Risk Assessment and Decision Support for Sustainable Traffic Safety in Hong Kong Waters

Abstract

Hong Kong's port is one of the busiest in the world. Such heavy traffic is associated with a high accident rate. The present study uses Bayesian Networks to analyze accident risk in Hong Kong waters using 331 accident reports during the period of 1999-2017. The methodology adopted is comprised of an analysis of present literature and expert judgments for the determination of nodes and states. The calculation of probabilities and conditional probability tables (CPT) were done based solely on the real data in accident reports through parameter estimation. The results indicate that the highest portion of accidents was categorized as “other” with a probability of 0.5174. The majority of such accidents took place in port waters. The second highest category was “collision” with a probability of 0.2256. Both of these accident types were associated with the highest fatality rate-one or two people killed. Poor judgment, negligence and insufficient training were found to be the most influential factors with regard to human actions. The highest rate of injuries was associated with passenger ships. The results offer valuable insights into various accident scenarios which involve setting evidence at different states of consequences and accident types to determine the most prominent contributing factors. Sensitivity analysis was also conducted to recognize the most critical variables. This study should prove useful to decision and policy makers seeking to enhance sustainable safety in maritime traffic operations.

Keywords

• Accident risk
• potential consequences
• Bayesian networks
• hong kong waters
• maritime traffic safety