FBEP - Risk management and resilience for ports and maritime operations

In response to port disruptions, shipping operators adopt adaptive strategies such as adjusting routes, modifying port call sequences, or skipping ports. Among these, port skipping is a critical decision that impacts operational efficiency and supply chain resilience. However, the decision-making mechanisms behind port skipping remain underexplored. This study develops a vessel behavioral model based on Automatic Identification System (AIS) data to analyze ship operators' responses to port disruptions. We employ a three-step methodology: first, we compute actual port call sequences and delay times; second, we identify global container port disruptions; and third, we detect affected vessels and construct a predictive behavioral model. Our results reveal that a significant proportion of vessels skip disrupted ports, with decision timing influenced by disruption severity and alternative routing options. Additionally, certain ports exhibit higher skip rates, reflecting variations in network resilience. These insights enhance maritime disruption management and contribute to the development of more robust global shipping networks.

Global supply chains regularly face widespread disruptions, with events such as the COVID-19 pandemic (2021-22), geopolitical incidents in the Red Sea, and water shortages at the Panama Canal impacting global maritime trade flows and shipping routes. Regardless of the cause, delays or rerouting in critical maritime supply lines have had a global impact. To quantify and assess the magnitude and location of such maritime disruptions, a proposed metric, the Global Supply Chain Stress Index (GSCSI), has been developed by the World Bank since 2021. The stress metric is derived from AIS (Automatic Identification System) tracking data. It calculates the equivalent delayed capacity measured in TEUs (Twenty-foot Equivalent Units), providing insights at the port, country, regional, and global levels.

The Northern Corridor (NC) faces pressure to continue to be a viable and competitive option for the overland China-Europe container trade. As the rail transport route between Chongqing in China and Duisburg in Germany that goes through Russia and Poland, an estimated 1.9 million TEUs of containers were transported via the China-Europe Railway Express in 2023. Although the route remains preferred for overland container trade between China and Europe, the continuation of geopolitical tensions between Ukraine and Russia threatens its operational viability. This paper aims to study the operational effects of such geopolitical disruptions in NC. The spillover effects from NC container trade to Red Sea (RS) and Cape of Good Hope (CGH) routes are realised with the development of a multi-objective mixed-integer programming model. This method allows the use of genetic algorithms and other optimisation techniques to evaluate a multi-objective function integrating transport volume, time, cost and carbon emission in the multimodal rail-land-sea transportation network.

This study uses a bottom-up approach using negative binomial models to examine events reported by European waterway authorities, leveraging the daily number of vessels passing through lock systems to analyze the impact of events in inland waterway transport flows. It presents the first empirical analysis of short-term and long-term events on major inland waterways such as the Rhine, Main, and Danube and introduces a novel data source and methodology. The findings identify various event types, their geographical distribution, and their impacts on IWT, providing insights for evidence-based policy development and strategies to improve the reliability of IWT.

Maritime transport plays a crucial role in global trade, yet it remains highly vulnerable to accidents, which can have severe human, economic, and environmental consequences. While traditional probabilistic risk analysis methods have advanced maritime safety, they often fail to account for the impacts of major shock events, such as pandemics and geopolitical conflicts, on accident trends. This study addresses this gap by conducting a comparative analysis of maritime accidents before and after significant crises, with a particular focus on the COVID-19 pandemic. The pandemic disrupted global supply chains, altered shipping operations, and introduced new safety challenges, leading to shifts in accident patterns and risk factors. To assess these evolving trends, this study employs a data-driven Bayesian Network (BN) approach, leveraging real-world accident data to uncover key differences in accident dynamics pre- and post-crisis. By analysing changes in risk factors, operational vulnerabilities, and safety measure effectiveness, the study provides a comprehensive evaluation of maritime safety in response to large-scale disruptions. The findings offer valuable insights for maritime policymakers, industry stakeholders, and safety regulators, guiding the development of enhanced risk mitigation strategies, crisis preparedness frameworks, and adaptive safety protocols. This research contributes to a more resilient and data-driven approach to maritime risk management, ensuring the industry's ability to navigate future global challenges effectively.