Dynamic Risk Assessment for New Types of Ro-Pax Ferries

Abstract

Advancements in maritime technology, expanding traffic, and stricter environmental standards present increasing challenges for operating RO-PAX vessels. Traditional risk assessment methods remain static and retrospective, which makes them ineffective for addressing the evolving complexities of navigational hazards in current RO-PAX vessels. The transition to predictive risk assessments that operate in real-time becomes essential to address modern navigation challenges. Researchers plan to create a complex dynamic risk assessment (DRA) model that will address the specific needs of modern RO-PAX vessels. The model combines operational, environmental, and technical data inputs to deliver real-time navigation and port operations risk analysis. The model aims at improving risk assessment precision and response time through machine learning techniques and Big Data analytics. The model will use machine learning to analyse previous incidents and adjust its algorithms based on new information. Big Data analytics will supply essential computational power for processing the extensive data generated by maritime operations. Implementing these technologies will improve safety measures at sea, alongside operational performance.