WBFQ - Risk management and resilience for ports and maritime operations

In recent years, various disruptions have had widespread effects on the shipping industry, making the resilience of shipping companies increasingly crucial. Limited research has linked resilience with the operational strategies of shipping alliances. This paper addresses this gap by proposing a framework named the Adjustment Processes of Shipping Alliances (APSA), which describes how alliances adjust their operations at a spatial level in response to crises, using the Red Sea crisis as a case study. By leveraging extensive shipping satellite data, data mining algorithms, and appropriately developed models, the response strategies, performance, and network characteristics of the three main shipping alliances are assessed. The findings reveal that during the crisis, the shipping networks of the alliances became significantly more dispersed, with alliances increasing their port skipping rates to maintain relatively stable schedule reliability. Ports in the Red Sea region were skipped the most, and other ports in Asia and the Middle East also showed some increase. Meanwhile, vessel allocation and port coverage remained relatively stable. We also discussed the possible relationship between response strategies and the performance of alliances and suggested that increasing port skipping can improve their performance during a crisis. This research provides insights into the resilience of shipping alliances when confronted with unexpected disruptions.

With the rapid growth of global shipping, increasing maritime traffic has heightened accident risks, posing threats to the economy, ecology, and public safety. This study introduces a data-driven Bayesian Network (BN) framework to address challenges in maritime risk analysis, including data deficiencies such as missing values and imbalances. The framework employs boxplot techniques to detect outliers and the ADASYN algorithm to balance datasets, ensuring data reliability. Accimap theory is integrated to systematically identify key risk factors, and K-means clustering is used for precise indicator classification. Validation shows the model outperforms advanced machine learning classifiers in predicting accident severity. Finally, our findings reveals some key findings: (1) "standardized operations" are the most influential factor, with a mutual information value of 0.13408;(2) Human behavioral norms gain importance as incident severity increases. (3) Scenario analysis highlights that favorable weather conditions can paradoxically lead to more severe accidents, likely due to reduced vigilance. This study offers valuable insights for policymakers and industry practitioners, providing a robust framework for maritime risk management and accident prevention.

This article focuses on risk assessment of owners of underwater marine infrastructure in relation to incidents off the coasts and analyses the applicability of both international and national law in relation to arrest of the vessels. During or after the completion of the criminal investigations, the arrest can be made on other grounds to secure compensation for damage by holding the vessel under arrest in order to complete the possible forced sale of the vessel. The owners of the underwater structures are also able to start procedures of arrest based on the damage they have suffered, on basis of Maritime Code to secure their demands. However, the economic risk for the owners of the infrastructure is in this case very different than in seizure for collateral damage initiated by police or prosecutors. These two possibilities are analyzed independently in the article from the risk assesment point of view of the owners of marine infrastructure. The liability for unfounded arrest or seizure of the vessel is strict and lies with the owners in the first option and state/public officials in the latter. Legal rules for lodging the security for arrest or seizure also differ from each other in these options.