Know your safety indicator – A determination of merchant vessels Bow Crossing Range based on big data analytics

Mateusz Gil, Kozioł, Paweł, Wróbel, Krzysztof and Jakub Montewka

Reliability Engineering and System Safety, 2022, vol. 220, issue C

Abstract: Even in the era of automatization maritime safety constantly needs improvements. Regardless of the presence of crew members on board, both manned and autonomous ships should follow clear guidelines (no matter as bridge procedures or algorithms). To date, many safety indicators, especially in collision avoidance have been proposed. One of such parameters commonly used in day-to-day navigation but usually omitted by researchers is Bow Crossing Range (BCR). Therefore, this paper aims to investigate, what are typical, empirical values of BCR during routine operations of merchant ships, as well as investigate what factors impact this indicator and to what extent. To this end, a ten-year big dataset of real maritime traffic obtained from the Automatic Identification System (AIS) was used to provide statistical and spatiotemporal analyses. The results indicate that BCR is strongly related to the type of navigational area (open sea or restricted waters) but not with the dimensions or speed of ships. Among analyzed vessel types, passenger ships were noted as vessels that cross other bows at the closes ranges. Results of this study may be found interesting by fleet managers and developers of Maritime Autonomous Surface Ships (MASS). The former could utilize the results to provide revised operational guidelines for deck officers while the latter - propose an early-detection warning system based on empirical data for prospective MASS.

Keywords: Bow Crossing Range (BCR); Automatic Identification System (AIS); ship collision avoidance; maritime risk and safety; big data analysis; Maritime Autonomous Surface Ships (MASS) (search for similar items in EconPapers)
Date: 2022
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Citations: View citations in EconPapers (12)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:reensy:v:220:y:2022:i:c:s095183202100781x

DOI: 10.1016/j.ress.2021.108311

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