Epistemology

Risk analysis (engineering)

Risk assessment

Engineering

Philosophy

Business

Artificial intelligence

Archaeology

China

Geography

Transport engineering

Accident (philosophy)

Water flow

Water transport

Bayesian network

Computer security

Environmental science

Computer science

Forensic engineering

Environmental engineering

Reliability Engineering & System Safety

The rapid development of the shipping industry requires the use of large vessels carrying high-volume cargoes. Accidents incurred by these vessels can lead to a heavy loss of life and damage to the environment and property. As a leading country in international trade, China has developed its waterway transport systems, including inland waterways and coastal shipping, in the past decades. A few catastrophic shipping accidents have occurred during this period. This paper aims to develop a new risk analysis approach based on Bayesian networks (BNs) to enable the analysis of accident severity in waterborne transportation. Although the risk data are derived from accidents that occurred in China's waters, the risk factors influencing accident severity and the risk modelling methodology are generic and capable of generating useful insights on waterway risk analysis in a broad sense. To develop the BN-based risk model, waterway accident data are first collected from all accident investigation reports by China's Maritime Safety Administration (MSA) from 1979 to 2015. Based on the derived quantitative data, we identify the factors related to the severity of waterway accidents and use them as nodes of the risk model. Second, based on a receiver operating characteristic (ROC) curve, an augmented naïve BN (ABN) model is selected through a comparative study with a naïve BN (NBN) model to analyse the key risk factors influencing waterway accident severity. The results show that the key factors influencing waterway safety include the type and location of the accident and the type and age of the ship. Moreover, a novel scenario analysis is conducted to predict accident severity in various situations by combining different states (e.g., high risk) of the key factors to generate useful insights for accident prevention. More specifically, the findings can aid transport authorities, ship owners and other stakeholders in improving waterborne transportation safety under uncertainty.

Bayesian network modelling and analysis of accident severity in waterborne transportation: A case study in China