Navigating uncertainty: A dynamic Bayesian network-based risk assessment framework for maritime trade routes

Abstract

Maritime safety is crucial for international seaborne trade and the global economy. Acknowledging the inevitable and multifaceted risks present in maritime navigation, we introduce a novel approach to evaluate the time-dependent risk performance of various routes, with the objective to mitigate vulnerabilities and fortify resilience. To achieve this goal, we have developed an effective framework using a Dynamic Bayesian network (DBN) model to capture the interactive relationships among the influential factors in probabilistic terms. This framework employs a combination of the synthetic minority over-sampling technique and edited nearest neighbor approach to address the issue of imbalanced maritime accident reports. Furthermore, the gradient descent algorithm is utilized to calculate the conditional probabilities among the influential factors, while the Markov chain model is applied to develop the DBN model. The framework enables dynamical consideration of the risk performance of the maritime trade routes and identifies the key influential factors during different time intervals. Our research scientifically identifies the devastating impact of terrorism on shipping routes, followed by other risk factors such as piracy, military conflicts, and various induced risks, ranked in descending order of severity. The case study on the Indian Ocean shipping route, which applies the proposed methodology, reveals an initial phase of fluctuating increases, succeeded by a prevailing downward trend over the span of 2009–2019. The methodology and results provide valuable support for maritime stakeholders in making informed decisions.