Abstract
This paper presents a big data analytics method for the evaluation of ship-ship collision risk in real operational conditions. The approach makes use of big data from Automatic Identification System (AIS) and nowcast data corresponding to time-dependent traffic situations and hydro-meteorological conditions respectively. An Avoidance Behavior-based Collision Detection Model (ABCD-M) is introduced to identify potential collision scenarios and Collision Risk Indices (CRIs) are quantified when evasive actions are taken for each detected collision scenario in various voyages. The method is applied on Ro-Pax ships operating over 13 months of the ice-free period in the Gulf of Finland. Results indicate that collision risk estimates may be extremely diverse among voyages, and in 97.5% of potential collision scenarios the evasive actions are triggered only when risk is at 45% or more of its maximum value. The overall CRI for ships operating over the given area tends to be lower for adverse hydro-meteorological conditions. It is therefore concluded that the proposed method may assist with the (1) identification of critical scenarios in various voyages not currently accounted for by existing accident databases, (2) definition of commonly agreed risk criteria to set off alarms, (3) the estimation of risk profile over the life cycle of fleet operations.
Highlights
Ship collisions and groundings are the most frequent maritime traffic accidents globally [38]
Results indicate that collision risk estimates may be extremely diverse among voyages, and in 97.5% of potential collision scenarios the evasive actions are triggered only when risk is at 45% or more of its maximum value
The paper introduces a big data analytics method for evaluation shipship collision risk based on collision avoidance behaviors, with a RoRo/ Passenger ship (RoPax) being considered as the struck ship
Summary
Ship collisions and groundings are the most frequent maritime traffic accidents globally [38]. It is desirable to develop a big data analytics method for evaluation of ship-ship collision risk in various voyages using now-cast data and AIS data, by recovering detailed time-dependent traffic situa tions and the hydro-meteorological conditions at the times. This would allow insight to be gained into collision risk reflecting real operational conditions, as well as exploring the time to trigger evasive actions in various voyages [53]. The method detects collision scenarios based on clustered ship trajectories encompassing AIS and hydro-meteorological big data streams at the time of collision avoidance maneuvers in various routes (see Section 2). The paper concludes on the potential of the method to develop intelligent decision support systems to mitigate collision risk by inspecting traffic patterns in various voyages and ship- ship collision risk (see Section 4)
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