Integration of individual encounter information into causation probability modelling of ship collision accidents

Journal article (2019)

Authors

P. Chen Safety and Security Science - TPM
Junmin Mou Wuhan University of Technology, Hubei Key Laboratory of Inland Shipping Technology
P.H.A.J.M. van Gelder Safety and Security Science - TPM
Research Group
Safety and Security Science (TPM) (TU Delft)
AIS data Ship collision Accident analysis Probabilistic risk analysis Credal network Causation modelling
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Published Date

2019

Language

English

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Faculty

Technology, Policy and Management

Department

Values Technology and Innovation

Research Group

Safety and Security Science

Abstract

Maritime accidents, especially ship collisions, have always been a threat to the safety of maritime transport industry, the regional and global economy, and societies, due to its dire consequences. In this paper, a novel method to model causational factors, one of the critical elements of probabilistic risk modelling of ship collision accidents, is proposed. A credal probabilistic graphical network model based on imprecise probabilities was established based on accident investigation reports and domain experts as the overall framework to represent expert knowledge and probabilistic inference under uncertainty. Causational probability is estimated from the micro-to-macroscopic perspective where information of ship encounters are integrated into the causational model to perform probabilistic inference on each encounter and to obtain collective results. The causation probability interval is obtained and compared between model with and without the availability of geometric encounter data. The results indicate that: (1) the encounter information (relative bearing, TCPA, and presence of other ship) has influence on causational probability of ship collision accident to certain extent; human and organisational factors play more significant role; and (2) with AIS data integration, causational probability analysis can be utilized to determine encounters with higher likelihood and obtain details of dangerous ship encounters in regional maritime traffic.