TY - JOUR
T1 - Integration of individual encounter information into causation probability modelling of ship collision accidents
AU - Chen, Pengfei
AU - Mou, Junmin
AU - van Gelder, P. H.A.J.M.
PY - 2019
Y1 - 2019
N2 - 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.
AB - 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.
KW - Accident analysis
KW - AIS data
KW - Causation modelling
KW - Credal network
KW - Probabilistic risk analysis
KW - Ship collision
UR - http://www.scopus.com/inward/record.url?scp=85070526783&partnerID=8YFLogxK
U2 - 10.1016/j.ssci.2019.08.008
DO - 10.1016/j.ssci.2019.08.008
M3 - Article
SN - 0925-7535
VL - 120
SP - 636
EP - 651
JO - Safety Science
JF - Safety Science
ER -