Waterway traffic complexity is one of the main reasons behind grounding accidents. It can be estimated qualitatively through expert judgment or quantitatively through the analysis of traffic data. In this paper we introduce a big data analytics methodology for the analysis of grounding avoidance behavior of selected Ro-Pax ships. To demonstrate the applicability of the method, detailed traffic data obtained from Automatic Identification System (AIS) are combined with the General Bathymetric Chart of the Oceans (GEBCO) for three year period of operations in the Gulf of Finland. The grounding avoidance behavior of a RoPax passenger ship operating in shallow waters is idealized under various traffic patterns that link to side or forward grounding scenarios. The results demonstrate improved understanding of grounding avoidance behaviors in real operation that may in turn lead to better evaluation of waterway complexity indices and critical operational scenarios not currently accounted for by existing accident databases.

INTRODUCTION

In recent years the influence of the economies of scale lead to increased ship sizes, traffic intensity and associated risks (e.g. Kujala, 2013; Wen et al., 2015 and Zhang et al., 2016). Modern passenger ships present a ship segment where understanding risks in relation to navigation patterns that may result in grounding accidents deems special consideration (Carter et al., 2019). This is mainly because of their potentially disastrous consequences resulting in life loss and environmental damage (e.g. see MAIB (2019); Di 2012; Baird, 2019). Statistical review of accidents records in the Baltic has shown that in terms of accident occurrence, collisions and groundings are the most common accidents (see Fig. 1). The former accounts for almost 32% of accidents between 2014–2017. Over the same period there have been 153 grounding or stranding events accounting for 24.8% of the accidents (Florent Nicolas et al., 2018). Notwithstanding, studies on the root cause of grounding scenarios remain limited due to lack of broadly available data records and/or techniques that may be used for their meaningful assessment.

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