Enhanced risk assessment framework for complex maritime traffic systems via data driven: A case study of ship navigation in Arctic

Abstract

The era of big data has been characterized by an increasing diversity of information and a deeper application of system safety. In this context, this study proposes an enhanced risk assessment (ERA) framework to estimate traffic risk from massive data obtained in complex maritime traffic systems. The ERA framework adopts a 4R model that includes risk perception, risk cognition, risk reasoning, and risk control. The ERA framework integrates the Systems Theoretic Accident Model and Process and Stochastic Petri Nets to analyze the ship traffic process and develop risk control schemes. The feasibility of the proposed framework is demonstrated by a case study in Arctic waters. The results indicate that ice concentration represents a key factor for ship traffic in Arctic waters and that the risk control scheme type is related to the ice resistance level of ships. Accordingly, for ships with low ice resistance or no ice-class ships, the traffic risk is high when they are passing through the East Siberian, Laptev, Kara Sea, and the Vilkitskogo Strait, and icebreakers are required in July and October. In contrast, for ships with a higher ice resistance, regular traffic is generally possible for the East Siberian and Laptev Seas.