Big data analytics of safety assessment for a port of entry: A case study in Keelung Harbor

Abstract

In order to reduce accidents at the port of entry that may be due to weather factors, it is necessary to not only improve navigation skills but also use current advanced nautical technology to provide inbound vessels with the necessary information and establish a safety assessment model for inbound vessels to assist port authorities in effective control. Therefore, this study plans to use automatic identification system data collected around Keelung Harbor as a basis, coupled with meteorological data with time, and then to use a geographic information system and a decision tree algorithm in big data analytics. This enables analysis of the navigation characteristics of all types of vessels that have entered Keelung Harbor under different meteorological conditions, thereby establishing a safety assessment model for the port of entry in Keelung Harbor. The model can not only provide the vessel traffic service control personnel with real-time analysis on whether the behavior of an inbound vessel at a given time has abnormalities but also be used to establish an operation model for port traffic flow.

Keywords

Marine traffic maneuverability path optimization safety

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