Probabilistic Risk Analysis for Ship Collision-Theory and Application for Conventional and Autonomous Ships

Probabilistic Risk Analysis for Ship Collision-Theory and Application for Conventional and Autonomous Ships

Chen, P. (TU Delft Safety and Security Science)

van Gelder, P.H.A.J.M. (promotor)
Papadimitriou, E. (copromotor)

Delft University of Technology

2020-06-10

Maritime transportation has been one of the major contributors to the global trade and economy. Accidents, however, have been continuously posing risks to individuals and societies in terms of loss of human life, economic and environmental consequences, etc. This thesis paid particular attention to the probabilistic risk analysis of ship collision accident and explored the possible influence of implementing MASS on the risk of collision in maritime traffic. A comprehensive literature review is conducted to investigate the stakeholders on maritime traffic safety and related methodologies for risk analysis of ship collision accident. A series of methods based on Non-linear Velocity Obstacle (NLVO) is proposed to identify the encounter between ships that have the potential for collision accident from the perspective of the whole encounter process. The causal relationships between accident contributing factors are modelled with credal network to estimate the causation probability of ship collision accident with consideration of encounter situation. Finally, an initial analysis of the potential influence of MASS on the collision risk in maritime traffic was also explored based on the proposed approaches. The objective of this research is to furtherly develop a quantitative risk analysis model for ship collision accident in waterways in an integrated manner that can introduce multiple sources of information into analysis and further to obtain insights of collision risk for safety management.

Maritime Safety
Probabilistic Risk Analysis
Ship Collision
Velocity Obstacles (VO)
AIS
Collision Candidate
Near Miss
Credal Network
Maritime Autonomous Surface Ship

https://doi.org/10.4233/uuid:b8275053-49a0-4d77-aaea-27a92758f6e8

9789464022742

2021-08-31

Institutional Repository

doctoral thesis

© 2020 P. Chen