date: Apr 13 2006 2:59PM